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Background: Malignant kidney tumors are not uncommon worldwide. They are heterogeneous but have unified pathological prognostic features. Objectives: we focused on studying renal tumor in our centre, a large tertiary centre in the western region of Saudi Arabia, addressing gross and microscopic features of prognostic significance. Methodology: Nephrectomies due to renal parenchymal neoplasm from July 2010 to June 2020 in King Abdulaziz University Hospital were studied. Patients' demographic and radiology data were extracted from the Hospital data system. Pathological features were reviewed by combining the radiology, gross and microscopic features and applying the recent diagnostic, grading, and staging guidelines. Results: 146 out of 154 renal neoplasms were malignant. The patient's average age was 53 years. 62% of the patients were male. Clear cell renal cell carcinoma is the most common subtype (66%), followed by papillary and chromophobe renal cell carcinoma. 59% of the tumors have low nuclear ISUP grade. 55% of tumors were pT1 stage. Two cases were up-staged according to the updated guidelines for renal vein invasion. A shift toward partial nephrectomy is observed in the second five years. Conclusion: The patients' demographics and pathological tumor features are similar to the results of regional studies in our country and the Middle East. The updated staging guidelines modified the stage of two tumors. Lacking information such as tumor laterality may impact the patients' follow-up. A shift toward partial nephrectomy reflects early-stage tumor detection and advanced radiology modalities and will improve the patient's outcome and save the residual kidney function. [ABSTRACT FROM AUTHOR]

Background: During active surveillance (AS) for Grade Group (GG) 2 prostate cancer, pathologic progression to GG3 on surveillance biopsy is a trigger for intervention. However, this ratio of GP3:GP4, may be obscured by increases of relatively indolent disease. We aimed to explore changes in GP4 quantity during AS and propose alternative definitions for progression based on GP4 changes., Design, Setting, and Participants: We assessed patients enrolled on AS between November 2014 and March 2020 with GG2 disease on diagnostic biopsy and subsequent surveillance biopsy approximately 1 year later. Outcome measures included change in overall %GP4 and total length GP4 (mm)., Results and Limitations: 61 patients met the inclusion criteria, the median change in total length of GP4 and %GP4 was -0.12 mm (IQR -0.31, 0.09) and -2.5% (IQR -8.6, 0.0), respectively. Excluding the 35 patients with no evidence of GP4 on surveillance biopsy, median change in total GP4 length and %GP4 was 0.19 mm (IQR -0.04, 0.67) and 1.2% (IQR -1.6, 6.6), respectively. Three patients progressed to GG3 disease on surveillance biopsy, one of whom had only a small increase in %GP4. Conversely, an additional 2 patients who did not meet the criterion for GG3 had a large increase (> 1 mm) in total GP4 length., Conclusions: Presence of GG3 disease on surveillance biopsy as a trigger for treatment in men on AS is of questionable use alone; we suggest including other measures that do not depend on a ratio, such as an increase in total GP4 length., Competing Interests: Disclouser All conflicts of interest, including specific financial interests, relations and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment, affiliations, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties filed, received or pending, personal relationships), are listed below: Peter T. Scardino is named on a patent for a statistical method to detect prostate cancer that has been commercialized by OPKO Health (from which he receives royalties and stock options) and chairs the Medical Advisory board of Insightec as an unpaid consultant. Andrew J. Vickers is named on a patent for a statistical method to detect prostate cancer that has been commercialized by OPKO Health (from which he receives royalties and stock options) and has received consulting funds from Insightec and Steba. None of these companies contributed to or directed any of the research reported in this article. The other authors declare no conflict of interest, including specific financial interests or relationships and affiliations relevant to the subject matter or materials discussed in the manuscript., (Copyright © 2024 Elsevier Inc. All rights reserved.)

We aimed to assess the correlation between ISUP 2014 grades of needle biopsy (NB) and radical prostatectomy (RP) specimens and the parameters effecting this correlation. A total of 353 patients, who underwent a radical prostatectomy with diagnose of prostate cancer, were included in the study. Especially, the maximum percentage of core involved by cancer (MPCI) of upgraded group was significantly higher than those of correlated group and downgraded group. MPCI might be used as a preoperative value to determine risk classification and to help counsel patients with regard to treatment decision and prognosis of disease. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is one of the most prevalent cancers worldwide. As the demand for prostate biopsies increases, a worldwide shortage and an uneven geographical distribution of proficient pathologists place a strain on the efficacy of pathological diagnosis. Deep learning (DL) is able to automatically extract features from whole-slide images of prostate biopsies annotated by skilled pathologists and to classify the severity of PCa. A whole-slide image of biopsies has many irrelevant features that weaken the performance of DL models. To enable DL models to focus more on cancerous tissues, we propose a Multi-Channel and Multi-Spatial (MCMS) Attention module that can be easily plugged into any backbone CNN to enhance feature extraction. Specifically, MCMS learns a channel attention vector to assign weights to channels in the feature map by pooling from multiple attention branches with different reduction ratios; similarly, it also learns a spatial attention matrix to focus on more relevant areas of the image, by pooling from multiple convolutional layers with different kernel sizes. The model is verified on the most extensive multi-center PCa dataset that consists of 11,000 H&E-stained histopathology whole-slide images. Experimental results demonstrate that an MCMS-assisted CNN can effectively boost prediction performance in accuracy (ACC) and quadratic weighted kappa (QWK), compared with prior studies. The proposed model and results can serve as a credible benchmark for future research in automated PCa grading. [ABSTRACT FROM AUTHOR]

Background and Objective : Renal cell carcinoma represents a significant global health challenge with a low survival rate. The aim of this research was to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patients requiring urgent treatment. Methods : The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction. Based on the 3D CNN architecture, the feature extractor module predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. Clinical variables are systematically selected using the Spearman score and random forest importance score as criteria. A deep learning-based network, trained with discrete LogisticHazard-based loss, performs the survival prediction. Nine distinct experiments are performed, with varying numbers of clinical variables determined by different thresholds of the Spearman and importance scores. Results : Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors. The best-performing experiment yielded a concordance index of 0.84 and an area under the curve value of 0.8 on the test cohort, which suggests strong predictive power. Conclusions : The multimodal deep-learning approach developed in this study shows promising results in estimating survival probabilities for renal cell carcinoma patients using CT imaging and clinical data. This may have potential implications in identifying patients who require urgent treatment, potentially improving patient outcomes. The code created for this project is available for the public on: GitHub • Developing a multimodal AI for renal cancer survival estimation. • Utilizing 3D CNN for integration of CT scans with clinical data. • Predicting personalized survival curves to aid patient treatment planning. • Optimization of clinical data selection for survival prediction. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is one of the most prevalent cancers worldwide. As the demand for prostate biopsies increases, a worldwide shortage and an uneven geographical distribution of proficient pathologists place a strain on the efficacy of pathological diagnosis. Deep learning (DL) is able to automatically extract features from whole-slide images of prostate biopsies annotated by skilled pathologists and to classify the severity of PCa. A whole-slide image of biopsies has many irrelevant features that weaken the performance of DL models. To enable DL models to focus more on cancerous tissues, we propose a Multi-Channel and Multi-Spatial (MCMS) Attention module that can be easily plugged into any backbone CNN to enhance feature extraction. Specifically, MCMS learns a channel attention vector to assign weights to channels in the feature map by pooling from multiple attention branches with different reduction ratios; similarly, it also learns a spatial attention matrix to focus on more relevant areas of the image, by pooling from multiple convolutional layers with different kernel sizes. The model is verified on the most extensive multi-center PCa dataset that consists of 11,000 H&E-stained histopathology whole-slide images. Experimental results demonstrate that an MCMS-assisted CNN can effectively boost prediction performance in accuracy (ACC) and quadratic weighted kappa (QWK), compared with prior studies. The proposed model and results can serve as a credible benchmark for future research in automated PCa grading.

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Purpose: To investigate the concordance of biopsy and pathologic International Society of Urological Pathology (ISUP) grading in salvage radical prostatectomy (SRP) patients for recurrent prostate cancer., Methods: Within a high-volume center database, we identified patients who underwent SRP for recurrent prostate cancer (PCa) between 2004 and 2020. Upgrading, downgrading, concordance, and any discordance between posttreatment biopsy ISUP and ISUP at SRP were tested. Logistic regression models were used to predict ISUP upgrading and ISUP discordance. Models were adjusted for prostatic specific antigen before SRP, age at surgery, initial prostatic specific antigen (PSA), type of primary treatment, time from primary PCa diagnosis to SRP, number of positive cores at biopsy, and original Gleason score., Results: Overall, 184 patients with available biopsy and pathologic ISUP grading were identified. Of those, 17.4% (n = 32), 40.8% (n = 75), 19.6% (n = 36), and 22.2% (n = 41) harbored biopsy ISUP 1, ISUP 2, ISUP 3, and ISUP 4-5 grading, respectively. Pathologic ISUP 1, ISUP 2, ISUP 3, and ISUP 4-5 grading was recorded in 6.0% (n = 11), 40.8% (n = 75), 32.1% (n = 59), and 21.2% (n = 39), respectively. Median PSA before SRP was 5.5 ng/ml (interquartile range [IQR]: 3.1-8.1 ng/ml), median age at SRP was 65.1 years (IQR:60.7-69.4 years) and median time from original PCa diagnosis to SRP was 47 months (IQR: 27.3-85.2 months). Concordance of biopsy and pathologic ISUP was identified in 45.1% (n = 83). Conversely, any ISUP discordance, upgrading and downgrading of at least one ISUP group was identified in 54.9% (n = 101), 35.3% (n = 65), and 19.6% (n = 36). In logistic models, none of the preoperative characteristics was associated with upgrading or ISUP discordance (all p > 0.1)., Conclusion: Discordance between biopsy and pathologic ISUP grading is common at SRP. However, in 45% of SRP cases biopsy ISUP is capable to predict pathologic ISUP. Further studies are necessary to identify characteristics for ISUP upgrading at SRP., (© 2021 The Authors. The Prostate published by Wiley Periodicals LLC.)

The current grading of prostate cancer is based on the classification system of the International Society of Urological Pathology (ISUP) following a consensus conference in Chicago in 2014. The foundations are based on the frequently modified grading system of Gleason. This article presents a brief description of the development to the current ISUP grading system.