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BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AIs role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AIs successful clinical implementation. FUNDING: No specific funding was provided for this study.

Recent advancements in computer‐assisted diagnosis (CAD) have catalysed significant progress in pathology, particularly in the realm of urine cytopathology. This review synthesizes the latest developments and challenges in CAD for diagnosing urothelial carcinomas, addressing the limitations of traditional urinary cytology. Through a literature review, we identify and analyse CAD models and algorithms developed for urine cytopathology, highlighting their methodologies and performance metrics. We discuss the potential of CAD to improve diagnostic accuracy, efficiency and patient outcomes, emphasizing its role in streamlining workflow and reducing errors. Furthermore, CAD tools have shown potential in exploring pathological conditions, uncovering novel biomarkers and prognostic/predictive features previously unknown or unseen. Finally, we examine the practical issues surrounding the integration of CAD into clinical practice, including regulatory approval, validation and training for pathologists. Despite the promising results, challenges remain, necessitating further research and validation efforts. Overall, CAD presents a transformative opportunity to revolutionize diagnostic practices in urine cytopathology, paving the way for enhanced patient care and outcomes. [ABSTRACT FROM AUTHOR]

Background/Aim: To evaluate the long-term oncological outcomes in men with intermediate risk prostate cancer (PCa) enrolled in active surveillance (AS). Patients and Methods: From April 2015 to December 2022, 30 men with Gleason score 3+4/ISUP Grade Group2 (GG2), greatest percentage of cancer (GPC) ≤50%, Gleason pattern 4 ≤10%, ≤3 positive biopsy cores were enrolled in AS. All patients underwent confirmatory transperineal saturation biopsy (SPBx: 20 cores) 12 months from diagnosis plus multiparametric magnetic resonance (mpMRI) evaluation. At the last follow-up, 68Ga prostate-specific membrane antigen (PSMA) positron-emission tomography (PET)/computed tomography (CT) was added: lesions with PIRADS score ≥3 and/or standardized uptake value (SUVmax) >5 were submitted to four targeted cores. Results: Three out of 30 (10%) men with GG2 PCa were reclassified at confirmatory biopsy. At the last follow-up (median 5.2 years), only 2 of 27 (7.4%) men were reclassified and 23/30 (76.6%) continued AS. Conclusion: Men with favorable GG2 PCa enrolled in AS have good long-term oncological results. The use of selective criteria (i.e., SPBx, mpMRI, PSMA PET/CT) reduces the risk of reclassification. [ABSTRACT FROM AUTHOR]

Two‐liner/synopsis: The digital cytology hub (DCH) has been established under the umbrella of the Cytopathology journal. DCH will help bring about the crucial changes needed to make digital cytology the way of practicing cytology in our laboratories. [ABSTRACT FROM AUTHOR]

Background/Aim: To evaluate the diagnostic accuracy of 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) in the diagnosis and staging of prostate cancer (PCa). Patients and Methods: From January 2021 to December 2022, 160 men (median age: 66 years) with PCa (median PSA of 11.7 ng/ml) before prostate biopsy underwent 68Ga-PET/CT imaging examinations (Biograph 6; Siemens, Knoxville, TN, USA). The location of focal uptake on 68Ga-PSMA PET/TC and standardized uptake values (SUVmax) were reported on a per-lesion basis for each International Society of Urological Pathology (ISUP) grade group (GG) PCa. Results: Overall, the median intraprostatic 68Ga-PSMA SUVmax was 26.1 (range=2.7-164); in the 15 men with not clinically significant PCa (ISUP grade group 1) median SUVmax was 7.5 (range=2.7-12.5). In the 145 men with csPCa (ISUP GG=2) median SUVmax was 33 (range=7.8-164). A SUVmax cut-off of 8 demonstrated a diagnostic accuracy in the diagnosis of PCa equal to 87.7% vs. 89.3% vs. 100% in the presence of a GG1 vs. GG2 vs. GG=3 PCa, respectively. In addition, median SUVmax in the bone and node metastases was 52.7 (range=25.3-92.8) and 47 (range=24.5-65), respectively. Conclusion: 68GaPSMA PET/CT with a SUVmax cut-off of 8 demonstrated a good accuracy in the diagnosis of csPCa (100% in the presence of GG=3) showing a good cost-benefit ratio as a single procedure for the diagnosis and staging of high-risk PCa. [ABSTRACT FROM AUTHOR]

Introduction: To evaluate the accuracy of 68Ga-prostate specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) in the diagnosis of clinically significant prostate cancer (csPCa: Grade Group = 2) in men enrolled in Active Surveillance (AS) protocol. Materials and methods: From May 2013 to December 2021 200 men aged between 52 and 74 years (median age 63) with very low risk PCa were enrolled in an AS protocol study. During the follow up 48/200 (24%) men were upgraded and 10/200 (5%) decided to leave the AS protocol. After five years from confirmatory biopsy (range: 48-60 months) 40/142 (28.2%) consecutive patients were submitted to mpMRI and 68Ga-PSMA PET/CT imaging examinations before scheduled repeated biopsy. All the mpMRI (PI-RADS = 3) and 68Ga-PET/TC standardized uptake value (SUVmax) = 5 index lesions underwent targeted cores (mpMRI-TPBx and PSMA-TPBx) combined with transperineal saturation prostate biopsy (SPBx: median 20 cores). Results: Multiparametric MRI and 68Ga-PSMA PET/CT showed 18/40 (45%) and 9/40 (22.5%) lesions suspicious for PCa. In 3/40 (7.5%) men a csPCa (GG2) was found; 68Ga-PSMA-TPBx vs. mpMRI-TPBx vs. SPBx diagnosed 2/3 (66.6%) vs. 2/3 (66.6%) vs. 3/3 (100%) csPCa, respectively. In detail, mpMRI and 68Ga-PSMA PET/TC demonstrated 16/40 (40%) vs. 7/40 (17.5%) false positive and 1 (33.3%) vs. 1 (33.3%) false negative results. Conclusion: Although 68PSMA PET/CT did not improve the detection for csPCa of SPBx (1 false negative result equal to 33.3% of the cases), at the same time, would have spared 31/40 (77.5%) scheduled biopsies showing a better diagnostic accuracy in comparison with mpMRI (83.3% vs. 70.2%). [ABSTRACT FROM AUTHOR]