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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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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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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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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