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Incidental Lung Nodule Detection May Help Save Lives, Study Finds

The study authors say their findings argue for careful reading of CT scans done for other reasons that may reveal the presence of lung nodules.

Imagine you're getting a chest scan for a potential heart problem, and instead of a heart issue, it reveals a small spot on your lungs. A recent study published in the Journal of Thoracic Oncology suggests that these unexpected findings could be a lifesaver, offering a rare opportunity to catch lung cancer early—when it's much easier to treat.

Lung cancer remains the leading cause of cancer-related deaths in the United States, with most cases diagnosed at later, less treatable stages. Although low-dose computed tomography (CT) screening has proven effective in reducing mortality by detecting early-stage lung cancer, fewer than 5% of eligible individuals currently participate in screening programs. As a result, researchers are now examining alternative ways to detect lung cancer early, including through incidental findings during imaging done for other health concerns.

A team of researchers, including Emanuela Taioli, M.D., Ph.D., director of the Institute for Translational Epidemiology at the Icahn School of Medicine at Mount Sinai in New York, explored the effect of incidental findings of pulmonary nodules on non-small cell lung cancer (NSCLC) mortality.

The researchers analyzed data from over 1,000 patients diagnosed with NSCLC using the Surveillance, Epidemiology, and End Results (SEER)-Medicare database. They focused on chest imaging performed up to a year before cancer diagnosis for reasons unrelated to cancer screening, such as evaluating heart issues.

At the time of diagnosis, the median tumor size was 25 millimeters (mm). The predicted tumor sizes at the time of prior imaging were 12.16 mm for fast-growing tumors, 17.3 mm for medium-growing tumors, and 20.42 mm for slow-growing tumors.

The study found that if the nodules had been detected earlier, it could have lowered the risk of death by 7.79% for fast-growing tumors, 4.5% for medium-growing tumors, and 2.45% for slow-growing tumors.

Emanuela Taioli, M.D., Ph.D.

"Our findings support the concept that CT scans performed for other clinical reasons, for example, a heart condition or other issues, should be read in their entirety and interpreted carefully because they may reveal the presence of lung nodules that may need clinical attention," Taioli, the study's corresponding author, told MHE in an interview. "This is particularly true for [patients] who do not meet the eligibility criteria for lung cancer screening."

The study also revealed that approximately 10.5% of patients diagnosed with NSCLC had undergone chest imaging for reasons other than cancer screening in the year prior to their diagnosis. The study's findings suggest that identifying tumors earlier, when they are smaller (less than 10 mm), could significantly improve survival rates. For fast-growing tumors, early detection could have decreased mortality by up to 8%.

However, the study had some limitations.The authors were unable to account for various factors, such as tumor density, which could affect tumor growth rates. Additionally, the study only included NSCLC patients, and it's expected that patients with more aggressive cancer types, such as small cell lung cancer (SCLC), could benefit even more from incidental detection. The study also had to rely on estimated tumor size due to limitations in the SEER database, which only reports tumor diameter.

Despite these limitations, the findings support the need for greater attention to chest imaging done for non-cancer reasons. In the discussion section of their paper, the researchers highlighted the importance of implementing programs to address incidental pulmonary nodules. Machine learning and artificial intelligence-driven tools, already approved for use in detecting lung nodules, could also play a significant role in identifying tumors early.

In many cases, "the incidental finding of a nodule in a CT scan performed for other reasons may be the only opportunity for early detection of lung cancer nodules," Taoli said. "Early detection of lung cancer has revolutionized lung cancer prognosis, from a deadly disease to a highly curable one."

Using Precision Diagnostics For Early Detection Of Lung Cancer

A key focus of R&D for Sanmed Biotech is precisely detecting and profiling lung nodules as a means to better diagnose lung cancer.Credit: Kateryna Kon/Science Photo Library/Getty

Lung cancer is the second most common type of cancer and the leading cause of cancer deaths worldwide. Early-stage lung cancer is often asymptomatic, meaning patients are typically diagnosed at an advanced stage, where tumours are growing rapidly and the prognosis is grave. A cross-disciplinary team at Sanmed Biotech, a precision medicine innovation firm founded in 2016, is hoping to improve the outlook for lung cancer through new solutions for early diagnosis.

"The survival rate of stage 1A lung cancer at 10 years is over 90%. Yet for late-stage lung cancer patients, the survival drops significantly and only the 5-year survival rate is calculated," says Frank Shi, CEO of Sanmed Biotech. "Early diagnosis of lung cancer is critical in reducing the mortality rate and prolonging survival."

Credit: Sanmed Biotech

Credit: Sanmed Biotech

Credit: Sanmed Biotech

As Shi explains, the company has its roots in Cynvenio Biosystems, a global pioneer in liquid biopsy technology founded in 2008 by Nobel-winning physicist and chemist, Alan J. Heeger, and company Zhuhai Livzon Diagnostics, one of China's first in vitro diagnostic products manufacturing enterprises.

Differentiating cancerous nodules

The research and development priority at Sanmed Biotech is to precisely detect and profile lung nodules — small lumps of tissue that can be benign, precancerous or metastatic tumours. Low dose computed tomography (LDCT) screening is the recommended test to detect and measure nodules, but it's fraught with challenges.

"Where nodules are found to be 15 or even 20 millimeters, clinicians may propose further testing, such as a positron emission tomography scan, bronchoscopy or tissue biopsy," says Xin Ye, product development director of Sanmed Biotech. "However, LDCT has proven difficult to accurately profile smaller suspect nodules, presenting a major diagnostic challenge for clinicians to determine whether the nodule is malignant or benign, or whether an invasive biopsy or immediate surgical resection is necessary."

An effective, non-invasive, early detection test is needed to improve the diagnostic efficacy of LDCT, and Sanmed Biotech's proprietary liquid biopsy technology, fits the bill, says Ye.

Sanmed's liquid biopsy assay uses a novel multiplex fluorescence in-situ hybridization (FISH) test to detect chromosomal aberrant cells (CACs) in peripheral blood: a technique developed in 2010 by Ruth Katz. In 2020, refinements of this technique by Katz's team led to improved diagnosis of benign and malignant lung nodules by detecting chromosomal abnormalities in the peripheral blood genome through a simple, safe, effective, non-invasive test.

Sanmed's test contrasts with other liquid biopsy technologies that detect 'antigen-dependent' circulating tumour cells (CTC): these may lose sensitivity if the tumour cells alter the antigen profile by leaking into the bloodstream. Sanmed's liquid biopsy assay can distinguish between benign and malignant lung nodules by identifying individual cells bearing gains and/or loss of specific chromosomal loci that are labeled with distinct fluorophores.

Towards greater diagnostic certainty

Early lung cancer diagnosis is also impeded by the absence of standardized methods for interpreting LDCT images. Due to imaging anomalies and human error, doctors may reach different diagnostic conclusions on the same scan. So, Sanmed Biotech has invested globally in specialized high-throughput computation and image recognition algorithms to automate the LDCT image and data analysis.

One outcome is the SANMED Target Call Lung Nodule Analysis Platform, which is built on deep convolutional neural networks and machine learning algorithms trained on a massive dataset of around 300,000 annotated pulmonary nodules. This platform can automatically reconstruct, segment, and analyse the LDCT images and label the lung nodules with the relevant parameter values indicating malignancy risks.

In 2022, Sanmed Biotech published its preliminary findings in Frontiers in Oncology. This study proposed a machine-learning-based prediction model, which integrates clinical characteristics (age and smoking history) and radiological profiles of nodules with the artificial intelligence (AI) analysis of LDCT data and Sanmed's liquid biopsy assay results. In a sample of 728 subjects, the model achieved optimal diagnostic performance, outperforming any approaches conducted alone.

Ramping up with applications

Sanmed has established a sound intellectual property protection system and its technological advantage has stretched to far-reaching applications and collaborations. At present, the products are in clinical use in more than 20 leading hospitals in China.

Furthermore, the company's comprehensive solution in early detection of lung cancer attracted a specialist in respiratory diseases. Chunxue Bai, chair of the Chinese Alliance against Lung Cancer and vice president of the Chinese Respiratory Association, plans to partner with the company to conduct a multi-center clinical study of AI-assisted Sanmed's liquid biopsy assay diagnosis of benign and malignant lung nodules in China, enrolling more than 10 hospitals and 100,000 participants.

"We strive to offer cutting-edge products to maximize benefits for both patients and physicians," says Ye. "We believe our non-invasive option will be a useful complementary tool for clinicians in assessing early lung cancer. It could help to improve early lung cancer diagnosis and treatment in patients with malignant nodules while sparing those with benign entities from unnecessary and potentially harmful surgery."

Contact Info

Phone: +86-0756-6931621

Email: info@sanmedbio.Com

Website: www.Sanmedbio.Com

This advertisement appears in Nature Spotlight 2022 Precision medicine, an editorially independent supplement. Advertisers have no influence over the content.

Use Of AI Lets Health System Find Lung Cancer At Early Stages

Artificial intelligence (AI) helps a Sarasota, Florida, health system catch lung nodules that appear on CT scans for patients treated for scores of conditions, allowing them to be referred for a possible lung cancer diagnosis.

For Amie J. Miller, MSN, APRN, AOCNP, ACHPN, CTTS, November always brings opportunity, and a little frustration.

She was excited at how many attendees at the Association of Cancer Care Centers' (ACCC) 51st Annual Meeting & Cancer Center Business Summit knew that November was Lung Cancer Awareness Month. But she admitted it's tough following the annual pink out to promote Breast Cancer Awareness Month that comes every October.

Lung cancer, she notes, kills more Americans than breast, colon, or prostate cancer combined.

Amie J. Miller, MSN, APRN, AOCNP, ACHPN, CTTSImage: LinkedIn

Miller shared this observation at Thursday's ACCC session on how artificial intelligence (AI) and business intelligence solutions can help health systems make practice improvements. She runs an AI-fueled effort that saves lives and brings more than $1 million a year to Sarasota Memorial Health Care System in southwest Florida.

At Sarasota's Brian D. Jellison Cancer Institute, Miller is coordinator of the Lung Cancer Early Detection and Prevention Program, which features an initiative to follow up on incidental pulmonary nodules (IPNs) detected through AI. IPNs are asymptomatic lesions that may be detected on a CT scan during an emergency room visit or other intervention; these could signal early lung cancer but often go unaddressed.

Recent therapeutic advances in the treatment of lung cancer mean survival rates are vastly improved—if cancer is caught early. However, as a 2024 report from the American Lung Association showed, disparities are widening in biomarker testing and lung cancer screening, meaning that that whether one dies of lung cancer may depend on access. Miller said national lung cancer screening rates remain stubbornly low at 18.1%, although there are pockets of the country—such as the catchment area for St. Elizabeth's in Kentucky—where about 45% of the eligible population is screened.

As Miller explained, understanding who is eligible for screening is key. Recommendations from the US Preventive Services Task Force (USPSTF) call for screening those aged 50 to 80 years who are current smokers or who have quit within the past 15 years, after smoking 20 years or more. "When we look at the data, only about 42% of lung cancer patients qualified for lung cancer screening," she said. "So, that's a real issue."

Thus, 44% of lung cancers are diagnosed at a late stage, where survival rates are significantly lower if cancer is caught at stage I or II. That's where an IPN program can make a difference, Miller said. According to a 2022 study by Penn Medicine, about 10% of patients with an IPN of more than 8 mm will receiving a lung cancer diagnosis.

So, who are these patients?

"Incidental pulmonary nodules are a different animal than lung cancer screening," Miller said. "With cancer screening, you have people who are well insured; they're well connected to their primary care doctor, they're educated, and they're advocating for themselves. Whereas, with IPNs, these patients are oftentimes within the emergency room for their health care, oftentimes they don't have a primary care physician, oftentimes they are uninsured."

Miller took the audience step by step through Sarasota's journey with its AI-driven lung cancer screening and IPN initiative:

Sarasota Memorial Healthcare System invested in AI-driven software in 2019, which boosted its lung cancer screening program and allowed the launch of an IPN program designed to catch cancers at earlier stages.

The AI technology reads close to 150,000 CT scans that come in across the 1100-bed system each year. A radiologist will accurately report what is on the scan; almost always, patients are not receiving a scan for a lung nodule.

Previously, the patients would discharge to a primary care physician who would be unaware of the IPN; a patient with very early stage lung cancer might return several years later with late-stage lung disease. Miller said, "This is something that is happening in all of your emergency rooms today. I guarantee it."

Under the IPN program, the AI flags IPNs that merit follow-up, and information on these patients is forwarded for follow-up. A communication tool kits was developed to help reach out to patients to educate them on what the CT scan showed and next steps.

After integrating AI, Sarasota's annual screening volume rose by 99%, from 676 patients in 2019 to 1342 patients in 2023. Screening rates surpass national benchmarks for early detection, with 70% of lung cancer screening and 44% of incidental pulmonary nodule cancer cases diagnosed at stages I and II.

Miller emphasized that the AI program has more than paid for itself: she wrote that the health care system produced a return rate of return of more than 85% for high-risk lung cancer patients, with an average of 91% from 2019 through Q2 2024, compared with the national average of 22.3%.

According to the session abstract, "The AI solution helped generate $8,321,128 in downstream charges and $803,106 in contribution margin from 1702 cases and $5,559,125 in charges and $349,121 in contribution margin from 275 cases, from the lung cancer screening and incidental pulmonary nodule programs, respectively."

A key to the AI program's success is that it doesn't interrupt the workflow; yet it tracks the patients and allows IPNs to be caught in a timely manner. Each day, the AI program scans CT scans and pulls a what Miller calls a "work list" of patients who need follow-up. "And I guarantee you, every single day I call a patient and say, 'By the way, you were in the in the emergency room last week having your CT scan done because you had a kidney stone—you actually have a spot on your lung and it measures 10 millimeters.' And I can't tell you how many times patients [say], 'I had no idea.'"

There's now a weekly meeting among a team of pulmonologists and a thoracic surgeon to discuss high-risk cases that emerge from the AI-driven caseload; Miller said they cover 10 cases in 30 minutes, and over time, they are building awareness about IPNs.

"There is ongoing community physician outreach," she said. "This is something that will generate increases in our screening numbers…. That's something I talk about every day—how can we increase our screening volumes, by engaging physicians and empowering and educating the communities?"

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