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After preprocessing and ROI segmentation, habitat sub-regions were defined by clustering, radiomics features extracted and filtered, and four Random Forest models built from T1WI/T2WI classic radiomics and habitat features. Results: Best clustering number was three; in external testing, T2WI habitat achieved AUC 0.919, outperforming other models. SHAP enhanced interpretability by highlighting key feature contributions. Conclusion: T2WI habitat robustly distinguishes benign from malignant VCFs and supports early clinical decision-making.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/mri-based-habitat-analysis-for-differentiating-benign-and-malignant-vertebral-compression-fractures-research-article/451962/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/mri-based-habitat-analysis-for-differentiating-benign-and-malignant-vertebral-compression-fractures-research-article/451962.png","ImageObject",300,407,{"name":42,"@type":43},"Kurz","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the study address for vertebral compression fractures?","Question",{"text":63,"@type":64},"Differentiating benign versus malignant VCFs using conventional MRI is challenging. The study aims to improve diagnostic discrimination with a dedicated MRI-based habitat approach.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were the MRI habitat sub-regions created and which models were built?",{"text":68,"@type":64},"After preprocessing and ROI segmentation, clustering determined the optimal number of habitat sub-regions. Radiomics features were extracted and used with a Random Forest to build four models: T1WI classic radiomics, T1WI habitat, T2WI classic radiomics, and T2WI habitat.",{"name":70,"@type":61,"acceptedAnswer":71},"Which model performed best on the external test set and what role did SHAP play?",{"text":72,"@type":64},"The T2WI habitat model performed best on the external test set with an AUC of 0.919. 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BMC Medical Imaging (2026) 26:5 [https://doi.org/10.1186/s12880-025-02090-5](https://doi.org/10.1186/s12880-025-02090-5)  \nBMC Medical Imaging  \nRESEARCH Open Access  \nMRI-based habitat analysis for differentiating  benign and malignant vertebral compression fractures  \nXiaoshan Liu 1, Zhiqin Gao2, Junqiao Guo 1, Haifeng Zhang 1, Hanlin Zhu 1, Ming Zhan 1, Bo Feng 1 and Wenyan Wei3*  \nAbstract  \nObjective Differentiating between benign and malignant vertebral compression fractures (VCFs) using conventional MRI images presents a significant challenge. This study seeks to develop an MRI-Based Habitat model, to effectively distinguish between benign and malignant VCFs. Additionally, the study aims to employ the SHapley Additive exPlanations (SHAP) method to provide interpretability for the radiomics model.  \nMethods A retrospective analysis of MRI data from 132 patients (ages range 40–97 years) with VCFs was performed across two medical centers (January 2021–December 2024), with 97 in the training set and 35 in the external test set. Following the preprocessing and segmentation of the regions of interest in the MRI images, a habitat analysis model was developed utilizing T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) data. The optimal number of clusters was identified to delineate sub-regions. Subsequently, radiomics features were extracted and filtered, and the Random Forest (RF) algorithm was employed to construct four distinct models: aT1WI classic radiomics model, aT1WI habitat model, aT2WI classic radiomics model, and aT2WI habitat model. The diagnostic performance of each model in distinguishing between benign and malignant VCFs was assessed using the area under the receiver operating characteristic curve (AUC) . The model demonstrating the highest performance was further visualized using the SHAP method.  \nResults The analysis determined that the optimal number of clusters for the habitat sub-regions in both T1WI and T2WI sequences was three. Within the external test set, the T2WI habitat model demonstrated superior diagnostic performance, achieving an AUC of 0 . 919, the T1WI habitat model achieved an AUC of 0 . 806, and the T1WI and T2WI classic radiomics models AUCs of 0.898 and 0. 863, respectively. SHAP value analysis indicated that, within the T2WI habitat model, the absolute weight of Inverse Variance in Habitat sub-region 1 was the highest, with a value of 0.253. Conclusion The T2WI habitat model developed from MRI images demonstrates a robust capability to distinguish between benign and malignant VCFs, surpassing the performance of classic radiomics models. Furthermore, the visualization of SHAP values enhances the model’s interpretability, offering valuable scientific indicators for the early clinical differentiation of benign and malignant VCFs.  \nKeywords Vertebral fracture, Habitat, Radiomics, SHAP, Machine learning  \n*Correspondence: Wenyan Wei [wyw628000@163.com](wyw628000@163.com)  \n1Department of Radiology, Hangzhou Ninth Hospital, Hangzhou Red Cross Hospital Qiantang Campus, Hangzhou 310012, China  \n2Department of Radiology, Hangzhou Xiaoshan District Traditional Chinese Medicine Hospital, Hangzhou 311201, China 3Department of Radiology, Hangzhou Cancer Hospital, Hangzhou, Zhejiang 310002, China  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit","cbCaiiEdDwShiUHT","https://ap.wps.com/l/cbCaiiEdDwShiUHT","pdf",3706797,11,"English","# Abstract\n## Objective\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What problem does the study address for vertebral compression fractures?\",\"answer\":\"Differentiating benign versus malignant VCFs using conventional MRI is challenging. The study aims to improve diagnostic discrimination with a dedicated MRI-based habitat approach.\"},{\"question\":\"How were the MRI habitat sub-regions created and which models were built?\",\"answer\":\"After preprocessing and ROI segmentation, clustering determined the optimal number of habitat sub-regions. Radiomics features were extracted and used with a Random Forest to build four models: T1WI classic radiomics, T1WI habitat, T2WI classic radiomics, and T2WI habitat.\"},{\"question\":\"Which model performed best on the external test set and what role did SHAP play?\",\"answer\":\"The T2WI habitat model performed best on the external test set with an AUC of 0.919. SHAP value visualization increased interpretability by indicating the contribution of key habitat sub-region features.\"}]","MRI-based habitat analysis for differentiating benign and malignant vertebral compression fractures - Research article | PDF",1790736231,28]