Prostate magnetic resonance imaging-targeted biopsy global grade correlates better than highest grade with prostatectomy grade.

BACKGROUND: Magnetic resonance imaging (MRI)-targeted prostate biopsy has become an increasingly common method of diagnosing prostate cancer. A previous study from our institution demonstrated that the biopsy global Grade Group (gGG, aggregate GG of all positive cores) and highest Grade Group (hGG in any core) both show substantial concordance with the Grade Group at radical prostatectomy (RPGG) while the discordance predominantly consists of upgrading in gGG and downgrading in hGG. We performed a larger cohort study focused on biopsy cases in which gGG and hGG differ, to determine their relative concordance with RPGG. METHODS: We conducted a retrospective review of radical prostatectomy specimens with prior MRI-targeted biopsies from our institution between 2016 and 2020. Separate gGG and hGG were assigned to each MRI-targeted lesion. Targeted lesions with different gGG versus hGG were segregated from those with identical gGG and hGG. The concordance of biopsy GG with RPGG was evaluated using κ coefficient analysis. RESULTS: Of the 489 lesions with MRI-targeted biopsies, 82 (17%) differed in gGG versus hGG. The gGG of 46 (56%), 33 (40%), and 3 (4%) lesions were unchanged, upgraded, and downgraded at radical prostatectomy, respectively (κ= 0.302, weighted κ = 0.334). The hGG of 24 (29%), 9 (11%), and 49 (60%) lesions were unchanged, upgraded, and downgraded at radical prostatectomy, respectively (κ = 0.040, weighted κ = 0.198). When stratified by the biopsy GG, gGG showed the highest concordance in GG2 (61%) and GG3 (54%) lesions. The hGG resulted in substantial downgrading (60%) with less optimal concordance regardless of the biopsy GG. Neither the prebiopsy prostate specific antigen level nor the PI-RADS score was predictive of upgrading of gGG. CONCLUSIONS: When gGG and hGG differ, gGG method more accurately predicts the RPGG than hGG, particularly in GG2 and GG3 lesions which comprised the majority of targeted lesions.

Stimulated Raman histology as a method to determine the adequacy of renal mass biopsy and identify malignant subtypes of renal cell carcinoma.

INTRODUCTION: Renal tumor biopsy requires adequate tissue sampling to aid in the investigation of small renal masses. In some centers the contemporary nondiagnostic renal mass biopsy rate may be as high as 22% and may be as high as 42% in challenging cases. Stimulated Raman Histology (SRH) is a novel microscopic technique which has created the possibility for rapid, label-free, high-resolution images of unprocessed tissue which may be viewed on standard radiology viewing platforms. The application of SRH to renal biopsy may provide the benefits of routine pathologic evaluation during the procedure, thereby reducing nondiagnostic results. We conducted a pilot feasibility study, to assess if renal cell carcinoma (RCC) subtypes may be imaged and to see if high-quality hematoxylin and eosin (H&E) could subsequently be generated. METHODS/MATERIALS: An 18-gauge core needle biopsy was taken from a series of 25 ex vivo radical or partial nephrectomy specimens. Histologic images of the fresh, unstained biopsy samples were obtained using a SRH microscope using 2 Raman shifts: 2,845 cm-1 and 2,930 cm-1. The cores were then processed as per routine pathologic protocols. The SRH images and hematoxylin and eosin (H&E) slides were then viewed by a genitourinary pathologist. RESULTS: The SRH microscope took 8 to 11 minutes to produce high-quality images of the renal biopsies. Total of 25 renal tumors including 1 oncocytoma, 3 chromophobe RCC, 16 clear cells RCC, 4 papillary RCC, and 1 medullary RCC were included. All renal tumor subtypes were captured, and the SRH images were easily differentiated from adjacent normal renal parenchyma. High quality H&E slides were produced from each of the renal biopsies after SRH was completed. Immunostains were performed on selected cases and the staining was not affected by the SRH image process. CONCLUSION: SRH produces high quality images of all renal cell subtypes that can be rapidly produced and easily interpreted to determine renal mass biopsy adequacy, and on occasion, may allow renal tumor subtype identification. Renal biopsies remained available to produce high quality H&E slides and immunostains for confirmation of diagnosis. Procedural application has promise to decrease the known rate of renal mass nondiagnostic biopsies, and application of convolutional neural network methodology may further improve diagnostic capability and increase utilization of renal mass biopsy among urologists.