Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC. (August 2022)

• Record Type: Journal Article
• Authors: Keek, Simon A.; Kayan, Esma; Chatterjee, Avishek; Belderbos, José S. A.; Bootsma, Gerben; van den Borne, Ben; Dingemans, Anne-Marie C.; Gietema, Hester A.; Groen, Harry J. M.; Herder, Judith; Pitz, Cordula; Praag, John; De Ruysscher, Dirk; Schoenmaekers, Janna; Smit, Hans J. M.; Stigt, Jos; Westenend, Marcel; Zeng, Haiyan; Woodruff, Henry C.; Lambin, Philippe; Hendriks, Lizza
• Is Part Of: Therapeutic advances in medical oncology. Volume 14(2022)
• Publication Date: 2022-08
• Subjects: CT -- metastatic brain tumours -- non-small-cell lung cancer -- predictive biomarker -- tumour biology; Oncology -- Periodicals; Cancer -- Treatment -- Periodicals; 616.994005
• DOI: 10.1177/17588359221116605
• Languages: English
• ISSNs: 1758-8340

Abstract

Introduction: Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC. Methods: Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [ 18 F-fluorodeoxyglucose positron emission tomography-computed tomography (18-FDG-PET-CT), contrast-enhanced chest CT, contrast-enhanced brain magnetic resonance imaging/CT] and radically treated stage III NSCLC, exclusion criteria: second primary within 2 years of NSCLC diagnosis and prior prophylactic cranial irradiation. Primary endpoint was BM development any time during follow-up (FU). CT-based radiomics features ( N = 530) were extracted from the primary lung tumour on 18-FDG-PET-CT images, and a list of clinical features ( N = 8) was collected. Univariate feature selection based on the area under the curve (AUC) of the receiver operating characteristic was performed to identify relevant features. Generalized linear models were trained using the selected features, and multivariate predictive performance was assessed through the AUC. Results: In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58–0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47–076 and 0.48–0.76, respectively). Conclusion: CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients.