A rapidly enlarging mass of the anterior compartment of the neck with compressive symptoms may represent, among other diagnosis, a neoplasm of the thyroid gland. We describe the case of a 59-year-old woman referred to the endocrine surgical unit because of compressive cervical symptoms for 3 months. The cervical ultrasound revealed a sub-sternal goiter with heterogeneous echo structure and the fine-needle aspirating cytology was inconclusive. Given the large impact of symptoms on life quality, she was submitted to a total thyroidectomy. Histological examination of the surgical specimen revealed the presence of a Diffuse Large B Cell Lymphoma of the thyroid. Primary thyroid lymphomas are rare and there are few randomized studies for diagnostic and therapeutic guidance. New immunohistochemical and molecular techniques have improved the diagnostic accuracy with corebiopsy limiting the role of surgery. The treatment should first include the control of local disease with radiotherapy and/or surgery combined with chemotherapy to control obscure or disseminated disease. Palliative surgery may be needed to relieve airway compression symptoms. Under these circumstances, surgery should be performed by a specialized surgeon to decrease the associated morbidity. The prognosis of patients depends on the histological classification of the tumor and the stage of the disease. Due to the rarity of the disease, each case must be evaluated and treated individually, since there is not a consensual therapeutic approach.

Actinomycosis remains characteristically uncommon, but is still an important cause of morbidity. Its clinical presentation is usually indolent and chronic as slow growing masses that can evolve into fistulae, and for that reason are frequently underdiagnosed. Actinomyces spp is often disregarded clinically and is classified as a colonizing microorganism. In this review of literature, we concomitantly present 11 cases of actinomycosis with different localizations, diagnosed at a tertiary hospital between 2009 and 2016. We outline the findings of at least one factor of immunosuppression in > 90% of the reported cases.

Background/Objectives: Accurate diagnosis, prognosis, and prediction of treatment response are essential in managing gynecologic cancers and maintaining patient quality of life. Computational pathology, powered by artificial intelligence (AI), offers a transformative opportunity for objective histopathological assessment. This review provides a comprehensive, user-oriented overview of existing AI tools for the characterization of gynecological cancers, critically evaluating their clinical applicability and identifying key challenges for future development. Methods: A systematic literature search was conducted in PubMed and Web of Science for studies published up to 2025. The search focused on AI tools developed for the diagnosis, prognosis, or treatment prediction of gynecologic cancers based on histopathological images. After applying selection criteria, 36 studies were included for in-depth analysis, covering ovarian, uterine, cervical, and other gynecological cancers. Studies on cytopathology and pure tumor detection were excluded. Results: Our analysis identified AI tools addressing critical clinical tasks, including histopathologic subtyping, grading, staging, molecular subtyping, and prediction of therapy response (e.g., to platinum-based chemotherapy or PARP inhibitors). The performance of these tools varied significantly. While some demonstrated high accuracy and promising results in internal validation, many were limited by a lack of external validation, potential biases from training data, and performance that is not yet sufficient for routine clinical use. Direct comparison between studies was often hindered by the use of non-standardized evaluation metrics and evolving disease classifications over the past decade. Conclusions: AI tools for gynecologic cancers represent a promising field with the potential to significantly support pathological practice. However, their current development is heterogeneous, and many tools lack the robustness and validation required for clinical integration. There is a pressing need to invest in the creation of clinically driven, interpretable, and accurate AI tools that are rigorously validated on large, multicenter cohorts. Future efforts should focus on standardizing evaluation metrics and addressing unmet diagnostic needs, such as the molecular subtyping of rare tumors, to ensure these technologies can reliably benefit patient care.

The aim of the present study was to develop and validate a quantitative image analysis (IA) algorithm to aid pathologists in assessing bright-field HER2 in situ hybridization (ISH) tests in solid cancers. A cohort of 80 sequential cases (40 HER2-negative and 40 HER2-positive) were evaluated for HER2 gene amplification with bright-field ISH. We developed an IA algorithm using the ISH Module from HALO software to automatically quantify HER2 and CEP17 copy numbers per cell as well as the HER2/CEP17 ratio. We observed a high correlation of HER2/CEP17 ratio, an average of HER2 and CEP17 copy number per cell between visual and IA quantification (Pearson’s correlation coefficient of 0.842, 0.916, and 0.765, respectively). IA was able to count from 124 cells to 47,044 cells (median of 5565 cells). The margin of error for the visual quantification of the HER2/CEP17 ratio and of the average of HER2 copy number per cell decreased from a median of 0.23 to 0.02 and from a median of 0.49 to 0.04, respectively, in IA. Curve estimation regression models showed that a minimum of 469 or 953 invasive cancer cells per case is needed to reach an average margin of error below 0.1 for the HER2/CEP17 ratio or for the average of HER2 copy number per cell, respectively. Lastly, on average, a case took 212.1 s to execute the IA, which means that it evaluates about 130 cells/s and requires 6.7 s/mm². The concordance of the IA software with the visual scoring was 95%, with a sensitivity of 90% and a specificity of 100%. All four discordant cases were able to achieve concordant results after the region of interest adjustment. In conclusion, this validation study underscores the usefulness of IA in HER2 ISH testing, displaying excellent concordance with visual scoring and significantly reducing margins of error.