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Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：本研究的目的是评估多参数超声成像组学模型在预测乳腺癌术后复发风险和分子分型方面的功效。
方法：回顾性分析534例经术前超声和病理确诊为乳腺癌的女性患者。2018年1月至2023年6月在新疆医科大学附属肿瘤医院就诊。单因素分析和多因素logistic回归模型用于确定与临床特征相关的独立危险因素。PyRadiomics软件包用于在选定的超声图像中描绘感兴趣的区域并提取放射学特征。随后，通过最小绝对收缩和选择算子（LASSO）回归和支持向量机（SVM）方法建立影像学评分。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)。通过校准曲线和判定曲线评价诊断效能和临床实用性。
结果：在训练集中，术后复发风险预测模型的AUC值为0.9489,他们是0.8491。关于分子分型预测模型，HER-2过表达表型的训练集和验证集中的AUC值分别为0.93和0.92，TNBC表型为0.94和0.74，腔A表型为1.00和0.97，腔B表型为1.00和0.89，分别。在全面分析校准和判定曲线的基础上,结果表明，该模型具有较强的预测性能和临床实用性。
结论：使用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该病的诊断和治疗提供了至关重要的指导。
方法：回顾性分析534例经术前超声和病理确诊为乳腺癌的女性患者。2018年1月至2023年6月在新疆医科大学附属肿瘤医院就诊。单因素分析和多因素logistic回归模型用于确定与临床特征相关的独立危险因素。PyRadiomics软件包用于在选定的超声图像中描绘感兴趣的区域并提取放射学特征。随后，通过最小绝对收缩和选择算子（LASSO）回归和支持向量机（SVM）方法建立影像学评分。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)。通过校准曲线和判定曲线评价诊断效能和临床实用性。
结果：在训练集中，术后复发风险预测模型的AUC值为0.9489,他们是0.8491。关于分子分型预测模型，HER-2过表达表型的训练集和验证集中的AUC值分别为0.93和0.92，TNBC表型为0.94和0.74，腔A表型为1.00和0.97，腔B表型为1.00和0.89，分别。在全面分析校准和判定曲线的基础上,结果表明，该模型具有较强的预测性能和临床实用性。
结论：使用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该病的诊断和治疗提供了至关重要的指导。
