A Radiomics-Based Model for Potentially More Accurate Identification of Subtypes of Breast Cancer Brain Metastases.

IF 2.6 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Seonghyeon Cho, Bio Joo, Mina Park, Sung Jun Ahn, Sang Hyun Suh, Yae Won Park, Sung Soo Ahn, Seung-Koo Lee
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引用次数: 0

Abstract

Purpose: Breast cancer brain metastases (BCBM) may involve subtypes that differ from the primary breast cancer lesion. This study aimed to develop a radiomics-based model that utilizes preoperative brain MRI for multiclass classification of BCBM subtypes and to investigate whether the model offers better prediction accuracy than the assumption that primary lesions and their BCBMs would be of the same subtype (non-conversion model) in an external validation set.

Materials and methods: The training and external validation sets each comprised 51 cases (102 cases total). Four machine learning classifiers combined with three feature selection methods were trained on radiomic features and primary lesion subtypes for prediction of the following four subtypes: 1) hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, 2) HR+/HER2+, 3) HR-/HER2+, and 4) triple-negative. After training, the performance of the radiomics-based model was compared to that of the non-conversion model in an external validation set using accuracy and F1-macro scores.

Results: The rate of discrepant subtypes between primary lesions and their respective BCBMs were 25.5% (n=13 of 51) in the training set and 23.5% (n=12 of 51) in the external validation set. In the external validation set, the accuracy and F1-macro score of the radiomics-based model were significantly higher than those of the non-conversion model (0.902 vs. 0.765, p=0.004; 0.861 vs. 0.699, p=0.002).

Conclusion: Our radiomics-based model represents an incremental advance in the classification of BCBM subtypes, thereby facilitating a more appropriate personalized therapy.

Abstract Image

Abstract Image

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基于放射组学的乳腺癌脑转移亚型更准确识别模型
目的:乳腺癌脑转移(BCBM)可能涉及不同于原发性乳腺癌病变的亚型。本研究旨在建立一种基于放射组学的模型,利用术前脑MRI对脑cbm亚型进行多类别分类,并研究该模型是否比在外部验证集中假设原发病变及其脑cbm属于同一亚型(非转换模型)具有更好的预测准确性。材料和方法:训练集和外部验证集各51例(共102例)。结合3种特征选择方法,对4种机器学习分类器进行放射学特征和原发病变亚型的训练,预测以下4种亚型:1)激素受体(HR)+/人表皮生长因子受体2 (HER2)-, 2) HR+/HER2+, 3) HR-/HER2+, 4)三阴性。训练后,使用准确性和f1 -宏评分将基于放射组学的模型的性能与外部验证集中的非转换模型的性能进行比较。结果:原发性病变与相应BCBMs亚型的差异率在训练集中为25.5% (n=13 / 51),在外部验证集中为23.5% (n=12 / 51)。在外部验证集中,基于放射组学的模型的准确性和F1-macro评分显著高于非转换模型(0.902 vs. 0.765, p=0.004;0.861 vs. 0.699, p=0.002)。结论:我们基于放射组学的模型代表了BCBM亚型分类的渐进进展,从而促进了更合适的个性化治疗。
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
Yonsei Medical Journal
Yonsei Medical Journal 医学-医学:内科
CiteScore
4.50
自引率
0.00%
发文量
167
审稿时长
3 months
期刊介绍: The goal of the Yonsei Medical Journal (YMJ) is to publish high quality manuscripts dedicated to clinical or basic research. Any authors affiliated with an accredited biomedical institution may submit manuscripts of original articles, review articles, case reports, brief communications, and letters to the Editor.
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