Liu Jinhui, Chen Junjun, Qiu Lin, Li Ruoxin, Li Yuning, Li Ting, Leng Xiaoling
Department of Ultrasonography, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, 523059, Guangdong, China.
Department of Ultrasonography, Chenzhou First People's Hospital, Chenzhou, 423000, Hunan, China.
Sci Rep. 2025 Jun 6;15(1):19953. doi: 10.1038/s41598-025-03704-2.
To investigate the diagnostic capability of multiple machine learning algorithms combined with intratumoral and peritumoral ultrasound radiomics models for non-massive breast cancer in dense breast backgrounds. Manual segmentation of ultrasound images was performed to define the intratumoral region of interest (ROI), and five peritumoral ROIs were generated by extending the contours by 1 to 5 mm. A total of 851 radiomics features were extracted from these regions and filtered using statistical methods. Thirteen machine learning algorithms were employed to create radiomics models for the intratumoral and peritumoral areas. The best model was combined with clinical ultrasound predictive factors to form a joint model, which was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).Based on this model, a nomogram was developed, demonstrating high predictive performance, with C-index values of 0.982 and 0.978.The model incorporating the intratumoral and peritumoral 2 mm regions outperformed other models, indicating its effectiveness in distinguishing between benign and malignant breast lesions. This study concludes that ultrasound imaging, particularly in the intratumoral and peritumoral 2 mm regions, has significant potential for diagnosing non-massive breast cancer, and the nomogram can assist clinical decision-making.
为研究多种机器学习算法结合瘤内及瘤周超声影像组学模型对致密乳腺背景下非肿块型乳腺癌的诊断能力。对超声图像进行手动分割以定义瘤内感兴趣区域(ROI),并通过将轮廓扩展1至5毫米生成五个瘤周ROI。从这些区域共提取851个影像组学特征,并使用统计方法进行筛选。采用13种机器学习算法为瘤内和瘤周区域创建影像组学模型。将最佳模型与临床超声预测因素相结合形成联合模型,使用ROC曲线、校准曲线和决策曲线分析(DCA)对其进行评估。基于该模型,开发了列线图,显示出较高的预测性能,C指数值分别为0.982和0.978。纳入瘤内和瘤周2毫米区域的模型优于其他模型,表明其在区分乳腺良恶性病变方面的有效性。本研究得出结论,超声成像,尤其是在瘤内和瘤周2毫米区域,在诊断非肿块型乳腺癌方面具有巨大潜力,且列线图可辅助临床决策。