Renal cell carcinoma is the third type of urologic cancer and has a poor prognosis with 30% of metastatic patients at diagnosis. The antiangiogenics and targeted immunotherapies led to treatment remodeling emphasizing the role of the tumour microenvironment. However, long-term responses are rare with a high rate of resistance. New strategies are emerging to improve the efficacy and the emerging drugs are under evaluation in ongoing trials. With the different treatment options, there is an urgent need to identify biomarkers in order to predict the efficacy of drugs and to better stratify patients. Owing to the limitations of programmed death-ligand 1 (PD-L1), the most studied immunohistochemistry biomarkers, and of the tumor mutational burden, the identification of more reliable markers is an unmet need. New technologies could help in this purpose.

BACKGROUND. Commercial gene expression assays are guiding clinical decision making in patients with prostate cancer, particularly when considering active surveillance. Given heterogeneity and multifocality of primary prostate cancer, such assays should ideally be robust to the coexistence of unsampled higher grade disease elsewhere in the prostate in order to have clinical utility. Herein, we comprehensively evaluated transcriptomic profiles of primary multifocal prostate cancer to assess robustness to clinically relevant multifocality. METHODS. We designed a comprehensive, multiplexed targeted RNA-sequencing assay capable of assessing multiple transcriptional classes and deriving commercially available prognostic signatures, including the Myriad Prolaris Cell Cycle Progression score, the Oncotype DX Genomic Prostate Score, and the GenomeDX Decipher Genomic Classifier. We applied this assay to a retrospective, multi-institutional cohort of 156 prostate cancer samples. Derived commercial biomarker scores for 120 informative primary prostate cancer samples from 44 cases were determined and compared. RESULTS. Derived expression scores were positively correlated with tumor grade ( r _S = 0.53–0.73; all P < 0.001), both within the same case and across the entire cohort. In cases of extreme grade-discordant multifocality (co-occurrence of grade group 1 [GG1] and ≥GG4 foci], gene expression scores were significantly lower in low- (GG1) versus high-grade (≥GG4) foci (all P < 0.001). No significant differences in expression scores, however, were observed between GG1 foci from prostates with and without coexisting higher grade cancer (all P > 0.05). CONCLUSIONS. Multifocal, low-grade and high-grade prostate cancer foci exhibit distinct prognostic expression signatures. These findings demonstrate that prognostic RNA expression assays performed on low-grade prostate cancer biopsy tissue may not provide meaningful information on the presence of coexisting unsampled aggressive disease. FUNDING. Prostate Cancer Foundation, National Institutes of Health (U01 CA214170, R01 CA183857, University of Michigan Prostate Specialized Program of Research Excellence [S.P.O.R.E.] P50 CA186786-05, Weill Cornell Medicine S.P.O.R.E. P50 CA211024-01A1), Men of Michigan Prostate Cancer Research Fund, University of Michigan Comprehensive Cancer Center core grant (2-P30-CA-046592-24), A. Alfred Taubman Biomedical Research Institute, and Department of Defense.

Renal cell carcinoma encompass distinct diseases with different pathologic features and distinct molecular pathways. Immune checkpoint inhibitors targeting the programmed death receptor ligand 1 (PD-L1)/programmed death receptor 1 (PD-1) pathway alone or in combination have greatly changed clinical management of metastatic renal cell carcinoma, now competing with antiangiogenic drugs in monotherapy for first-line treatment. However, long-term response rates are low, and biomarkers are needed to predict treatment response. Quantification of PD-L1 expression by immunohistochemistry was developed as a promising biomarker in clinical trials, but with many limitations (different antibodies, tumour heterogeneity, specimens, and different thresholds of positivity). Other biomarkers, including tumour mutational burden and molecular signatures, are also developed and discussed in this review.

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.

Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. Objective: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. Results: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. Conclusions: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

Digital Pathology (DP) revolutionizes the diagnostic workflow. Digitized scanned slides enhance operational efficiency by facilitating remote access, slide storage, reporting and automated AI image analysis, and enabling collaboration and research. However, substantial upfront and maintenance costs remain significant barriers to adoption. This study evaluates DP’s financial and qualitative value, exploring whether the long-term financial benefits justify investments and addressing implementation challenges in large public and private European laboratory settings. A targeted literature review, semi-structured interviews, surveys, and a net present value (NPV) model were employed to assess DP’s impact on clinical practice and laboratory financials. Qualitative findings validate the key benefits of DP, including optimized workflow, enhanced logistics, and improved laboratory organization. Pathologists reported a smooth integration, improved training, teaching, and research capabilities, and increased flexibility through remote work. Collaboration within multidisciplinary teams was strengthened, while case examination efficiency and access to archival slides were notably improved. Quantitative results indicate that DP demonstrates strong financial potential, achieving cost recovery within 6 years. DP investment results in a 7-year NPV of + €0.21 million (m) driven by increased productivity and diagnosis volumes. Although the high upfront costs for scanners, training, and system integration pose a significant barrier to the adoption of DP, larger institutions are better positioned to leverage economies of scale. This study underscores the importance of sustained financial support to cope with the initial investment and regional collaboration in driving widespread adoption of DP. Expanding reimbursement policies for pathology procedures could significantly reduce financial barriers.