Jintan Peoples Hospital, Jiangsu, Changzhou, China.
Changzhou Second People's Hospital Affiliated to Nanjing Medical University, Jiangsu, Changzhou, China.
Ultrason Imaging. 2024 Nov;46(6):357-366. doi: 10.1177/01617346241276168. Epub 2024 Sep 10.
We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. We enrolled 155 patients with pathologically confirmed breast cancer who underwent NAC. The patients were randomly divided into the training set and the validation set in the ratio of 7:3. The deep learning and radiomics features of pre-treatment ultrasound images were extracted, and the random forest recursive elimination algorithm and the least absolute shrinkage and selection operator were used for feature screening and DL-Score and Rad-Score construction. According to multifactorial logistic regression, independent clinical predictors, DL-Score, and Rad-Score were selected to construct the comprehensive prediction model DLRC. The performance of the model was evaluated in terms of its predictive effect, and clinical practicability. Compared to the clinical, radiomics (Rad-Score), and deep learning (DL-Score) models, the DLRC accurately predicted the pCR status, with an area under the curve (AUC) of 0.937 (95%CI: 0.895-0.970) in the training set and 0.914 (95%CI: 0.838-0.973) in the validation set. Moreover, decision curve analysis confirmed that the DLRC had the highest clinical value among all models. The comprehensive model DLRC based on ultrasound radiomics, deep learning, and clinical features can effectively and accurately predict the pCR status of breast cancer after NAC, which is conducive to assisting clinical personalized diagnosis and treatment plan.
我们研究了一种基于术前超声放射组学、深度学习和临床特征的综合模型对新辅助化疗（NAC）后乳腺癌病理完全缓解（pCR）的预测价值。我们纳入了 155 名经病理证实的乳腺癌患者，这些患者均接受了 NAC。患者按 7:3 的比例随机分为训练集和验证集。提取预处理超声图像的深度学习和放射组学特征，采用随机森林递归消除算法和最小绝对收缩和选择算子进行特征筛选和 DL-Score 和 Rad-Score 构建。根据多因素逻辑回归，选择独立的临床预测因子、DL-Score 和 Rad-Score 构建综合预测模型 DLRC。根据预测效果和临床实用性评估模型性能。与临床、放射组学（Rad-Score）和深度学习（DL-Score）模型相比，DLRC 准确预测了 pCR 状态，在训练集和验证集的曲线下面积（AUC）分别为 0.937（95%CI：0.895-0.970）和 0.914（95%CI：0.838-0.973）。此外，决策曲线分析证实，DLRC 在所有模型中具有最高的临床价值。基于超声放射组学、深度学习和临床特征的综合模型 DLRC 可以有效准确地预测 NAC 后乳腺癌的 pCR 状态，有助于辅助临床制定个性化的诊断和治疗方案。
