The emerging and challenging role of PD-L1 in patients with gynecological cancers: An updating review with clinico-pathological considerations.

Over recent years, there has been significant progress in the development of immunotherapeutic molecules designed to block the PD-1/PD-L1 axis. These molecules have demonstrated their ability to enhance the immune response by prompting T cells to identify and suppress neoplastic cells. PD-L1 is a type 1 transmembrane protein ligand expressed on T lymphocytes, B lymphocytes, and antigen-presenting cells and is considered a key inhibitory checkpoint involved in cancer immune regulation. PD-L1 immunohistochemical expression in gynecological malignancies is extremely variable based on tumor stage and molecular subtypes. As a result, a class of monoclonal antibodies targeting the PD-1 receptor and PD-L1, known as immune checkpoint inhibitors, has found successful application in clinical settings. In clinical practice, the standard method for identifying suitable candidates for immune checkpoint inhibitor therapy involves immunohistochemical assessment of PD-L1 expression in neoplastic tissues. The most commonly used PD-L1 assays in clinical trials are SP142, 28-8, 22C3, and SP263, each of which has been rigorously validated on specific platforms. Gynecologic cancers encompass a wide spectrum of malignancies originating from the ovaries, uterus, cervix, and vulva. These neoplasms have shown variable response to immunotherapy which appears to be influenced by genetic and protein expression profiles, including factors such as mismatch repair status, tumor mutational burden, and checkpoint ligand expression. In the present paper, an extensive review of PD-L1 expression in various gynecologic cancer types is discussed, providing a guide for their pathological assessment and reporting.

Atypical glandular cells and predictive features of malignancy in Pap smears: A retrospective monocentric study.

OBJECTIVE: The introduction of cytological screening with the Papanicolau smear significantly reduced cervical cancer mortality. However, Pap smear examination can be challenging, being based on the observer ability to decode different cytological and architectural features. This study aims to evaluate the malignancy rate of AGC (atypical glandular cells) category, investigating the relationships between cytological and histological diagnosis. METHODS: Eighty-nine patients, diagnosed as AGC at cytological evaluation and followed up with biopsy or surgical procedure at Policlinico Gemelli Hospital, Rome, Italy, were included in the study. The cytopathological architectural (feathering, rosette formation, overlapping, loss of polarity, papillary formation, three-dimensional formation) and nuclear (N/C ratio, nuclear enlargement and hyperchromasia, mitoses, nuclei irregularity, evident nucleoli) features of AGC were evaluated. Statistical analyses were performed to assess cyto-histological correlation and determine the relevance of architectural and nuclear features in the diagnosis of malignancy. RESULTS: Of the 89 AGC patients, 48 cases (53.93%) were diagnosed as AGC-NOS and 41 (46.07%) were diagnosed as AGC-FN, according to the Bethesda classification system. The follow-up biopsies or surgical resections revealed malignancy in 46 patients (51.69%). The rates of malignancy for AGC-NOS and AGC-FN were 35.41% and 70.73% respectively. Furthermore, analysing cytopathological features, we found that both architectural and nuclear criteria were statistically significant (p < 0.05). Only overlapping, nuclear irregularity and increased N/C ratio were not found to be statistically significant for detecting malignancy. CONCLUSIONS: Cytological diagnosis of glandular lesions remains a valid tool, when appropriate clinical correlation and expert evaluation are available.

Beyond the WHO 2020 Classification of Female Genital Tumors: Types of Endometrial Cancer: A Pathological and Molecular Focus on Challenging Rare Variants.

Endometrial carcinoma is a heterogeneous group of malignancies characterized by distinct histopathological features and genetic underpinnings. The 2020 WHO classification has provided a comprehensive framework for the categorization of endometrial carcinoma. However, it has not fully addressed the spectrum of uncommon entities that are currently not recognized by the 2020 WHO and have only been described in the form of small case series and case reports. These neoplasms represent a real diagnostic challenge for pathologists; furthermore, their therapeutic management still remains controversial and information regarding tumor prognosis is very limited. This review aims to elucidate these lesser-known variants of endometrial carcinoma. We discuss the challenges of identifying these rare subtypes and the molecular alterations associated with them. Furthermore, we propose the need for expanded classification systems that include these variants to enhance clinical outcomes and research efforts. We believe that a better histological typing characterization of these entities may lead to more reproducible and accurate diagnoses and more personalized treatments. By raising awareness of these rare entities, we also hope to encourage further investigation and integration into clinical practice to improve patient care in endometrial carcinoma.

Microscopic sex cord-stromal proliferations in extraovarian heterotopic sites: Report of a rare case and review of the literature.

Microscopic heterotopic extraovarian sex cord-stromal proliferations were first reported in the literature in 2015 by McCluggege. Afterwards, few similar cases have been described. Herein, we report the fourteenth case of microscopic heterotopic sex cord-stromal proliferation and the third case sited in the pelvic peritoneum. The clinical history of these rare cases suggests their benign nature. Knowledge of this histological pattern is important for differential diagnoses such as malignant pathologies and metastatic diseases.

Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep-learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed-models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10-point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process.