Department of Breast and Thyroid Surgery, Tumor Hospital Affiliated to Xinjiang Medical University, No. 789 of Suzhou Street, Xinshi District, Urumqi, 830000, China.
Department of Artificial Intelligence and Smart Mining Engineering Technology Center, Xinjiang Institute of Engineering, Urumqi, 830023, China.
BMC Womens Health. 2024 Jul 2;24(1):380. doi: 10.1186/s12905-024-03231-8.
The aim of this study is to assess the efficacy of a multiparametric ultrasound imaging omics model in predicting the risk of postoperative recurrence and molecular typing of breast cancer.
A retrospective analysis was conducted on 534 female patients diagnosed with breast cancer through preoperative ultrasonography and pathology, from January 2018 to June 2023 at the Affiliated Cancer Hospital of Xinjiang Medical University. Univariate analysis and multifactorial logistic regression modeling were used to identify independent risk factors associated with clinical characteristics. The PyRadiomics package was used to delineate the region of interest in selected ultrasound images and extract radiomic features. Subsequently, radiomic scores were established through Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine (SVM) methods. The predictive performance of the model was assessed using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) was calculated. Evaluation of diagnostic efficacy and clinical practicability was conducted through calibration curves and decision curves.
In the training set, the AUC values for the postoperative recurrence risk prediction model were 0.9489, and for the validation set, they were 0.8491. Regarding the molecular typing prediction model, the AUC values in the training set and validation set were 0.93 and 0.92 for the HER-2 overexpression phenotype, 0.94 and 0.74 for the TNBC phenotype, 1.00 and 0.97 for the luminal A phenotype, and 1.00 and 0.89 for the luminal B phenotype, respectively. Based on a comprehensive analysis of calibration and decision curves, it was established that the model exhibits strong predictive performance and clinical practicability.
The use of multiparametric ultrasound imaging omics proves to be of significant value in predicting both the risk of postoperative recurrence and molecular typing in breast cancer. This non-invasive approach offers crucial guidance for the diagnosis and treatment of the condition.
本研究旨在评估多参数超声成像组学模型预测乳腺癌术后复发风险和分子分型的疗效。
回顾性分析 2018 年 1 月至 2023 年 6 月新疆医科大学附属肿瘤医院经术前超声和病理诊断为乳腺癌的 534 例女性患者。采用单因素分析和多因素 logistic 回归建模，确定与临床特征相关的独立危险因素。使用 PyRadiomics 包在选定的超声图像中描绘感兴趣区域并提取放射组学特征。然后，通过最小绝对值收缩和选择算子（LASSO）回归和支持向量机（SVM）方法建立放射组学评分。使用受试者工作特征（ROC）曲线评估模型的预测性能，并计算曲线下面积（AUC）。通过校准曲线和决策曲线评估诊断效能和临床实用性。
在训练集中，术后复发风险预测模型的 AUC 值为 0.9489，在验证集中，AUC 值为 0.8491。对于分子分型预测模型，在训练集和验证集中，HER-2 过表达表型的 AUC 值分别为 0.93 和 0.74，三阴性乳腺癌（TNBC）表型为 0.94 和 0.74，Luminal A 表型为 1.00 和 0.97，Luminal B 表型为 1.00 和 0.89。基于校准和决策曲线的综合分析，该模型表现出较强的预测性能和临床实用性。
多参数超声成像组学在预测乳腺癌术后复发风险和分子分型方面具有重要价值。这种非侵入性方法为诊断和治疗提供了重要指导。
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