Radiomics based on brain-to-tumor interface enables prediction of metastatic tumor type of brain metastasis: a proof-of-concept study.

IF 9.7 1区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiologia Medica Pub Date : 2024-11-22 DOI:10.1007/s11547-024-01934-4

Mingchen Jiang, Yiyao Sun, Chunna Yang, Zekun Wang, Ming Xie, Yan Wang, Dan Zhao, Yuqi Ding, Yan Zhang, Jie Liu, Huanhuan Chen, Xiran Jiang

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引用次数: 0

Abstract

Background: Early and accurate identification of the metastatic tumor types of brain metastasis (BM) is essential for appropriate treatment and management.

Methods: A total of 450 patients were enrolled from two centers as a primary cohort who carry 764 BMs originated from non-small cell lung cancer (NSCLC, patient = 173, lesion = 187), small cell lung cancer (SCLC, patient = 84, lesion = 196), breast cancer (BC, patient = 119, lesion = 200), and gastrointestinal cancer (GIC, patient = 74, lesion = 181). A third center enrolled 28 patients who carry 67 BMs (NSCLC = 24, SCLC = 22, BC = 10, and GIC = 11) to form an external test cohort. All patients received contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI scans at 3.0 T before treatment. Radiomics features were calculated from BM and brain-to-tumor interface (BTI) region in the MRI image and screened using least absolute shrinkage and selection operator (LASSO) to construct the radiomics signature (RS). Volume of peritumor edema (VPE) was calculated and combined with RS to create a joint model. Performance of the models was assessed by receiver operating characteristic (ROC).

Results: The BTI-based RS showed better performance compared to BM-based RS. The combined models integrating BTI features and VPE can improve identification performance in AUCs in the training (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.803 vs. 0.949 vs. 0.918), internal validation (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.717 vs. 0.854 vs. 0.840), and external test (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.744 vs. 0.839 vs. 0.800) cohorts.

Conclusion: This study indicated that BTI-based radiomics features and VPE are associated with the metastatic tumor types of BM.

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基于脑-肿瘤界面的放射组学可预测脑转移瘤的转移瘤类型：概念验证研究。

背景：早期准确识别脑转移瘤（BM）的转移瘤类型对适当的治疗至关重要：早期准确识别脑转移瘤（BM）的转移瘤类型对于适当的治疗和管理至关重要：方法：两个中心共招募了 450 名患者作为主要队列，他们携带的 764 个脑转移瘤分别来自非小细胞肺癌（NSCLC，患者 = 173，病灶 = 187）、小细胞肺癌（SCLC，患者 = 84，病灶 = 196）、乳腺癌（BC，患者 = 119，病灶 = 200）和胃肠道癌（GIC，患者 = 74，病灶 = 181）。第三个中心招募了28名携带67个BM的患者（NSCLC=24人，SCLC=22人，BC=10人，GIC=11人）组成外部测试队列。所有患者在治疗前都接受了3.0 T对比增强T1加权（T1CE）和T2加权（T2W）磁共振成像扫描。根据核磁共振成像中的BM和脑-肿瘤界面（BTI）区域计算放射组学特征，并使用最小绝对收缩和选择算子（LASSO）进行筛选，以构建放射组学特征（RS）。计算瘤周水肿体积（VPE）并将其与 RS 结合以创建联合模型。通过接收者操作特征（ROC）评估模型的性能：结果：与基于BM的RS相比，基于BTI的RS显示出更好的性能。在训练（LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.803 vs. 0.949 vs. 0.918）、内部验证（LC/NLC vs. SCLC/NSCLC vs. BC/GIC，0.717 vs. 0.854 vs. 0.840）和外部测试（LC/NLC vs. SCLC/NSCLC vs. BC/GIC，0.744 vs. 0.839 vs. 0.800）队列的 AUC：该研究表明，基于 BTI 的放射组学特征和 VPE 与 BM 的转移性肿瘤类型相关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Radiologia Medica 医学-核医学

CiteScore

14.10

自引率

7.90%

发文量

133

审稿时长

4-8 weeks

期刊介绍： Felice Perussia founded La radiologia medica in 1914. It is a peer-reviewed journal and serves as the official journal of the Italian Society of Medical and Interventional Radiology (SIRM). The primary purpose of the journal is to disseminate information related to Radiology, especially advancements in diagnostic imaging and related disciplines. La radiologia medica welcomes original research on both fundamental and clinical aspects of modern radiology, with a particular focus on diagnostic and interventional imaging techniques. It also covers topics such as radiotherapy, nuclear medicine, radiobiology, health physics, and artificial intelligence in the context of clinical implications. The journal includes various types of contributions such as original articles, review articles, editorials, short reports, and letters to the editor. With an esteemed Editorial Board and a selection of insightful reports, the journal is an indispensable resource for radiologists and professionals in related fields. Ultimately, La radiologia medica aims to serve as a platform for international collaboration and knowledge sharing within the radiological community.