Galileo—an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies Albino Eccher, Vincenzo L’Imperio, Liron Pantanowitz, Giorgio Cazzaniga, Fabio Del Carro, et al. Journal of Nephrology, 2025 Background Pre-transplant procurement biopsy interpretation is challenging, also because of the low number of renal pathology experts. Artificial intelligence (AI) can assist by aiding pathologists with kidney donor biopsy assessment. Herein we present the “Galileo” AI tool, designed specifically to assist the on-call pathologist with interpreting pre-implantation kidney biopsies. Methods A multicenter cohort of whole slide images acquired from core-needle and wedge biopsies of the kidney was collected. A deep learning algorithm was trained to detect the main findings evaluated in the pre-implantation setting (normal glomeruli, globally sclerosed glomeruli, ischemic glomeruli, arterioles and arteries). The model obtained on the Aiforia Create platform was validated on an external dataset by three independent pathologists to evaluate the performance of the algorithm. Results Galileo demonstrated a precision, sensitivity, F1 score and total area error of 81.96%, 94.39%, 87.74%, 2.81% and 74.05%, 71.03%, 72.5%, 2% in the training and validation sets, respectively. Galileo was significantly faster than pathologists, requiring 2 min overall in the validation phase (vs 25, 22 and 31 min by 3 separate human readers, p < 0.001). Galileo-assisted detection of renal structures and quantitative information was directly integrated in the final report. Conclusions The Galileo AI-assisted tool shows promise in speeding up pre-implantation kidney biopsy interpretation, as well as in reducing inter-observer variability. This tool may represent a starting point for further improvements based on hard endpoints such as graft survival. Graphical Abstract
iForensic, multicentric validation of digital whole slide images (WSI) in forensic histopathology setting according to the College of American Pathologists guidelines Nicola Pigaiani, Antonio Oliva, Vito Cirielli, Simone Grassi, Vincenzo Arena, et al. International Journal of Legal Medicine, 2025 Pathology has benefited from the rapid progress of image-digitizing technology during the last decade. However, the application of digital whole slide images (WSI) in forensic pathology still needs to be improved. WSI validation is crucial to ensure diagnostic performance, at least equivalent to glass slides and light microscopy. The College of American Pathologists Pathology and Laboratory Quality Center recently updated internal digital pathology system validation recommendations. Following these guidelines, this pilot study aimed to validate the performance of a digital approach for forensic histopathological diagnosis. Six independent skilled forensic pathologists from different forensic medicine institutes evaluated 100 glass slides of forensic interest (80 stained with standard hematoxylin and eosin, 20 with special staining), including different organs and tissues, with light microscopy (Olympus BX51, Tokyo, Japan). Glass slides were scanned using the Aperio GT 450 DX Digital Slides Scanner (Leica Biosystems, Nussloch, Germany). After two wash-out weeks, forensic pathologists evaluated WSIs in front of a widescreen using computer devices with dedicated software (O3 viewer, O3 Enterprise, Zucchetti, Trieste, Italy). Side-by-side comparisons between diagnoses performed on tissue glass slides versus WSIs were above the threshold stated in the validation guidelines (mean concordance of 97.8%). CSUQ Version 3 questionnaire showed high satisfaction for all pathologists (mean result: 6.6/7). Our institutional digital forensic pathology system has been validated for practical casework application. This approach opens new scenarios in practical forensic casework investigations, such as sharing live histological ex-glass slides online, as well as educational and research perspectives, with improving impacts on the whole daily workflow.
Forensic value of soft tissue detachments from the hyoid bone in death due to strangulation asphyxia Giovanna Del Balzo, Guido Pelletti, Dario Raniero, Alessia Farinelli, Andrea Uberti, et al. Advances in Clinical and Experimental Medicine, 2025 BACKGROUND There are no unequivocal histopathological findings for the diagnosis of fatal asphyxia due to neck compression. From the observation of a series of asphyxiation cases, we noted, during microscopic analysis, a high frequency of "detachment" of soft tissues from the hyoid bone. This specifically refers to the presence of an optical space between the surface of the hyoid bone and soft tissues. OBJECTIVES We aimed to evaluate the detachment of soft tissues from the hyoid bone as specific histological evidence of death due to strangulation asphyxia. MATERIAL AND METHODS Ten blocks were taken from deaths due to external mechanical compression of the neck (strangulation asphyxia, group A), 22 blocks were taken from deaths for other causes without trauma to the neck (group B), and 38 blocks were obtained from living subjects that have undergone laryngectomies (group C). The presence/absence of detachments were compared between the 3 groups (A, B and C) using Fisher's exact test. RESULTS The detachment of soft tissues from the hyoid bone was observed in 5 cases (50%) in group A, 6 cases (27.2%) in group B, and 17 cases (44.3%) in group C. The sensitivity and specificity of the presence of the detachment in group A were 0.5 (95% confidence interval (95% CI): 0.38-0.62) and 0.57 (95% CI: 0.45-0.69), respectively. The comparison between the 3 groups and the presence/absence of soft tissue detachment showed no statistically significant differences between the groups (p = 0.329), clarifying that soft tissue detachment is a nonspecific variable for all 3 situations. CONCLUSIONS Detachment of soft tissues has poor value as a single element to favor the diagnosis of asphyxia due to violent compression of the neck and should be interpreted as an artifactual finding, unrelated to the neck injury or injury vitality.
Artificial intelligence-based algorithms for the diagnosis of prostate cancer: A systematic review Stefano Marletta, Albino Eccher, Filippo Maria Martelli, Nicola Santonicco, Ilaria Girolami, et al. American Journal of Clinical Pathology, 2024 Objectives The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine. Methods A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer. Results Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival. Conclusions The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI’s adoption in prostate pathology, as well as expanding its prognostic predictive potential.
Characterization of two transcriptomic subtypes of marker-null large cell carcinoma of the lung suggests different origin and potential new therapeutic perspectives Michele Simbolo, Giovanni Centonze, Anastasios Gkountakos, Valentina Monti, Patrick Maisonneuve, et al. Virchows Archiv, 2024 Pulmonary large cell carcinoma (LCC) is an undifferentiated neoplasm lacking morphological, histochemical, and immunohistochemical features of small cell lung cancer, adenocarcinoma (ADC), or squamous cell carcinoma (SCC). The available molecular information on this rare disease is limited. This study aimed to provide an integrated molecular overview of 16 cases evaluating the mutational asset of 409 genes and the transcriptomic profiles of 20,815 genes. Our data showed that TP53 was the most frequently inactivated gene (15/16; 93.7%) followed by RB1 (5/16; 31.3%) and KEAP1 (4/16; 25%), while CRKL and MYB genes were each amplified in 4/16 (25%) cases and MYC in 3/16 (18.8%) cases; transcriptomic analysis identified two molecular subtypes including a Pure-LCC and an adenocarcinoma like-LCC (ADLike-LCC) characterized by different activated pathways and cell of origin. In the Pure-LCC group, POU2F3 and FOXI1 were distinctive overexpressed markers. A tuft cell-like profile and the enrichment of a replication stress signature, particularly involving ATR, was related to this profile. Differently, the ADLike-LCC were characterized by an alveolar-cell transcriptomic profile and association with AIM2 inflammasome complex signature. In conclusion, our study split the histological marker-null LCC into two different transcriptomic entities, with POU2F3, FOXI1, and AIM2 genes as differential expression markers that might be probed by immunohistochemistry for the differential diagnosis between Pure-LCC and ADLike-LCC. Finally, the identification of several signatures linked to replication stress in Pure-LCC and inflammasome complex in ADLike-LCC could be useful for designing new potential therapeutic approaches for these subtypes.
Serous Neoplasms Paola Capelli, Paolo Tinazzi Martini, Giovanni Morana, Riccardo de Robertis, Claudio Luchini, et al. Imaging and Pathology of Pancreatic Neoplasms A Pictorial Atlas Second Edition, 2022
Neuroendocrine Neoplasms Riccardo De Robertis, Mirko D’Onofrio, Paolo Tinazzi Martini, Stefano Gobbo, Maria Gaia Mastrosimini, et al. Imaging and Pathology of Pancreatic Neoplasms A Pictorial Atlas Second Edition, 2022
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