Kenneth Iczkowski

PURPOSE: This study investigates the potential of DALL·E 3, an artificial intelligence (AI) model, to generate synthetic pathologic images of prostate cancer (PCa) at varying Gleason grades. The aim is to enhance medical education and research resources, particularly by providing diverse case studies and valuable teaching tools. METHODS: This study uses DALL·E 3 to generate 30 synthetic images of PCa across various Gleason grades, guided by standard Gleason pattern descriptions. Nine uropathologists evaluated these images for realism and accuracy compared with actual hematoxylin and eosin (H&E)–stained slides using a scoring system. RESULTS: The average realism and representativeness scores were 6.04 and 6.17, indicating satisfactory quality. Scores varied significantly among Gleason patterns ( P < .05), with Gleason 5 images achieving the highest scores and accurately depicting critical pathologic characteristics. Limitations included a lack of fine nuclear detail, essential for identifying malignancy, which may affect the images' diagnostic utility. CONCLUSION: DALL·E 3 shows promise in generating customized pathologic images that can aid in education and resource expansion within pathology. However, ethical concerns, such as the potential misuse of AI-generated images for data falsification, highlight the need for responsible oversight. Collaboration between technology firms and pathologists is essential for the ethical integration of AI in pathology practices.

Aims: Tumour content in prostatic biopsies is an important indicator of prostate cancer volume and patient prognosis. Consequently, guidelines typically recommend reporting it as a percentage or linear length (mm). This study aimed to determine the current practices for reporting tumour content in prostatic biopsies and evaluated the consistency among pathologists in diagnosing 10 standard biopsy cases of prostate cancer to assess interobserver variability.Methods and results: A web‐based survey gathered data on demographics, experience and attitudes regarding the reporting of prostate cancer and its extent in biopsies. Virtual microscopy allowed analysis of 10 biopsy cases, each consisting of a single slide of prostate cancer. Self‐reports from 304 participants recruited via the International Society of Urological Pathology and the German Society of Pathology were analysed. Most participants (43.4%) reported tumour extent as percentage of the biopsy core, 37.6% reported percentages and mm and 18.3% reported mm exclusively. The methods used to determine percentages showed an unexpected spread of choices, leading to considerable variability in results. Additionally, 40.8% of participants took part in the practical segment of the survey. The reported measures of tumour extent confirmed a notable interobserver variability, which was significantly higher for reported percentages.Conclusion: A high rate of interobserver variability in reporting tumour content in prostatic biopsies was found. This matter is especially critical for patients who are candidates for active surveillance. Reporting absolute measures of tumour content has the advantage of lower variability in comparison to percentages.

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predict eventual BCR with an AUC of 0.97 and classifying cancer at an accuracy of 89.9%. This study showcases the application for a radiomics-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Malignant mesothelioma of the testicular tunics is rare. About one third of cases are metastatic and carry a poor prognosis. This paper reviews the epidemiology, clinicopathologic features, treatment, and outcome of this entity.