Qin Jing, Qin Xiachuan, Duan Yayang, Xie Yuchen, Zhou Yuanyuan, Zhang Chaoxue
Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Transl Cancer Res. 2024 Jan 31;13(1):317-329. doi: 10.21037/tcr-23-1042. Epub 2024 Jan 15.
Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction.
We retrospectively analyzed 420 patients with pathologically confirmed benign or malignant breast tumors. Clinical data and examination images were collected, and the population was divided into training (n=294) and validation (n=126) groups at a ratio of 7:3. The region of interest (ROI) was manually segmented along the tumor boundary using MaZda software, and the features of each ROI was extracted. After dimension reduction and screening, the best features were retained. Subsequently, random forest (RF), support vector machines, and K-nearest neighbor classifiers were used to establish prediction models in an US and combined-methods group.
Finally, 8 of the 379 features were retained in the US group. Random forest was found to be the best model, and the area under the curve (AUC) of the training and validation groups was 0.90 [95% confidence interval (CI): 0.852-0.942] and 0.85 (95% CI: 0.775-0.930), respectively. Meanwhile, 12 of the 750 features were retained in the combined group. In this regard, random forest proved to be the best model, and the AUC of the training and validation group was 0.95 (95% CI: 0.918-0.981) and 0.92 (95% CI: 0.866-0.969), respectively. The calibration curve showed a good fit of the model. The decision curve showed that the clinical net benefit of the combined group was far greater than that of any single examination, and the prediction model of the combined group exhibited a degree of practical clinical value.
The combined model based on US and CT images has potential application value in the prognostic prediction of benign and malignant breast diseases.
早期诊断对乳腺癌治疗至关重要，但传统影像学检测具有挑战性。放射组学有潜力以非侵入性方式提高早期诊断效能。本研究探讨基于超声（US）模型整合计算机断层扫描（CT）放射组学信息是否能提高乳腺癌预测效能。
我们回顾性分析了420例经病理证实的乳腺良性或恶性肿瘤患者。收集临床数据和检查图像，并按7:3的比例将人群分为训练组（n = 294）和验证组（n = 126）。使用MaZda软件沿肿瘤边界手动分割感兴趣区域（ROI），并提取每个ROI的特征。经过降维和筛选，保留最佳特征。随后，在超声组和联合方法组中使用随机森林（RF）、支持向量机和K近邻分类器建立预测模型。
最终，超声组保留了379个特征中的8个。发现随机森林是最佳模型，训练组和验证组的曲线下面积（AUC）分别为0.90 [95%置信区间（CI）：0.852 - 0.942]和0.85（95% CI：0.775 - 0.930）。同时，联合组保留了750个特征中的12个。在这方面，随机森林被证明是最佳模型，训练组和验证组的AUC分别为0.95（95% CI：0.918 - 0.981）和0.92（95% CI：0.866 - 0.969）。校准曲线显示模型拟合良好。决策曲线表明联合组的临床净效益远大于任何单一检查，联合组的预测模型具有一定的临床实用价值。
基于超声和CT图像的联合模型在乳腺良恶性疾病的预后预测中具有潜在应用价值。
