Single- and multi-site radiomics may improve overall survival prediction for patients with metastatic lung adenocarcinoma

• Published in: Diagnostic and interventional imaging 2024-11, Vol.105 (11), p.439-452
• Main Authors: Masson-Grehaigne, Cécile, Lafon, Mathilde, Palussière, Jean, Leroy, Laura, Bonhomme, Benjamin, Jambon, Eva, Italiano, Antoine, Cousin, Sophie, Crombé, Amandine
• Format: Article
• Language: English
• Subjects: Adenocarcinoma of lung; Adenocarcinoma of Lung - diagnostic imaging; Adenocarcinoma of Lung - mortality; Adenocarcinoma of Lung - pathology; Adenocarcinoma of Lung - secondary; Adult; Aged; Biomarkers; Female; Humans; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - mortality; Lung Neoplasms - pathology; Male; Middle Aged; Neoplasm metastasis; Prognosis; Radiomics; Retrospective Studies; Tomography, X-Ray Computed
• ISSN: 2211-5684; 2211-5684
• DOI: 10.1016/j.diii.2024.07.005

•Radiomics scores based on the largest tumor lesion and averaged radiomics features, as well as intra-patient inter-tumor heterogeneity are associated with overall survival in a testing cohort.•Radiomics score based on the largest tumor lesion significantly improves the clinicopathological prognostic model.•Multi-site radiomics appears as a promising prognostic tool in patients with metastatic cancer. The purpose of this study was to assess whether single-site and multi-site radiomics could improve the prediction of overall survival (OS) of patients with metastatic lung adenocarcinoma compared to clinicopathological model. Adults with metastatic lung adenocarcinoma, pretreatment whole-body contrast-enhanced computed tomography examinations, and performance status (WHO-PS) ≤ 2 were included in this retrospective single-center study, and randomly assigned to training and testing cohorts. Radiomics features (RFs) were extracted from all measurable lesions with volume ≥ 1 cm3. Radiomics prognostic scores based on the largest tumor (RPSlargest) and the average RF values across all tumors per patient (RPSaverage) were developed in the training cohort using 5-fold cross-validated LASSO-penalized Cox regression. Intra-patient inter-tumor heterogeneity (IPITH) metrics were calculated to quantify the radiophenotypic dissimilarities among all tumors within each patient. A clinicopathological model was built in the training cohort using stepwise Cox regression and enriched with combinations of RPSaverage, RPSlargest and IPITH. Models were compared with the concordance index in the independent testing cohort. A total of 300 patients (median age: 63.7 years; 40.7% women; median OS, 16.3 months) with 1359 lesions were included (200 and 100 patients in the training and testing cohorts, respectively). The clinicopathological model included WHO-PS = 2 (hazard ratio [HR] = 3.26; P < 0.0001), EGFR, ALK, ROS1 or RET mutations (HR = 0.57; P = 0.0347), IVB stage (HR = 1.65; P = 0.0211), and liver metastases (HR = 1.47; P = 0.0670). In the testing cohort, RPSaverage, RPSlargest and IPITH were associated with OS (HR = 85.50, P = 0.0038; HR = 18.83, P = 0.0082 and HR = 8.00, P = 0.0327, respectively). The highest concordance index was achieved with the combination of clinicopathological variables and RPSaverage, significantly better than that of the clinicopathological model (concordance index = 0.7150 vs. 0.695, respectively; P = 0.0049) Single-site and multi-site radiomics-based