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- Marra, Giancarlo, et al.
(author)
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Impact of Epithelial Histological Types, Subtypes, and Growth Patterns on Oncological Outcomes for Patients with Nonmetastatic Prostate Cancer Treated with Curative Intent: A Systematic Review.
- 2023
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In: European urology. - 0302-2838 .- 1873-7560. ; 84:1, s. 65-85
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Journal article (peer-reviewed)abstract
- The optimal management for men with prostate cancer (PCa) with unconventional histology (UH) is unknown. The outcome for these cancers might be worse than for conventional PCa and so different approaches may be needed.To compare oncological outcomes for conventional and UH PCa in men with localized disease treated with curative intent.A systematic review adhering to the Referred Reporting Items for Systematic Reviews and Meta-Analyses was prospectively registered on PROSPERO (CRD42022296013) was performed in July 2021.We screened 3651 manuscripts and identified 46 eligible studies (reporting on 1871814 men with conventional PCa and 6929 men with 10 different PCa UHs). Extraprostatic extension and lymph node metastases, but not positive margin rates, were more common with UH PCa than with conventional tumors. PCa cases with cribriform pattern, intraductal carcinoma, or ductal adenocarcinoma had higher rates of biochemical recurrence and metastases after radical prostatectomy than for conventional PCa cases. Lower cancer-specific survival rates were observed for mixed cribriform/intraductal and cribriform PCa. By contrast, pathological findings and oncological outcomes for mucinous and prostatic intraepithelial neoplasia (PIN)-like PCa were similar to those for conventional PCa. Limitations of this review include low-quality studies, a risk of reporting bias, and a scarcity of studies that included radiotherapy.Intraductal, cribriform, and ductal UHs may have worse oncological outcomes than for conventional and mucinous or PIN-like PCa. Alternative treatment approaches need to be evaluated in men with these cancers.We reviewed the literature to explore whether prostate cancers with unconventional growth patterns behave differently to conventional prostate cancers. We found that some unconventional growth patterns have worse outcomes, so we need to investigate if they need different treatments. Urologists should be aware of these growth patterns and their clinical impact.
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- Schröder, Fritz H, et al.
(author)
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Prostate-cancer mortality at 11 years of follow-up.
- 2012
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In: The New England journal of medicine. - 1533-4406 .- 0028-4793. ; 366:11, s. 981-90
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Journal article (peer-reviewed)abstract
- Several trials evaluating the effect of prostate-specific antigen (PSA) testing on prostate-cancer mortality have shown conflicting results. We updated prostate-cancer mortality in the European Randomized Study of Screening for Prostate Cancer with 2 additional years of follow-up.
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- Lopez-Beltran, Antonio, et al.
(author)
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International Society of Urological Pathology (ISUP) Consensus Conference on Current Issues in Bladder Cancer : Working Group 3: Subcategorization of T1 Bladder Cancer
- 2024
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In: American Journal of Surgical Pathology. - 0147-5185. ; 48:1, s. 24-31
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Journal article (peer-reviewed)abstract
- Emerging data on T1 bladder cancer subcategorization (aka substaging) suggests a correlation with oncological outcomes. The International Society of Urological Pathology (ISUP) organized the 2022 consensus conference in Basel, Switzerland to focus on current issues in bladder cancer and tasked working group 3 to make recommendations for T1 subcategorization in transurethral bladder resections. For this purpose, the ISUP developed and circulated a survey to their membership querying approaches to T1 bladder cancer subcategorization. In particular, clinical relevance, pathological reporting, and endorsement of T1 subcategorization in the daily practice of pathology were surveyed. Of the respondents of the premeeting survey, about 40% do not routinely report T1 subcategory. We reviewed literature on bladder T1 subcategorization, and screened selected articles for clinical performance and practicality of T1 subcategorization methods. Published literature offered evidence of the clinical rationale for T1 subcategorization and at the conference consensus (83% of conference attendants) was obtained to report routinely T1 subcategorization of transurethral resections. Semiquantitative T1 subcategorization was favored (37%) over histoanatomic methods (4%). This is in line with literature findings on practicality and prognostic impact, that is, a shift of publications from histoanatomic to semiquantitative methods or by reports incorporating both methodologies is apparent over the last decade. However, 59% of participants had no preference for either methodology. They would add a comment in the report briefly stating applied method, interpretation criteria (including cutoff), and potential limitations. When queried on the terminology of T1 subcategorization, 34% and 20% of participants were in favor of T1 (microinvasive) versus T1 (extensive) or T1 (focal) versus T1 (nonfocal), respectively.
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- Ploussard, Guillaume, et al.
(author)
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The Contemporary Concept of Significant Versus Insignificant Prostate Cancer
- 2011
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In: European Urology. - : Elsevier BV. - 1873-7560 .- 0302-2838. ; 60:2, s. 291-303
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Research review (peer-reviewed)abstract
- Context: The notion of insignificant prostate cancer (Ins-PCa) has progressively emerged in the past two decades. The clinical relevance of such a definition was based on the fact that low-grade, small-volume, and organ-confined prostate cancer (PCa) may be indolent and unlikely to progress to biologic significance in the absence of treatment. Objective: To review the definition of Ins-PCa, its incidence, and the clinical impact of Ins-PCa on the contemporary management of PCa. Evidence acquisition: A review of the literature was performed using the Medline, Scopus, and Web of Science databases with no restriction on language up to September 2010. The literature search used the following terms: insignificant, indolent, minute, microfocal, minimal, low volume, low risk, and prostate cancer. Evidence synthesis: The most commonly used criteria to define Ins-PCa are based on the pathologic assessment of the radical prostatectomy specimen: (1) Gleason score <= 6 without Gleason pattern 4 or 5, (2) organ-confined disease, and (3) tumour volume < 0.5 cm(3). Several preoperative criteria and prognostication tools for predicting Ins-PCa have been suggested. Nomograms are best placed to estimate the risk of progression on an individualised basis, but a substantial proportion of men with a high probability of harbouring Ins-PCa are at risk for pathologic understaging and/or undergrading. Thus, there is an ongoing need for identifying novel and more accurate predictors of Ins-PCa to improve the distinction between insignificant versus significant disease and thus to promote the adequate management of PCa patients at low risk for progression. Conclusions: The exciting challenge of obtaining the pretreatment diagnostic tools that can really distinguish insignificant from significant PCa should be one of the main objectives of urologists in the following years to decrease the risk of overtreatment of Ins-PCa. (C) 2011 European Association of Urology. Published by Elsevier B. V. All rights reserved.
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- Strom, Peter, et al.
(author)
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies : a population-based, diagnostic study
- 2020
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In: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Journal article (peer-reviewed)abstract
- BackgroundAn increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.MethodsWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.FindingsThe AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73).InterpretationAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.
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