[Interpretation of the pathological diagnostic criteria and characteristics of high-grade thyroid follicular-derived carcinoma in the 5th edition WHO classification].

The 5th edition WHO classification of thyroid tumors proposed high-grade non-anaplastic thyroid carcinoma, which includes traditional poorly differentiated thyroid carcinoma (PDTC) and differentiated high-grade thyroid carcinoma (DHGTC), with a prognosis between highly differentiated thyroid carcinoma and anaplastic thyroid carcinoma (ATC), in which about 50% of patients do not take radioactive iodine. Therefore, this classification is of great clinical significance. This article interprets the diagnostic criteria and genetic features of high-grade non-anaplastic thyroid carcinoma in 5th edition WHO classification, comparing with ATC.

Anaplastic thyroid carcinoma: Updates on WHO classification, clinicopathological features and staging.

Anaplastic thyroid carcinoma is an uncommon carcinoma representing 1 to 4% of all thyroid cancers. The carcinoma is most common in females of the eight decades. It is a locally advanced cancer with frequent infiltration of surrounding organs, blood vessels and skin of neck. Paraneoplastic manifestations could occur. Approximately half of the patients with anaplastic thyroid carcinoma had distant metastasis with lung and brain as the most frequent sites of metastasis. The median survival of patients with anaplastic thyroid carcinoma reported was from 1 to 6 months. The terminology of the cancer in World Health Organization is "anaplastic thyroid carcinoma" rather than "undifferentiated thyroid carcinoma". In the latest American Joint Committee on Cancer (AJCC) TNM staging system for anaplastic thyroid carcinoma, there are updates on T and N categories. To conclude, updated knowledge of clinicopathological features, classification, pathological staging will improve our understanding of the cancer and will help in the management of the patients with this aggressive cancer.

Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images.

BACKGROUND Thyroid nodules are extremely common and typically diagnosed with ultrasound whether benign or malignant. Imaging diagnosis assisted by Artificial Intelligence has attracted much attention in recent years. The aim of our study was to build an ensemble deep learning classification model to accurately differentiate benign and malignant thyroid nodules. MATERIAL AND METHODS Based on current advanced methods of image segmentation and classification algorithms, we proposed an ensemble deep learning classification model for thyroid nodules (EDLC-TN) after precise localization. We compared diagnostic performance with four other state-of-the-art deep learning algorithms and three ultrasound radiologists according to ACR TI-RADS criteria. Finally, we demonstrated the general applicability of EDLC-TN for diagnosing thyroid cancer using ultrasound images from multi medical centers. RESULTS The method proposed in this paper has been trained and tested on a thyroid ultrasound image dataset containing 26 541 images and the accuracy of this method could reach 98.51%. EDLC-TN demonstrated the highest value for area under the curve, sensitivity, specificity, and accuracy among five state-of-the-art algorithms. Combining EDLC-TN with models and radiologists could improve diagnostic accuracy. EDLC-TN achieved excellent diagnostic performance when applied to ultrasound images from another independent hospital. CONCLUSIONS Based on ensemble deep learning, the proposed approach in this paper is superior to other similar existing methods of thyroid classification, as well as ultrasound radiologists. Moreover, our network represents a generalized platform that potentially can be applied to medical images from multiple medical centers.