Israeli biotech firm Medial EarlySign, which develops artificial intelligence (AI)-based clinical data solutions for early detection and prevention of high-burden diseases, announced this week the publication of new research impacting the early diagnosis of non-small cell lung cancer (NSCLC).

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Together with researchers from Kaiser Permanente Southern California, the Department of Health Systems Science from Kaiser Permanente Bernard J. Tyson School of Medicine, and the Department of Health Sciences, Brock University, St. Catharines, ON, Canada, study authors found that EarlySign's machine-learning model was more accurate for early diagnosis of NSCLC than either standard eligibility criteria for screening or the modified PLCOm2012, demonstrating the potential to help prevent lung cancer deaths through early detection.

The peer-reviewed retrospective data study, "Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data," was published in the American Journal of Respiratory and Critical Care Medicine.

The rationale for the study is that most lung cancers are diagnosed at an advanced stage, while pre-symptomatic identification of high-risk individuals can prompt earlier intervention and improve long-term outcomes. The objective was to develop a model to predict a future diagnosis of lung cancer based on routine clinical and laboratory data, using machine-learning.

Results of the study indicated that based on clinical characteristics and laboratory testing performed nine to 12 months before a clinical diagnosis of cancer, the EarlySign model was able to identify lung cancer with a sensitivity and specificity of 40.3% and 95%, respectively, with a positive test result indicating a 13-fold elevation in the odds of lung cancer. With further validation and refinement, this model has the potential to help prevent lung cancer deaths through earlier diagnosis.

"Lung cancer is the leading cancer killer of both men and women in the US with over 150,000 deaths expected each year," commented Michael K. Gould, MD, MS, and a professor of Health System Science from Kaiser Permanente Bernard J. Tyson School of Medicine.

"Earlier identification of high-risk individuals has the potential to improve lung cancer survival rates by finding the disease at a localized stage when it is more likely to be curable.  The machine learning models from EarlySign can help advance lung cancer identification by nine to twelve months which can lead to earlier diagnosis and treatment, when it matters the most," Gould said.

According to EarlySign VP of Clinical Research Eran Choman, "The recent pandemic has led to significant delay of diagnosis and treatment across the board, with delays in screening meaning that cancers may be more advanced and with more serious consequences."

Choman noted that "the collaborative efforts with the research team have been extraordinary in revealing how advanced AI predictive modeling can increase the predictive power of a model that could have a significant beneficial impact leading to additional early diagnosis and treatment of this serious disease."

Ori Geva, co-founder and CEO of EarlySign, said the company was "seeking to harness these results to further establish the value of this model to partner with providers, payers and life cciences and augment the identification of lung cancer and thus the treatment and better outcome for patients."

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Maccabi Healthcare Services' KSM Research and Innovation Center has partnered with Ibex Medical Analytics in a pilot of Ibex Medical Analytics' Galen Breast artificial intelligence-based diagnostic solution, Maccabi and Ibex announced last week.

Breast cancer is the most common malignant disease in women worldwide, with over 2 million new cases each year. Early and accurate detection is critical for effective treatment and saving women's lives.

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The pilot, operating at Maccabi's Pathology Institute includes 2,000 breast biopsies on which pathologists will use Galen Breast as a First Read application. This is the first-ever deployment of an AI application for primary diagnosis of breast cancer.

During the pilot, all breast biopsies examined at Maccabi will be digitized using a digital pathology scanner, and automatically analyzed by the Galen Breast solution prior to review by a pathologist. The solution detects suspicious findings on biopsies, such as regions with high probability of including cancer cells, and classifies them to one of three risk levels, ranging from high risk of cancer to benign.

Ibex's AI solution has been used at Maccabi's Pathology Institute since 2018, and all breast and prostate biopsies undergo an AI-based second read. The technology alerts pathologists when there are discrepancies between the pathologist's diagnosis and the AI algorithm's findings, thus providing a safety net in case of error or misdiagnosis.

"We are proud to use AI as an integral part of breast cancer diagnosis," said Dr. Judith Sandbank, director of the Pathology Institute at Maccabi. "We have already had a successful experience with Ibex's AI solution, enabling us to implement quality control and perform second read on biopsies, and now we are making a significant leap forward with the integration of AI into primary cancer diagnosis."

Ibex co-founder and CTO Dr. Chaim Linhart said, "Artificial intelligence is revolutionizing healthcare, and its integration into clinical practice will significantly improve the ability to diagnose cancer quickly and efficiently."

"Our solutions are used in routine practice in pathology laboratories worldwide, and have already helped detect breast and prostate cancers that were misdiagnosed by pathologists as benign. It is now time to take AI to the next level and employ its capabilities across a broader range of the diagnostic workflow," Linhart said.

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The  prize carries an award of $1 million.

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Barzilay has increasingly focused her work on health care. Her algorithms for early breast cancer diagnosis and risk assessment have been tested at hospitals around the world.

Barzilay herself is a breast cancer survivor. Through her research, she came to the conclusion that if the tools she is developing had existed when she was diagnosed, doctors could have discovered her cancer two to three years earlier.

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In addition to the allocation of 40 million shekels ($11.5 million) to pay for the tests, a national database to record the results will be established.

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The pilot program, which was designed by Professor Siegal Sadetzki, head of public health services in the Health Ministry, the nation's four HMOs (Clalit, Maccabi, Meuhedet, and Leumit) will identify current and former smokers aged 55-74 who smoke or used to smoke a pack of cigarettes a day for 30 years or two packs a day for 15 years.

Smokers who fit this profile will be sent for a type of CT scan that has been proven to help spot lung cancer in its early stages.

Each year, nearly 2,000 Israeli die of lung cancer. Research shows that early detection can help reduce the fatality rate of lung cancer by 40% among women and 26% among men.

The decision comes after a four-year battle to secure government coverage of the early detection tests, which was spearheaded by the Israeli Lung Foundation.

Founder and director of the ILCF Dr. Shani Shilo welcomed the news of the pilot early detection program and called it "a historic decision that will no doubt reduce the rate of fatality from the most lethal cancer."

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