MRI-Based Quantification of Intratumoral Heterogeneity for Predicting Progression-Free Survival in Patients with Lung Cancer Brain Metastasis Receiving Radiotherapy.
Authors
Affiliations (5)
Affiliations (5)
- From the Department of Radiology (W.G., L.L., Y.W., X.L., D.C.), the First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
- Department of Radiology (W.G., L.L., Y.W., X.L., D.C.), National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
- Shanghai Key Laboratory of Magnetic Resonance (G.Y.), East China Normal University, Shanghai, China.
- MR Scientific Marketing (Y.S.), Siemens, Shanghai, China.
- From the Department of Radiology (W.G., L.L., Y.W., X.L., D.C.), the First Affiliated Hospital of Fujian Medical University, Fuzhou, China [email protected].
Abstract
Our aim was to investigate the potential of using MRI-based habitat features for predicting progression-free survival (PFS) in patients with lung cancer brain metastasis (LCBM) receiving radiotherapy. One hundred and forty-six lesions from 68 patients with LCBM receiving radiotherapy were retrospectively reviewed and divided into training, random test (R-test), and time-independent test (TI-test) cohorts. Conventional radiomics and habitat features were extracted from the whole-tumor area and tumor subregions, respectively. Different machine learning risk models for predicting PFS were developed on the basis of clinical, radiomics, and habitat features, and their combination (clinical + habitat), respectively. The performance of the risk models was evaluated using the concordance index (C-Index) and Brier scores. The Kaplan-Meier curve was used to assess the prognostic value of the models. The habitat risk model achieved the best prediction ability among 4 different risk models in the TI-test cohort (C-Index: 0.716; 95% CI, 0.548-0.890). Additionally, the habitat and radiomics risk models outperformed the clinical risk model in the training (C-Index: 0.721-0.762 versus 0.697) and TI-test cohorts (C-Index: 0.630-0.716 versus 0.377). A habitat risk model based on intratumoral heterogeneity could be a reliable biomarker for predicting PFS in patients with LCBM receiving radiotherapy.