The absolute tumor-capsule contact length in the diagnosis of extraprostatic extension of prostate cancer

Abstract Purpose This study aimed to calculate an applicable relative ratio threshold value instead of the absolute threshold value for simultaneous 68Ga prostate-specific membrane antigen/positron emission tomography ([68Ga]Ga-PSMA-11 PET) in patients with prostate cancer (PCa). Materials and methods Our study evaluated thirty-two patients and 170 focal prostate lesions. Lesions are classified into groups according to Prostate Imaging Reporting and Data System (PI-RADS). Standardized uptake values maximum (SUVmax), corresponding lesion-to-background ratios (LBRs) of SUVmax, and LBR distributions of each group were measured based on regions of interest (ROI). We examined LBR with receiver operating characteristic analysis to determine threshold values for differentiation between multiparametric magnetic resonance imaging (mpMRI)-positive and mpMRI-negative lesions. Results We analyzed a total of 170 focal prostate lesions. Lesions number of PI-RADS 2 to 5 was 70, 16, 46, and 38. LBR of SUVmax of each PI-RADS scores was 1.5 (0.9, 2.4), 2.5 (1.6, 3.4), 3.7 (2.6, 4.8), and 6.7 (3.5, 12.7). Based on an optimal threshold ratio of 2.5 to be exceeded, lesions could be classified into MRI-positive lesion on [68Ga]Ga-PSMA PET with a sensitivity of 85.2%, a specificity of 72.0%, with the corresponding area under the receiver operating characteristic curve (AUC) of 0.83, p < 0.001. This value matches the imaging findings better. Conclusion The ratio threshold value of SUVmax, LBR, has improved clinical and research applicability compared with the absolute value of SUVmax. A higher threshold value than the background’s uptake can dovetail the imaging findings on MRI better. It reduces the bias from using absolute background uptake value as the threshold value.

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MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study

European Radiology ◽

10.1007/s00330-021-07856-3 ◽

2021 ◽

Author(s):

Renato Cuocolo ◽

Arnaldo Stanzione ◽

Riccardo Faletti ◽

Marco Gatti ◽

Giorgio Calleris ◽

...

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Support Vector Machine ◽

Support Vector ◽

Index Lesion ◽

Pairwise Correlation ◽

Experienced Radiologist ◽

Expert Radiologist ◽

External Test ◽

Extraprostatic Extension

Abstract Objectives To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions. Methods Consecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on the two remaining institutions’ data and compared with a baseline reference and expert radiologist assessment of EPE. Results In total, 193 patients were included. From an initial dataset of 2436 features, 2287 were excluded due to either poor stability, low variance, or high collinearity. Among the remaining, 14 features were used to train the ML model, which reached an overall accuracy of 83% in the training set. In the two external test sets, the SVM achieved an accuracy of 79% and 74% respectively, not statistically different from that of the radiologist (81–83%, p = 0.39–1) and outperforming the baseline reference (p = 0.001–0.02). Conclusions A ML model solely based on radiomics features demonstrated high accuracy for EPE detection and good generalizability in a multicenter setting. Paired to qualitative EPE assessment, this approach could aid radiologists in this challenging task. Key Points • Predicting the presence of EPE in prostate cancer patients is a challenging task for radiologists. • A support vector machine algorithm achieved high diagnostic accuracy for EPE detection, with good generalizability when tested on multiple external datasets. • The performance of the algorithm was not significantly different from that of an experienced radiologist.

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Limited Sampling of Radical Prostatectomy Specimens With Excellent Preservation of Prognostic Parameters of Prostate Cancer

Archives of Pathology & Laboratory Medicine ◽

10.5858/133.8.1278 ◽

2009 ◽

Vol 133 (8) ◽

pp. 1278-1284

Author(s):

Kyungeun Kim ◽

Pil June Pak ◽

Jae Y. Ro ◽

Dongik Shin ◽

Soo-Jin Huh ◽

...

Keyword(s):

Prostate Cancer ◽

Radical Prostatectomy ◽

Sampling Method ◽

Resection Margin ◽

Gleason Grade ◽

Posterior Half ◽

Margin Involvement ◽

Partial Sampling ◽

Prostate Cancers ◽

Extraprostatic Extension

Abstract Context.—The widespread use of the serum prostate-specific antigen test has increased the early detection of prostate cancer and consequently reduced grossly definable prostate cancers. Objective.—To find the most efficient gross sampling method for radical prostatectomy specimens not only preserving important prognostic factors but also being cost effective. Design.—We initially analyzed clinicopathologic features of the entire prostate sections from 148 radical prostatectomy specimens, which then were used to examine the impact of 5 partial sampling methods on tumor stage, Gleason score, extraprostatic extension, resection margin status, and paraffin block numbers. The methods included submission of (1) alternative slices, (2) alternative slices plus biopsy-positive posterior quarters, (3) every posterior half, (4) every posterior half plus one midanterior half, and (5) alternative slices plus peripheral 3-mm rim of the remaining prostate. Results.—Prostate cancers and their extraprostatic extension and resection margin involvement were commonly located in the right posterior portion of the prostate. Method 5 was most efficient, detecting all cases with extraprostatic extension and resection margin involvement and reducing 25% of paraffin blocks compared with the entire sampling of the prostate. The Gleason scores were retained in most of cases, except reversal of the primary and secondary Gleason grade component in only 2 cases (1%). Only 4 cases (3%) were downstaged within the same T2 stage. Conclusions.—These results demonstrate that sampling of alternative slices plus peripheral rim of the remaining prostate is the most efficient partial sampling method for radical prostatectomy specimens.

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Extraprostatic Extension into Periprostatic Fat is a More Important Determinant of Prostate Cancer Recurrence than an Invasive Phenotype

The Journal of Urology ◽

10.1016/j.juro.2013.06.050 ◽

2013 ◽

Vol 190 (6) ◽

pp. 2061-2067 ◽

Cited By ~ 14

Author(s):

Jada Kapoor ◽

Benjamin Namdarian ◽

John Pedersen ◽

Chris Hovens ◽

Daniel Moon ◽

...

Keyword(s):

Prostate Cancer ◽

Important Determinant ◽

Cancer Recurrence ◽

Invasive Phenotype ◽

Prostate Cancer Recurrence ◽

Extraprostatic Extension

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Predictive Factors for Positive Surgical Margins in Patients With Prostate Cancer After Radical Prostatectomy: A Systematic Review and Meta-Analysis

Frontiers in Oncology ◽

10.3389/fonc.2020.539592 ◽

2021 ◽

Vol 10 ◽

Author(s):

Lijin Zhang ◽

Hu Zhao ◽

Bin Wu ◽

Zhenlei Zha ◽

Jun Yuan ◽

...

Keyword(s):

Prostate Cancer ◽

Gleason Score ◽

Predictive Factors ◽

Meta Analysis ◽

Positive Lymph Node ◽

Pathological Stage ◽

Positive Surgical Margins ◽

Subgroup Analyses ◽

Biopsy Gleason Score ◽

Extraprostatic Extension

Background and ObjectivesPrevious studies have demonstrated that positive surgical margins (PSMs) were independent predictive factors for biochemical and oncologic outcomes in patients with prostate cancer (PCa). This study aimed to conduct a meta-analysis to identify the predictive factors for PSMs after radical prostatectomy (RP).MethodsWe selected eligible studies via the electronic databases, such as PubMed, Web of Science, and EMBASE, from inception to December 2020. The risk factors for PSMs following RP were identified. The pooled estimates of standardized mean differences (SMDs)/odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. A fixed effect or random effect was used to pool the estimates. Subgroup analyses were performed to explore the reasons for heterogeneity.ResultsTwenty-seven studies including 50,014 patients with PCa were eligible for further analysis. The results showed that PSMs were significantly associated with preoperative prostate-specific antigen (PSA) (pooled SMD = 0.37; 95% CI: 0.31–0.43; P < 0.001), biopsy Gleason Score (<6/≥7) (pooled OR = 1.53; 95% CI:1.31–1.79; P < 0.001), pathological Gleason Score (<6/≥7) (pooled OR = 2.49; 95% CI: 2.19–2.83; P < 0.001), pathological stage (<T2/≥T3) (pooled OR = 3.90; 95% CI: 3.18–4.79; P < 0.001), positive lymph node (PLN) (pooled OR = 3.12; 95% CI: 2.28–4.27; P < 0.001), extraprostatic extension (EPE) (pooled OR = 4.44; 95% CI: 3.25–6.09; P < 0.001), and seminal vesicle invasion (SVI) (pooled OR = 4.19; 95% CI: 2,87–6.13; P < 0.001). However, we found that age (pooled SMD = 0.01; 95% CI: −0.07–0.10; P = 0.735), body mass index (BMI) (pooled SMD = 0.12; 95% CI: −0.05–0.30; P = 0.162), prostate volume (pooled SMD = −0.28; 95% CI: −0.62–0.05; P = 0.097), and nerve sparing (pooled OR = 0.90; 95% CI: 0.71–1.14; P = 0.388) had no effect on PSMs after RP. Besides, the findings in this study were found to be reliable by our sensitivity and subgroup analyses.ConclusionsPreoperative PSA, biopsy Gleason Score, pathological Gleason Score, pathological stage, positive lymph node, extraprostatic extension, and seminal vesicle invasion are independent predictors of PSMs after RP. These results may helpful for risk stratification and individualized therapy in PCa patients.

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