AI Tool That Identifies Patterns on CT Scans Offers New Promise for Treating Patients with Small Cell Lung Cancer

Researchers 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.

The researchers at the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University (Cleveland, OH, USA) identified a set of radiomic patterns from CT scans taken before treatment that allow them to predict a patient’s response to chemotherapy. They also examined the association between AI-derived image features with longer-term outcomes.

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. Small cell lung cancer patients can be challenging to treat. During their efforts to ascertain which SCLC patients would respond to treatment, the researchers found 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, patterns were revealed by the AI that corresponded to patients who ended up living longer after treatment compared to those who did not. Finally, the AI revealed that there was notably more heterogeneity, or variability, in the scanned images of patients who did not respond to chemo and had poorer chances of survival. These findings from a retrospective study now sets the stage for prospective AI driven clinical trials for treatment management of SCLC patients. Their findings are significant because chemotherapy remains the backbone of systemic treatment, the researchers said. 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 at reducing unnecessary chemotherapeutic treatments and thus reducing patient suffering,” said the study’s co-lead author Mohammadhadi Khorrami, a CCIPD researcher and PhD student in 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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