Radiomics-based machine learning to evaluate immunotherapy efficacy in non-small cell lung cancer patients with bone metastases.
Authors
Affiliations (6)
Affiliations (6)
- University of Calgary, Human Performance Laboratory, Faculty of Kinesiology, Calgary, Alberta, Canada.
- University of Calgary, McCaig Institute for Bone and Joint Health, Calgary, Alberta, Canada.
- University of Calgary, Schulich School of Engineering, Department of Biomedical Engineering, Calgary, Alberta, Canada.
- University of Calgary, Cumming School of Medicine, Calgary, Alberta, Canada.
- University of Calgary, Cumming School of Medicine, Department of Radiology, Calgary, Alberta, Canada.
- University of Calgary, Department of Surgery, Orthopaedic Surgery, Calgary, Alberta, Canada.
Abstract
Assessing treatment response in bone metastases from non-small cell lung cancer (NSCLC) remains a major clinical challenge, particularly for patients receiving immune checkpoint inhibitors (ICIs). The existing response criteria are not optimized for osseous disease, leading to inconsistent evaluation. We aimed to develop and validate a radiomics-based machine learning (ML) framework to non-invasively distinguish immunotherapy response categories-progression, stable disease, and partial response-in NSCLC patients with bone metastases. Chest computed tomography (CT) scans from 99 NSCLC patients were analyzed before and during ICI therapy. Bone structures were automatically segmented using TotalSegmentator, and 1051 radiomic features were extracted per time point. Clinical variables were incorporated as optional features. Three ML classifiers-random forest, XGBoost, and support vector machine-were trained using fivefold cross-validation. A multistep feature selection pipeline (correlation filtering, mutual information, recursive feature elimination, and ReliefF ranking) was applied. Model performance was evaluated using area under the curve (AUC), <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>F</mi> <mn>1</mn></mrow> </math> -score, accuracy, sensitivity, and specificity, with additional statistical testing using Kruskal-Wallis, Mann-Whitney <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>U</mi></mrow> </math> , bootstrapping, and permutation analysis. Inter-rater agreement for radiological response categories was high (Cohen's kappa = 0.91). Post-treatment radiomic features yielded the best performance. The random forest model achieved an AUC of 0.94, an <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>F</mi> <mn>1</mn></mrow> </math> -score of 0.79, an accuracy of 0.79, a sensitivity of 0.80, and a specificity of 0.83. Clinical features did not meaningfully improve performance. Models based on the largest lesion showed lower accuracy than those using the overall response. Post-treatment CT radiomics captured therapy-induced skeletal changes and enabled differentiation of immunotherapy response categories in NSCLC bone metastases. These findings highlight radiomics as a non-invasive tool for response assessment and guiding personalized treatment strategies.