Frontiers in oncology · 2025
Multiparametric-MRI habitat radiomics analysis for discriminating pathological types of brain metastases.
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Zhu Jinling, Xie Xin, Deng Jixuan, Xu Ruizhe, Zou Li, Tian Ye, Cai Wu, Zhang Bo
Аннотация
BACKGROUND: Early identification of the primary tumor types in brain metastases (BMs) is crucial for developing effective treatment strategies. This study aimed to evaluate the potential of multiparametric MRI (mpMRI)-based habitat radiomics analysis in differentiating the pathological types of BMs.
MATERIALS AND METHODS: Pre-treatment MR images from 328 BMs patients at a single center were retrospectively collected and randomly divided into a training set (229 cases) and a test set (99 cases). Tumor regions were manually segmented on contrast-enhanced T1-weighted images (CE-T1WI), and the K-means clustering algorithm was employed to classify the tumor into four distinct sub-regions. Radiomics features were extracted separately from each sub-region to construct the habitat model. The resulting habitat model was compared alongside a traditional whole-tumor radiomics model, a clinical model, and a combined model (integrating habitat and clinical variables). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), as well as accuracy.
RESULTS: The combined model achieved the highest overall performance (training AUC: 0.992, accuracy: 0.952; test AUC: 0.939, accuracy: 0.845), outperforming the habitat model (training AUC: 0.965, accuracy: 0.876; test AUC: 0.888, accuracy: 0.835), traditional radiomics model (training AUC: 0.984, accuracy: 0.866; test AUC: 0.884, accuracy: 0.754), and clinical model (training AUC: 0.788, accuracy: 0.731; test AUC: 0.716, accuracy: 0.653). However, class-specific evaluation revealed substantial performance variation, with F1-scores of 0.874 for lung cancer BMs, but only 0.333 and 0.200 for breast and gastrointestinal cancer BMs, respectively.
CONCLUSIONS: This study demonstrates that while habitat radiomics shows potential for classifying BMs, its current performance is constrained by class imbalance and scanner heterogeneity. Consequently, our primary contribution lies in providing a critical baseline and a clear direction, prioritizing data-centric solutions as the essential next step for the field.
MATERIALS AND METHODS: Pre-treatment MR images from 328 BMs patients at a single center were retrospectively collected and randomly divided into a training set (229 cases) and a test set (99 cases). Tumor regions were manually segmented on contrast-enhanced T1-weighted images (CE-T1WI), and the K-means clustering algorithm was employed to classify the tumor into four distinct sub-regions. Radiomics features were extracted separately from each sub-region to construct the habitat model. The resulting habitat model was compared alongside a traditional whole-tumor radiomics model, a clinical model, and a combined model (integrating habitat and clinical variables). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), as well as accuracy.
RESULTS: The combined model achieved the highest overall performance (training AUC: 0.992, accuracy: 0.952; test AUC: 0.939, accuracy: 0.845), outperforming the habitat model (training AUC: 0.965, accuracy: 0.876; test AUC: 0.888, accuracy: 0.835), traditional radiomics model (training AUC: 0.984, accuracy: 0.866; test AUC: 0.884, accuracy: 0.754), and clinical model (training AUC: 0.788, accuracy: 0.731; test AUC: 0.716, accuracy: 0.653). However, class-specific evaluation revealed substantial performance variation, with F1-scores of 0.874 for lung cancer BMs, but only 0.333 and 0.200 for breast and gastrointestinal cancer BMs, respectively.
CONCLUSIONS: This study demonstrates that while habitat radiomics shows potential for classifying BMs, its current performance is constrained by class imbalance and scanner heterogeneity. Consequently, our primary contribution lies in providing a critical baseline and a clear direction, prioritizing data-centric solutions as the essential next step for the field.