背景信号抑制弥散全身成像（DWIBS）的放射组学模型在乳腺癌腋窝淋巴结状态预测中的应用。

Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer.

机构信息

Department of Advanced Biomedical Imaging and Informatics, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.

Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.

出版信息

J Xray Sci Technol. 2023;31(3):627-640. doi: 10.3233/XST-230009.

DOI:10.3233/XST-230009

PMID:37038802

Abstract

BACKGROUND

In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy.

OBJECTIVE

This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status.

METHODS

A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models.

RESULTS

For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548-0.982), 0.801 (0.597-1.000), and 0.779 (0.567-0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548-0.982), 0.757 (0.538-0.977), and 0.779 (0.567-0.992), respectively.

CONCLUSIONS

Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.

摘要

背景

在乳腺癌的诊断和治疗中，对腋窝淋巴结（ALN）转移进行非侵入性预测，有助于避免前哨淋巴结活检相关的并发症。

目的

本研究旨在开发和评估基于扩散加权全身成像（DWIBS）检查中提取的放射组学特征的机器学习模型，以预测 ALN 状态。

方法

共纳入 100 例经组织学证实的、浸润性的、临床 N0 乳腺癌患者，这些患者在术前均接受了包括短回波反转恢复（STIR）和 DWIBS 序列的 DWIBS 检查。使用 DWIBS 和 STIR 序列中的分段原发性病变计算放射组学特征，并根据检查日期将其分为训练（n=75）和测试（n=25）数据集。使用训练数据集，通过最小绝对收缩和选择算子算法进行最佳特征选择，然后使用 DWIBS、STIR 或 DWIBS 和 STIR 序列的组合构建逻辑回归模型和支持向量机（SVM）分类器模型，以预测 ALN 状态。使用受试者工作特征曲线评估放射组学模型的预测性能。

结果

对于测试数据集，使用 DWIBS、STIR 和两者组合的逻辑回归模型的曲线下面积（AUC）分别为 0.765（95%置信区间：0.548-0.982）、0.801（0.597-1.000）和 0.779（0.567-0.992），而使用 DWIBS、STIR 和两者组合的 SVM 分类器模型的 AUC 分别为 0.765（0.548-0.982）、0.757（0.538-0.977）和 0.779（0.567-0.992）。