Habitat-Based MRI Radiomics to Predict the Origin of Brain Metastasis

无线电技术 脑转移 栖息地 转移 医学 人工智能 生态学 计算机科学 生物 内科学 癌症
作者
Yiyao Sun,Peng Zhao,Mingchen Jiang,Jia Wei,Huan‐Huan Chen,Huan Wang,Yuqi Ding,Xiaoyu Wang,Juan Su,Xianzheng Sha,Chunna Yang,Dan Zhao,Bo Huang,Xiran Jiang
标识
DOI:10.2139/ssrn.4812498
摘要

Background: This study aims to explore the value of habitat-based MRI radiomics for predicting the origin of brain metastasis (BM).Methods: A primary cohort was developed with 384 patients from two centers, which comprises 734 BM lesions. An independent cohort was developed with 28 patients from a third center, which comprises 70 BM lesions. All patients underwent T1-weighted contrast-enhanced (T1-CE) and T2-weighted (T2W) MRI scans before treatment. Radiomics features were extracted from tumor active area (TAA) and peritumoral edema area (PEA) selected using the least absolute shrinkage and selection operator (LASSO) to construct radiomics signatures (Rads). The Rads were further integrated with volume of peritumoral edema (VPE) to build combined models for predicting the metastatic type of BM. Performance of the models were assessed through receiver operating characteristic (ROC) curve analysis.Findings: Rads derived from TAA and PEA both showed predictive power for identifying the origin of BM. The developed combined models generated the best performance in the training (AUCs, lung cancer (LC)/non-lung cancer (NLC) vs. small cell lung cancer (SCLC)/ non-small cell lung cancer (NSCLC) vs. breast cancer (BC)/ gastrointestinal cancer (GIC), 0.870 vs. 0.946 vs. 0.886), internal validation (AUCs, LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.786 vs. 0.863 vs. 0.836) and external validation (AUCs, LC /NLC vs. SCLC/NSCLC vs. BC/GIC, 0.805 vs. 0.877 vs. 0.774) cohort.Interpretation: The developed habitat-based radiomics models can effectively identificat the metastatic tumor type of BM, and may be considered as a potential preoperative basis for timely treatment planning.Funding: The study was supported by the National Key R&D Program of China: BTIT (Grant NO.2022YFF1202803), and General Program from Department of Education of Liaoning Province (JYTMS20230132).Declaration of Interest: The authors declare that they have no competing interests.Ethical Approval: The ethics review boards of our hospitals granted ethical approval for this retrospective analysis, waiving the requirement for informed consent from patients.

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