Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer.
Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer.
复制标题
DOI：
10.1155/2022/8616535
复制
发表时间：
2022
影响因子：
--
通讯作者：
Jayadhas, S. Arockia
中科院分区：
文献类型：
--
作者：
Ashokkumar, N.;Meera, S.;Anandan, P.;Murthy, Mantripragada Yaswanth Bhanu;Kalaivani, K. S.;Alahmadi, Tahani Awad;Alharbi, Sulaiman Ali;Raghavan, S. S.;Jayadhas, S. Arockia
全世界第二大死亡原因是乳腺癌，主要发生在女性身上。早期诊断改善了进一步的治疗，降低了死亡率。提出了一种独特的深度学习算法，用于预测早期乳腺癌。该方法利用多个层从源输入中检索大量信息。它可以对医学领域中复杂的图像属性进行自动定量评价，提高诊断的准确性和可靠性。乳腺癌患者的腋窝淋巴结数据集来自伊拉斯谟医学中心。在2018年至2021年期间，共研究了850名患者的1050张图像。对于独立测试，数据样本收集了来自国家癌症研究所95名患者的100张图像。病理检查证实腋窝淋巴结的存在。前馈、径向基函数和Kohonen自组织是人工神经网络（ANN），用于训练伊拉斯谟医学中心数据集的84%，并测试剩余的16%的独立数据集。根据准确性（Ac）、灵敏度（Sn）、特异性（Sf）和受试者工作曲线（Roc）的结果确定所提出的模型性能，并将其与其他四位放射科医生的机制进行比较。研究结果表明，所提出的机制实现了95%的灵敏度，96%的特异性和98%的准确性，这高于放射科医生的模型（90%的灵敏度，92%的特异性和94%的准确性）。深度学习算法可以通过利用初始乳腺癌患者的图像准确预测腋窝淋巴结转移的临床阴性。该方法为腋窝淋巴结阴性改变的患者提供了腋窝淋巴结转移的早期诊断技术。
The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists' mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists' models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.
登录
查看更多内容
影响因子：
28.4
作者：
Morrow M
通讯作者：
Morrow M
DOI：
10.1186/bcr3375
发表时间：
2013-01-15
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Kloten V;Becker B;Winner K;Schrauder MG;Fasching PA;Anzeneder T;Veeck J;Hartmann A;Knüchel R;Dahl E
通讯作者：
Dahl E
影响因子：
4.4
作者：
Ha, Richard;Chin, Christine;Jambawalikar, Sachin
通讯作者：
Jambawalikar, Sachin
影响因子：
3.1
作者：
Augasta, M. Gethsiyal;Kathirvalavakumar, T.
通讯作者：
Kathirvalavakumar, T.
影响因子：
--
作者：
Elizabeth Jesi V;Mohamed Aslam S;Ramkumar G;Sabarivani A;Gnanasekar AK;Thomas P
通讯作者：
Thomas P