A comparative analysis of techniques for measuring tumor contact length in predicting extraprostatic extension.

PURPOSE: This study aims to evaluate the diagnostic performance of curvilinear and linear measurement methods in different magnetic resonance imaging (MRI) sequences for detecting extraprostatic extension (EPE) in prostate cancer, and to evaluate the added value of apparent diffusion coefficient (ADC) in detecting EPE. METHODS: A retrospective analysis was conducted on 84 patients who underwent multiparametric MRI (mp-MRI) prior to radical prostatectomy between January 2019 and February 2022. Tumor contact length (TCL) was assessed curvilinearly and linearly on T2-weighted imaging (T2WI), ADC maps, and dynamic contrast-enhanced (DCE) MRI by two radiologists. MRI-based EPE positivity was defined as a curvilinear or linear contact length of >15 mm. Statistical comparisons were conducted using chi-squared and independent samples t-tests, with interreader agreement evaluated using weighted κ statistics. Univariate and multivariate logistic regression identified independent predictors of EPE, and two prediction models were constructed. Diagnostic performance was assessed using receiver operator characteristic (ROC) curve analysis. RESULTS: A total of 32 (38%) and 52 (62%) patients with EPE and non-EPE, respectively, were included in this study. Patients with EPE demonstrated significantly larger tumor sizes, lower ADC values, and lower ADC ratios than those without EPE (p < 0.001). The curvilinear and linear TCL measurements for each sequence exhibited statistically significant correlations with EPE for both readers, with strong interreader agreement. Curvilinear TCL (c-TCL) and linear TCL (l-TCL) on DCE-MRI showed higher area under the curve (AUC) values than the other measurements for EPE prediction (reader 1: 0.815 and 0.803, reader 2: 0.746 and 0.713, respectively). However, there was no statistically significant difference between c-TCL and l-TCL. Multivariable models with mean ADC value improved predictive performance. Model 2 (ADC, ISUP, and c-TCL on DCE images) surpassed model 1 (ADC and c-TCL on DCE images) with an AUC of 0.919 and 0.874, respectively. CONCLUSION: DCE-MRI demonstrated superior performance in predicting EPE compared to other sequences. Linear and curvilinear measurements had comparable diagnostic performance. Being more practical and easier, radiologists may use l-TCL measurement in daily practice. The mean ADC value provided additional diagnostic value.

Bicenter validation of a risk model for the preoperative prediction of extraprostatic extension of localized prostate cancer combining clinical and multiparametric MRI parameters.

BACKGROUND: This study aimed to validate a previously published risk model (RM) which combines clinical and multiparametric MRI (mpMRI) parameters to predict extraprostatic extension (EPE) of prostate cancer (PC) prior to radical prostatectomy (RP). MATERIALS AND METHODS: A previously published RM combining clinical with mpMRI parameters including European Society of Urogenital Radiology (ESUR) classification for EPE was retrospectively evaluated in a cohort of two urological university hospitals in Germany. Consecutive patients (n = 205, January 2015 -June 2021) with available preoperative MRI images, clinical information including PSA, prostate volume, ESUR classification for EPE, histopathological results of MRI-fusion biopsy and RP specimen were included. Validation was performed by receiver operating characteristic analysis and calibration plots. The RM's performance was compared to ESUR criteria. RESULTS: Histopathological T3 stage was detected in 43% of the patients (n = 89); 45% at Essen and 42% at Düsseldorf. Discrimination performance between pT2 and pT3 of the RM in the entire cohort was AUC = 0.86 (AUC = 0.88 at site 1 and AUC = 0.85 at site 2). Calibration was good over the entire probability range. The discrimination performance of ESUR classification alone was comparable (AUC = 0.87). CONCLUSIONS: The RM showed good discriminative performance to predict EPE for decision-making for RP as a patient-tailored risk stratification. However, when experienced MRI reading is available, standardized MRI reading with ESUR scoring is comparable regarding information outcome. A main limitation is the potentially limited transferability to other populations because of the high prevalence of EPE in our subgroups.

Does the extent of extraprostatic extension at radical prostatectomy predict outcome?-a systematic review and meta-analysis.

Extraprostatic extension (EPE) of prostate cancer is usually reported as either focal (F-EPE) or established (E-EPE), but data on the implication for outcomes of this subdivision are conflicting and no systematic review (SR) evaluating this exists. This SR aims to address this gap in the literature, focusing on the impact of F-EPE and E-EPE on outcome in radical prostatectomy (RP) patients. Searches on Embase, Medline(R), and Pubmed databases were conducted. Studies were included if they investigated the extent of EPE in RP patients and correlated this with defined outcomes (biochemical recurrence [BCR], death, metastasis). Quality was assessed using the Newcastle-Ottawa Scale. A random effects model was used for studies reporting hazard ratios (EPE extent and biochemical recurrence). 24 studies, including 49,187 men, were included. Six studies were of high quality. 20 studies reported how they measured EPE. 13 studies reported that the extent of EPE was associated significantly with BCR. Meta-analysis showed there was a significant correlation between BCR and both F-EPE and E-EPE when compared to organ-confined disease; no significant difference was found between F-EPE and E-EPE. This is the only SR to investigate the extent of EPE on outcomes after RP. EPE alone predicts outcome, but the value of subdivision by extent could not be demonstrated. Comparisons are limited due to variability in EPE assessment and in the methods used to report outcomes in the literature. Further work to standardize EPE reporting methods, in larger cohorts, may be helpful to resolve remaining questions.

Incidence and Pitfalls of Adipose Tissue Encountered in Prostatic Transurethral Resections and Related Specimens.

While the presence of adipose tissue and its involvement by prostatic cancer (extraprostatic extension) is well-recognized in prostate biopsies, adipose tissue in transurethral resections of the prostate (TURP) is largely unexplored. Herein, 200 consecutive TURPs and related specimens were reviewed, including a separate 3-year analysis of specimens containing prostatic cancer, with the following data collected: presence of fat, presence of cancer within fat, and quantity of fat. For specimens with both fat and prostatic cancer, specimen weight and tumor volume were recorded. Within the 200 consecutive TURPs and related specimens, adipose tissue was identified in 20%; 55% had 2.5 mm of adipose tissue; the number of fragments with adipose tissue ranged from 1 to 14. No correlation between specimen weight and measured extent of adipose tissue or number of fragments with adipose tissue was identified. Of all the specimens with prostatic cancer, 15/56 (27%) involved adipose tissue, with two specimens with large cancer volume (>90%) demonstrating extensive involvement of adipose tissue. Adipose tissue is frequently present within TURP and related specimens with variability in extent. The etiology behind encountering adipose tissue is uncertain, and it could represent resection into peri-prostatic fat, intraprostatic fat, or bladder neck fat sampling. Although encountering adipose tissue involved by cancer in TURP and related specimens may imply extraprostatic extension (pT3a), further studies are needed to corroborate these findings as well as to determine if these should be included in reported synoptics.

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification.

RATIONALE AND OBJECTIVES: Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI. MATERIAL AND METHODS: An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology. RESULTS: A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth. CONCLUSION: Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.

Predicting contralateral extraprostatic extension in unilateral high-risk prostate cancer: a multicentric external validation study.

PURPOSE: Accurate prediction of extraprostatic extension (EPE) is crucial for decision-making in radical prostatectomy (RP), especially in nerve-sparing strategies. Martini et al. introduced a three-tier algorithm for predicting contralateral EPE in unilateral high-risk prostate cancer (PCa). The aim of the study is to externally validate this model in a multicentric European cohort of patients. METHODS: The data from 208 unilateral high-risk PCa patients diagnosed through magnetic resonance imaging (MRI)-targeted and systematic biopsies, treated with RP between January 2016 and November 2021 at eight referral centers were collected. The evaluation of model performance involved measures such as discrimination (AUC), calibration, and decision-curve analysis (DCA) following TRIPOD guidelines. In addition, a comparison was made with two established multivariable logistic regression models predicting the risk of side specific EPE for assessment purposes. RESULTS: Overall, 38%, 48%, and 14% of patients were categorized as low, intermediate, and high-risk groups according to Martini et al.'s model, respectively. At final pathology, EPE on the contralateral prostatic lobe occurred in 6.3%, 12%, and 34% of patients in the respective risk groups. The algorithm demonstrated acceptable discrimination (AUC 0.68), comparable to other multivariable logistic regression models (p = 0.3), adequate calibration and the highest net benefit in DCA. The limitations include the modest sample size, retrospective design, and lack of central revision. CONCLUSION: Our findings endorse the algorithm's commendable performance, supporting its utility in guiding treatment decisions for unilateral high-risk PCa patients.

Amide proton transfer imaging has added value for predicting extraprostatic extension in prostate cancer patients.

Background: Prostate cancer invades the capsule is a key factor in selecting appropriate treatment methods. Accurate preoperative prediction of extraprostatic extension (EPE) can help achieve precise selection of treatment plans. Purpose: The aim of this study is to verify the diagnostic efficacy of tumor size, length of capsular contact (LCC), apparent diffusion coefficient (ADC), and Amide proton transfer (APT) value in predicting EPE. Additionally, the study aims to investigate the potential additional value of APT for predicting EPE. Method: This study include 47 tumor organ confined patients (age, 64.16 ± 9.18) and 50 EPE patients (age, 61.51 ± 8.82). The difference of tumor size, LCC, ADC and APT value between groups were compared. Binary logistic regression was used to screen the EPE predictors. The receiver operator characteristic curve analysis was performed to assess the diagnostic performance of variables for predicting EPE. The diagnostic efficacy of combined models (model I: ADC+LCC+tumor size; model II: APT+LCC+tumor size; and model III: APT +ADC+LCC+tumor size) were also analyzed. Results: APT, ADC, tumor size and the LCC were independent predictors of EPE. The area under the curve (AUC) of APT, ADC, tumor size and the LCC were 0.752, 0.665, 0.700 and 0.756, respectively. The AUC of model I, model II, and model III were 0.803, 0.845 and 0.869, respectively. The cutoff value of APT, ADC, tumor size and the LCC were 3.65%, 0.97×10-3mm2/s, 17.30mm and 10.78mm, respectively. The sensitivity/specificity of APT, ADC, tumor size and the LCC were 76%/89.4.0%, 80%/59.6%, 54%/78.9%, 72%/66%, respectively. The sensitivity/specificity of model I, Model II and Model III were 74%/72.3%, 82%/72.5% and 84%/80.9%, respectively. Data conclusion: Amide proton transfer imaging has added value for predicting EPE. The combination model of APT balanced the sensitivity and specificity.

Prostate Cancer Risk Stratification by Simple Scoring of the Current pT3 Lesions: A Proposal for a New Pathologic T-Staging System.

Cancer spread beyond the prostate, including extraprostatic extension (other than seminal vesicle or bladder invasion; EPE)/microscopic bladder neck invasion and seminal vesicle invasion (SVI) currently classified as pT3a and pT3b lesions, respectively, does not uniformly indicate poor oncologic outcomes. Accurate risk stratification of current pT3 disease is therefore required. We herein further determined the prognostic impact of these histopathologic lesions routinely assessed and reported by pathologists, particularly their combinations. We assessed consecutive 2892 patients undergoing radical prostatectomy for current pT2 (n = 1692), pT3a (n = 956), or pT3b (n = 244) disease at our institution between 2009 and 2018. Based on our preliminary findings, point(s) were given (1 point to focal EPE, microscopic bladder neck invasion, or unilateral SVI; 2 points to nonfocal/established EPE or bilateral SVI) and summed up in each case. Our cohort had 0 point (n = 1692, 58.5%; P0), 1 point (n = 243, 8.4%; P1), 2 points (n = 657, 22.7%; P2), 3 points (n = 192, 6.6%; P3), 4 points (n = 76, 2.6%; P4), and 5 points (n = 32, 1.1%; P5). Univariate analysis revealed associations of higher points with significantly worse biochemical progression-free survival, particularly when P4 and P5 were combined. In multivariable analysis (P0 as a reference), P1 (hazard ratio [HR], 1.57; P = .033), P2 (HR, 3.25; P < .001), P3 (HR, 4.01; P < .001), and P4 + P5 (HR, 5.99; P < .001) showed significance for the risk of postoperative progression. Meanwhile, Harrell C-indexes for the current pT staging, newly developed point system, and the Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S) score were 0.727 (95% CI, 0.706-0.748), 0.751 (95% CI, 0.729-0.773), and 0.774 (95% CI, 0.755-0.794), respectively, for predicting progression. We believe our data provide a logical rationale for a novel pathologic T-staging system based on the summed points, pT1a (0 point), pT1b (1 point), pT2 (2 points), pT3a (3 points), and pT3b (4 or 5 points), which more accurately stratifies the prognosis of prostate cancer.

Risk of Biochemical Recurrence in Patients With Grade Group 1 Prostate Cancer With Extraprostatic Extension Treated With Radical Prostatectomy.

PURPOSE: We sought to examine the association of extraprostatic extension (EPE) with biochemical recurrence (BCR) separately in men with Grade Group (GG) 1 and GG2 prostate cancer (PCa) treated with radical prostatectomy. MATERIALS AND METHODS: We reviewed our institutional database of patients who underwent radical prostatectomy for PCa between 2005 and 2022 and identified patients with GG1 and GG2 disease on final pathology. Fine-Gray competing risk models with an interaction between EPE (yes vs no) and GG (GG1 vs GG2) were used to examine the relationship between disease group and BCR-free survival. RESULTS: The cohort consisted of 6309 men, of whom 169/2740 (6.2%) with GG1 disease had EPE while 1013/3569 (28.4%) with GG2 disease had EPE. Median follow-up was 4 years. BCR occurred in 400/6309 (6.3%) patients. For men with GG1, there was no statistically significant difference in BCR-free survival for men with vs without EPE (subdistribution HR = 0.88; 95% CI: 0.37-2.09). However, for GG2 patients BCR-free survival was significantly worse for those with vs without EPE (subdistribution HR = 1.97, 95% CI: 1.54-2.52). CONCLUSIONS: Although there is a subset of GG1 PCas capable of invading through the prostatic capsule, patients with GG1 PCa and EPE at prostatectomy experience similar biochemical recurrence and survival outcomes compared to GG1 patients without EPE. However, among men with GG2, EPE connotes a worse prognosis.

The Development and External Validation of Artificial Intelligence-Driven MRI-Based Models to Improve Prediction of Lesion-Specific Extraprostatic Extension in Patients with Prostate Cancer.

Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%, p = 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer.

[Comparison of the efficiency of surgical excision methods for detecting extraprostatic extension and positive resection margin in prostate cancer]. / Sravnenie effektivnosti metodov vyrezki operatsionnogo materiala dlya vyyavleniya ekstraprostaticheskogo rasprostraneniya i polozhitel'nogo kraya rezektsii pri rake prostaty.

On the samples of 26 prostatectomies, the method of excision of the prostate gland according to Kim was tested. This method increased the number of blocks by 30.2% and increased the detectability of extraprostatic extension by 41.7% and positive surgical margin by 40.0% compared to the method of alternate prostate sections. Also, the method according to Kim reduced the number of blocks of prostate tissue by 34.3% compared to the method of complete prostate excision.

Detection of extraprostatic extension by transperineal multiparametric magnetic resonance imaging-ultrasound fusion targeted combined with systemic template prostate biopsy.

BACKGROUND: Extraprostatic extension (EPE) of prostate cancer (PCa) on transrectal (TR) needle core biopsy (Bx) is a rare histopathological finding that can help in clinical decision-making. The detection efficiency of the transperineal (TP) approach is yet to be explored. METHODS: We retrospectively reviewed 2848 PCa cases using concomitant systemic template biopsy (SBx) and multiparametric magnetic resonance imaging (MRI)-ultrasound fusion-targeted biopsy (TBx) using the TR (n = 1917) or TP (n = 931) approach at our institution between January 2015 and July 2022. We assessed and compared clinical, MRI, and biopsy characteristics using different approaches (TP and TR) and methods (SBx and TBx). RESULTS: In total, 40 EPE cases were identified (40/2848, 1.4%). TP showed a significantly higher EPE detection rate compared to TR in SBx (TR:0.7% vs. TP:1.6%; p = 0.028) and TBx (TR:0.5% vs. TP:1.2%; p = 0.033), as well as the combined methods (2.1% vs. 1.1%, p = 0.019). A significantly higher incidence of EPEs was found at non-base sites in TP than in TR (76.7% vs. 50%, p = 0.038). SBx showed a higher EPE detection rate than TBx; however, the difference was not statistically significant. TP showed higher prostate-specific antigen density (0.35 vs. 0.17, p = 0.005), higher frequency of GG4-5 in the cores with EPE (65.0% vs. 50.0%, p = 0.020), and more PCa-positive SBx cores (10 vs. 8, p = 0.023) compared to the TR. CONCLUSIONS: TP may improve EPE detection compared with TR and should be applied to patients with adverse pre-biopsy features.

Comprehensive analysis of predictive factors for upstaging in intraprostatic cancer after radical prostatectomy: Different patterns of spread exist in lesions at different locations.

BACKGROUND: Accurate assessment of the clinical staging is crucial for determining the need for radical prostatectomy (RP) in prostate cancer (PCa). However, the current methods for PCa staging may yield incorrect results. This study aimed to comprehensively analyze independent predictors of postoperative upstaging of intraprostatic cancer. METHODS: We conducted a retrospective analysis of data from intraprostatic cancer patients who underwent radical surgery between March 2019 and December 2022. Intraprostatic cancer was defined as a lesion confined to the prostate, excluding cases where multiparameter magnetic resonance imaging (mpMRI) showed the lesion in contact with the prostatic capsule. We assessed independent predictors of extraprostatic extension (EPE) and analyzed their association with positive surgical margin (PSM) status. In addition, based on the distance of the lesion from the capsule on mpMRI, we divided the patients into non-transition zone and transition zone groups for further analysis. RESULTS: A total of 500 patients were included in our study. Logistic regression analysis revealed that biopsy Gleason grade group (GG) (odds ratio, OR: 1.370, 95% confidence interval, CI: 1.093-1.718) and perineural invasion (PNI) (OR: 2.746, 95% CI: 1.420-5.309) were predictive factors for postoperative EPE. Both biopsy GG and PNI were associated with lateral (GG: OR: 1.270, 95% CI: 1.074-1.501; PNI: OR: 2.733, 95% CI: 1.521-4.911) and basal (GG: OR: 1.491, 95% CI: 1.194-1.862; PNI: OR: 3.730, 95% CI: 1.929-7.214) PSM but not with apex PSM (GG: OR: 1.176, 95% CI: 0.989-1.399; PNI: OR: 1.204, 95% CI: 0.609-2.381) after RP. Finally, PNI was an independent predictor of EPE in the transition zone (OR: 11.235, 95% CI: 2.779-45.428) but not in the non-transition zone (OR: 1.942, 95% CI: 0.920-4.098). CONCLUSION: PNI and higher GG may indicate upstaging of tumors in patients with intraprostatic carcinoma. These two factors are associated with PSM in locations other than the apex of the prostate. Importantly, cancer in the transition zone of the prostate is more likely to spread externally through nerve invasion than cancer in the non-transition zone.

Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis.

PURPOSE: In recent decades, diverse nomograms have been proposed to predict extraprostatic extension (EPE) in prostate cancer (PCa). We aimed to systematically evaluate the accuracy of MRI-inclusive nomograms and traditional clinical nomograms in predicting EPE in PCa. The purpose of this meta-analysis is to provide baseline summative and comparative estimates for future study designs. MATERIALS AND METHODS: The PubMed, Embase, and Cochrane databases were searched up to May 17, 2023, to identify studies on prediction nomograms for EPE of PCa. The risk of bias in studies was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Summary estimates of sensitivity and specificity were obtained with bivariate random-effects model. Heterogeneity was investigated through meta-regression and subgroup analysis. RESULTS: Forty-eight studies with a total of 57 contingency tables and 20,395 patients were included. No significant publication bias was observed for either the MRI-inclusive nomograms or clinical nomograms. For MRI-inclusive nomograms predicting EPE, the pooled AUC of validation cohorts was 0.80 (95% CI: 0.76, 0.83). For traditional clinical nomograms predicting EPE, the pooled AUCs of the Partin table and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram were 0.72 (95% CI: 0.68, 0.76) and 0.79 (95% CI: 0.75, 0.82), respectively. CONCLUSION: Preoperative risk stratification is essential for PCa patients; both MRI-inclusive nomograms and traditional clinical nomograms had moderate diagnostic performance for predicting EPE in PCa. This study provides baseline comparative values for EPE prediction for future studies which is useful for evaluating preoperative risk stratification in PCa patients. CRITICAL RELEVANCE STATEMENT: This meta-analysis firstly evaluated the diagnostic performance of preoperative MRI-inclusive nomograms and clinical nomograms for predicting extraprostatic extension (EPE) in prostate cancer (PCa) (moderate AUCs: 0.72-0.80). We provide baseline estimates for EPE prediction, these findings will be useful in assessing preoperative risk stratification of PCa patients. KEY POINTS: • MRI-inclusive nomograms and traditional clinical nomograms had moderate AUCs (0.72-0.80) for predicting EPE. • MRI combined clinical nomogram may improve diagnostic accuracy of MRI alone for EPE prediction. • MSKCC nomogram had a higher specificity than Partin table for predicting EPE. • This meta-analysis provided baseline and comparative estimates of nomograms for EPE prediction for future studies.

Predictors of Extraprostatic Extension in Patients with Prostate Cancer.

PURPOSE: To identify effective factors predicting extraprostatic extension (EPE) in patients with prostate cancer (PCa). METHODS: This retrospective cohort study recruited 898 consecutive patients with PCa treated with robot-assisted laparoscopic radical prostatectomy. The patients were divided into EPE and non-EPE groups based on the analysis of whole-mount histopathologic sections. Histopathological analysis (ISUP biopsy grade group) and magnetic resonance imaging (MRI) (PI-RADS v2.1 scores [1-5] and the Mehralivand EPE grade [0-3]) were used to assess the prediction of EPE. We also assessed the clinical usefulness of the prediction model based on decision-curve analysis. RESULTS: Of 800 included patients, 235 (29.3%) had EPE, and 565 patients (70.7%) did not (non-EPE). Multivariable logistic regression analysis showed that the biopsy ISUP grade, PI-RADS v2.1 score, and Mehralivand EPE grade were independent risk factors for EPE. In the regression assessment of the models, the best discrimination (area under the curve of 0.879) was obtained using the basic model (age, serum PSA, prostate volume at MRI, positive biopsy core, clinical T stage, and D'Amico risk group) and Mehralivand EPE grade 3. Decision-curve analysis showed that combining Mehralivand EPE grade 3 with the basic model resulted in superior net benefits for predicting EPE. CONCLUSION: Mehralivand EPE grades and PI-RADS v2.1 scores, in addition to basic clinical and demographic information, are potentially useful for predicting EPE in patients with PCa.

Quality assessment of prostate MRI by PI-QUAL score: Inter-reader agreement and impact on prostate cancer local staging at 3 Tesla.

PURPOSE: To evaluate whether the Prostate Imaging Quality (PI-QUAL) score impacts prostate cancer (PCa) staging on MRI. The secondary goal was to test inter-reader agreement among radiologists experienced in prostate imaging. METHOD: A retrospective, single-center study with patients who underwent 3 Tesla prostate MRI scans and were submitted to radical prostatectomy (RP) between January 2018 and November 2021 and were eligible for our study. Extraprostatic extension (EPE) data were collected from original MR reports (EPEm) and pathological reports of RP specimens (EPEp). Three expert prostate radiologists (ESUR/ESUI criteria R1, R2, R3) independently evaluated all MRI exams according to PI-QUAL score for image quality (1 to 5; 1: poor, 5: excellent), blinded to original imaging reports and clinical data. We studied the diagnostic performance of MRI using pooled data from PI-QUAL scores (≤3 vs. ≥4). We also performed univariate and multivariate analyses to assess the PI-QUAL score impact on local PCa staging. Cohen's K and Tau-b Kendall tests were used to assess the inter-reader agreement for PI-QUAL score, T2WI, DWI, and DCE. RESULTS: Our final cohort included 146 patients, of which 27.4% presented EPE on pathology. We observed no impact of imaging quality on accuracy for EPE prediction: AUC of 0.750 (95% CI 0.26-1) for PI-QUAL ≤ 3 and 0.705 (95% CI 0.618-0.793) for PI-QUAL ≥ 4. The multivariate analysis demonstrated a correlation of EPEm (OR 3.25, p 0.001) and ISUP grade group (OR 1.89, p 0.012) to predict EPEp. The inter-reader agreement was moderate to substantial (0.539 for R1-R2, 0.522 for R2-R3, and 0.694 for R1-R3). CONCLUSION: Our clinical impact evaluation showed no direct correlation between MRI quality by PI-QUAL score and accuracy in detecting EPE in patients undergoing RP. Additionally, we had moderate to a substantial inter-reader agreement for the PI-QUAL score.

MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review.

PURPOSE: Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature. METHODS: We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores. RESULTS: We identified 33 studies-22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science. CONCLUSION: Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.

Significance of extraprostatic extension by Grade Groups 1-3 prostatic carcinoma on needle biopsy.

BACKGROUND: It is rare for extraprostatic extension (EPE) on biopsy to be seen with Grade Groups (GG) 1-3 (Gleason scores 3 + 3 = 6; 3 + 4 = 7; 4 + 3 = 7) prostatic adenocarcinoma, and there is no data whether this finding should be a contraindication for performing radical prostatectomy (RP). METHODS: Thirty eight cases with GG 1-3 prostatic adenocarcinoma as the highest grade in the case with EPE on biopsy were identified from our consultation files. Highly unfavorable findings at RP were those that if they could have been predicted preoperatively, might have factored into the decision of whether to proceed with surgery. For these purposes, highly unfavorable pathology at RP was defined as either the presence of seminal vesicle invasion or lymph node metastases or GG5 (Gleason score 9-10). RESULTS: Among 37 patients with clinical follow-up data, 18 (49%) received radiation and/or hormonal therapy (RT/HT), 13 patients (35%) either underwent (n = 11) or are planning (n = 2) RP, and 6 patients (16%) received either ablation therapy or active surveillance. Based on the 11 RP pathology reports, 8 were GG2, one GG3 with tertiary pattern 5, and two GG3. Ten cases were reported to have EPE and six cases had positive margins. Only one had highly unfavorable pathology with pT3bN1 disease. The only difference between the RP and the RT/HT groups in their pretreatment parameters was the mean age of the RP patients was 61 compared with 69 for the RT/HT men (p = 0.02); the lack of many cases with highly unfavorable pathology at RP cannot be attributable to a selection bias of men with lower volume cancer on biopsy or lower serum prostate-specific antigen levels choosing RP over RT/HT. CONCLUSIONS: Despite EPE on biopsy, most men do not have highly unfavorable pathology at RP, and this treatment should remain an option in this setting.

3D-Reconstructed Contact Surface Area and Tumour Volume on Magnetic Resonance Imaging Improve the Prediction of Extraprostatic Extension of Prostate Cancer.

This study is to determine whether the volume and contact surface area (CSA) of a tumour with an adjacent prostate capsule on MRI in a three-dimensional (3D) model that can predict side-specific extraprostatic extension (EPE) at radical prostatectomy (RP). Patients with localised prostate cancer (PCa) who underwent robot-assisted RP between July 2015 and March 2021 were included in this retrospective study. MRI-based 3D prostate models incorporating the PCa volume and location were reconstructed. The tumour volume and surface variables were extracted. For the prostate-to-tumour and tumour-to-prostate CSAs, the areas in which the distances were ≤ 1, ≤ 2, ≤ 3, ≤ 4, and ≤ 5 mm were defined, and their surface (cm2) were determined. Differences in prostate sides with and without pathological EPE were analysed. Multivariable logistic regression analysis to find independent predictors of EPE. Overall, 75/302 (25%) prostate sides showed pathological EPE. Prostate sides with EPE had higher cT-stage, higher PSA density, higher percentage of positive biopsy cores, higher biopsy Gleason scores, higher radiological tumour stage, larger tumour volumes, larger prostate CSA, and larger tumour CSA (all p < 0.001). Multivariable logistic regression analysis showed that the radiological tumour stage (p = 0.001), tumour volume (p < 0.001), prostate CSA (p < 0.001), and tumour CSA (p ≤ 0.001) were independent predictors of pathological EPE. A 3D reconstruction of tumour locations in the prostate improves prediction of extraprostatic extension. Tumours with a higher 3D-reconstructed volume, a higher surface area of tumour in contact with the prostate capsule, and higher surface area of prostate capsule in contact with the tumour are at increased risk of side-specific extraprostatic extension.

Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning.

The localization of extraprostatic extension (EPE), i.e., local spread of prostate cancer beyond the prostate capsular boundary, is important for risk stratification and surgical planning. However, the sensitivity of EPE detection by radiologists on MRI is low (57% on average). In this paper, we propose a method for computational detection of EPE on multiparametric MRI using deep learning. Ground truth labels of cancers and EPE were obtained in 123 patients (38 with EPE) by registering pre-surgical MRI with whole-mount digital histopathology images from radical prostatectomy. Our approach has two stages. First, we trained deep learning models using the MRI as input to generate cancer probability maps both inside and outside the prostate. Second, we built an image post-processing pipeline that generates predictions for EPE location based on the cancer probability maps and clinical knowledge. We used five-fold cross-validation to train our approach using data from 74 patients and tested it using data from an independent set of 49 patients. We compared two deep learning models for cancer detection: (i) UNet and (ii) the Correlated Signature Network for Indolent and Aggressive prostate cancer detection (CorrSigNIA). The best end-to-end model for EPE detection, which we call EPENet, was based on the CorrSigNIA cancer detection model. EPENet was successful at detecting cancers with extraprostatic extension, achieving a mean area under the receiver operator characteristic curve of 0.72 at the patient-level. On the test set, EPENet had 80.0% sensitivity and 28.2% specificity at the patient-level compared to 50.0% sensitivity and 76.9% specificity for the radiologists. To account for spatial location of predictions during evaluation, we also computed results at the sextant-level, where the prostate was divided into sextants according to standard systematic 12-core biopsy procedure. At the sextant-level, EPENet achieved mean sensitivity 61.1% and mean specificity 58.3%. Our approach has the potential to provide the location of extraprostatic extension using MRI alone, thus serving as an independent diagnostic aid to radiologists and facilitating treatment planning.