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Nearly all cervical cancers are caused by persistent high-risk human papillomavirus (hrHPV) infection. There are 14 recognized hrHPV genotypes (HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68), and hrHPV genotypes 16 and 18 comprise approximately 66% of all cases worldwide. An additional 15% of cervical cancers are caused by hrHPV genotypes 31, 33, 45, 52, and 58. Screening patients for hrHPV as a mechanism for implementation of early treatment is a proven strategy for decreasing the incidence of HPV-related neoplasia, cervical cancer in particular. Here, we present population data from an HPV screening initiative in Kosovo designed to better understand the prevalence of the country’s HPV burden and local incidence of cervical cancer by hrHPV genotype. Nearly 2000 women were screened for hrHPV using a real-time polymerase chain reaction (real-time PCR) assay followed by melt curve analysis to establish the prevalence of hrHPV in Kosovo. Additionally, DNA was extracted from 200 formalin-fixed, paraffin embedded cervical tumors and tested for hrHPV using the same method. Cervical screening samples revealed a high prevalence of hrHPV genotypes 16 and 51, while cervical cancer specimens predominantly harbored genotypes 16, 18, and 45. This is the first comprehensive screening study for evaluating the prevalence of hrHPV genotypes in Kosovo on screening cervical brush samples and cervical neoplasms. Given the geographic distribution of hrHPV genotypes and the WHO’s global initiative to eliminate cervical cancer, this study can support and direct vaccination efforts to cover highly prevalent hrHPV genotypes in Kosovo’s at-risk population.

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.

Tubo-ovarian high-grade serous carcinoma is believed to originate in the fallopian tubes, arising from precursor lesions like serous tubal intraepithelial carcinoma (STIC) and serous tubal intraepithelial lesion (STIL). Adequate diagnosis of these precursors is important, but can be challenging for pathologists. Here we present a deep-learning algorithm that could assist pathologists in detecting STIC/STIL. A dataset of STIC/STIL ( n  = 323) and controls ( n  = 359) was collected and split into three groups; training ( n  = 169), internal test set ( n  = 327), and external test set ( n  = 186). A reference standard was set for the training and internal test sets, by a panel review amongst 15 gynecologic pathologists. The training set was used to train and validate a deep-learning algorithm (U-Net with resnet50 backbone) to differentiate STIC/STIL from benign tubal epithelium. The model’s performance was evaluated on the internal and external test sets by ROC curve analysis, achieving an AUROC of 0.98 (95% CI: 0.96–0.99) on the internal test set, and 0.95 (95% CI: 0.90–0.99) on the external test set. Visual inspection of all cases confirmed the accurate detection of STIC/STIL in relation to the morphology, immunohistochemistry, and the reference standard. This model’s output can aid pathologists in screening for STIC, and can contribute towards a more reliable and reproducible diagnosis.

Endometrial cancer, the fourth most common cancer in females in the United States, with the lifetime risk for developing this disease is approximately 2.8% in women. Precise histologic evaluation and molecular classification of endometrial cancer is important for effective patient management and determining the best treatment modalities. This study introduces EndoNet, which uses convolutional neural networks for extracting histologic features and a vision transformer for aggregating these features and classifying slides based on their visual characteristics into high- and low- grade. The model was trained on 929 digitized hematoxylin and eosin-stained whole-slide images of endometrial cancer from hysterectomy cases at Dartmouth-Health. It classifies these slides into low-grade (Endometroid Grades 1 and 2) and high-grade (endometroid carcinoma FIGO grade 3, uterine serous carcinoma, carcinosarcoma) categories. EndoNet was evaluated on an internal test set of 110 patients and an external test set of 100 patients from the public TCGA database. The model achieved a weighted average F1-score of 0.91 (95% CI: 0.86-0.95) and an AUC of 0.95 (95% CI: 0.89-0.99) on the internal test, and 0.86 (95% CI: 0.80-0.94) for F1-score and 0.86 (95% CI: 0.75-0.93) for AUC on the external test. Pending further validation, EndoNet has the potential to support pathologists without the need of manual annotations in classifying the grades of gynecologic pathology tumors.