Cryptococcal Prostatitis Forming Caseous and Suppurative Granulomas Diagnosed by Needle Biopsy: A Case Report

Cryptococcal granulomatous prostatitis is extremely rare, and there have been few reports of its diagnosis by prostate needle biopsy. The patient, an 81–year–old man, was receiving immunosuppressive treatment for rheumatoid arthritis. He had an oropharyngeal ulcer, and it was diagnosed alongside a methotrexate-related diffuse large B-cell lymphoma. A systemic imaging examination revealed a prostatic tumor-like mass clinically suspected to be prostatic cancer, and a needle biopsy was performed. The biopsy specimen showed various types of inflammatory cell infiltration, and suppurative granuloma and caseous granuloma were observed. Both granulomas showed multiple round and oval organisms that were revealed with Grocott methenamine silver staining. Acid–fast bacilli were not detected by Ziehl–Neelsen staining. We histologically diagnosed granulomatous prostatitis caused by Cryptococcus infection. Caseous granulomas often develop in the prostate after bacillus Calmette–Guerin immunotherapy for bladder cancer, although the possibility of cryptococcal granulomatous prostatitis should also be considered.

Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery

Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis.

Prostate biopsy: a survey of practice among Nigerian urologists

Background Prostate biopsy is a commonly performed outpatient procedure in urology. It is a rapidly changing field with wide variation in practice pattern. The aim of this study is to document the current practice of prostate biopsy among Nigerian urologists. Methods A prospectively designed, self-administered, 16-item survey questionnaire was distributed among urologists and trainees at the 24th Annual General Meeting and Scientific Conference of the Nigerian Association of Urological Surgeons (NAUS). The survey covers various aspect of prostate biopsy including indications for biopsy, prophylactic antibiotic regimen use, methods of bowel preparation, number of biopsy cores taken, complications among others. Results Fifty-one completed questionnaires were returned, out of 76 distributed, giving a response rate of 67%. Majority of the respondents were Consultant urologist 47 (92%), most of them practice in the public health system 46 (90.2%), and performed more than 5 prostate needle biopsy per month 37 (72.5%). All respondents administer prophylactic antibiotics prior to biopsy, with intravenous Gentamycin being the most commonly administered prophylactics 14 (27.5%), only a few perform bowel preparations prior to biopsy 8 (15.7%) with Dulcolax suppository being the most commonly employed agents 5 (63%). Most of the biopsy were done under transrectal ultrasound guidance 29 (56.9%). None of the respondents performed MRI-guided transrectal biopsy. Most respondents take 8–12 core biopsy 20 (39.2%), using 18G trucut biopsy needle 31 (60.8%), with the patient in left lateral decubitus position 26 (51%), under 2% intrarectal xylocaine instillation 28 (54.9%). The commonest complication after the procedure was bleeding per rectum 20 (39.2%), followed by haematuria 9 (17.6%), and infection 8 (15.7%). Conclusion There is universal use of prophylactic antibiotic prior to biopsy. However, bowel preparation prior to biopsy is not common among Nigerian urologist, and MRI-guided biopsy is very rarely done for prostate biopsy. There is need for a prostate biopsy guideline among Nigerian urologists to ensure uniformity of practice, and enhance standardized service delivery.

Preference and Demand for Digital Pathology and Computer-Aided Diagnosis among Korean Pathologists: A Survey Study Focused on Prostate Needle Biopsy

Digital pathology systems (DPSs) have been globally implemented, and computer-assisted diagnosis (CAD) software has been actively developed in recent years. This study aimed to investigate perceptions of digital pathology and the demand for CAD. An online survey involving members of the Korean Society of Pathologists was conducted, and a demonstration clip of the diagnostic assistant software for a prostate needle biopsy was shown to them to provide a simple experience with CAD. One hundred sixty-four Korean pathologists (13.6% of 1210 Korean pathologists) participated. The majority (77.4%) answered affirmatively regarding the necessity of implementing a DPS, and 26.8% had plans to implement or increase the use of DPSs in the following 2–3 years at their medical institutions. Pathologists felt that multidisciplinary care or conference accessibility (56.7%), remote consultation (49.4%), and big data building (32.9%) were useful parts of DPSs. Most pathologists (81.7%) responded that CAD software would assist with the diagnostic process. In a prostate needle biopsy, pathologists used the software to improve the measurement of tumor volume and/or length and core length but not to suggest a diagnostic name or Gleason grade. Korean pathologists who participated in the survey had highly positive perceptions of digital pathology and maintained a positive attitude toward the use of CAD software.

Clinical accuracy of information extracted from prostate needle biopsy pathology reports using natural language processing.

1557 Background: Patients with prostate cancer are diagnosed through a prostate needle biopsy (PNB). Information contained in PNB pathology reports is critical for informing clinical risk stratification and treatment; however, patient comprehension of PNB pathology reports is low, and formats vary widely by institution. Natural language processing (NLP) models trained to automatically extract key information from unstructured PNB pathology reports could be used to generate personalized educational materials for patients in a scalable fashion and expedite the process of collecting registry data or screening patients for clinical trials. As proof of concept, we trained and tested four NLP models for accuracy of information extraction. Methods: Using 403 positive PNB pathology reports from over 80 institutions, we converted portable document formats (PDFs) into text using the Tesseract optical character recognition (OCR) engine, removed protected health information using the Philter open-source tool, cleaned the text with rule-based methods, and annotated clinically relevant attributes as well as structural attributes relevant to information extraction using the Brat Rapid Annotation Tool. Text pre-processing for classification and extraction was done using Scispacy and rule-based methods. Using a 75:25 train:test split (N = 302, 101), we tested conditional random field (CRF), support vector machine (SVM), bidirectional long-short term memory network (Bi-LSTM), and Bi-LSTM-CRF models, reserving 46 training reports as a validation subset for the latter two models. Model-extracted variables were compared with values manually obtained from the unprocessed PDF reports for clinical accuracy. Results: Clinical accuracy of model-extracted variables is reported in the Table. CRF was the highest performing model, with accuracies of 97% for Gleason grade, 82% for percentage of positive cores ( < 50% vs. ≥50%), 90% for perineural or lymphovascular invasion, and 100% for presence of non-acinar carcinoma histology. On manual review of inaccurate results, model performance was limited by PDF image quality, errors in OCR processing of tables or columns, and practice variability in reporting number of biopsy cores. Conclusions: Our results demonstrate successful proof of concept for the use of NLP models in accurately extracting information from PNB pathology reports, though further optimization is needed before use in clinical practice.[Table: see text]