Is Low-risk Prostate Cancer Really Low-risk? Consider Factors Affecting Biochemical Recurrence and Tumour Upgrade

Söğütdelen, Emrullah; Citamak, Burak

doi:10.29271/jcpsp.2023.02.193

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Studies with more high-Gleason tumors (>50%) had significantly higher sensitivity and specificity values compared with those without (<50%). This could be because high-grade PCa was associated with lower prostate volume and longer diameters of lesions on MRI [40,41]. PCa is detected earlier in patients with enlarged prostates because of the elevated levels of prostate-specific antigen produced by the enlarged tissue, and PCa diagnosed in small glands may be more aggressive and associated with more unfavorable histopathological findings.…”

Section: Discussionmentioning

confidence: 99%

The Diagnostic Performance of Tumor Stage on MRI for Predicting Prostate Cancer-Positive Surgical Margins: A Systematic Review and Meta-Analysis

Wang

Zhu

et al. 2023

Diagnostics

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Purpose: Surgical margin status in radical prostatectomy (RP) specimens is an established predictive indicator for determining biochemical prostate cancer recurrence and disease progression. Predicting positive surgical margins (PSMs) is of utmost importance. We sought to perform a meta-analysis evaluating the diagnostic utility of a high clinical tumor stage (≥3) on magnetic resonance imaging (MRI) for predicting PSMs. Method: A systematic search of the PubMed, Embase databases, and Cochrane Library was performed, covering the interval from 1 January 2000 to 31 December 2022, to identify relevant studies. The Quality Assessment of Diagnostic Accuracy Studies 2 method was used to evaluate the studies’ quality. A hierarchical summary receiver operating characteristic plot was created depicting sensitivity and specificity data. Analyses of subgroups and meta-regression were used to investigate heterogeneity. Results: This meta-analysis comprised 13 studies with 3924 individuals in total. The pooled sensitivity and specificity values were 0.40 (95% CI, 0.32–0.49) and 0.75 (95% CI, 0.69–0.80), respectively, with an area under the receiver operating characteristic curve of 0.63 (95% CI, 0.59–0.67). The Higgins I2 statistics indicated moderate heterogeneity in sensitivity (I2 = 75.59%) and substantial heterogeneity in specificity (I2 = 86.77%). Area, prevalence of high Gleason scores (≥7), laparoscopic or robot-assisted techniques, field strength, functional technology, endorectal coil usage, and number of radiologists were significant factors responsible for heterogeneity (p ≤ 0.01). Conclusions: T stage on MRI has moderate diagnostic accuracy for predicting PSMs. When determining the treatment modality, clinicians should consider the factors contributing to heterogeneity for this purpose.

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Section: Discussionmentioning

confidence: 99%

The Diagnostic Performance of Tumor Stage on MRI for Predicting Prostate Cancer-Positive Surgical Margins: A Systematic Review and Meta-Analysis

Wang

Zhu

et al. 2023

Diagnostics

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Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study

Chen,

Fu,

Dong

et al. 2024

Urology

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Machine learning discrimination of Gleason scores below GG3 and above GG4 for HSPC patients diagnosis

Zhu,

Dai,

Wang

et al. 2024

Sci Rep

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This study aims to develop machine learning (ML)-assisted models for analyzing datasets related to Gleason scores in prostate cancer, conducting statistical analyses on the datasets, and identifying meaningful features. We retrospectively collected data from 717 hormone-sensitive prostate cancer (HSPC) patients at Yunnan Cancer Hospital. Of these, data from 526 patients were used for modeling. Seven auxiliary models were established using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Extreme gradient boosting tree (XGBoost), Adaptive Boosting (Adaboost), and artificial neural network (ANN) based on 21 clinical biochemical indicators and features. Evaluation metrics included accuracy (ACC), precision (PRE), specificity (SPE), sensitivity (SEN) or regression rate(Recall), and f1 score. Evaluation metrics for the models primarily included ACC, PRE, SPE, SEN or Recall, f1 score, and area under the curve(AUC). Evaluation metrics were visualized using confusion matrices and ROC curves. Among the ensemble learning methods, RF, XGBoost, and Adaboost performed the best. RF achieved a training dataset score of 0.769 (95% CI: 0.759—0.835) and a testing dataset score of 0.755 (95% CI: 0.660—0.760) (AUC: 0.786, 95%CI: 0.722—0.803), while XGBoost achieved a training dataset score of 0.755 (95% CI: 95%CI: 0.711—0.809) and a testing dataset score of 0.745 (95% CI: 0.660—0.764) (AUC: 0.777, 95% CI: 0.726—0.798). Adaboost scored 0.789 on the training dataset (95% CI: 0.782—0.857) and 0.774 on the testing dataset (95% CI: 0.651—0.774) (AUC: 0.799, 95% CI: 0.703—0.802). In terms of feature importance (FI) in ensemble learning, Bone metastases at first visit, prostatic volume, age, and T1-T2 have significant proportions in RF’s FI. fPSA, TPSA, and tumor burden have significant proportions in Adaboost’s FI, while f/TPSA, LDH, and testosterone have the highest proportions in XGBoost. Our findings indicate that ensemble learning methods demonstrate good performance in classifying HSPC patient data, with TNM staging and fPSA being important classification indicators. These discoveries provide valuable references for distinguishing different Gleason scores, facilitating more accurate patient assessments and personalized treatment plans.

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Is Low-risk Prostate Cancer Really Low-risk? Consider Factors Affecting Biochemical Recurrence and Tumour Upgrade

Abstract: Objective: To evaluate the demographics, preoperative or pathological factors, and findings of magnetic resonance imaging (MRI) to predict the factors affecting biochemical recurrence and tumour upgrade in low-risk prostate cancer.

Cited by 3 publications

References 3 publications

The Diagnostic Performance of Tumor Stage on MRI for Predicting Prostate Cancer-Positive Surgical Margins: A Systematic Review and Meta-Analysis

The Diagnostic Performance of Tumor Stage on MRI for Predicting Prostate Cancer-Positive Surgical Margins: A Systematic Review and Meta-Analysis

Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study

Machine learning discrimination of Gleason scores below GG3 and above GG4 for HSPC patients diagnosis

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