medRxiv - Urology最新文献

{"title":"Discovery of tumour indicating morphological changes in benign prostate biopsies through AI","authors":"Eduard Chelebian, Christophe Avenel, Helena Järemo, Pernilla Andersson, Anders Bergh, Carolina Wählby","doi":"10.1101/2024.06.18.24309064","DOIUrl":"https://doi.org/10.1101/2024.06.18.24309064","url":null,"abstract":"Background and Objective: Diagnostic needle biopsies that miss clinically significant prostate cancers (PCa) likely sample benign tissue adjacent to cancer. Such samples may contain changes indicating the presence of cancer elsewhere in the organ. Our goal is to evaluate if artificial intelligence (AI) can identify morphological characteristics in benign biopsies of men with raised PSA that predict the future detection of clinically significant PCa during a 30-month follow-up. Methods: A retrospective cohort of 232 patients with raised PSA and benign needle biopsies, paired by age, year of diagnosis and PSA levels was collected. Half were diagnosed with PCa within 30 months, while the other half remained cancer-free for at least eight years. AI model performance was assessed using the area under the receiver operating characteristic curve (AUC) and attention maps were used to visualise the morphological patterns relevant for cancer diagnosis as captured by the model. Key findings and Limitations: The AI model could identify patients that were later diagnosed with PCa from their initial benign biopsies with an AUC of 0.82. Distinctive morphological patterns, such as altered stromal collagen and changes in glandular epithelial cell composition, were revealed. Conclusions and Clinical Implications: AI applied to standard haematoxylin-eosin sections identifies patients initially diagnosed as negative but later found to have clinically significant PCa. Morphological patterns offer insights into the long-ranging effects of PCa in the benign parts of the tumour-bearing organ. Patient Summary: Using AI, we identified subtle changes in normal prostate tissue suggesting the presence of tumours elsewhere in the prostate. This could aid in the early identification of potentially high-risk tumours, limiting overuse of prostate biopsies.","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning for the prediction of urosepsis using electronic health record data","authors":"Varuni Sarwal, Nadav Rakocz, Georgina Dominique, Jeffrey N. Chiang, A. Lenore Ackerman","doi":"10.1101/2024.05.28.24306956","DOIUrl":"https://doi.org/10.1101/2024.05.28.24306956","url":null,"abstract":"Urosepsis, a medical condition resulting from the progression of urinary tract infection (UTI), is a leading cause of death in hospitals in the United States. Urosepsis commonly occurs due to complicated UTI and constitutes approximately 25% of all sepsis cases. Early prediction of urosepsis is critical in providing personalized care, reducing diagnostic uncertainty, and ultimately lowering mortality rates. While machine learning techniques have the potential to aid healthcare professionals in identifying potential risk factors, and high-risk patients, and recommending treatment options, no existing study has been developed so far to predict the development of urosepsis in patients with a suspected UTI presenting to an outpatient setting. In this research study, we develop and evaluate the utility of multiple machine learning models to predict the likelihood of hospital admission and urosepsis diagnosis for patients with an outpatient UTI encounter, leveraging de-identified electronic health records sourced from a large health care system encompassing a wide range of encounters spanning primary to quaternary care. Inclusion criteria included a positive diagnosis of urinary tract infection indicated by ICD-10 code N30 or N93.0 and positive bacteria result via urinalysis in an ambulatory setting (primary or emergent care settings). For these patients, we extracted demographic information, urinalysis findings, and any antibiotics prescribed for each instance of UTI. Reencounters we defined as all encounters within seven days of the initial UTI encounter. The reencounters were considered urosepsis-related if matching positive blood and urine cultures were found with a sepsis ICD-10 code of A41, R78, or R65. A variety of machine learning models were trained on this rich feature set and were evaluated on two tasks: the prediction of a reencounter leading to hospitalization, and the prediction of Urosepsis. Model performances were stratified by the patient ethnicities. Our models demonstrated high predictive performance with an area under the ROC curve (AUC) of 79.5% AUC and an area under the precision-recall curve (APR) of 13% APR for reencounters, and 90% ROC and 31% APR for Urosepsis. We computed shapley values to interpret our model predictions and found the patient age, sex, and urinary WBC count were the top three predictive features. Our study has the potential to assist clinicians in the identification of high-risk patients, making more informed decisions about antibiotic prescription and providing improved patient care.","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141193734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A qualitative and quantitative analysis of changes in prostate MRI T2WI signals with different abstinence durations","authors":"Wenjun Ma, Baoming Ren, Yanjun Gao, Weixian Bai","doi":"10.1101/2024.05.03.24306819","DOIUrl":"https://doi.org/10.1101/2024.05.03.24306819","url":null,"abstract":"<strong>PURPOSE</strong> To investigate the effect of abstinence duration on image quality of prostate with high-field magnetic resonance imaging(MRI);","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140940225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of a urine- based proteomics test to predict clinically significant prostate cancer: complementing MRI pathway","authors":"Maria Frantzi, Ana Cristina Morillo, Guillermo Lendinez, Ana Blanca-Pedregosa, Daniel Lopez Ruiz, Jose Parada, Isabel Heidegger, Zoran Culig, Emmanouil Mavrogeorgis, Antonio Lopez Beltran, Marina Mora-Ortiz, Julia Carrasco-Valiente, Harald Mischak, Rafael A Medina, Juan Pablo Campos Hernandez, Enrique Gómez Gómez","doi":"10.1101/2024.04.16.24305475","DOIUrl":"https://doi.org/10.1101/2024.04.16.24305475","url":null,"abstract":"<strong>Purpose</strong> Prostate cancer (PCa) is the most frequently diagnosed cancer in men. One major clinical need is to accurately predict clinically significant PCa (csPCa). A proteomics based 19-biomarker model (19-BM) was previously developed using Capillary Electrophoresis-Mass Spectrometry (CE-MS) and validated in 1000 patients at risk for PCa. Here, our objective was to validate 19-BM in a multicentre prospective cohort of 101 biopsy-naive patients using current diagnostic pathways.","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of positive surgical margin parameters and pathological stage on biochemical recurrence after radical prostatectomy: a systematic review and meta-analysis","authors":"HONG GUO, Lei Zhang, Yuan Shao, Kunyang An, Caoyang Hu, Xuezhi Liang, Dongwen Wang","doi":"10.1101/2024.03.21.24304691","DOIUrl":"https://doi.org/10.1101/2024.03.21.24304691","url":null,"abstract":"I ntroduction : To systematically review and perform a meta-analysis on the predictive value of the primary Gleason grade (PGG) at the positive surgical margin (PSM), length of PSM, number of PSMs, and pathological stage of the primary tumor on biochemical recurrence (BCR) in patients with prostate cancer (PCa) after radical prostatectomy (RP).\u0000Methods: A systematic literature search was performed using electronic databases, including PubMed, EMBASE, Cochrane Library, and Web of Science, from January 1, 2005, to October 1, 2023. The protocol was pre-registered in PROSPERO. Subgroup analyses were performed according to the different treatments and study outcomes. Pooled hazard ratios with 95% confidence intervals were extracted from multivariate analyses, and a fixed or random effect model was used to pool the estimates. Subgroup analyses were performed to explore the reasons for the heterogeneity.\u0000Results: Thirty studies that included 46,572 patients with PCa were eligible for this meta-analysis. The results showed that, compared to PGG3, PGG4/5 was associated with a significantly increased risk of BCR. Compared with PSM ≤3 mm, PSM ³3 mm was associated with a significantly increased risk of BCR. Compared with unifocal PSM, multifocal PSM (mF-PSM) was associated with a significantly increased risk of BCR. In addition, pT &gt;2 was associated with a significantly increased risk of BCR compared to pT2. Notably, the findings were found to be reliable based on the sensitivity and subgroup analyses.\u0000Conclusions: PGG at the PSM, length of PSM, number of PSMs, and pathological stage of the primary tumor in patients with PCa were found to be associated with a significantly increased risk of BCR. Thus, patients with these factors should be treated differently in terms of receiving adjunct treatment and more frequent monitoring. Large-scale, well-designed prospective studies with longer follow-up periods are needed to validate the efficacy of these risk factors and their effects on patient responses to adjuvant and salvage therapies and other oncological outcomes.","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140301790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Cost, Label-Free Blue Light Cystoscopy through Digital Staining of White Light Cystoscopy Videos","authors":"Shuang Chang, Greyson A Wintergerst, Camella J Carlson, Haoli Yin, Kristen R Scarpato, Amy N Luckenbaugh, Sam Chang, Soheil Kolouri, Audrey K Bowden","doi":"10.1101/2024.03.21.24304656","DOIUrl":"https://doi.org/10.1101/2024.03.21.24304656","url":null,"abstract":"Bladder cancer is 10th most common malignancy and carries the highest treatment cost among all cancers. The high cost of bladder cancer treatment stems from its high recurrence rate, which necessitates frequent surveillance. White light cystoscopy (WLC), the standard of care surveillance tool to examine the bladder for lesions, has limited sensitivity for early-stage bladder cancer. Blue light cystoscopy (BLC) utilizes a fluorescent dye to induce contrast in cancerous regions, improving the sensitivity of detection by 43%. Nevertheless, the added cost and lengthy administration time of the dye limits the availability of BLC for surveillance. Here, we report the first demonstration of digital staining on clinical endoscopy videos collected with standard-of-care clinical equipment to convert WLC images to accurate BLC-like images. We introduce key pre-processing steps to circumvent color and brightness variations in clinical datasets needed for successful model performance; the results show excellent qualitative and quantitative agreement of the digitally stained WLC (dsWLC) images with ground truth BLC images as measured through staining accuracy analysis and color consistency assessment. In short, dsWLC can provide the fluorescent contrast needed to improve the detection sensitivity of bladder cancer, thereby increasing the accessibility of BLC contrast for bladder cancer surveillance use without the cost and time burden associated with the dye and specialized equipment.","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NOVEL SOLUTION BASED ON DETECTION OF MIRS-410-3P AND 141-5P FOR DIAGNOSTIC OF PROSTATE CANCER EVOLUTION","authors":"Maria Fernanda Carbache, EVELIN RAQUEL NUNEZ, YOUNESS OUAHID, ENRIQUE SAINZ, JUAN JOSE MONTOYA, ANA MOLERA, AURA SOOUTO, DAVID VAZQUEZ, PABLO CASTAN, JOAQUIN CARBALLIDO","doi":"10.1101/2024.03.11.24303774","DOIUrl":"https://doi.org/10.1101/2024.03.11.24303774","url":null,"abstract":"To date, prostate cancer (PCa) is both the most common tumour diagnosed in males and the second most common\u0000cause of cancer-related mortality(1,) . Prevention programs and screening protocols have proven useful to detect\u0000the disease at population level, but they lack sensitivity and specificity in comparison to the molecular tests\u0000routinely available for screening of other types of cancer, leading to unnecessary biopsies and overtreatment in\u0000many cases. In this context, a new set of small RNA biomarkers are surfacing with promising results to predict\u0000tumour progression, risk reclassification and treatment response(2) such as miR-410-3p -3p and miR-141-5p.\u0000Former studies where these biomarkers were examined in prostate cancer tissues and cell lines by qRT-PCR\u0000have shown that high expression of miR-410-3p -3p correlates to both a) accurate diagnosis in certain groups\u0000where the PSA levels do not match results from biopsy, surgery and/or digital rectal examination and b) poor\u0000prognosis of prostate cancer patients(3) . Likewise, miR-141-5p shows a parallel behaviour suggesting a potential\u0000combo for fine molecular analysis of the ratios. In this sense, recent studies have demonstrated that miR-410-3p\u0000-3p exert oncogenic functions through downregulating PTEN, proving that miR-410-3p -3p inhibits prostate\u0000cancer progression via downregulating PTEN/AKT/mTOR signalling pathway. Curiously enough, different\u0000behaviour has been reported for the biomarker in both, peripheral blood from patients and cancer-cell line(s)\u0000(34.5.6) models further pointing at the advantages of a dual gauging made possible by parallel semi-quantitation of\u0000miR-141-5p. In this sense, miR-141-5p has been clearly identified as to be upregulated in large cohorts (n over\u0000500) of prostate cancer patients confirming overexpression in multivariate analysis in tumour epithelium and\u0000tumour stroma. This overexpression taken into the context of a peripheral blood reduction of miR-410-3p appears\u0000to be associated with increased risk of biochemical cancer recurrence in an independent study over 500 patients\u0000(7)\u0000. Here we present the design, molecular set up and preclinical assessment of a novel system that uses the\u0000discarded volume from PSA blood tests to predict prostate cancer progression and biochemical cancer recurrence\u0000via detection of the biomarkers. The method described could potentially eliminate the need of invasive means\u0000such as biopsy, surgery and digital rectal examination.","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140129789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRAGMATIC PRostate cancer diAGnosis and MAnagement Triage In the Clinical care pathway.","authors":"Abishek Sharma, Teresa Campbell, Anthony Bates, Rincy John, Charlotte Adams, Aisling Brassill, Bryony Lennon, Philip Camilleri, Ami Sabharwal, Philip Charlton, Gerard Andrade, Mark Tuthill, Andrew Protheroe, Alastair D Lamb, Tom Leslie, Aaron Leiblich, Francisco Lopez, Clare Verrill, Fergus Gleeson, Ruth MacPherson, Freddie C Hamdy, Richard C Bell, Richard J Bryant","doi":"10.1101/2024.03.05.24303844","DOIUrl":"https://doi.org/10.1101/2024.03.05.24303844","url":null,"abstract":"Background: It is important to investigate, diagnose and commence treatment for locally advanced and metastatic prostate cancer quickly to optimise treatment outcomes. Since the introduction of national 2-week wait and 31/62-day targets in the United Kingdom for investigation of suspected prostate cancer over 2 decades ago, the clinical pathway has become increasingly complex. This may lead to some patients with the most clinically significant disease having the rapidity of their diagnosis and commencement of treatment compromised by resource use in diagnosing less significant, or clinically insignificant, disease. Methods:\u0000We will conduct a retrospective review of timelines for diagnosis and commencement of treatment for all men referred to a tertiary unit for investigation of suspected prostate cancer on the 2-week wait pathway in a 3-month period in 2023. In parallel, we will introduce triaging of all new 2-week wait referrals in a prospective 3-month period, with a dedicated nurse navigator streamlining patients for the most rapid investigation and treatment, based on pre-specified risk criteria including PSA, pre-biopsy mpMRI findings including TNM staging, and histology results. We hypothesise that this bespoke triaging system, above and beyond the 2-week wait and 2022 Faster Diagnostic Pathway guidance issued by NHS England, will improve timings for investigation and commencement of treatment for the most clinically significant prostate cancer cases.\u0000Conclusions:\u0000The use of in-house criteria for triaging and stratification of the most clinically urgent and significant prostate cancer cases, identified by a nurse specialist navigator, may improve clinical outcomes for patients with greatest need for rapid prostate cancer imaging, diagnosis and treatment.","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140070949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-performing Multi-task Model of Urinary Tract Dilation (UTD) Classification for Neonatal Ultrasound Reports Through Natural Language Processing","authors":"Yining Hua, Anudeep Mukkamala, Carlos Estrada, Michael Lingzhi Li, Hsin-Hsiao Wang","doi":"10.1101/2024.01.23.24301680","DOIUrl":"https://doi.org/10.1101/2024.01.23.24301680","url":null,"abstract":"Objective: The urinary tract dilation (UTD) classification system provides objective assessment relevant to hydronephrosis management for children. However, the lack of uniform language regarding UTD in radiology reports leads to significant difficulty in both clinical management and research. We seek to develop a unified multi-task/multi-class model that can effectively extract UTD components and classifications from early postnatal ultrasound (US) reports.\u0000Methods: Radiology records from our institution were reviewed to identify infants aged 0-90 days undergoing early ultrasound for antenatal UTD. The report and images were reviewed by the study team to create the ground truth of UTD classification and components (primary outcome). Bio_ClinicalBERT, a variant of the Bidirectional Encoder Representations from Transformers (BERT) model, was used as the embedding layers of the classification model. The model was fine-tuned with 11 linear classification layers. All but the last BERT layer were frozen during the fine-tuning process. The model performance was evaluated with five-fold cross-validation with an 80:20 train-test ratio.\u0000Results: 2460 early (0-90 days) US reports were included. The five-fold cross-validated model performance is satisfactory (Weighted F1 &gt; 0.9 for all UTD components). We report the weighted F1 scores, accuracies, and standard deviations for all 11 tasks and their average performance. Conclusions: By applying deep state-of-the-art NLP neural networks, we developed a high-performing, efficient, and scalable solution to extract UTD components from unstructured ultrasound reports using one single multi-task model. This can potentially help standardize and facilitate large-scale computer vision research for pediatric hydronephrosis. Key Words: machine learning, efficiency, ambulatory care, forecasting","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139554327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-Targeted Prostate Biopsy Introduces Grade Inflation and Overtreatment","authors":"Abderrahim Oussama Batouche, Eugen Czeizler, Timo-Pekka Lehto, Andrew Erickson, Tolou Shadbahr, Teemu Daniel Laajala, Joona Pohjonen, Andrew Vickers, Tuomas Mirtti, Antti Sakari Rannikko","doi":"10.1101/2024.01.10.24300922","DOIUrl":"https://doi.org/10.1101/2024.01.10.24300922","url":null,"abstract":"Abstract\u0000Purpose: The use of MRI-targeted biopsies has led to lower detection of Gleason Grade Group 1 (GG1) prostate cancer and increased detection of GG2 disease. Although this finding is generally attributed to improved sensitivity and specificity of MRI for aggressive cancers, it might also be explained by grade inflation. Our objective was to determine the likelihood of definitive treatment and risk of post-treatment recurrence for patients with GG2 cancer diagnosed using targeted biopsies relative to men with GG1 cancer diagnosed using systematic biopsies. Materials and Methods: We performed a retrospective study on a large tertiary center registry (HUS Acamedic Datalake) to retrieve data on prostate cancer diagnosis, treatment, and cancer recurrence. We included patients with either GG1 with systematic biopsies (3317 men) or GG2 with targeted biopsies (554 men) from 1993 to 2019. We assessed the risk of curative treatment and recurrence after treatment. Kaplan-Meier survival curves were computed to assess treatment- and recurrence-free survival. Cox proportional hazards regression analysis was performed to assess the risk of posttreatment recurrence. Results: Patients with systematic biopsy detected GG1 cancer had a significantly longer median time-to-treatment (31 months) than those with targeted biopsy detected GG2 cancer (4 months, p&lt;0.0001). Risk of recurrence after curative treatment was similar between groups with the upper bound of the 95% CI, excluding an important difference (HR: 0.94, 95% CI [0.71-1.25], p=0.7). Conclusions: GG2 cancers detected by MRI-targeted biopsy are treated more aggressively than GG1 cancers detected by systematic biopsy, despite having similar oncologic risk. To prevent further overtreatment related to the MRI pathway, treatment guidelines from the pre-MRI era need to be updated to consider changes in the diagnostic pathway.","PeriodicalId":501140,"journal":{"name":"medRxiv - Urology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139422254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}