Jan H. Rüschoff

The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen's quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model's Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.

Despite advances in treatment, lung cancer survival rates remain low. A better understanding of the cellular heterogeneity and interplay of cancer-associated fibroblasts (CAFs) within the tumor microenvironment will support the development of personalized therapies. We report a spatially resolved single-cell imaging mass cytometry (IMC) analysis of CAFs in a non-small cell lung cancer cohort of 1,070 patients. We identify four prognostic patient groups based on 11 CAF phenotypes with distinct spatial distributions and show that CAFs are independent prognostic factors for patient survival. The presence of tumor-like CAFs is strongly correlated with poor prognosis. In contrast, inflammatory CAFs and interferon-response CAFs are associated with inflamed tumor microenvironments and higher patient survival. High density of matrix CAFs is correlated with low immune infiltration and is negatively correlated with patient survival. In summary, our data identify phenotypic and spatial features of CAFs that are associated with patient outcome in NSCLC.

Background Recent studies have reported the additive value of combined gallium 68 (68Ga)-labeled Glu-urea-Lys (Ahx)-HBED-CC ligand targeting the prostate-specific membrane antigen (PSMA) (hereafter called 68Ga-PSMA-11) PET/MRI for the detection and localization of primary prostate cancer compared with multiparametric MRI. Purpose To compare the diagnostic accuracy and interrater agreement of multiparametric MRI and 68Ga-PSMA-11 PET/MRI for the detection of extracapsular extension (ECE) and seminal vesicle infiltration (SVI) in patients with prostate cancer. Materials and Methods Retrospective analysis of 40 consecutive men who underwent multiparametric MRI and 68Ga-PSMA-11 PET/MRI within 6 months for suspected prostate cancer followed by radical prostatectomy between April 2016 and July 2018. Four readers blinded to clinical and histopathologic findings rated the probability of ECE and SVI at multiparametric MRI and PET/MRI by using a five-point Likert-type scale. The prostatectomy specimen served as the reference standard. Accuracy was assessed with a multireader multicase analysis and by calculating reader-average areas under the receiver operating characteristics curve (AUCs), sensitivity, and specificity for ordinal and dichotomized data in a region-specific and patient-specific approach. Interrater agreement was assessed with the Fleiss multirater κ. Results For multiparametric MRI versus PET/MRI in ECE detection, respectively, AUC, sensitivity, and specificity in the region-specific analysis were 0.67 and 0.75 (P = .07), 28% (21 of 76) and 47% (36 of 76) (P = .09), and 94% (529 of 564) and 90% (509 of 564) (P = .007). For the patient-specific analysis, AUC, sensitivity, and specificity were 0.66 and 0.73 (P = .19), 46% (22 of 48) and 69% (33 of 48) (P = .04), and 75% (84 of 112) and 67% (75 of 112) (P = .19), respectively. For multiparametric MRI versus PET/MRI in SVI detection, respectively, AUC, sensitivity, and specificity of the region-specific analysis were 0.66 and 0.74 (P = .21), 35% (seven of 20) and 50% (10 of 20) (P = .25), and 98% (295 of 300) and 94% (282 of 300) (P < .001). For the patient-specific analysis, AUC, sensitivity, and specificity were 0.65 and 0.79 (P = .25), 35% (seven of 20) and 55% (11 of 20) (P = .20), and 98% (137 of 140) and 94% (131 of 140) (P = .07), respectively. Interrater reliability for multiparametric MRI versus PET/MRI did not differ for ECE (κ, 0.46 vs 0.40; P = .24) and SVI (κ, 0.23 vs 0.33; P = .39). Conclusion Our results suggest that gallium 68 (68Ga)-labeled Glu-urea-Lys (Ahx)-HBED-CC ligand targeting the prostate-specific membrane antigen (PSMA) (68Ga-PSMA-11) PET/MRI and multiparametric MRI perform similarly for local staging of prostate cancer in patients with intermediate-to-high-risk prostate cancer. The increased sensitivity of 68Ga-PSMA-11 PET/MRI for the detection of extracapsular disease comes at the cost of a slightly reduced specificity. © RSNA, 2019