Context:Telomerase promoter mutations (TERT) were recently described in follicular cell-derived thyroid carcinomas (FCDTC) and seem to be more prevalent in aggressive cancers.Objectives:We aimed to evaluate the frequency of TERT promoter mutations in thyroid lesions and to investigate the prognostic significance of such mutations in a large cohort of patients with differentiated thyroid carcinomas (DTCs).Design:This was a retrospective observational study.Setting and Patients:We studied 647 tumors and tumor-like lesions. A total of 469 patients with FCDTC treated and followed in five university hospitals were included. Mean follow-up (±SD) was 7.8 ± 5.8 years.Main Outcome Measures:Predictive value of TERT promoter mutations for distant metastasization, disease persistence at the end of follow-up, and disease-specific mortality.Results:TERT promoter mutations were found in 7.5% of papillary carcinomas (PTCs), 17.1% of follicular carcinomas, 29.0% of poorly differentiated carcinomas, and 33.3% of anaplastic thyroid carcinomas. Patients with TERT-mutated tumors were older (P < .001) and had larger tumors (P = .002). In DTCs, TERT promoter mutations were significantly associated with distant metastases (P < .001) and higher stage (P < .001). Patients with DTC harboring TERT promoter mutations were submitted to more radioiodine treatments (P = .009) with higher cumulative dose (P = .004) and to more treatment modalities (P = .001). At the end of follow-up, patients with TERT-mutated DTCs were more prone to have persistent disease (P = .001). TERT promoter mutations were significantly associated with disease-specific mortality [in the whole FCDTC (P < .001)] in DTCs (P < .001), PTCs (P = .001), and follicular carcinomas (P < .001). After adjusting for age at diagnosis and gender, the hazard ratio was 10.35 (95% confidence interval 2.01–53.24; P = .005) in DTC and 23.81 (95% confidence interval 1.36–415.76; P = .03) in PTCs.Conclusions:TERT promoter mutations are an indicator of clinically aggressive tumors, being correlated with worse outcome and disease-specific mortality in DTC. TERT promoter mutations have an independent prognostic value in DTC and, notably, in PTC.

Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

A B S T R A C TBreast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time-and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

Follicular thyroid carcinoma is being diagnosed less and less frequently despite the increasing incidence of well-differentiated thyroid carcinomas everywhere. This review will discuss the reasons underlying such an observation focusing on the evolution of the morphological and immunohistochemical diagnostic criteria of follicular thyroid tumors. It will address the differential diagnosis between follicular carcinoma and three tumor types-follicular adenoma, follicular variant of papillary carcinoma and poorly differentiated carcinoma-as well as the problems raised by the newly described categories of follicular tumors: follicular tumor of uncertain malignant potential, well-differentiated tumor of uncertain malignant potential and welldifferentiated carcinoma, not otherwise specified. Finally, the prognostic and therapeutic significance of some promising molecular biomarkers will be discussed within the frame of the aforementioned histopathological classification.

Cribriform-morular variant of thyroid carcinoma is classically associated with familial adenomatous polyposis but, it can also occur as a sporadic neoplasm. This neoplasm is much more frequently observed in women than in men (ratio of 61:1). In familial adenomatous polyposis patients, tumors are generally multifocal and/or bilateral (multinodular appearance), whereas in the sporadic cases tumors tend to occur as single nodules. The tumors are well delimited, and characteristically show a blending of follicular, cribriform, papillary, trabecular, solid, and morular patterns. Neoplastic cells are tall or cuboidal with the occasional nuclear features of classic papillary thyroid carcinoma. The morules include cells with peculiar nuclear clearing and show positivity for CDX2 and CD10. Angioinvasion and capsular invasion have been described in about 30 and 40% of cases, respectively, with lymph node metastases in less than 10% of patients and distant metastases in 6%. Although this tumor has good prognosis, neuroendocrine and/or poor differentiation have been associated with aggressive behavior. Tumor cells can be focally positive or negative for thyroglobulin, but are always positive for TTF-1, estrogen and progesterone receptors, and negative for calcitonin and cytokeratin 20. Nuclear and cytoplasmic staining for β-catenin is the hallmark of this tumor type; this feature plays a role in fine needle aspiration biopsy. Cribriform-morular variant of thyroid carcinoma has a peculiar endodermal (intestinal-like) type phenotype, activation of the WNT/β-catenin signaling pathway, and belongs to the non-BRAF-non-RAS subtype of the molecular classification of thyroid tumors. Elevated expression of estrogen and progesterone receptors and activation of the WNT/β-catenin pathway may prove useful as putative therapeutic targets in cases that do not respond to conventional therapy. Clinicians should be alerted to the possibility of familial adenomatous polyposis when a diagnosis of cribriform-morular variant of thyroid carcinoma is made. Instead of being considered as a variant of papillary thyroid carcinoma its designation as cribriform-morular thyroid carcinoma seems more appropriate.