基于深度卷积神经网络模型在甲状腺结节细胞学评估中的应用价值。

Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules.

机构信息

Department of Pathology, The Affiliated People's Hospital of Ningbo University, Ningbo 315000, Zhejiang Province, China.

出版信息

J Healthc Eng. 2021 Nov 9;2021:6076135. doi: 10.1155/2021/6076135. eCollection 2021.

DOI:10.1155/2021/6076135

PMID:34795882

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8594987/

Abstract

OBJECTIVE

To investigate the application value of a deep convolutional neural network (CNN) model for cytological assessment of thyroid nodules.

METHODS

117 patients with thyroid nodules who underwent thyroid cytology examination in the Affiliated People's Hospital of Ningbo University between January 2017 and December 2019 were included in this study. 100 papillary thyroid cancer samples and 100 nonmalignant samples were collected respectively. The sample images were translated vertically and horizontally. Thus, 900 images were separately created in the vertical and horizontal directions. The sample images were randomly divided into training samples ( = 1260) and test samples ( = 540) at the ratio of 7 : 3 per the training sample to test sample. According to the training samples, the pretrained deep convolutional neural network architecture Resnet50 was trained and fine-tuned. A convolutional neural network-based computer-aided detection (CNN-CAD) system was constructed to perform full-length scan of the test sample slices. The ability of CNN-CAD to screen malignant tumors was analyzed using the threshold setting method. Eighty pathological images were collected from patients who received treatment between January 2020 and May 2020 and used to verify the value of CNN in the screening of malignant thyroid nodules as verification set.

RESULTS

With the number of iterations increasing, the training and verification loss of CNN model gradually decreased and tended to be stable, and the training and verification accuracy of CNN model gradually increased and tended to be stable. The average loss rate of training samples determined by the CNN model was (22.35 ± 0.62) %, and the average loss rate of test samples determined by the CNN model was (26.41 ± 3.37) %. The average accuracy rate of training samples determined by the CNN model was (91.04 ± 2.11) %, and the average accuracy rate of test samples determined by the CNN model was (91.26 ± 1.02)%.

CONCLUSION

A CNN model exhibits a high value in the cytological diagnosis of thyroid diseases which can be used for the cytological diagnosis of malignant thyroid tumor in the clinic.

摘要

目的

探讨深度卷积神经网络（CNN）模型在甲状腺结节细胞学评估中的应用价值。

方法

收集 2017 年 1 月至 2019 年 12 月在宁波大学附属人民医院行甲状腺细胞学检查的 117 例甲状腺结节患者的临床资料。其中甲状腺癌患者 100 例，甲状腺良性结节患者 100 例。分别对甲状腺癌及良性结节患者的甲状腺细胞学检查标本进行纵横双向切片，共获得 900 张切片图像，分别对 900 张图像进行纵横双向翻转，共计 1800 张图像。将所有图像随机分为训练样本（1260 张）和测试样本（540 张），训练样本与测试样本比例为 7∶3。根据训练样本对预训练的 Resnet50 深度卷积神经网络架构进行训练和微调。构建基于卷积神经网络的计算机辅助检测（CNN-CAD）系统，对测试样本切片进行全长扫描。采用阈值设定法分析 CNN-CAD 筛查恶性肿瘤的能力。收集 2020 年 1 月至 2020 年 5 月间经治疗的 80 例病理图像作为验证集，验证 CNN 在恶性甲状腺结节筛查中的价值。

结果

随着迭代次数的增加，CNN 模型的训练损失和验证损失逐渐降低并趋于稳定，训练和验证准确率逐渐升高并趋于稳定。CNN 模型确定的训练样本平均损失率为（22.35±0.62）%，测试样本平均损失率为（26.41±3.37）%。CNN 模型确定的训练样本平均准确率为（91.04±2.11）%，测试样本平均准确率为（91.26±1.02）%。