CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction
Open accessAbstract
Background Use of adjuvant chemotherapy in patients with early-stage lung cancer is controversial because no definite biomarker exists to identify patients who would receive added benefit from it. We aimed to develop and validate a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (NSCLC) that is prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy following surgery. Methods We did a retrospective multicohort study of individuals with early-stage NSCLC (stage I and II) who either received surgery alone or surgery plus adjuvant chemotherapy. We selected patients for whom we had available pre-treatment diagnostic CT scans and corresponding survival information. We used radiomic texture features derived from within and outside the primary lung nodule on chest CT scans of patients from the Cleveland Clinic Foundation (Cleveland, OH, USA; cohort D 1 ) to develop QuRiS. A least absolute shrinkage and selection operator-Cox regularisation model was used for data dimension reduction, feature selection, and QuRiS construction. QuRiS was independently validated on a cohort of patients from the University of Pennsylvania (Philadephia, PA, USA; cohort D 2 ) and a cohort of patients whose CT scans were derived from The Cancer Imaging Archive (cohort D 3 ). QuRNom was constructed by integrating QuRiS with tumour and node descriptors (according to the tumour, node, metastasis staging system) and lymphovascular invasion. The primary endpoint of the study was the assessment of the performance of QuRiS and QuRNom in predicting disease-free survival. The added benefit of adjuvant chemotherapy estimated using QuRiS and QuRNom was validated by comparing patients who received adjuvant chemotherapy versus patients who underwent surgery alone in cohorts D 1 –D 3 . Findings We included: 329 patients in cohort D 1 (73 [22%] had surgery plus adjuvant chemotherapy and 256 (78%) had surgery alone); 114 patients in cohort D 2 (33 [29%] had surgery plus adjuvant chemotherapy and 81 (71%) had surgery alone); and 82 patients in cohort D 3 (24 [29%] had surgery plus adjuvant chemotherapy and 58 (71%) had surgery alone). QuRiS comprised three intratumoral and 10 peritumoral CT-radiomic features and was found to be significantly associated with disease-free survival (ie, prognostic validation of QuRiS; hazard ratio for predicted high-risk vs predicted low-risk groups 1·56, 95% CI 1·08–2·23, p=0·016 for cohort D 1 ; 2·66, 1·24–5·68, p=0·011 for cohort D 2 ; and 2·67, 1·39–5·11, p=0·0029 for cohort D 3 ). To validate the predictive performance of QuRiS, patients were partitioned into three risk groups (high, intermediate, and low risk) on the basis of their corresponding QuRiS. Patients in the high-risk group were observed to have significantly longer survival with adjuvant chemotherapy than patients who underwent surgery alone (0·27, 0·08–0·95, p=0·042, for cohort D 1 ; 0·08, 0·01–0·42, p=0·0029, for cohorts D 2 and D 3 combined). As concerns QuRNom, the nomogram-estimated survival benefit was predictive of the actual efficacy of adjuvant chemotherapy (0·25, 0·12–0·55, p<0·0001, for cohort D 1 ; 0·13, <0·01–0·99, p=0·0019 for cohort D 3 ). Interpretation QuRiS and QuRNom were validated as being prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy, especially in clinically defined low-risk groups. Since QuRiS is based on routine chest CT imaging, with additional multisite independent validation it could potentially be employed for decision management in non-invasive treatment of resectable lung cancer. Funding National Cancer Institute of the US National Institutes of Health, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, Department of Defence, National Institute of Diabetes and Digestive and Kidney Diseases, Wallace H Coulter Foundation, Case Western Reserve University, and Dana Foundation.
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A key issue with prior multimodal AI applications using handcrafted features was that difficulties requiring particular domain expertise were multiplied, as expert clinicians would also need to be involved in the feature design phase.
“A key issue with these approaches was that the difficulties requiring particular domain expertise are multiplied, as expert clinicians would also need to be involved in the feature design phase (Vaidya et al., 2020; Tortora et al., 2023).”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Vaidya et al., 2020
The Patients and Methods sections (pages unavailable) describe experienced radiologists identifying nodules and the extraction of radiomic features from annotated regions. This supports clinician involvement in preparing the imaging data, but the paper does not say clinicians were required for feature design or that domain-expertise difficulties were multiplied. (LLM, conf=0.82)
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- Authors
- Vaidya, P., Bera, K., Gupta, A., Wang, X., Corredor, G., Fu, P., Beig, N., Prasanna, P., Patil, P. D., Velu, P. D., Rajiah, P., Gilkeson, R., Feldman, M. D., Choi, H., Velcheti, V., & Madabhushi, A.
- Affiliations
- Case Western Reserve University (US)
- University Hospitals of Cleveland (US)
- Cleveland Clinic (US)
- Weill Cornell Medicine (US)
- Mayo Clinic (US)
- University of Pennsylvania (US)
- NYU Langone’s Laura and Isaac Perlmutter Cancer Center (US)
- Louis Stokes Cleveland VA Medical Center (US)
- Corresponding author
- Madabhushi, Anant
- ORCID
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 2
- Issue
- 3
- Pages
- e116-e128
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC7051021
- OpenAlex
- W3006347021
- Cited by
- 134
- References
- 27
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 22 · 2023: 18 · 2022: 30 · 2021: 28 · 2020: 6
- FWCI
- 9.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Defense (W81XWH-18\u20131-0440, W81XWH-18, 1-0440)
- National Institutes of Health (P50CA174523, 01A1, 1U24CA199374\u201301, 1P20 CA233216\u201301, R01 CA220581\u201301A1, 1U01 CA239055\u201301, R01CA202752\u201301A1, R01CA208236\u201301A1, R01 CA216579\u201301A1)
- DOD Peer Reviewed Cancer Research Program (W81XWH-16\u20131-0329, 1-0329, W81XWH-16)
- National Institute of Diabetes and Digestive and Kidney Diseases (1K25 DK115904\u201301A1)
- National Cancer Institute
- Wallace H. Coulter Foundation
- Case Western Reserve University
- Dana Foundation
- National Center for Research Resources (IBX004121A, 1 C06 RR12463, 1 C06 RR12463\u201301)
- Government of South Australia
- U.S. Department of Veterans Affairs
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Oncology › Inflammatory Biomarkers in Disease Prognosis
- Keywords
- radiomics, non-small cell lung cancer, adjuvant chemotherapy, early-stage lung cancer, disease-free survival, nomogram, texture features, computed tomography, prognostic score, lymphovascular invasion, TNM staging, surgical resection, treatment selection, LASSO Cox regression, model validation, The Cancer Imaging Archive, Cleveland Clinic Foundation, University of Pennsylvania
- MeSH terms
- Aged, Carcinoma, Non-Small-Cell Lung, Female, Forecasting, Humans, Male, Middle Aged, Neoplasm Staging, Retrospective Studies, Tomography, X-Ray Computed, Chemotherapy, Adjuvant, Outcome Assessment, Health Care, Disease-Free Survival
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