FRONT PAGE: AI Predicts Treatment Response And Survival In Small Cell Lung Cancer Patients

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Newswise — CLEVELAND–Researchers at the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University have used artificial intelligence (AI) to identify patterns on computed tomography (CT) scans that offer new promise for treating patients with small cell lung cancer.

Small cell lung cancer (SCLC) represents about 13% of all lung cancers, but grows faster and is more likely to spread than non-small cell lung cancer, according to the American Cancer Society.

And while a lot of AI research has been performed on non-small cell lung cancer, little work has been done on SCLC, said CCIPD Director Anant Madabhushi, the Donnell Institute Professor of Biomedical Engineering at Case Western Reserve.

Lung cancer patients with small cells can be difficult to treat, Madabhushi stated. His laboratory worked closely with University Hospitals Cleveland oncologists to determine which SCLC patients would be able to receive treatment.

The researchers discovered radiomic patterns in CT scans that were taken before chemotherapy. These patterns allow them to predict the patient’s response. The association of AI-derived image features and longer-term outcomes was also studied.

Specifically, the researchers noted that computationally extracted textural patterns of the tumor itself–as well as the region surrounding it–were found to be different in SCLC patients who responded well to a certain chemotherapy, compared to those who did not.

Further, the AI revealed patterns that indicated patients who lived longer after treatment than those who didn’t.

Finally, Madabhushi stated that patients who didn’t respond to chemotherapy had significantly more variability and higher survival rates.

What’s next: possible human trials

These findings from a retrospective study now sets the stage for prospective AI driven clinical trials for treatment management of SCLC patients, Madabhushi said.

Results from the research were published in Frontiers in Oncology in October.

The researchers believe that chemotherapy is still the foundation of systemic treatment.

” Although most patients respond to treatment initially, relapses are common and some patients are resistant to chemotherapy,” Prantesh Jain (co-lead author of this study) said. He was working with the Department of Hematology and Oncology of University Hospitals. He is now an associate professor of oncology in Roswell Park Comprehensive Cancer Center, Buffalo.

“Currently,” Jain said, “there are no clinically validated predictive biomarkers to select a subpopulation of patients with primary chemoresistance or early recurrence.”

Broader AI initiative

The study is part of broader research conducted at CCIPD to develop and apply novel AI and machine-learning approaches to diagnose and predict therapy responses for various diseases and indications of cancer, including breast, prostate, head and neck, brain, colorectal, gynecologic and skin cancer. “Our efforts are aimed to reduce unnecessary chemotherapeutic treatment and so reduce patient suffering,” stated Mohammadhadi Khorrami (a CCIPD researcher, and PhD student of biomedical engineering at Case Western Reserve).

“By knowing which patients will benefit from therapy, we can decrease ineffective treatments and increase more aggressive therapy in patients who have suboptimal or no response to the first-line therapy.”

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Case Western Reserve University is one of the country’s leading private research institutions. We are located in Cleveland and offer unique educational opportunities in a stimulating cultural environment. In a collaborative and hands-on setting, our faculty members are able to teach and conduct research. Arts and sciences, dentistry, engineering, law and management, as well as nursing and social work, are some of our nationally recognized programs. About 5,800 undergraduate and 6,300 graduate students comprise our student body. Visit case.edu to see how Case Western Reserve thinks beyond the possible.

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