Evolution, controversies and the future of prostate cancer grading

Egevad, Lars; Delahunt, Brett; Yaxley, John; Samaratunga, Hemamali

doi:10.1111/pin.12761

Cited by 6 publications

(5 citation statements)

References 60 publications

(118 reference statements)

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“…This parameter is highly correlated with the aggressiveness of the malignancy, and highly aggressive tumors progress more rapidly and are usually associated with LN metastasis. In our study, the XGB model showed that the weight value of GS was the highest, showing the importance of this parameter and indicated that it contributed most to the results obtained ( 40 , 41 ). As our results demonstrated, the XGB model assigned weighted values to all variables and arranged them by order of importance, thus allowing for more variables to be involved in the analysis and helping physicians to better understand the risk factors.…”

Section: Discussionsupporting

confidence: 56%

Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

et al. 2021

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BackgroundA more accurate preoperative prediction of lymph node involvement (LNI) in prostate cancer (PCa) would improve clinical treatment and follow-up strategies of this disease. We developed a predictive model based on machine learning (ML) combined with big data to achieve this.MethodsClinicopathological characteristics of 2,884 PCa patients who underwent extended pelvic lymph node dissection (ePLND) were collected from the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Eight variables were included to establish an ML model. Model performance was evaluated by the receiver operating characteristic (ROC) curves and calibration plots for predictive accuracy. Decision curve analysis (DCA) and cutoff values were obtained to estimate its clinical utility.ResultsThree hundred and forty-four (11.9%) patients were identified with LNI. The five most important factors were the Gleason score, T stage of disease, percentage of positive cores, tumor size, and prostate-specific antigen levels with 158, 137, 128, 113, and 88 points, respectively. The XGBoost (XGB) model showed the best predictive performance and had the highest net benefit when compared with the other algorithms, achieving an area under the curve of 0.883. With a 5%~20% cutoff value, the XGB model performed best in reducing omissions and avoiding overtreatment of patients when dealing with LNI. This model also had a lower false-negative rate and a higher percentage of ePLND was avoided. In addition, DCA showed it has the highest net benefit across the whole range of threshold probabilities.ConclusionsWe established an ML model based on big data for predicting LNI in PCa, and it could lead to a reduction of approximately 50% of ePLND cases. In addition, only ≤3% of patients were misdiagnosed with a cutoff value ranging from 5% to 20%. This promising study warrants further validation by using a larger prospective dataset.

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Section: Discussionsupporting

confidence: 56%

Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

et al. 2021

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“…Histological grading of prostate cancer is an important parameter for predicting the treatment and prognosis response. To date, Gleason grading forms the basis of prostate cancer grading (Egevad et al, 2019). The Gleason grade is assigned based on the architecture and arrangement of the malignant cells within the tumor.…”

Section: Introductionmentioning

confidence: 99%

RNA m6A Methylation Regulators Multi-Omics Analysis in Prostate Cancer

Wang

2021

Front. Genet.

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RNA N6-methyladenosine (m6A) methylation is known to be the most popular RNA modification in animals. Many research reports have elaborated on the effects of m6A regulators in medical practice, such as diagnosis, prognosis, and treatment. M6A modification has evident impacts on many aspects of RNA metabolism, just like RNA splicing, processing, translation, and stability. M6A also has a magnificent role in numerous types of cancers. We analyzed the prostate cancer datasets, from The Cancer Genome Atlas (TCGA) database, for every recognized m6A regulator in their gene expression, DNA methylation status and copy number variations (CNVs). We also systematically analyzed the relationship between different m6A regulators and the prognosis of prostate cancer. The results illustrated considerable differences in the expression of various m6A regulators between the prostate and normal cancer samples. At the same time, there were evident differences in the expression of various m6A regulators in prostate cancers with different Gleason scores. Subsequently, we determined CBLL1, FTO, YTHDC1, HNRNPA2B1 as crucial m6A regulators of prostate cancer. Premised on the expression of CBLL1, we also identified potential therapeutic agents for prostate cancer, and knockdown of HNRNPA2B1 prominently inhibited prostate cells migration and invasion in vitro experiment.

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“…Gleason score takes into account the two most common cell subtypes rather than the worst pattern, which can lead to underestimation of the aggressiveness of the tumour. Additionally, different rules exist for reporting Gleason scores in biopsy specimens, which can lead to undergrading the tumour compared with radical prostatectomy biopsy [152][153][154] . Gleason score is further categorized into one of five prognostic ISUP groups, ranging from 1 (low risk) to 5 (high risk) 56 , with group 1, 2, 3, 4 and 5 tumours corresponding to Gleason scores ≤3+3, 3+4, 4+3, 4+4, and ≥4+5, respectively 56 .…”

Section: [H2] Pathologymentioning

confidence: 99%

Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway

et al. 2022

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Multiparametric magnetic resonance imaging (MRI) of the prostate is now recommended as the initial diagnostic test for men presenting with suspected prostate cancer, with a negative MRI enabling safe avoidance of biopsy and a positive result enabling MRI-directed sampling of lesions. The diagnostic pathway consists of several steps, from initial patient presentation and preparation to performing and interpreting MR images, communicating the imaging findings, outlining the prostate and intra-prostatic target lesions, performing the biopsy, and assessing the cores. Each component of this pathway requires experienced clinicians, optimised equipment, good inter-disciplinary communication between specialists, and standardised workflows in order to achieve the expected outcomes. Assessment of quality and mitigation measures are essential for the success of the MRI-directed prostate cancer diagnostic pathway. Quality assurance processes including Prostate Imaging-Reporting and Data System (PI-RADS), template biopsy, and pathology guidelines help minimise variation and ensure optimisation of the diagnostic pathway. Quality control systems including the Prostate Imaging Quality (PI-QUAL) scoring system, patient-level outcomes (such as PI-RADS MRI score assignment and cancer detection rates), multidisciplinary meeting review and audits might also be used to provide consistency of outcomes and ensure that all the benefits of the MRI-directed pathway are achieved. Key points-Multiparametric magnetic resonance imaging (MRI) is now recommended as the initial diagnostic test for men presenting with suspected prostate cancer -A negative MRI enables patients to safely avoid biopsy, whereas a positive MRI prompts targeted biopsy and pathologically accurate tissue sampling.-The MRI-directed prostate cancer diagnostic pathway involves several steps including acquiring and interpreting MR images, communicating MRI findings, outlining suspicious target lesions, performing biopsy, and evaluating the cores.-All steps in the pathway are prone to variation; assessment and mitigation of poor quality and variance are essential for a successful delivery of the MRI-directed pathway -Quality assurance systems to minimise variation in performance and prevent poor quality include Prostate Imaging-Reporting and Data System (PI-RADS) imaging guidelines for radiologists, prostate biopsy templates, and International Society of Urological Pathology (ISUP) guidelines for histopathologists.-Quality control measures include checking MRI compliance with PI-RADS, image quality assessment with the Prostate Imaging Quality (PI-QUAL) scoring system, radiologist certification, multidisciplinary team meeting ( MDT) review and pathology re-review of images, and audits of cancer detection rates and biopsy core quality.

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Evolution, controversies and the future of prostate cancer grading

Cited by 6 publications

References 60 publications

Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

RNA m6A Methylation Regulators Multi-Omics Analysis in Prostate Cancer

Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway

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