Atypical small acinar proliferation and two or more cores of high-grade intraepithelial neoplasia on a previous prostate biopsy are significant predictors of cancer during a transperineal template-guided saturation biopsy aimed at sampling one core for each 1 mL of prostate volume

Artificial intelligence-assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies

Paige Prostate is a clinical-grade artificial intelligence tool designed to assist the pathologist in detecting, grading, and quantifying prostate cancer. In this work, a cohort of 105 prostate core needle biopsies (CNBs) was evaluated through digital pathology. Then, we compared the diagnostic performance of four pathologists diagnosing prostatic CNB unaided and, in a second phase, assisted by Paige Prostate. In phase 1, pathologists had a diagnostic accuracy for prostate cancer of 95.00%, maintaining their performance in phase 2 (93.81%), with an intraobserver concordance rate between phases of 98.81%. In phase 2, pathologists reported atypical small acinar proliferation (ASAP) less often (about 30% less). Additionally, they requested significantly fewer immunohistochemistry (IHC) studies (about 20% less) and second opinions (about 40% less). The median time required for reading and reporting each slide was about 20% lower in phase 2, in both negative and cancer cases. Lastly, the average total agreement with the software performance was observed in about 70% of the cases, being significantly higher in negative cases (about 90%) than in cancer cases (about 30%). Most of the diagnostic discordances occurred in distinguishing negative cases with ASAP from small foci of well-differentiated (less than 1.5 mm) acinar adenocarcinoma. In conclusion, the synergic usage of Paige Prostate contributes to a significant decrease in IHC studies, second opinion requests, and time for reporting while maintaining highly accurate diagnostic standards.

A Hybrid Human-Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy

CONTEXT.—

Prostate cancer is a common malignancy, and accurate diagnosis typically requires histologic review of multiple prostate core biopsies per patient. As pathology volumes and complexity increase, new tools to improve the efficiency of everyday practice are keenly needed. Deep learning has shown promise in pathology diagnostics, but most studies silo the efforts of pathologists from the application of deep learning algorithms. Very few hybrid pathologist-deep learning approaches have been explored, and these typically require complete review of histologic slides by both the pathologist and the deep learning system.

OBJECTIVE.—

To develop a novel and efficient hybrid human-machine learning approach to screen prostate biopsies.

DESIGN.—

We developed an algorithm to determine the 20 regions of interest with the highest probability of malignancy for each prostate biopsy; presenting these regions to a pathologist for manual screening limited the initial review by a pathologist to approximately 2% of the tissue area of each sample. We evaluated this approach by using 100 biopsies (29 malignant, 60 benign, 11 other) that were reviewed by 4 pathologists (3 urologic pathologists, 1 general pathologist) using a custom-designed graphical user interface.

RESULTS.—

Malignant biopsies were correctly identified as needing comprehensive review with high sensitivity (mean, 99.2% among all pathologists); conversely, most benign prostate biopsies (mean, 72.1%) were correctly identified as needing no further review.

CONCLUSIONS.—

This novel hybrid system has the potential to efficiently triage out most benign prostate core biopsies, conserving time for the pathologist to dedicate to detailed evaluation of malignant biopsies.

Comparison of cancer detection rates by transrectal prostate biopsy for prostate cancer using two different nomograms based on patient's age and prostate volume

Background

The aim of this study is to evaluate the efficacy of two different Nara Urological Research and Treatment Group (NURTG) nomograms allocating 6–12 biopsy cores based on age and prostate volume.

Materials and methods

From April 2006 to July 2014, a total of 1,605 patients who underwent initial prostate biopsy were enrolled. Based on a nomogram taking the patient’s age and prostate volume into consideration, 6–12 biopsy cores were allocated. Two types of nomogram were used, for the former group (before March 2009) and latter group (March 2009 onward). Cancer detection rates in all patients and those with prostate-specific antigen values in the gray zone (4.0–10 ng/mL) were compared. Predictive parameters for detection of prostate cancer in gray-zone patients were also investigated.

Results

The cancer detection rates in all patients and those in the gray zone were 48% and 38% in the former group and 54% and 41% in the latter group, respectively. The cancer detection rate in all patients was significantly higher in the latter group compared with the former group, but detection in gray-zone patients did not show a significant difference between the two groups (P=0.011 and P=0.37, respectively). Multivariate analysis indicated that age, digital rectal examination, prostate volume, transrectal ultrasonography findings, and volume/biopsy ratio were significant predictive parameters in gray-zone patients. The clinically insignificant cancer detection rate was significantly lower in the latter group compared with the former group (P=0.0008).

Conclusion

The latter nomogram provided more acceptable detection rates of clinically significant and insignificant cancer than the former one, and we consider that an initial maximum 12-core transrectal ultrasound-guided needle biopsy may be sufficient for prostate cancer diagnosis.