Tuberculosis (TB) presents a substantial health risk to autopsy staff, given its three to five times higher incidence of TB compared to clinical staff. This risk is notably accentuated in South Korea, which reported the highest TB incidence rate and the third highest TB mortality rate among OECD member countries in 2020. The standard TB diagnostic method, histopathological examination of sputum or tissue for acid-fast bacilli (AFB) using Ziehl–Neelsen staining, demands microscopic examination of slides at 1000× magnification, which is labor-intensive and time-consuming. This article proposes a computer-aided diagnosis (CAD) system designed to enhance the efficiency of TB diagnosis at magnification less than 1000×. By training nine neural networks with images taken from 30 training slides and 10 evaluation slides at 400× magnification, we evaluated their ability to detect M. tuberculosis. The N model achieved the highest accuracy, with 99.77% per patch and 90% per slide. We discovered that the model could aid pathologists in preliminary TB screening, thereby reducing diagnostic time. We anticipate that this research will contribute to minimizing autopsy staff’s infection risk and rapidly determining the cause of death.

Distinguishing prostatic stromal invasion (PSI) by urothelial carcinoma (UC) from in situ UC involving prostatic ducts or acini with no stromal invasion (in situ involvement) may be challenging on hematoxylin and eosin stained sections. However, the distinction between them is important because cases with PSI show worse prognosis. This study was performed to assess the utility of double cocktail immunostains with high molecular weight cytokeratin (HMWCK) and GATA-3 to discriminate PSI by UC from in situ UC involvement of prostatic ducts or acini in the prostate. Among 117 radical cystoprostatectomy specimens for bladder UCs, 25 cases showed secondary involvement of bladder UC in prostatic ducts/acini only or associated stromal invasion and of these 25 cases, seven cases revealed equivocal PSI. In these seven cases with equivocal PSI, HMWCK, and GATA-3 double immunohistochemical stains were performed to identify whether this cocktail stain is useful to identify the stromal invasion. In all cases, basal cells of prostate glands showed strong cytoplasmic staining for HMWCK and UC cells showed strong nuclear staining for GATA-3. In cases with stromal invasion of UC, GATA-3-positive tumor cells in the prostatic stroma without surrounding HMWCK-positive basal cells were highlighted and easily recognized. Among seven equivocal cases, two cases showed PSI and five in situ UC in the prostate. In two cases, the original diagnoses were revised. Our study suggested that HMWCK and GATA-3 double stains could be utilized as an adjunct method in the distinction between PSI by UC from in situ UC involving prostatic ducts or acini.

Background: Urothelial carcinoma (UC) accounts for roughly 90% of bladder cancer, and has a high propensity for diverse differentiation. Recently, certain histologic variants of UC have been recognized to be associated with unfavorable clinical outcomes. Several UC studies have also suggested that tumor budding is a poor prognostic marker. Distant metastasis of UC after radical cystectomy is not uncommon. However, these metastatic lesions are not routinely confirmed with histology. Methods: We investigated the histopathologic features of 13 cases of UC with biopsy-proven distant metastases, with a special emphasis on histologic variants and tumor budding. Results: Lymph nodes (6/13, 46%) were the most common metastatic sites, followed by the lung (4/13, 31%), liver (4/13, 31%), and the adrenal gland (2/13, 15%). The histologic variants including squamous (n=1), micropapillary (n=4), and plasmacytoid (n=1) variants in five cases of UC. Most histologic variants (4/5, 80%) of primary UCs appeared in the metastatic lesions. In contrast, high-grade tumor budding was detected in six cases (46%), including one case of non-muscle invasive UC. Our study demonstrates that histologic variants are not uncommonly detected in distant metastatic UCs. Most histologic variants seen in primary UCs persist in the distant metastatic lesions. In addition, high-grade tumor budding, which occurs frequently in primary tumors, may contribute to the development of distant metastasis. Conclusions Therefore, assessing the presence or absence of histologic variants and tumor budding in UCs of the urinary bladder, even in non-muscle invasive UCs, may be useful to predict distant metastasis.

This case study highlights craniofacial identification technology (CFIT) as a complementary and translational tool for reconstruction of cranial gunshot wounds (GSWs) and presenting evidence beyond forensic identification. In forensic cases involving GSWs, a visual demonstration of the bullet trajectory can improve communication between forensic pathologists and non-medical judicial agencies and the court. Postmortem computed tomography (PMCT) images and autopsy images are essential medical evidence, providing a robust visual display of the GSW and its bullet trajectory. PMCT images are useful for identifying the precise projectile localization and characteristics of bone fractures. However, PMCT images may not adequately present soft tissue injuries well, and autopsy images can be unpleasant to non-medical professionals, making it difficult for them to understand such specialized scientific evidence. CFIT is a well-established scientific tool with which forensic experts in craniofacial identification can create more advanced reconstructed three-dimensional (3D) images based on both postmortem findings and PMCT data. Intracranial bullet trajectory can be shown simply and directly in reconstructed 3D cranial images. CFIT can serve as an adjunctive tool to overcome the limitations of both PMCT images and autopsy images, thereby facilitating better understanding of such specialized medical evidence by non-medical professionals. Here, we present two cases of head GSWs, in which CFIT was newly implemented to reconstruct the cranial GSW including bullet trajectory, for evidence presentation—expanding its traditional use in forensic identification. Therefore, CFIT can help provide better forensic medical services for non-medical professionals.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a rare, inherited, arrhythmic syndrome that can lead to sudden cardiac death, particularly in children and adolescents, during physical or emotional stress. It typically presents as adrenergically mediated polymorphic or bidirectional ventricular tachycardia in otherwise structurally and histologically normal hearts. In many cases, conventional autopsies fail to reveal the definitive cause of death; as such, postmortem genetic testing is a valuable diagnostic alternative in such cases. The present report describes a case involving a 15-year-old boy who collapsed during outdoor physical activity and died despite prompt attempts at resuscitation. Autopsy findings were unremarkable. Postmortem next-generation sequencing, however, identified a pathogenic heterozygous RYR2 variant (c.7202G>T, p.Arg2401Leu) consistent with CPVT type 1. Genetic findings established the cause of death and supported the recommendation for cascade testing of biological relatives. Despite its benefits, postmortem genetic testing has not yet been implemented in routine forensic practice in South Korea. This case supports the integration of structured postmortem genetic testing and family counseling into modern death investigations, along with collaborative systems with clinical institutions to improve diagnostic certainty and preventive care.

Sphenoid sinus fluid is considered a supportive indicator of drowning in forensic medicine, but traditional manual assessment on postmortem computed tomography (PMCT) is labor-intensive and observer-dependent. Efficient, reproducible methods for quantitative evaluation are needed in forensic practice. This study developed deep learning-based approaches for the automated segmentation and volumetric estimation of sphenoid sinus fluid using PMCT images from 165 autopsy-confirmed drowning cases. Three U-Net-based models (2D, 2.5D, and 3D) were developed and evaluated against manually annotated reference standards. In the test dataset, mean Dice coefficients were 0.866 (2D), 0.869 (2.5D), and 0.798 (3D). Volumetric estimates showed no statistically significant differences from the reference standard, with strong correlations (Spearman's ρ = 0.976-0.988). Mean absolute errors were 0.218 (2D), 0.206 (2.5D), and 0.310 ml (3D). The 2.5D approach provided the most balanced performance between segmentation accuracy and volumetric estimation. These findings demonstrate the feasibility of automated PMCT-based segmentation and volumetric quantification of sphenoid sinus fluid, enabling quantitative assessment on PMCT images prior to autopsy.