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Burning Issue: Why Opportunistic Screening for Lung Cancer is the need of the hour

'Cancer Cures Smoking'

Did the above line make you look twice and think thrice? Years ago, the Cancer Patients Aid Association published this thought-provoking message, a genuinely fresh view on the relationship between tobacco and cancer. And why not?

Extensive research from across the world indicates that cigarette smoking can explain almost 90% of lung cancer risk in men and 70 to 80% in women. The WHO lists tobacco use as the first risk factor for cancer. The World Cancer Research Fund International goes a step further and plainly calls out smoking. With lung cancer racking up 2.21 million cases in 2021 and 1.8 million deaths, one can understand why healthcare stakeholders want to focus efforts on targeting common causes and reducing incidents of the disease.

Yet, a recent study indicates troubling trends.

Medanta Hospital is one of India’s leading medical facilities. Their research on lung cancer prevalence, conducted over a decade between 2012 – 2022 amongst 304 patients threw up a startling statistic – 50% of their lung cancer patient cohort were non-smokers. According to the doctors who conducted the research, Dr Arvind Kumar, Dr. Belal Bin Asaf and Dr. Harsh Puri, this was a sharp rise from earlier figures for non-smoking lung cancer patients (10-20%). But, there’s more.

The study indicates that, be it smokers or non-smokers, the risk group for lung cancer has expanded to a relatively more youthful population.

The WHO previously flagged a key factor for the rising trend in young, non-smokers being at risk for lung diseases – air pollution. Dr. Tedros Adhanom Ghebreyesus called air pollution a ‘silent public health emergency’ and ‘the new tobacco’. It presents clinicians working to treat and prevent lung cancer with a new conundrum – evaluating risk factors for the disease.

Simply put, how does one tackle the risk of lung cancer in a 25-year-old, non-smoking individual living a reasonably healthy lifestyle when a risk factor could be the simple act of breathing?

According to Dr. Matthew Lungren, the answer could be Opportunistic Screening – which he calls, “… the BEST use case for AI in radiology”

Qure.ai concurs. qXR, our artificial intelligence (AI) solution for chest X-rays, has been tried, tested and trusted to assist in identifying and reporting missing nodules, which highlights the importance of opportunistic screening for identifying potential lung cancers early.

All our recent studies, including the one with Massachusetts General Hospital (MGH) in a retrospective multi-center study, investigated and concluded that Qure’s CE approved qXR could identify critical findings on Chest X-Rays, including malignant nodules.  This spurs the possibility that opportunistic screening for indicators of lung cancer and other pulmonary diseases should become the norm.

Qure.ai’s solutions, can truly make the difference, augmenting the efforts of clinicians and radiologists any and every time a Chest X-ray or Chest CT is conducted.

November is Lung Cancer Awareness Month. What better moment than the last day of the month to urge everyone to think outside the box when it comes to demographics, risk factors, screening, and the role of AI in healthcare.

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Role of an AI for Reliable Screening of Abnormality in X-rays

Role of an AI for Reliable Screening of Abnormality in X-rays: A Prospective Multicenter Study on Operational Efficiency using a CE approved solution

Chest X-rays are the most common diagnostic imaging technique used in clinical practice. The patient care pathway is, however, significantly hampered in most high-volume healthcare centers. Therefore, an Artificial Intelligence system that can quickly, reliably, and accurately identify anomalies in chest X-rays is being agreed as essential for enhancing radiological workflow.   

Here are some of the key challenges in correctly identifying the abnormalities on a chest X-ray (CXR)  

  • Scarcity of radiologists with the necessary training  
  • Overwhelming workload in large healthcare facilities  
  • CXR interpretations are highly subjective due to the presence of overlapping tissue structures.  

For example: Sometime even well-trained radiologists find it challenging to differentiate between the lesions or correctly identify very obscure pulmonary nodules   

Objective: To conduct a CE-approved post-market study which is also a prospective multi-center and multi-reader study prospective multicenter quality-improvement study. The team evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings. 

Method: A team of expert radiologists used Qure.ai’s CE- approved AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multicentre study. This study took place in a large radiology network in India for a period of 10 months. This was done is over 35 + sites by ~120 expert radiologists.  

Study Highlights

  • A total of 65,604 chest X-rays (CXRs) were processed from a network of 35 centers during the study period. 
  • Turnaround Time (TAT) decreased by about 40.63% from pre-AI period to post-AI period. 
  • The high NPV (98.9%) in categorizing normal and abnormal CXR with confidence demonstrates the utility of Qure’s AI as a screening tool in high-volume settings. 
  • The 1.1% missed CXRs were non-critical and non-actionable x-rays which don't need follow-up. 

Investigator Comments

“AI-based chest X-ray solution (qXR) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways.” 

“qXR helped decrease the mean TAT by over 40%, and 99% of the AI reported normal CXRs were actually normal.” 

Dr. Arunkumar Govindarajan,
 Director and Radiologist, Aarthi Scans and Labs
 

Conclusion

  • The study has prospectively demonstrated that using AI as an assistance tool can be beneficial in high-workload healthcare facilities. 
  • The Study showcased how AI can shorten the time patients must wait to receive the final report, particularly in normal circumstances. Normal and abnormal CXR are on the same worklist in a typical system, making it impossible to separate or prioritize normal CXR without opening the CXR.  
  • AI can reduce report errors and missed diagnoses by acting as a secondary reader. 
  • The use of AI will result in more appropriate treatments for the disease beyond a reduction in reporting time and an improvement in report quality. 

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Gain CoR endorsed super user training

CoR endorsed CPD Super User Training by Qure.ai

Qure.ai became the first company to receive an endorsement from The College of Radiographers (CoR) for CPD Super User Training. This training helps in obtaining Qure.ai’s Artificial Intelligence Super User Certificate. This training is a fantastic opportunity to receive and get a CPD Certificate.

Qure.ai is the first AI company in the UK to receive this kind of endorsement.

COR knowledge outcomes are,

[CoR 02] Knowledge base
[CoR 03] Work safely
[CoR 06] Manage knowledge/information
[CoR 09] Interprofessional/agency working or learning
[CoR 11] Workforce development or staff governance

What is CPD and how does it help:

Continuing Professional Development (CPD) is to help improve the safety and quality of care provided for patients and the public in the UK. 

CPD Helps
-in updating the latest changes in medical practices. 
-in maintaining the professional standards required. 
-in annual appraisal to show that one has met the requirements for revalidation.

Health professionals are responsible for:

  1. identifying their CPD needs
  2. undertaking CPD activities that are relevant to their practice and support professional development 
  3.  

CPD should be focussed on four primary domains: 

  1. knowledge, skills, and performance  
  2. safety and quality  
  3. communication, partnership, and teamwork  
  4. maintaining trust 

Evidence of CPD is vital in the annual appraisals. A CPD portfolio would typically include a selection of activities in at least 3 of the following categories: 

  1. Work-based 
  2. Professional 
  3. Formal 
  4. Self-directed 
  5. Other learning 

Healthcare professionals must undertake 35 hours of Continuing Professional Development (CPD) relevant to their scope of practice over the three years before their registration renewal. 

Who are all eligible for this training

All medical professionals registered under The Health and Care Professionals Council are eligible for this training

  1. Maintain a continuous, up-to-date, and accurate record of their CPD activities. 
  2. Demonstrate that their CPD activities are a mixture of learning activities relevant to current or future practice. 
  3. Seek to ensure that their CPD has contributed to the quality of their practice and service delivery. 
  4. Seek to ensure that their CPD benefits the service user. 
  5. Upon request, present a written profile (which must be their work and supported by evidence) explaining how they have met the Standards for CPD. 

For more details, reach out to us

    Qure.ai is committed to protecting and respecting your privacy and processes your information as our Privacy Policy.

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    Taking No Chances: Opportunistic Screening’s Role in Early Lung Cancer Detection

    Key Highlights

    • Over 20M Chest CTs are performed every year in the USA alone  
    • Every chest CT scan is a potential lung cancer screening opportunity 
    • Chest CT scanning increased significantly during the pandemic 
    • Qure.ai conducted a deep-learning study to use CT scans for COVID to screen for actionable nodules

    Introduction

    Jackson Brown, Jr. once said that nothing is more expensive than a missed opportunity. Lung cancer is perhaps the ideal example of this because incidental/early detection via opportunistic screening can play a significant role in helping to successfully combat the malady. 

    Lung cancer accounts for 1 in 5 cancer deaths yearly; the leading cause of cancer-related deaths worldwide. It accounts for the greatest economic and public health burden of all cancers annually; approximately $180 billion. This is also because the prognosis for lung cancer is poor compared to other cancers, largely due to a high proportion of cases being detected at an advanced stage  where treatment options are limited, and the 5-year survival rate is only 5-15%.The global pandemic strained healthcare systems worldwide also leading to significant increase in the chest CT volumes.  

    “Earlier we would conduct approximately 300 chest CT scans per month. During the pandemic, this number rose to 7000 per month. It put a severe strain on doctors who must review every scan. Qure’s AI solution, qCT, made a significant difference to us by flagging missed actionable nodules on chest CT scans for further follow-ups & investigations.”
    – Arpit Kothari, CEO, bodyScans.in

    The large volume of scans during the pandemic allowed Qure.ai to conduct a study using a deep-learning approach towards opportunistic screening for actionable lung nodules.

    Methodology

    The study uses Qure.ai’s deep-learning approach to identify lung nodules on CT scans from patients who were scanned for COVID-19 from 5 radiology centers across different cities in India.  

    The scans were sourced from bodyScans.in, a leading radiology service provider in Central India and Aarthi Scans & Labs, yet another major diagnostic provider with 40 full-fledged diagnostic centers across India.

    2502 scans were randomly selected from Chest CTs performed at 5 sites in two specialist radiology chains, Aarthi Scans and bodyScans during India’s 2nd and 3rd wave of Covid. They were processed by qCT, Qure’s AI capable of detecting and characterizing lung nodules. The radiologist report of the cases flagged by qCT were investigated for findings suggestive of cancer. Flagged cases for which the nodule was not reported were re-read by an independent radiologist with AI assistance on a web portal. They were asked to either confirm or reject the flag, rate the nodule for malignancy potential if confirmed or provide alternate finding if rejected (See Figure). 

    Results

    • 2502 CT scans were processed in total.  
    • Of these, 23.7% were flagged by qCT and re-read by an independent thoracic radiologist.  
    • In 19.4% of these flagged cases, the radiologist agreed that there were unreported actionable nodules.  
    • There were 19 cases where radiologists did not rule out the risk of malignancy and 2 out of these were rated as probably malignant.  

    Conclusion

    In the study, Qure.ai’s AI tool has assisted in reporting missed nodules which highlights the importance of opportunistic screening for identifying potential lung cancers early.  The need to improve efficiency and speed of clinical care continues to drive multiple innovations into practice, including AI. With the increasing demand for superior health care services and the large volumes of data generated daily from parallel streams, streamlining of clinical workflows has become a pressing issue. In our study, by using AI as a safety net, we found 21 chest CTs that should have warranted follow-up management for the patients. 

    “Early detection plays a critical role in successfully treating Lung Cancer. Yet, there are several factors which contribute to the significant risk of these nodules getting missed in chest CT scans. Qure’s AI solution, qCT is immensely useful because it acts as a safety net; another pair of eyes to ensure that we clinicians can identify those patients who need immediate help. Eventually, AI can augment our efforts to defeat the disease.”
    – Dr. Arunkumar Govindarajan, Director, Aarthi Scans & Labs

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    AI-Based Gaze Deviation Detection to Aid LVO Diagnosis in NCCT

    Introduction

    Strokes occur when blood supply to the brain is interrupted or reduced, depriving brain tissue of oxygen and nutrients. It is estimated that a patient can lose 1.9 million neurons each minute when a stroke is untreated. So, the treatment of stroke is a medical emergency that requires early intervention to minimize brain damage and complications. Furthermore, a stroke caused by emergent large vessel occlusion (LVO) requires a much more prompt identification to improve clinical outcomes.

    Neuro interventionalists need to activate their operating rooms to prepare candidates identified for endovascular therapy (EVT) as soon as possible. As a result, identifying imaging findings on non-contrast computed tomography (NCCT) that are predictive of LVO would aid in identifying potential EVT candidates. We present and validate gaze deviation as an indicator to detect LVO using NCCT. In addition, we offer an Artificial Intelligence (AI) algorithm to detect this indicator.

    What is LVO?

    Large vessel occlusion (LVO) stroke is caused by a blockage in one of the following brain vessels:

    1. Internal Carotid Artery (ICA) 
    2. ICA terminus (T-lesion; T occlusion) 
    3. Middle Cerebral Artery (MCA) 
    4. M1 MCA 
    5. Vertebral Artery 
    6. Basilar Artery

    Image source: Science direct

    LVO strokes are considered one of the more severe kinds of strokes, accounting for approximately 24% to 46% of acute ischemic strokes. For this reason, acute LVO stroke patients often need to be treated at comprehensive centers that are equipped to handle LVOs. 

    Endovascular Treatment (EVT)

    EVT is a treatment given to patients with acute ischemic stroke. Using this treatment, clots in large vessels are removed, helping deliver better outcomes. EVT evaluation needs to be done at the earliest for the patients that meet the criteria and are eligible. Early access to EVT increases better outcomes for patients.  The timeframe to perform is usually between 16 – 24 hours in most acute ischemic cases.

    Image Source: PennMedicine

    Goal for EVT

    Since it is important to perform this procedure as early as possible, how do we get there?

    LVO detection on NCCT

    There is a 3 point step to consider for this:

    1. Absence of blood
    2. Hyperdense vessel sign or dot sign
    3. Gaze deviation (often overlooked on NCCT) 

    Gaze deviation and its relationship with acute stroke

    Several studies suggest that gaze deviation is largely associated with the presence of LVO [1,2,3].

    Stroke patients with eye deviation on admission CT have higher rates of disability/death and hemorrhagic transformation. Consistent assessment and documentation of radiological eye deviation on acute stroke CT scan may help with prognostication [4].

    AI algorithm to identify gaze deviation

    We developed an AI algorithm that reports the presence of gaze deviation given an NCCT scan. Such AI algorithms have tremendous potential to aid in this triage process. The AI algorithm was trained using a set of scans to identify gaze direction and midline of the brain. The gaze deviation is calculated by measuring the angle between the gaze direction and the midline of the brain. We used this AI algorithm to identify clinical symptoms of ipsiversive gaze deviation in stroke patients with LVO treated with EVT. The AI algorithm has a sensitivity and specificity of 80.8% and 80.1% to detect LVO using gaze deviation as the sole indicator. The test set had 150 scans with LVO-positive cases where thrombectomy was performed.

    Discussion

    Ipsiversive Gaze deviation on NCCT is a good predictor of LVO due to proximal vessel occlusions in ICA terminus and M1 occlusions. However, it is a poor predictor of LVO due to M2 occlusion. We report an AI algorithm that can identify this clinical sign on NCCT. These findings can aid in the triage of LVO patients and expedite the identification of EVT candidates. 

    We are presenting this AI method at SNIS 2022, Toronto. Please attend our oral presentation on 28th July 2022 at 12:15 PM (Toronto time).

     

    Upadhyay, Ujjwal & Golla, Satish & Kumar, Shubham & Szweda, Kamila & Shahripour, Reza & Tarpley, Jason. (2022). Society of NeuroInterventional Surgery SNIS

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    Ultrasound AI for Cardiovascular Disease Prevention

    Key Using ultrasound AI to prevent cardiovascular diseases through early detection of atherosclerosis.

    Key Highlights

    • Cardiovascular diseases (CVD) cause ~32% of deaths; the leading cause of death globally
    • qVH is the first known solution for AI-guided vascular ultrasound (carotid) that can boost disease prevention using point-of-care-ultrasound (POCUS) devices.  
    • With qVH, high-risk individuals can be screened by any clinician/ remote health worker at any convenient location 
    • Patients can be identified before symptoms appear, allowing for earlier disease management, improved patient outcomes, and reduced costs

    Using AI to maximize the potential of POCUS for disease prevention

    Qure.ai has developed an AI product for vascular health called qVH. It is the first known solution that guides clinicians during a carotid artery scan. Here’s how qVH’s AI works:

    Probe Navigation Guidance: Detects probe location (CCA, ICA etc.) & orientation (long/short axis) and recommends best way to reach next step of protocol.

    Plaque Detection & Characterization Guidance: Auto-detects abnormalities in a live scan (video) and quantifies it when a diagnostic quality image is available

    Image Quality Guidance: Tracks image quality while scanning, recommends steps to maintain diagnostic quality & auto-captures images.

    Device Setting Guidance: Detects common errors in ultrasound device settings in Pulse Wave (PW) mode and recommends changes for accurate PW velocity measurements. Thereby, preventing errors that could lead to misdiagnosis. 

    *qVH is not FDA approved/CE marked yet and is currently meant for investigational or research use only.

    The need for qVH

    ~20% of strokes in adults are caused by the narrowing of the carotid artery (see image; bottom). Buildup of plaque (fatty deposits) in the arteries is the root cause for this narrowing; a condition that is known as atherosclerosis (see image; top).

    Plaques can develop in different vessels leading to artery narrowing/clots and hence reduced blood flow to various parts of our body. This leads to critical events such as:

    • Stroke (Carotid artery)
    • Heart Attack (Coronary Artery)
    • Renal Ischemia (Renal Artery), etc.

    Evidence suggests that ~0-3% of the general population have a severe form of this disease but without any symptoms, while ~35% of diabetic patients have carotid plaques with or without symptoms. 

    However, preventive measures to reduce disease burden have been limited due to:

    • Lack of clear guidelines
    • Logistical challenges like hospital visits for USG scan
    • Depending on operator skills for accurate scanning & reporting using USG
    • High cost of vascular USG
    • Lack of sufficiently skilled clinicians to perform vascular USG. 

    Significant developments in the last decade 

    Price: USG machines have become cheaper (by ~80%), more portable (handheld & wireless) and easily accessible with the arrival of point-of-care ultrasounds (POCUS).

    Preference: USG is gradually becoming the preferred modality for disease prevention.

    • American Heart Association (AHA) recommends carotid artery duplex scanning in patients with high-risk features undergoing coronary artery bypass graft (CABG) surgery.
    • European Society of Cardiology (ESC) recommends carotid duplex ultrasound for evaluating the extent and severity of carotid stenosis.

    Proof: Real-world evidence suggests benefits in risk-stratification through carotid artery screening.

    • Reduction in mortality rates (~10%), early disease diagnosis (~4.5 yrs), reduction in patient costs (by ~50%). [VIPVIZA]
    • Re-classification of low-risk patients to mid/high-risk based on the presence of carotid plaque. [Swiss AGLA]

    Advantages: Govt. backed reimbursements for using advanced ultrasound technology.

    • US Hospitals can claim NTAP reimbursement of ~$1868 per patient diagnosed using ultrasound guidance technology (Caption Guidance) for CVD prevention.
    • American Medical Association (AMA) has introduced 2 new CPT codes for quantitative USG tissue characterization with ACR proposing an additional $82/scan.

    What is holding back ultrasound-based CVD prevention programs?

    POCUS devices have solved problems related to accessibility and affordability. But they have amplified issues related to operator skills since these ultra-portable POCUS devices can be used in any setting (remote areas, sports ground, battlefieldetc.) by most clinicians. The existing issues are:

    • Training is needed to perform the ultrasound scan as per the defined protocol.
    • Training is needed to capture diagnostic quality images from a running video (cine loop).
    • There are chances of misdiagnosis due to errors in:
      • Performing ultrasound measurements manually. Eg: plaque length/area, PW ESV/PDV, Degree of Stenosis etc.
      • Optimizing device settings manually. Ex: gate size/angle/position, box angle.
      • Detecting & quantifying abnormalities (plaques, stenosis, etc.).
    • Inter-operator variability due to operator dependence for probe navigation, abnormality detection, image capture and for optimization of USG device settings. 

    In 2020, POCUS accounted for only ~3-5% of the ultrasound market by revenues. However,  global POCUS market revenue is predicted to increase to $4B by 2030 from $2B in 2020.

    qVH has been designed to address all of the issues outlined above. qVH validation has begun at 2 sites in India & Argentina and will be expanded to 8 sites across Asia, Europe and North America within the next 3 months. qVH can be used with all existing ultrasound machines. Our beta sites are using Cart and POCUS machines.

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    Need for Speed: AI, AstraZeneca, and early lung cancer diagnosis

    The AstraZeneca-Qure partnership

    A thousand miles begins with a single step. In 2020, Qure.ai and AstraZeneca took the first step together to integrate advanced artificial intelligence (AI) solutions to identify lung diseases early in patients across AstraZeneca’s Emerging Markets region – Latin America, Asia, Africa, and the Middle East. In the past 2 years, the partnership has made significant progress, incorporating the use of AI technology with chest X-rays for multi-disease screening, including tuberculosis and heart failure along with lung cancer.

    Lung Cancer: The need for early detection

    In more than 40% of people suffering from Lung Cancer, it is detected at Stage 4, when their likelihood of surviving 5 years is under 10%. Only 20% are diagnosed at Stage 1 when the survival rate is between 68-92%. That’s why Lung Cancer is responsible for every 1 in 5 cancer deaths worldwide.

    Though early detection facilitates early diagnosis and better patient outcomes, the disease’s silent progress to advanced stages makes it a challenge like none other. Low Dose CT (LDCT) remains the most effective means of screening for Lung Cancer. However, in LMICs, CTs can be prohibitively expensive, priced between USD 500 – 700, limiting their access. However, there is some hope.

    Chest X-rays are one the most routinely performed exams in the world, representing 40% of the approximately 3.6 billion imaging tests that are performed annually. As a non-invasive diagnostic test with easy access and low costs, the chest X-ray is a valuable first line test to screen for radiological indications of issues in the lungs, heart, ribs, and more. Acquiring chest X-ray scans only takes minutes; but it warrants expert radiologists to read and analyze them.

    Augmenting X-rays with the power AI

    qXR, Qure.ai's AI-powered chest X-ray interpretation tool, can automatically detect and localize up to 30 abnormalities, including indicators of Lung Cancer, TB, and COVID-19. This is particularly impactful when millions of scans are examined using qXR to report any abnormalities that could otherwise be missed due to:

    • Lack of experienced personnel
    • Increased workloads, limiting access and time for detailed reads of abnormal scans
    • Incidental nodules indicative for Lung Cancer being missed because physicians are only looking at the results for which the X-rays were ordered and not incidental findings.

    How Qure is making a difference

    1. Working with grassroot level healthcare professionals

    A. Leveraging Primary Care GP clinics in Malaysia

    Qualitas Medical Group (QMG) is a chain of integrated general practice (GP) clinics, dental clinics, medical imaging centers, and ambulatory care management centers that play an integral role in Malaysia’s health system. Along with Lung Cancer Network Malaysia, QMG uses qXR to triage all chest X-rays taken of local workers, identifying incidental lung nodules that maybe indicative of lung cancer for further testing. qXR has also helped GPs to reduce their dependency on radiologists for second reads and reduced reporting turnaround time for chest X-rays from 2 days to the same day.

    “Qure.ai’s state of the art deep learning technology is a potential game changer that will enhance and expedite diagnosis with rapid referral to relevant specialty “, said Dr Anand Sachithanandan, President, Lung Cancer Network Malaysia

    B. Empowering Primary Care Physicians in Latin America

    Primary care centers are the first medical care touchpoint and are crucial stakeholders for early diagnosis in disease care pathways. In collaboration with Lung Ambition Alliance, Latin America, Qure is empowering primary care physicians in 12 different countries with AI-enabled smart phone-based chest X-ray analysis and lung nodule screening.

    In the absence of digital X-rays, physicians only need to click a picture of the X-ray film against a lightbox and upload it on the app to receive instant qXR analysis. Based on the results, they can guide the patient to the next appropriate steps.

    2. Collaborating with Cancer Care Foundations

    Assam is called India’s Cancer Capital as the state’s average cancer incident rate is double the national average. The high cancer burden, low public awareness, and a lack of specialised health-care infrastructure led the Govt. of Assam to partner with Tata Trusts and build the Assam Cancer Care Foundation (ACCF).

    Potential lung cancer suspects are identified via door-to-door screenings as well as via a screening kiosk set up at the Fakhruddin Ali Ahmed Medical College and Hospital, Barpeta where ACCF have built a specialised cancer care unit. Chest X-rays of these individuals will be screened for detection of suspicious lung nodule(s) using qXR. Based on the result, they will either be called back for an LDCT/Biopsy or an oncology consultation.

    3. Surveillance of all chest X-rays taken in a tertiary care hospital

    The VPS Lakeshore, Kerala is a tertiary care hospital and a centre of excellence in Oncology and other specialities. It is well equipped to take up largescale screening programs and facilitate the required care continuum for high risk, suspected and confirmed disease cases. The hospital has a program in place where a tool surveys all chest X-rays taken to facilitate early detection of Lung Cancer.

    Through our partnership with AstraZeneca, we have deployed qXR to scan all chest X-rays performed at the hospital to pick up possibly early cases of Lung Cancer. If any abnormal/nodule indicative cases are picked up by the software, it is instantly flagged to the radiologist/referring physician so that they can guide the patient along the next steps in the patient care pathway.

    4. Public screening road shows

    The Ministry Of Public Health, Thailand along with the AstraZeneca team initiated the “Don’t Wait. Get Checked” Lung Cancer Campaign in April ’22 at Central World Mall, in partnership with Banphaeo General Hospital, Digital Economy Promotion Agency (DEPA) and the Central Group. On the occasion of World No Tobacco Day, Qure.ai’s qXR was used to screen close to 200 people. The objective of this program was to directly impact Thailand's public health policies revolving around lung cancer.

    Way Forward

    “Building health systems that are resilient and sustainable will require finding new ways to prevent disease, diagnose patients earlier, and treat them more effectively. The benefits of the technology that Qure.ai offers align well with our corporate values, ultimately supporting our strategic objective to reshape healthcare delivery, close the cancer care gap and better chronic disease management, especially in low-to-middle income countries. We believe that innovative technology has the potential to transform patients’ outcomes, enabling more people to access care in timely, reliable and affordable ways, regardless of where they live”, said Pei-Chieh Fong, Medical VP, AstraZeneca International.

    At the Davos World Economic Forum 2022, AstraZeneca pledged to join the WEF EDISON Alliance and committed to screening 5 million patients for lung cancer by 2025 in partnership with Qure.ai.

    With the support of AstraZeneca Turkey, Qure.ai collaborated with Mersin University Hospital on a landmark study for the use of AI in Heart Failure detection, using our qXR suite. This study is an important indicator for the future of AI in healthcare and the use of technology to augment the efforts of physicians in the early detection of other diseases.

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    Qure’s AI for detecting risk of heart failure

    Every year, approximately 17.9 million lives are lost to cardiovascular diseases (CVD), the leading cause of death across the world. The rates of heart failure misdiagnosis range from 16.1% in hospitals to 68.5% in GP referral settings. In the EU alone, the economic burden of cardiovascular diseases exceeds €210 Bn.

    A systematic analysis on 10 studies done across 5 countries found patients groups with comorbidities and COPD, and the elderly population in nursing homes were more likely to have unrecognized heart failure.

    Turkey is known to have a higher prevalence of heart failure and Atrioventricular Septal Defect (AVSD) as compared to the western world. With millions of chest radiographs done annually for a host of reasons, a tool that could screen the data used in these studies to predict the early signs for risk of heart failure could be ground-breaking for care and patient outcomes.

    Output generated by Chest X-ray AI Solution

    Enlargement of heart in cases of heart failure

    At the start of 2021, the Department of Cardiology at the Mersin University Faculty of Medicine initiated a study under Digital Transformation with Artificial Intelligence in Health with support from AstraZeneca Turkey to use Qure’s AI solutions to understand the role of AI in predicting heart failures early from incidental findings on Chest X-rays. Only patients who were previously not suspected or identified for signs of heart failure were included in the study.

    Post risk assessment using the AI tool on chest radiographs, the department approached at-risk patients for follow-up tests. A larger number of patients were identified as at-risk but since this study was conducted during the pandemic restrictions, not all individuals came back to the hospital for follow-up. Of the high risk patients who came for follow up tests, 86% were identified to be confirmed heart failure patients. These individuals had confirmatory diagnoses with tests such as NT-proBNP and Echocardiography.

    The results of this year-long exercise have the potential to change the use of AI in cardiology altogether.

    Prof. Dr. Ahmet Çelik, President at Heart Failure Working Group of Turkish Society of Cardiology and the Principal Investigator in this research said,

    “In this study, which was carried out for the early diagnosis of heart failure, the power of artificial intelligence to predict heart failure by looking at lung X-rays was realized with a sensitivity of 89.1 percent and a selectivity of 86.4 percent. More importantly 65.3 percent of patients diagnosed with heart failure had Preserved Ejection Fraction Heart Failure which is difficult to diagnose.”

     Qure’s AI solution has been found to have 95%+ sensitivity for both cardiomegaly and pleural effusion. It could potentially be a game-changer as a silent reader, without increasing the work burden on healthcare professionals or adding significant costs by changing care pathways. It could screen all chest radiographs done worldwide on non-suspecting cases adding thousands of undiagnosed cases onto the cardiology risk assessment, diagnosis, and eventually treatment pathway. With a well-thought-through system for detection and diagnosis, this technology could mean more lives saved with minimal additional investment.

    AstraZeneca Middle East and Africa Region Medical Director Dr. Viraj Rajadhyaksha stated,

    “By applying advanced artificial intelligence and machine learning approaches to patients who go to different units for many reasons, this project will enable them to touch the lives of patients who are diagnosed early and to meet the right treatments much earlier. The results of the research have the potential to create an early detection tool for heart failure for the first time in the world.”

    AstraZeneca team aims to expand the project nationally and apply it to every lung x-ray taken. There has been research exploring the possibility of using AI for X-ray-based cardiac failure detection in a study setting. However, the potential impact on patients has not been demonstrated at such a scale before. This opens a world of opportunities for further focussed research evaluations to ascertain protocols of bringing in clinical practice.

    Prof. Dr. Ahmet Çamsari, the Rector of Mersin University is a strong believer in the potential of AI to impact the diagnostic pathway for patients. He said,

    “Our project will be one of the first projects where artificial intelligence is used in the early diagnosis of undiagnosed and suspected heart failure patients in our country and even in the world. In line with the results obtained, we aim to expand the project nationally and apply it to every lung x-ray taken. Again, we hope that these systems can be used in other fields such as radiological oncology and that artificial intelligence projects that touch the lives of patients can be implemented.”

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    Aarthi Scans: Scripting tele-radiology growth in India

    The rapidly increasing need for radiology diagnostic and image interpretation services around the world has brought two major issues to light. The first is the lack of radiologists and the other is the dearth of specialized knowledge.

    Building reliable communication and image transfer systems to tap into the expertise of radiologists who are not on-site can solve these issues to some extent. Hospitals, mobile imaging firms, urgent care clinics, and even some private practices all around the world are increasingly using tele-radiology. Tele-radiology improves patient care by allowing radiologists to provide services without having to be physically present at the imaging site so that the patients can receive round-the-clock access to trained specialists.

    Tele-radiology is significantly less expensive than having a radiologist on-site. These services are typically priced per exam, with the cost as low as $1 per X-ray Tele-radiology has transformed the practice of many radiology clinics around the world, allowing them to provide results faster and by facilitating access to the radiologist, adding enormous value to the diagnostic process.

    To set up a tele-radiology system between two centres, one requiring radiologist’s services and one providing radiologist’s services, the following elements are needed:

    1. Modality – a system that captures the medical image and has the facility to send these images in the preferred format i.e., DICOM
    2. PACS – a system that stores, sends, and receives medical images (DICOMs) and that can be identifiable by a unique address (like IP address, port number, etc.)
    3. Gateway – a medium that handles communication between the two centers (source and the destination) – receives the medical images from the source and sends back the output in the required format) using API calls. Complying with the data security and Protected Health Information (PHI) standards, de-identification and re-identification of confidential information can be performed by the Gateway.
    4. API Hub – a single place where all the Application Programming Interfaces (APIs) can be published and shared with external parties/clients by the intended service provider (tele-radiology) com

    Introducing Aarthi Scans: India’s largest Tele-radiology service provider

    India with its population of around 1.44 billion, right now we have around 1 radiologist for 100,000 people. Most rural India still lacks adequate radiological services and personnel, and not all imaging centers have subspecialty expertise, tele-radiology plays a significant role in quality diagnostics.

    Aarthi Scans and Labs, one of the largest diagnostic centers were started in the year 2000 by Mr. Govindarajan. Today Aarthi Scans has more than 100+ branches across 10 states.It was in 2011 that they started their tele-radiology services to provide quick reporting for emergency cases and nighttime reporting and to ensure continuous reporting even when radiologists go on leave. Their radiologists' review over 200,000 CTs, 250,000 MRIs, and 2.1 million chest X-rays annually.

    “At that point of time in 2011, when we started tele-radiology, telemedicine as a concept had not evolved in India. Radiologists giving reports without being present at the scanning location was viewed with skepticism. But once the referring doctors started viewing the benefits of tele-radiology, like nighttime reports, subspecialty reporting, they were impressed. Radiologists also needed a lot of convincing to report tele-radiology images. We standardised and digitised patient history, records, improved communication channels between tele-radiologists and radiographer in scanning sites and there was a slow and steady adoption by the Radiologist community. Our PACS vendor – Mr Ravindran from Innowave Healthcare Technologies helped us a great deal in solving the workflow related issues and helping us choose the right technology for us.”

     Govindarajan, Aarthi Scans and Labs

    Aarthi Scans has taken a step forward by incorporating Artificial Intelligence (AI) into their reporting procedure, demonstrating their commitment to staying on the cutting edge of technology. The ratio of one radiologist to more than 100,000 people in India has resulted from stress in radiology reporting, scan misreads and reporting delays. Any solution that might assist radiologists to relax and improve their productivity is always welcome at the technologically advanced setup at Aarthi Scans, and AI can be of immense value add in this scenario.

    Qure.ai's qXR, a Chest X-ray interpretation software – a CE Class II certified product – has been installed in Aarthi Scans diagnostic centers. The most common application of qXR in this setting is for Radiologist assistance to triage any scans with abnormalities on the worklist. The images are scanned and interpreted in under a minute. All scans that qXR identifies some findings in, are saved as a draft in the radiology worklist for further assessment and reporting. The report is generated in a natural language, significantly reducing the typing time that constitutes a significant portion of the reporting time. The final report is released in 30% lesser time due to this triaging mechanism and reading assistance by qXR.

    Qure.ai is a leading solution provider and we validated a few solutions before choose Qure.ai.  We chose qXR because the accuracy in categorising a study as normal or abnormal is very high (95%)!”.  “We have been using qXRin our day-to-day radiological practice across India in all our branches. We are huge fans of qXR's accuracy and utility.”

    – Dr. Arunkumar Govindarajan, Director, Aarthi Scans and Labs

    Technical Integration

    Qure PACS gateway for acquiring the Chest X-rays is integrated with Freedom Nano PACS which is present in every center of Aarthi Scans. Each center is authenticated with a unique token for the transmission of the studies and their corresponding results. The end-to-end transmission is supported by API calls that communicate with the Qure API hub to send the studies to the qXR AI models for processing. The AI interpretations are sent back to Freedom Nano PACS, where the radiologist can view the result from the individual centers. API and back to Freedom Nano PACS, where the radiologist can view the result from the individual centers. API and https-based communication make the data secured even on the cloud.

    Since partnering with Aarthi Scans four months ago, qXR has processed over 45,000 scans, triaging 55% of scans with abnormal findings. On a daily basis, qXR processes 200 chest X-rays.

    Scans that are classified as normal by qXR can be evaluated by the radiologist more quickly, giving them more time to review the abnormal scans and cutting down on overall reporting time. This has led to a reduction in the TAT by 30%. The qXR Secondary Capture output also uses contours to localize anomalies in the lungs, enabling radiologists to recognize abnormalities more accurately, without spending as much time as a regular scan.

    Finalising a Chest X-ray report as 'normal' is like passing through the valley of uncertainty for every Radiologist and qXR is like that friendly colleague who assists you with a second opinion / confirmation without bias. qXR saves our Radiologists' time and removes doubt while reporting.qXR has resulted in a 30% reduction in reporting time for our Radiologists.

    – Dr. Aarthi, Director, Aarthi Scans and Labs

    Insights into incorporating AI into the practice – Dr. Arunkumar Govindarajan

    “Learning about basics of Artificial Intelligence (AI) has helped me a lot to understand the inner workings of AI and terminologies. To start and understand in deep about AI one can take Coursera courses like – “AI for Everyone" by Andrew Ng and “AI for Medical Diagnosis” by DeepLearningAI. There are a lot of AI solutions out there, one can research in google to find which will suit your patients' and radiologist's needs. Once you fix a good AI vendor –

    • do your own validation and provide transparent feedback
    • partner with a good IT & PACS vendor and fix a workflow suited to your organization's needs. AI integration into PACS takes a bit of effort from the AI and PACS vendor. Be present during the meetings to ease the process and quickly resolve doubts”

    What's Next?

    After successful operations with qXR in all our centers, we will be next deploying Qure’s AI solution, qER for detecting brain abnormalities from head CT scans.

    “Time is Brain and quick qER report never goes in vain” Dr. Arunkumar Govindarajan, Director, Aarthi Scans and Labs

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    qCT-Lung: Catching lung cancer early

    In this blog, we will unbox qCT-Lung – our latest AI powered product that analyses Chest CT scans for lung cancer. At Qure.ai, we have always taken a holistic approach towards building solutions for lung health. qXR provides automated interpretation of chest X-rays and is complemented by qTrack, a disease & care pathway management platform with AI at its core. qCT-Lung augments our lung health suite with the ability to detect lung nodules & emphysema on chest CTs and analyze their malignancy. It can quantify & track nodules over subsequent scans. qCT-Lung is a CE certified product.

    qCT-Lung banner

    qCT-Lung: Catching lung cancer early

    Introduction

    Medical Imaging has seen the biggest healthcare advancements in artificial intelligence (AI) and lung health has been at the forefront of these improvements. Lung health has also been a key domain of our product portfolio. We’ve built AI algorithms like qXR, which provides automated interpretation of chest X-rays. We augmented its capabilities with qTrack – our AI powered disease management platform, which solves for active case finding & tracking patients in care pathways. These applications have empowered healthcare practitioners at all stages of the patient journey in TB, Covid-19 & lung cancer screenings.

    We’re adding a new member to our lung health suite: qCT-Lung. Its AI-powered algorithms can interpret chest CTs for findings like lung nodules & emphysema, and analyze their malignancy. It empowers clinicians to detect lung cancer in both screening programs as well as opportunistic screening settings.

    qXR & qCT-Lung’s abilities to support clinicians with detection of lung cancer on chest X-rays & CTs complement qTrack’s disease management & patient tracking capability. Together, they round up our lung health portfolio to make it a comprehensive, powerful & unique offering.

    Lung Cancer – The most fatal cancer

    Lung cancer is the second most common cancer in both men & women. 2.2 million people were diagnosed with lung cancer worldwide in 2020 [1]. With 1.74 million deaths in 2020, lung cancer is also the leading cause of cancer related deaths (18.4%) resulting in more deaths than the second and third deadliest cancers combined (colorectal – 9.2% & stomach – 8.2%).

    Future projections don’t look good either. Lung cancer incidents are projected to rise by 38% and the mortality is projected to rise by 39% by 2030 [2].

    There are two main types of lung cancer:

    • Non-small cell lung cancer (NSCLC): NSCLC comprises of 80-85% of all lung cancer cases. Their major subtypes are adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. They are grouped together because of shared similarity in treatment & prognoses.
    • Small cell lung cancer (SCLC): SCLC tends to grow and spread faster than NSCLC. 10-15% of all lung cancers are SCLC.

    There are also cancers that start in other organs (like breast) and spread to lung, but they don’t come under the vicinity of lung cancer.

    Early detection & outcomes

    Survival rates

    The 5-year survival is a measure of what percent of people live at least 5 years after the cancer is found. The 5-year survival rates for both NSCLC & SCLC look as follows [4]:

    Lung Cancer Survival rates

    Lung Cancer Survival rates

    The data shows that lung cancer mortality can be reduced significantly if detected & treated early.

    Early detection

    Data from England shows that the chances of surviving for at least a year decrease from 90% to 20% for the earliest to most advanced stage of lung cancer [5]. WHO elaborates on two components for early detection [6]:

    Early diagnosis

    Early identification of cancer results in better response to treatment, greater chances of survival, lesser morbidity & less expensive treatment. It comprises of 3 components:

    • Being aware of early symptoms of lung cancer like persistent cough, coughing up blood, pain in breathing, continuous breathlessness, loss of appetite, unexplained weight loss, etc [7].
    • access to clinical evaluation and diagnostic services
    • timely referral to treatment services.

    Screening

    Screening is aimed at identifying individuals with findings suggestive of lung cancer before they have developed symptoms. Further tests are conducted to establish if the diagnosis should be followed or referral for treatments should be made. They’re effective because symptoms of lung cancer do not appear until the disease is already at an advanced stage.

    Lung Cancer Screening Programs

    Screening programs use regular chest X-rays and low dose CT/ CAT scans to study people at higher risk of getting lung cancer. CT scans have proven to be more effective than X-rays. They resulted in a 20% reduction in lung cancer-specific deaths as compared to X-rays [2]. However, X-rays are more accessible and cheaper and thus, are important for low-income settings.

    The U.S. Preventive Services Task Force (USPSTF) recommends yearly lung cancer screening with LDCT for people who [9]:

    • Have a 20 pack-year or more smoking history, and
    • Smoke now or have quit within the past 15 years, and
    • Are between 50 and 80 years of age.

    Challenges in radiology screening today

    Chest CTs are comparatively more accurate than chest X-rays for identification of thoracic abnormalities. This is because of lack of superimposition, greater contrast, and spatial resolution. However, there are many challenges in identifying & reporting lung cancer on Chest CTs. These challenges can be divided into the following categories:

    Misdiagnosis

    A study revealed that 42.5% of malpractice suits on radiologists are because of failure to diagnose lung cancer [14]. These lawsuits can cost as high as $10M [15]. Misdiagnosis can occur due to two reasons [11]:

    • Lesion characteristics: Small dimension, poor conspicuousness , ill-defined margins and central location are the most common lesion characteristics that lead to missed lung cancers incidences.
    • Observer Error: There are multiple sources of observer error like:
      • Recognition error consists of missed detection of lesions.
      • Decision making error includes cases of inaccurately interpreted characteristics of a detected malignant lesion as benign/ normal.
      • Satisfaction of search error occurs when the observer fails to continue to search for subsequent abnormalities after identifying an initial one. Typically, this happens due to two possible mechanisms: ceasing the search for other abnormalities early in a positive exam and focusing on the wrong part of the exam.

    Analysis & Tracking

    Post detection of a lesion, a major challenge is to analyse its characteristics and determine malignancy. Even when the lesion’s malignancy is determined correctly, tracking them over subsequent scans is challenging for screening programs due to lack of appropriate CADs & tools.

    Structured reporting & Follow-ups

    Structured reporting helps to categorize results and recommend follow-ups based on chances of malignancy by considering size, appearance, and growth of the lesion. Further, volume measurement & volume doubling times (VDT) have been proposed in the management protocol of NELSON lung cancer screening trial [13]. All these metrics are challenging to calculate & report in absence of appropriate tools. This makes it hard to standardize follow up recommendations based on guidelines like Fleishner Society or Lung-RADS scores.

    Detecting relevant co-findings

    Certain other pulmonary findings like COPD (chronic obstructive pulmonary disease) are an independent risk factor for lung cancer. Lung cancer screening subjects have a high prevalence of COPD which accounts for significant morbidity and mortality.
    One of the major benefits of emphysema (a type of COPD) quantification in lung cancer screening patients is an earlier diagnosis and therapy of COPD with smoking cessation strategies. It can potentially lead to less COPD-related hospitalizations.

    Time constraints

    Interpreting CT scans is a time intensive process. A CT scan can have 16 to 320 slices compared to one or two images in an X-ray. Radiologists spend 5-10 minutes to interpret & report each CT scan.

    For chest CTs, detecting small nodules through hundreds of slices consumes a lot of time. There are tools that help with some of these issues but none of them solve for lung cancer screening comprehensively.

    qCT-Lung: AI powered lung nodule interpretation tool

    qCT-Lung empowers lung cancer screening programs and facilitates opportunistic screening by detecting malignant lesions using AI. It is aimed at helping clinicians with all the issues discussed in the previous section – misdiagnosis, analysis, reporting, detection of co-findings & reducing time constraints. The algorithm is trained on more than 200k chest CTs and can detect, analyze, monitor and auto-report lung nodules.
    This is how qCT-Lung assists clinicians in interpreting chest CTs for lung nodules:

    Detecting & Quantifying Lesions

    Secondary Capture

    Secondary Capture with detected nodule

    qCT can distinguish lung lesions from complex anatomical structures on lung CTs and minimize instances of letting lung cancers go undetected, by preventing nodules from being overlooked on scans. Faster and more accurate detection helps decrease time to treatment and improves patient outcomes.

    Advantages:

    • Detects lung nodules as small as 3mm with high accuracy (sensitivity of 95% and less than 1 false positives per scan)
    • Detects emphysema
    • Reduces the chance of missed nodules
    • Auto quantification of diameter & volume

    Analysis & Growth Monitoring

    Nodule Analysis & Malignancy Risk

    Nodule Analysis & Malignancy Risk

    qCT analyzes nodule characteristics to determine malignancy. The algorithm also assigns a malignancy risk score for each of the nodules that helps clinicians plan treatments.

    Advantages:

    • Analyses localization, spiculation, size, calcification & texture (solid, sub-solid & ground glass nodules)
    • Calculates Malignancy Risk Score
    • Measures volumetry and tracks growth of nodules
    • Predicts nodule volume doubling time
    • Precisely quantifies response to treatment

    Reporting Assistance

    Pre-filled report with suggested follow-ups

    Pre-filled report with suggested follow-ups

    qCT-Lung utilizes pre-populated results to offer clinicians faster reporting, that reduces time to treatment and further diagnosis. It can also recommend timelines for follow-up scans.

    Advantages:

    • Automates reporting to save time and reduce reporting workload
    • Pre-fed with the Lung-RADS & Fleischer Society Guidelines to suggest follow-ups.

    Modifiable Results

    qCt-Lung also offers a lung nodule reporting platform that is designed for screening programs. It enables clinicians to choose which nodules to include in the report and also to add new nodules. The platform pre-populates the image viewer with nodules identified by qCT-Lung. Clinicians can then exclude or add new nodules to this list. The final list after these changes is sent to the RIS.

    Advantages

    The platform empowers physicians to modify the results generated by qCT-Lung and report on what’s profoundly important for them.

    Qure’s Lung Health suite: A 3–pronged approach

    Qure's Lung Health Suite

    We have built an end-to-end portfolio for managing lung cancer screenings in all kinds of resource-settings. Lung cancer screening has many challenges. While CTs are recommended imaging modality, resource limited settings must depend on X-rays for its cost benefit and easy availability. Patient tracking, disease management and long term follow up for individuals with high-risk cases are also a challenge. Our comprehensive lung health suite takes care of these challenges.

    1. qXR – our chest X-ray interpretation algorithm detects lung nodules on X-rays with high accuracy.
    2. qCT-lungs does the same on chest CTs.
    3. qTrack is built and designed for community screening to track an individual’s disease and manage care pathways.

    Together, these solutions can help in active case screening, monitoring disease progression, reducing turn-around-time, linking care to treatment, & improving care pathways.

    Write to us at qct-lung@qure.ai to integrate qCT-Lung in your lung nodule management pathway.

    References

    1. Cancer.org: Key Statistics for Lung Cancer
    2. Chestnet.org: Lung Cancer Fact Sheet
    3. Cancer.org: What Is Lung Cancer?
    4. Cancer.org: Lung Cancer Survival Rates
    5. Cancer Research UK: Why is early diagnosis important?
    6. WHO: Fact Sheet on Cancer
    7. NHS UK: Lung Cancer Symptoms
    8. Cancer.org: Can Lung Cancer Be Found Early?
    9. CDC: Who Should Be Screened for Lung Cancer?
    10. National Lung Screening Trial Research Team, Aberle DR, Berg CD, et al. “The National Lung Screening Trial: overview and study design.” Radiology. 2011;258(1):243–253.
    11. del Ciello A, et al. “Missed lung cancer: when, where, and why? Diagnos.” Intervent. Radiol. 2017;23:118–126. doi: 10.5152/dir.2016.16187.
    12. Widmann, G. “Challenges in implementation of lung cancer screening—radiology requirements.” memo 12, 166–170 (2019).
    13. Dong Ming Xu, Hester Gietema, Harry de Koning, René Vernhout, Kristiaan Nackaerts, Mathias Prokop, Carla Weenink, Jan-Willem Lammers, Harry Groen, Matthijs Oudkerk, Rob van Klaveren, “Nodule management protocol of the NELSON randomised lung cancer screening trial”, Lung Cancer, Volume 54, Issue 2, 2006, Pages 177-184, ISSN 0169-5002
    14. Baker SR, Patel RH, Yang L, Lelkes VM, Castro A 3rd. “Malpractice suits in chest radiology: an evaluation of the histories of 8265 radiologists.” J Thorac Imaging. 2013 Nov;28(6):388-91.
    15. HealthImaging: Lung cancer missed on CT prompts $10M lawsuit against U.S. government