基于磁共振成像的机器学习预测乳腺癌新辅助化疗反应的系统评价和荟萃分析。

Machine learning with magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis.

机构信息

Department of Radiology, People's Hospital of Chongqing Banan District, Banan District, Chongqing, China.

Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China.

出版信息

Eur J Radiol. 2022 May;150:110247. doi: 10.1016/j.ejrad.2022.110247. Epub 2022 Mar 10.

DOI:10.1016/j.ejrad.2022.110247

PMID:35290910

Abstract

PURPOSE

The aim of this meta-analysis was to determine the diagnostic accuracy of machine learning (ML) models with MRI in predicting pathological response to neoadjuvant chemotherapy in patients with breast cancer. Furthermore, we compared the pathologic complete response (pCR) prediction performance of ML + radiomics with that of a deep learning (DL) algorithm.

METHODS

A search for relevant studies published until December 20, 2021 was conducted in MEDLINE and EMBASE databases. The quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies -2 criteria. The I value assessed the heterogeneity of the included studies as well as the decision to adopt a random effects model. The area under the receiver operating characteristic curves (AUC) was pooled to quantify the predictive accuracy. Subgroup analysis, meta-regression analysis, and sensitivity analysis were performed to detect potential sources of study heterogeneity. A funnel plot was used to investigate publication bias. The PROSPERO ID of our study was CRD42022284071.

RESULT

Seventeen eligible studies encompassing 3392 patients were evaluated in the analysis. ML + MRI showed high accuracy (AUC = 0.87, 95% CI = 0.84-0.91) in predicting response to neoadjuvant therapy. In subgroup analysis, the AUC of the DL subgroup (AUC = 0.92, 95% CI = 0.88-0.97) was higher than that of the ML + radiomics subgroup (AUC = 0.85, 95% CI = 0.82-0.90) (P = 0.030). In the ML + radiomics subgroup, the studies using MRI combined with other parameters (clinical or histopathologic information; AUC = 0.90, 95% CI = 0.85-0.96) reported better performance than studies using only MRI parameters (AUC = 0.82, 95% CI = 0.78-0.86) (P = 0.009).

CONCLUSIONS

ML applied to MRI enabled moderate accuracy in predicting pathological response to neoadjuvant therapy in patients with breast cancer. Furthermore, the meta-analysis showed that DL had higher predictive accuracy than ML + radiomics.

摘要

目的

本荟萃分析旨在确定机器学习（ML）模型与 MRI 联合在预测乳腺癌患者新辅助化疗病理反应方面的诊断准确性。此外，我们比较了 ML+放射组学与深度学习（DL）算法在病理完全缓解（pCR）预测性能上的差异。

方法

在 MEDLINE 和 EMBASE 数据库中检索截至 2021 年 12 月 20 日发表的相关研究。使用诊断准确性研究质量评估-2 标准评估研究质量。I ² 值评估纳入研究的异质性以及采用随机效应模型的决策。汇总受试者工作特征曲线下面积（AUC）以量化预测准确性。进行亚组分析、meta 回归分析和敏感性分析以检测研究异质性的潜在来源。使用漏斗图评估发表偏倚。本研究的 PROSPERO ID 为 CRD42022284071。

结果

共评估了 17 项纳入 3392 例患者的符合条件的研究。ML+MRI 在预测新辅助治疗反应方面具有较高的准确性（AUC=0.87，95%CI=0.84-0.91）。在亚组分析中，DL 亚组的 AUC（AUC=0.92，95%CI=0.88-0.97）高于 ML+放射组学亚组（AUC=0.85，95%CI=0.82-0.90）（P=0.030）。在 ML+放射组学亚组中，使用 MRI 联合其他参数（临床或组织病理学信息）的研究（AUC=0.90，95%CI=0.85-0.96）的表现优于仅使用 MRI 参数的研究（AUC=0.82，95%CI=0.78-0.86）（P=0.009）。