Department of Radiology, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey.
Department of Software Engineering, Firat University, Elazig, Turkey.
J Ultrasound Med. 2024 Nov;43(11):2051-2068. doi: 10.1002/jum.16535. Epub 2024 Jul 25.
Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer. In addition to causing unnecessary anxiety and stress to patients, such diagnosis can also lead to unnecessary biopsy procedures, which are painful, expensive, and prone to misdiagnosis. Therefore, there is a need for the development of more accurate and reliable methods for breast cancer diagnosis.
In this study, we proposed an artificial intelligence (AI)-based method for automatically classifying breast solid mass lesions as benign vs malignant. In this study, a new breast cancer dataset (Breast-XD) was created with 791 solid mass lesions belonging to 752 different patients aged 18 to 85 years, which were examined by experienced radiologists between 2017 and 2022.
Six classifiers, support vector machine (SVM), K-nearest neighbor (K-NN), random forest (RF), decision tree (DT), logistic regression (LR), and XGBoost, were trained on the training samples of the Breast-XD dataset. Then, each classifier made predictions on 159 test data that it had not seen before. The highest classification result was obtained using the explainable XGBoost model (XGAI) with an accuracy of 94.34%. An explainable structure is also implemented to build the reliability of the developed model.
The results obtained by radiologists and the XGAI model were compared according to the diagnosis obtained from the biopsy. It was observed that our developed model performed well in cases where experienced radiologists gave false positive results.
乳腺癌是一种由乳腺组织中细胞失控生长引起的癌症。在少数情况下，专家错误诊断乳腺癌和不必要的活检会导致各种负面后果。在某些情况下，放射学检查或临床发现可能会怀疑乳腺癌，但随后的详细评估可能无法确认癌症。除了给患者带来不必要的焦虑和压力外，这种诊断还可能导致不必要的活检程序，这些程序既痛苦、昂贵又容易误诊。因此，需要开发更准确、可靠的乳腺癌诊断方法。
在这项研究中，我们提出了一种基于人工智能（AI）的方法，用于自动将乳腺实性肿块病变分类为良性与恶性。本研究创建了一个新的乳腺癌数据集（Breast-XD），其中包含 752 名不同患者的 791 个实性肿块病变，这些病变由经验丰富的放射科医生在 2017 年至 2022 年期间进行了检查。
在 Breast-XD 数据集的训练样本上训练了 6 个分类器，包括支持向量机（SVM）、K-最近邻（K-NN）、随机森林（RF）、决策树（DT）、逻辑回归（LR）和 XGBoost。然后，每个分类器对其之前未见过的 159 个测试数据进行预测。使用可解释的 XGBoost 模型（XGAI）获得了最高的分类结果，准确率为 94.34%。还实现了一个可解释的结构来构建开发模型的可靠性。
根据活检获得的诊断，将放射科医生和 XGAI 模型的结果进行了比较。观察到，我们开发的模型在经验丰富的放射科医生给出假阳性结果的情况下表现良好。
