Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer
Year: 2025
Type: article
Study design: Observational Study
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 remains a significant challenge. This study explored the combined use of radiomics from CT scans and pathomics from H&E slides in three contexts: (1) predicting disease recurrence in early-stage NSCLC, (2) predicting immunotherapy response in advanced-stage NSCLC, and (3) predicting chemotherapy response in SCLC. The integrated radio-pathomic model significantly outperformed individual models. In early-stage NSCLC (N = 194), it achieved an HR of 8.35 (C-index: 0.71, p = 0.0043). In advanced-stage NSCLC (N = 35), the combined model improved predictive performance (AUC: 0.75, p = 0.042). In SCLC (N = 50), the integrated model showed an AUC of 0.78, surpassing both radiomic and pathomic models. These findings highlight the potential of combining radiomics and pathomics for improved lung cancer risk stratification and treatment prediction.
Source: npj Precision Oncology
Institutions: Case Western Reserve University, The Wallace H. Coulter Department of Biomedical Engineering, University Hospitals Cleveland Medical Center, Cleveland Clinic, NYU Langone Health, NYU Langone’s Laura and Isaac Perlmutter Cancer Center, Veterans Health Administration, Atlanta VA Medical Center
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
Funders: U.S. Department of Defense, U.S. Department of Veterans Affairs, Eli Lilly and Company, AstraZeneca, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, DOD Prostate Cancer Research Program, DOD Peer Reviewed Cancer Research Program
Awards: G100871905, G1031957857, RadxTools for assessing tumor treatment response on imaging, SYSTEMS BIOLOGY APPROACH TO PREDICTING AND ASSESSING RESPONSE TO CHEMORADIATION FOR BRAIN TUMORS, G1266336578, G1313646905, G1478154455, G1505200503, G1596146291, G1817069843, G1828163000, G2228475719, G2291306530, BIOMEDICAL ENGINEERING RESEARCH FACILITIES, G2689512122, G278880248, G2800195642, G2917785245, G2931214990, G3068466812, G3137332008, G338730684, G3564444776, G3716872827, G406353596, G4119534169, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, FUSED BIOMARKER-BASED PREDICTION OF AGGRESSIVE FROM INDOLENT PROSTATE CANCER, G5758079931, G5850682332, G5861339792, NOVEL CT-DERIVED RADIOMIC BIOMARKERS FOR LUNG NODULE CHARACTERIZATION IN THE SCREENING POPULATION, G6123616905, G620757052, G7124118669, Computer-Assisted Histologic Evaluation of Cardiac Allograft Rejection, QUANTITATIVE VESSEL TORTUOSITY AS BIOMARKER FOR PREDICTING AND MONITORING RESPONSE TO IMMUNOTHERAPY FOR NON-SMALL CELL LUNG CANCER FROM ROUTINE CT SCANS, G761790451, G7935030457, G8166743225, G854738250, G86714149, COMPUTATIONAL HISTOMORPHOMETRIC PREDICTOR OF PATHOLOGIC RESPONSE AND DISEASE RECURRENCE FOR TRIPLE-NEGATIVE BREAST CANCER, G9000000780, G9000021175, G9000023716, G9000064711, G9000153031, G9000175579, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, G942918344
OpenAlex ID: W4411032134