Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer

Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer

Abstract Lung cancer, the leading cause of cancer-related deaths globally, includes non-small cell lung cancer (NSCLC) (85% of cases) and small cell lung cancer (SCLC) (13–15%). While accurate diagnosis and treatment selection are critical, the absence of reliable predictive or prognostic biomarkers...

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Main Authors: Pranjal Vaidya, Mohammadhadi Khorrami, Kaustav Bera, Pingfu Fu, Lukas Delasos, Amit Gupta, Cristian Barrera, Nathan A. Pennell, Vamsidhar Velcheti, Anant Madabhushi
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:npj Precision Oncology
Online Access:https://doi.org/10.1038/s41698-025-00939-0
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https://doi.org/10.1038/s41698-025-00939-0

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