Department of Pathology, National Cancer Institute, Cairo University, Egypt.
Department of computer science, Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt.
Asian Pac J Cancer Prev. 2023 Apr 1;24(4):1379-1387. doi: 10.31557/APJCP.2023.24.4.1379.
Fine needle aspiration cytology has higher sensitivity and predictive value for diagnosis of thyroid nodules than any other single diagnostic methods. In the Bethesda system for reporting thyroid, the category IV, encompasses both adenoma and carcinoma, but it is not possible to differentiate both lesions in the cytology practice and can be only differentiated after resection. In this work, we aim at exploring the ability of a convolutional neural network (CNN) model to sub-classifying cytological images of Bethesda category IV diagnosis into follicular adenoma and follicular carcinoma.
We used a cohort of cytology cases n= 43 with extracted images n= 886 to train CNN model aiming to sub-classify follicular neoplasm (Bethesda category IV) into either follicular adenoma or follicular carcinoma.
In our study, the model subclassification of follicular neoplasm into follicular adenoma (n = 28/43, images n = 527/886) from follicular carcinoma (n = 15/43, images n= 359/886), has achieved an accuracy of 78%, with a sensitivity of 88.4%, and a specificity of 64% and an area under the curve (AUC) score of 0.87 for each of follicular adenoma and follicular carcinoma.
Our CNN model has achieved high sensitivity in recognizing follicular adenoma amongest cytology smears of follciualr neoplasms, thus it can be used as an ancillary technique in the subcalssification of Bethesda Iv category cytology smears.
细针抽吸细胞学检查对甲状腺结节的诊断具有比任何其他单一诊断方法更高的敏感性和预测价值。在甲状腺报告的 Bethesda 系统中，类别 IV 包括腺瘤和癌，但在细胞学实践中无法区分这两种病变，只能在切除后才能区分。在这项工作中，我们旨在探索卷积神经网络（CNN）模型将 Bethesda 类别 IV 诊断的细胞学图像细分为滤泡性腺瘤和滤泡状癌的能力。
我们使用了一个细胞学病例队列 n=43，提取的图像 n=886，旨在训练 CNN 模型，将滤泡性肿瘤（Bethesda 类别 IV）细分为滤泡性腺瘤或滤泡状癌。
在我们的研究中，模型将滤泡性肿瘤细分为滤泡性腺瘤（n=28/43，图像 n=527/886）和滤泡状癌（n=15/43，图像 n=359/886）的分类准确率为 78%，滤泡性腺瘤的敏感性为 88.4%，特异性为 64%，曲线下面积（AUC）评分为 0.87；滤泡状癌的敏感性为 88.4%，特异性为 64%，AUC 评分为 0.87。
我们的 CNN 模型在识别滤泡性腺瘤中的滤泡性肿瘤细胞学涂片方面具有很高的敏感性，因此可作为 Bethesda Iv 类细胞学涂片分类的辅助技术。
