Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson’s Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost.
IMPORTANCE Magnetic resonance imaging (MRI) guidance improves the accuracy of prostate biopsy for the detection of clinically significant prostate cancer, but the optimal use of such guidance is not yet clear. OBJECTIVE To determine the cancer detection rate (CDR) of targeting MRI-visible lesions vs systematic prostate sampling in the diagnosis of clinically significant prostate cancer in men who were biopsy naive. DESIGN, SETTING, AND PARTICIPANTS This paired cohort trial, known as the Prospective Assessment of Image Registration in the Diagnosis of Prostate Cancer (PAIREDCAP) study, was conducted in an academic medical center from January 2015 to April 2018. Men undergoing first-time prostate biopsy were enrolled. Paired-cohort participants were a consecutive series of men with MRI-visible lesions (defined by a Prostate Imaging Reporting & Data System version 2 score Ն 3), who each underwent 3 biopsy methods at the same sitting: first, a systematic biopsy; second, an MRI-lesion biopsy targeted by cognitive fusion; and third, an MRI-lesion targeted by software fusion. Another consecutive series of men without MRI-visible lesions underwent systematic biopsies to help determine the false-negative rate of MRI during the trial period. MAIN OUTCOMES AND MEASURES The primary end point was the detection rate of clinically significant prostate cancer (Gleason grade group Ն2) overall and by each biopsy method separately. The secondary end points were the effects of the Prostate Imaging Reporting & Data System version 2 grade, prostate-specific antigen density, and prostate volume on the primary end point. Tertiary end points were the false-negative rate of MRI and concordance of biopsy-method results by location of detected cancers within the prostate. RESULTS A total of 300 men participated; 248 had MRI-visible lesions (mean [SD] age, 65.5 [7.7] years; 197 were white [79.4%]), and 52 were control participants (mean [SD] age, 63.6 [5.9] years; 39 were white [75%]). The overall CDR was 70% in the paired cohort group, achieved by combining systematic and targeted biopsy results. The CDR by systematic sampling was 15% in the group without MRI-visible lesions. In the paired-cohort group, CDRs varied from 47% (116 of 248 men) when using cognitive fusion biopsy alone, to approximately 60% when using systematic biopsy (149 of 248 men) or either fusion method alone (154 of 248 men), to 70% (174 of 248 men) when combining systematic and targeted biopsy. Discordance of tumor locations suggests that the different biopsy methods detect different tumors. Thus, combining targeting and systematic sampling provide greatest sensitivity for detection of clinically significant prostate cancer. For all biopsy methods, the Prostate Imaging Reporting & Data System version 2 grade and prostate-specific antigen density were directly associated with CDRs, and prostate volume was inversely associated. CONCLUSIONS AND RELEVANCE An MRI-visible lesion in men undergoing first-time prostate biopsy identifies those with a heightened risk of clinically ...
GENITOURINARY IMAGINGM ultiparametric MRI is an important tool in the diagnosis of prostate cancer (PCa) (1,2). However, multiparametric MRI still misses PCa in up to 45% of men and faces challenges in distinguishing clinically significant PCa from indolent PCa (2,3). Thus, histopathologic examination of PCa remains the reference standard. A Gleason score based on the microscopic appearance of PCa is assigned to indicate its aggressiveness (4).Diffusion-weighted MRI is a critical component of multiparametric MRI and is sensitive to tissue microstructure changes in PCa (5). However, current clinical analysis using a monoexponential signal model to calculate apparent diffu-Materials and Methods: Men with PCa who underwent 3-T MRI and robotic-assisted radical prostatectomy between June 2018 and January 2019 were prospectively studied. After prostatectomy, the fresh whole prostate specimens were imaged in patient-specific threedimensionally printed molds by using 3-T MRI with DR-CSI and were then sliced to create coregistered WMHP slides. The DR-CSI spectral signal component fractions (f A , f B , f C ) were compared with epithelial, stromal, and luminal area fractions (f epithelium , f stroma , f lumen ) quantified in PCa and benign tissue regions. A linear mixed-effects model assessed the correlations between (f A , f B , f C ) and (f epithelium , f stroma , f lumen ), and the strength of correlations was evaluated by using Spearman correlation coefficients. Differences between PCa and benign tissues in terms of DR-CSI signal components and microscopic tissue compartments were assessed using two-sided t tests.Results: Prostate specimens from nine men (mean age, 65 years 6 7 [standard deviation]) were evaluated; 20 regions from 17 PCas, along with 20 benign tissue regions of interest, were analyzed. Three DR-CSI spectral signal components (spectral peaks) were consistently identified. The f A , f B , and f C were correlated with f epithelium , f stroma , and f lumen (all P , .001), with Spearman correlation coefficients of 0.74 (95% confidence interval [CI]: 0.62, 0.83), 0.80 (95% CI: 0.66, 0.89), and 0.67 (95% CI: 0.51, 0.81), respectively. PCa exhibited differences compared with benign tissues in terms of increased f A (PCa vs benign, 0.37 6 0.05 vs 0.27 6 0.06; P , .001), decreased f C (PCa vs benign, 0.18 6 0.06 vs 0.31 6 0.13; P = .01), increased f epithelium (PCa vs benign, 0.44 6 0.13 vs 0.26 6 0.16; P , .001), and decreased f lumen (PCa vs benign, 0.14 6 0.08 vs 0.27 6 0.18; P = .004). Conclusion:Diffusion-relaxation correlation spectrum imaging signal components correlate with microscopic tissue compartments in the prostate and differ between cancer and benign tissue.
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