Molecular classification of endometrial carcinoma on endometrial biopsy: an early prognostic value to guide personalized treatment.

OBJECTIVE: Molecular features are essential for estimating the risk of recurrence and impacting overall survival in patients with endometrial cancer. Additionally, the surgical procedure itself could be personalized based on the molecular characteristics of the tumor. This study aims to assess the feasibility of obtaining reliable molecular classification status from biopsy specimens collected during hysteroscopy to better modulate the appropriate surgical treatment. METHODS: This monocentric, retrospective, observational study was conducted on 106 patients who underwent a biopsy procedure followed by radical surgery for endometrial cancer, with concurrent molecular investigation. The molecular classification was determined through immunohistochemical staining for p53 and mismatch repair proteins, along with gene sequencing for POLE. RESULTS: Overall, 106 patients underwent molecular investigation, which was finally achieved on 99 patients (93.4%). Among these, the molecular analysis was conducted in 71 patients (67%) on the pre-operative endometrial biopsy and on the final uterine specimen in 28 patients (26.4%). Most of the endometrial biopsies were performed using Bettocchi hysteroscopy (66%). Molecular analysis was not possible in seven patients (6.6%), with six cases due to sample inadequacy and one case attributed to intra-mucosal carcinoma. The molecular results showed that the copy number low sub-group was the most common, and five cases of 'multiple classifiers' were observed in the low-risk category. CONCLUSION: Our experience in obtaining molecular information from biopsy samples underscores the feasibility and efficacy of this technique, even in small tissue samples. This capability helps define the prognostic group of patients, facilitates timely decision-making, and develops a personalized strategy for each patient.

Application of novel algorithm on a retrospective series to implement the molecular classification for endometrial cancer.

INTRODUCTION: The study aimed to validate the Betella algorithm, focusing on molecular analyses exclusively for endometrial cancer patients, where molecular classification alters risk assessment based on ESGO/ESTRO/ESP 2020 guidelines. MATERIALS AND METHODS: Conducted between March 2021 and March 2023, the retrospective research involved endometrial cancer patients undergoing surgery and comprehensive molecular analyses. These included p53 and mismatch repair proteins immunohistochemistry, as well as DNA sequencing for POLE exonuclease domain. We applied the Betella algorithm to our population and evaluated the proportion of patients in which the molecular analysis changed the risk class attribution. RESULTS: Out of 102 patients, 97 % obtained complete molecular analyses. The cohort exhibited varying molecular classifications: 10.1 % as POLE ultra-mutated, 30.3 % as mismatch repair deficient, 11.1 % as p53 abnormal, and 48.5 % as non-specified molecular classification. Multiple classifiers were present in 3 % of cases. Integrating molecular classification into risk group calculation led to risk group migration in 11.1 % of patients: 7 moved to lower risk classes due to POLE mutations, while 4 shifted to higher risk due to p53 alterations. Applying the Betella algorithm, we can spare the POLE sequencing in 65 cases (65.7 %) and p53 immunochemistry in 17 cases (17.2 %). CONCLUSION: In conclusion, we externally validated the Betella algorithm in our population. The application of this new proposed algorithm enables assignment of the proper risk class and, consequently, the appropriate indication for adjuvant treatment, allowing for the rationalization of the resources that can be allocated otherwise, not only for the benefit of settings with low resources, but of all settings in general.

Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification.

This paper introduces a self-attention Vision Transformer model specifically developed for classifying breast cancer in histology images. We examine various training strategies and configurations, including pretraining, dimension resizing, data augmentation and color normalization strategies, patch overlap, and patch size configurations, in order to evaluate their impact on the effectiveness of the histology image classification. Additionally, we provide evidence for the increase in effectiveness gathered through geometric and color data augmentation techniques. We primarily utilize the BACH dataset to train and validate our methods and models, but we also test them on two additional datasets, BRACS and AIDPATH, to verify their generalization capabilities. Our model, developed from a transformer pretrained on ImageNet, achieves an accuracy rate of 0.91 on the BACH dataset, 0.74 on the BRACS dataset, and 0.92 on the AIDPATH dataset. Using a model based on the prostate small and prostate medium HistoEncoder models, we achieve accuracy rates of 0.89 and 0.86, respectively. Our results suggest that pretraining on large-scale general datasets like ImageNet is advantageous. We also show the potential benefits of using domain-specific pretraining datasets, such as extensive histopathological image collections as in HistoEncoder, though not yet with clear advantages.

Mesonephric-Like Adenocarcinomas a Rare Tumor: The Importance of Diagnosis.

Mesonephric-like adenocarcinomas (MLA) are rare neoplasms that arise in the uterine body and ovary and have been added to the World Health Organisation's recent 2020 classification of female genital cancers. The pathogenesis of MLA is unknown and it remains debated whether they represent mesonephric carcinomas (Wolffian) arising in the endometrium/ovary or endometrioid carcinomas (Müllerian) closely mimicking mesonephric carcinomas. Here we report the case of a 57-year-old woman with an initial misdiagnosis of endometrioid adenocarcinoma on diagnostic biopsy. The patient came to our clinical evaluation for the appearance of menometrorrhagia complicated by anemia for several months. Therefore, she underwent pelvic echo-flowmetry, with indication for diagnostic hysteroscopy with endometrial biopsy, which yielded a positive result for endometrioid endometrial adenocarcinoma. Following staging CT scan and targeted examinations on pulmonary findings, the patient underwent surgery with surprise of definitive diagnosis deponent for endometrial MLA. Our intention is to establish a brief review of the scientific evidence in the literature and the tools available for a correct histological diagnosis, in the light of the scant anatomopathological evidence. Our question gives rise to the motive for the publication: is immunohistochemistry the right way to resolve the diagnostic error at histology, which is usually the only source of diagnostic certainty? This case is intended to alert of diagnostic error that risked having the patient treated as a neoplasm with a favorable prognosis and low degree of aggressiveness instead of for a very aggressive and poor prognosis tumor such as MLA.

Intrauterine fetal death in a COVID positive pregnant woman: a case report and literature review.

SARS-CoV-2 disease mechanisms are not yet fully understood, especially in cases affecting pregnant women. In fact, although they suffer from the same symptoms as non-pregnant women, they are more susceptible to adverse outcomes of COVID-19 as well as pregnancy complications leading to stillbirth, premature rupture of membrane, or intrauterine fetal demise (IUFD). The consequences on pregnancy are not clearly correlated with the severity of the infection. We present a case of fetal death in a 30-year-old woman with a confirmed SARS-CoV-2 infection without any other significant clinical or obstetric disorders. A histopathological examination of the placenta indicated massive and diffuse intervillar fibrinoid deposits associated with chronic histiocytic intervillositis that can be associated with SARS-CoV-2 placentitis. Given the high rates of SARS-CoV-2 infections in pregnancy, it is important to understand the factors that determine negative pregnancy outcomes, regardless of the severity of the patient's symptoms.

Primary mesenchymal chondrosarcoma of the orbit in a young female: imaging and histopathological features.

Mesenchymal chondrosarcoma (MCS) is a rare high-grade sarcoma of bone and soft tissue with highly aggressive behavior and a peak incidence in the second and third decades. We report a case of primary orbital MCS in a 30 year-old female, with radiological and clinicopathological features. Orbital MCS is an entity that should be considered in the differential diagnosis of calcified orbital lesions.