AJNR. American journal of neuroradiology · 2025
MRI-Based Quantification of Intratumoral Heterogeneity for Predicting Progression-Free Survival in Patients with Lung Cancer Brain Metastasis Receiving Radiotherapy.
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Guo Wei, Lin Lijuan, Wu Yanqing, Lin Xiang, Yang Guang, Song Yang, Chen Dehua
Аннотация
BACKGROUND AND PURPOSE: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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).
CONCLUSIONS: A habitat risk model based on intratumoral heterogeneity could be a reliable biomarker for predicting PFS in patients with LCBM receiving radiotherapy.
MATERIALS AND METHODS: 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.
RESULTS: 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).
CONCLUSIONS: A habitat risk model based on intratumoral heterogeneity could be a reliable biomarker for predicting PFS in patients with LCBM receiving radiotherapy.