RATIONALE AND OBJECTIVES
Breast cancer exhibits high biological heterogeneity and variable invasion patterns. Tumor budding (TB) is a key histopathological marker of aggressive behavior and poor prognosis. However, preoperative TB assessment is limited by biopsy sampling issues. This study aims to develop a multiparametric magnetic Resonance Imaging (MRI)-based habitat radiomics model for noninvasive preoperative prediction of TB status, providing an imaging biomarker to support personalized surgical planning.
MATERIALS AND METHODS
This retrospective study included 281 breast cancer patients who underwent preoperative multiparametric MRI. Patients were divided into a training set (n = 175), an internal validation set (n = 76), and an external validation set (n = 30). Intratumoral habitats were generated by combining voxel-based K-means clustering with a 3 × 3 × 3 neighborhood expansion, resulting in 16 subregions per patient, from which radiomic features were extracted. Multiple TB prediction models were constructed using two machine learning algorithms and evaluated comprehensively. Furthermore, a nomogram was built by integrating key clinical factors and heterogeneity features to assess clinical utility, and the model was subjected to SHapley Additive exPlanations (SHAP) interpretability analysis.
RESULTS
Multivariate analysis identified Ki-67 and enhancement pattern as independent predictors of TB. The combined model (logistic regression [LR]) achieved optimal performance, with area under the receiver operating characteristic curve values of 0.927, 0.858, and 0.858 in the training, internal validation, and external validation sets, respectively. SHAP analysis revealed that T2 habitat elongation, diffusion-weighted imaging habitat flatness, enhancement pattern, and Ki-67 were the core contributing features of the model. Higher elongation, lower flatness, and higher Ki-67 levels were associated with an increased risk of TB, providing potentially interpretable biological evidence for model decision-making.
CONCLUSION
An interpretable, multiparametric MRI-based intratumoral habitat heterogeneity machine learning model can noninvasively predict TB status preoperatively, offering a reliable reference for individualized treatment planning in breast cancer patients.
Min Sun, Weining Zhao, Yuwei Wang et al.· Academic Radiology· 0 citations
Objective To investigate white matter microstructural alterations in amyotrophic lateral sclerosis (ALS) using free-water-corrected diffusion tensor imaging (FW-DTI), compare its findings with those of conventional DTI, and examine the clinical correlations and preliminary diagnostic value of these metrics. Methods 44 ALS patients and 42 healthy controls underwent multi-b-value diffusion MRI. Conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AxD], radial diffusivity [RD]), free-water-corrected metrics (FW-FA, FW-MD, FW-AxD, FW-RD), and the free-water fraction (FWF) were calculated. Tract-based spatial statistics (TBSS) was used for voxelwise group comparisons. Correlations between clinical parameters, including disease progression rate (ΔFS) and the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) score, and DTI metrics were examined. A diagnostic nomogram was constructed using logistic regression based on imaging markers that showed significant differences between groups. Results Conventional DTI identified white matter abnormalities in ALS-related regions, including corticospinal tract-related regions, the corpus callosum, and the cingulate gyrus. FW-DTI showed additional and partially distinct alterations, including changes in the fornix, bilateral superior corona radiata, anterior and posterior corona radiata, and the posterior limb of the internal capsule. The free-water fraction did not differ between groups. Correlation analysis revealed that ΔFS was negatively associated with FA in the left posterior limb of the internal capsule (r = −0.432), and the ALSFRS-R score was positively associated with FW-FA in the right anterior corona radiata (r = 0.389). A diagnostic nomogram combining FA in the right cerebral peduncle and FW-FA in the right anterior corona radiata showed preliminary discriminative performance (area under the curve [AUC] = 0.860). Conclusion FW-DTI may provide complementary model-derived information for characterizing ALS-related white matter alterations beyond conventional DTI. Specific regional metrics were associated with ΔFS and the ALSFRS-R score, and the preliminary diagnostic nomogram yielded an AUC of 0.860.
Ze-Lin Liu, Hai-Qing Yang, J. Cui et al.· Frontiers in Neuroscience· 0 citations
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