Pathological Extraprostatic Extension Research Articles

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI.

To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm. This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE. A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24). Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

How Can We Identify Extraprostatic Extension (EPE) Before Surgery? The Use of a Preoperative Prostate MRI EPE Scoring System to Assess Postprostatectomy Locally Advanced Prostate Cancer.

Background: Distinguishing between organ-confined disease and extraprostatic extension (EPE) is crucial for the treatment of patients with prostate cancer. EPE is associated with an increased risk of biochemical recurrence, positive surgical margins, and metastatic disease. An MRI-based EPE scoring system was developed by Mehralivand in 2019; however, it has not been adopted in the Urology community. The purpose of this study is to evaluate the association of MRI-based EPE scoring with the pathologic EPE (pEPE) after radical prostatectomy. Methods: We conducted a retrospective review on a prospectively collected database of male patients who underwent a prostate MRI with EPE scoring by a trained genitourinary radiologist and subsequent robotic radical prostatectomy at our institution from September 2020 to December 2022. The associations between MRI EPE (mEPE) score and the presence of EPE on surgical pathology (pEPE) were examined using multivariable logistic regression. Results: A total of 194 patients met inclusion criteria with a median age of 63 years and prostate specific antigen (PSA) 7 ng/mL. Among those with mEPE score 3, 96% had pEPE. Those patients with an mEPE score ≥2 had an increased risk of pEPE compared with those with mEPE score 0 (odds ratio 3.79; 95% confidence interval 1.28-11.3) Furthermore, those with an mEPE score 3 were significantly more likely to have pEPE compared with those with mEPE score 0, 1 and 2 independently. Conclusion: MRI EPE is a straightforward tool that strongly correlates with the presence of pEPE. If validated prospectively, this scoring system could assist in counseling patients regarding nerve-sparing approach.

Less qualitative multiparametric magnetic resonance imaging in prostate cancer can underestimate extraprostatic extension in higher grade tumors.

Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and preoperative staging of prostate cancer. It remains unclear how Grade Group (GG) interacts with the ability of mpMRI to determine the presence of extraprostatic extension (EPE) on surgical pathology. A retrospective review of a robotic assisted laparoscopic radical prostatectomy (RALP) database from 2016-2020 was performed. Radiology mpMRI reports by multiple attending radiologists and without clear standardization or quality control were retrospectively assessed for EPE findings and compared with surgical pathology reports. The data were stratified by biopsy-based GG and a multivariable cluster analysis was performed to incorporate additional preoperative variables (age at diagnosis, PSA, etc.). Hazard ratios were calculated to determine how mpMRI findings and radiographic EPE relate to positive surgical margins. 289 patients underwent at least one mpMRI prior to RALP. Preoperative mpMRI demonstrated sensitivity of 39.3% and specificity of 88.8% for pathological EPE and had a negative predictive value (NPV) of 49.5%, and positive predictive value (PPV) of 84.0%. Stratification of NPV by GG yielded the following values: GG 1-5 (49.5%), GG 3-5 (40.8%), GG 4-5 (43.4%), and GG 5 (30.4%). Additionally, positive EPE on preoperative mpMRI was associated with a significantly decreased risk of positive surgical margins (RR: 0.655; 95% CI: 0.557-0.771). NPV of prostate mpMRI for EPE may be decreased for higher grade tumors. A detailed reference reading and image quality optimization may improve performance. However, urologists should exercise caution in nerve sparing approaches in these patients.

Development and Validation of MRI Imaging Biomarkers for Prostate Cancer Using Deep Learning.

Given the increasing number of treatment options for patients with localized prostate cancer (PCa), there is a need for biomarkers to aid in risk stratification. Specifically, novel biomarkers can aid in the identification of high-risk phenotypes among similar patients in traditional NCCN risk groupings. One promising area for development is using pre-treatment prostate MRI to identify imaging biomarkers to identify prostate cancer patients at highest risk for recurrence. We hypothesized that deep learning could be leveraged to identify imaging biomarkers of aggressive PCa from pre-treatment prostate MRIs. Our study included 1,020 patients treated at our institution between 2010-2022. Given pathologic extraprostatic extension (EPE) and seminal vesicle invasion (SVI) are associated with higher risk of treatment failure, we hypothesized that deep learning models which identified radiographic EPE and SVI would provide non-invasive imaging biomarkers associated with PCa prognosis. We trained two separate deep learning models using convolutional neural networks to predict SVI and EPE respectively. The model inputs were T2W prostate MRIs (n = 894) and models consisted of 8 convolutional layers. Dropout, L2 regularization, and data augmentation were used to improve model generalizability and reduce overfitting. Discriminatory ability of each model was measured using AUC on a blinded external test set of 221 patients. To assess the clinical utility of our imaging biomarkers, log-rank tests were used to evaluate biochemical free survival (BFS) for patients classified as high risk to patients classified as low risk. Biochemical failure was defined as post-treatment PSA >0.1 for patients who underwent radical prostatectomy (RP) or PSA >2ng/ml above nadir for patients receiving radiation therapy. Within our cohort of 1,020 patients the median age was 66 with a median follow up of 4 years. 49.3% (n = 503) underwent RP and 50.7% (n = 517) received EBRT. 4% (n = 41) were low risk, 62.4% (n = 636) were intermediate risk, and 33% (n = 337) were high risk based on NCCN criteria. Deep learning models showed good discriminatory ability for both EPE (AUC 0.66) and SVI (AUC 0.74). Both imaging biomarkers showed prognostic ability to identify high risk prostate phenotypes. Patients deemed high risk based on EPE classifier had worse BFS (median 5 vs 9 years, p<.001). Similarly, patients classified as high risk based on SVI also showed worse BFS (median 5 vs 9 years, p = 0.024). Among intermediate risk patients, EPE biomarker showed an ability to identify high risk phenotypes (median 6 vs 9 years, p = 0.024). Deep learning classifiers of prostate MRIs demonstrated the ability to stratify high-risk prostate cancer phenotypes beyond traditional risk paradigms. Imaging biomarkers represent a non-invasive method to help aid in the personalization of treatment for patients with localized prostate cancer and identify patients who potentially require treatment escalation.

Impact of Multiparametric Magnetic Resonance Imaging for the Prediction of Extraprostatic Extension According to the National Comprehensive Cancer Network Risk Stratification in Prostate Cancer

Purpose: The accuracy of extraprostatic extension (EPE) on multiparametric magnetic resonance imaging (mpMRI) for the preoperative staging of prostate cancer (PCa) remains controversial. This study aimed to determine the effect of mpMRI for EPE prediction in the final pathology after radical prostatectomy (RP) according to the National Comprehensive Cancer Network (NCCN) risk stratification in patients with clinically localized PCa.Materials and Methods: This retrospective study analyzed 340 consecutive patients diagnosed with clinically localized PCa who underwent RP with preoperative mpMRI between March 2020 and December 2021. They were stratified according to the NCCN risk stratification into low (LR), favorable intermediate (FIR), unfavorable intermediate (UIR), and high risk (HR) groups to assess final pathological EPE. The accuracy of staging mpMRI was assessed in each group. Univariate and multivariate analyses evaluated the predictors of EPE in the final pathology after RP.Results: Preoperative mpMRI showed suspicious EPE in 87 patients (25.6%), whereas postoperative pathological evaluation revealed EPE in 137 patients (40.3%). The LR group showed relatively low sensitivity and positive predictive value compared with other groups. In the multivariate analysis, suspicious EPE on mpMRI was a significant predictive factor for EPE in the final pathology in the FIR, UIR, and HR groups (p=0.012, p=0.011, and p=0.001, respectively), whereas no correlation was observed in the LR group (p=0.711).Conclusions: A strong correlation was observed between suspicious EPE on mpMRI and EPE in the final pathology in the FIR, UIR, and HR groups but not in the LR group.

Diagnostic value of multiparametric MRI-based models in the assessment of extra-prostatic extension of prostate cancer

Objective: To evaluate the diagnostic value of multiparametric magnetic resonance imaging (mpMRI) based models in the assessment of extra-prostatic extension (EPE) of prostate cancer. Methods: This retrospective study included 168 consecutive men with prostate cancers [aged 48 to 82 (66.6±6.8) years] who underwent radical prostatectomy and preoperative mpMRI examinations at the First Medical Center of the PLA General Hospital from January 2021 to February 2022. According to European Society of Urogenital Radiology (ESUR) score, EPE grade and mEPE score, all cases were independently evaluated by two radiologists, with disagreement reviewed by a senior radiologist as the final result. The diagnostic performance of each MRI-based model for pathologic EPE prediction was assessed using receiver operating characteristic curve (ROC), and the differences between the corresponding area under the curve (AUC) were compared using the DeLong test. The weighted Kappa test was used to evaluate the inter-reader agreement of each MRI-based model. Results: A total of 62 (36.9%) prostate cancer patients had pathologic confirmed EPE after radical prostatectomy. The AUC of ESUR score, EPE grade and mEPE score for predicting pathologic EPE were 0.836 (95%CI: 0.771-0.888), 0.834 (95%CI: 0.769-0.887) and 0.785 (95%CI: 0.715-0.844), respectively. The AUC of ESUR score and EPE grade were both superior to that of mEPE score with significant differences (all P<0.05), while there was no significant difference between the ESUR score and EPE grade models (P=0.900). EPE grading and mEPE score had good inter-reader consistency, with weighted Kappa values of 0.65 (95%CI: 0.56-0.74) and 0.74 (95%CI: 0.64-0.84), respectively. The inter-reader consistency of ESUR score was moderate, and the weighted Kappa value was 0.52 (95%CI: 0.40-0.63). Conclusion: All MRI-based models showed good preoperative diagnostic value in predicting EPE, among which the EPE grade resulted in more reliable performance with substantial inter-reader agreement.

MP61-18 STIMULATED RAMAN HISTOLOGY, A NOVEL METHOD FOR INTRAOPERATIVE SURGICAL MARGIN ASSESSMENTS DURING ROBOTIC ASSISTED LAPAROSCOPIC PROSTATECTOMY

You have accessJournal of UrologyCME1 Apr 2023MP61-18 STIMULATED RAMAN HISTOLOGY, A NOVEL METHOD FOR INTRAOPERATIVE SURGICAL MARGIN ASSESSMENTS DURING ROBOTIC ASSISTED LAPAROSCOPIC PROSTATECTOMY Miles P. Mannas, Jonathan Suderman, Fang-Ming Deng, William Huang, James Wysock, Daniel A. Orringer, and Samir S. Taneja Miles P. MannasMiles P. Mannas More articles by this author , Jonathan SudermanJonathan Suderman More articles by this author , Fang-Ming DengFang-Ming Deng More articles by this author , William HuangWilliam Huang More articles by this author , James WysockJames Wysock More articles by this author , Daniel A. OrringerDaniel A. Orringer More articles by this author , and Samir S. TanejaSamir S. Taneja More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003319.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Routine pathologic assessment of neurovascular bundles during robot assisted laparoscopic prostatectomy (RALP) enables nerve sparing surgery without negatively affecting oncologic outcomes; however, many urologists do not have access to rapid frozen sections interpreted by genitourinary pathologists. We hypothesized that stimulated Raman histology (SRH) may allow surgeons to identify positive margins intra-operatively. METHODS: 23 subjects underwent RALP with surgical margin resections conducted per surgeon assessment from pre-surgical and surgical factors. Pre-operatively all subjects underwent a 3 Tesla multiparametric prostate MRI (reporting per PI-RADSv2) indicating region of dominant tumor with degree of suspicion and location for possible ECE. Surgeons resected and imaged ∼5mm samples from prostate bed along bilateral neurovascular bundles and/or areas of concern with a clinical stimulated Raman histology (SRH) microscope (raman spectra 2845cm-1 and 2930cm-1). Samples ground truth was provided by pathologic processing with hematoxylin and eosin staining or frozen section after SRH visualization. RESULTS: 11 subjects had extra-prostatic extension (EPE), 8 of which had multifocal EPE. 7 subjects had a positive surgical margin, 6 of which had a non-focal positive margin. 10 of the 11 subjects with pathologic EPE had an associated MRI region of interest with broad capsular contact. 121 individual SRH margin assessments (mean 5.5) were conducted, with a mean time per assessment of 7.5minutes. The accuracy of surgical team SRH interpretation of resected margins was 95.5%, with a sensitivity of 83.3% and a specificity of 98.3%. All positive SRH margins were extra-prostatic and associated with EPE in a sextant zone in or adjacent to the MRI region of interest. CONCLUSIONS: SRH can provide surgeons with a rapid tool for accurate identification of PCa at surgical margins in real-time during RALP. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e859 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Miles P. Mannas More articles by this author Jonathan Suderman More articles by this author Fang-Ming Deng More articles by this author William Huang More articles by this author James Wysock More articles by this author Daniel A. Orringer More articles by this author Samir S. Taneja More articles by this author Expand All Advertisement PDF downloadLoading ...

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 ≤ 5mm 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.

Which one is better for predicting extraprostatic extension on multiparametric MRI: ESUR score, Likert scale, tumor contact length, or EPE grade?

PurposeTo evaluate the European Society of Urogenital Radiology (ESUR) score, the Likert scale, tumor contact length (TCL) > 1 cm, and EPE (extraprostatic extension) grade in predicting EPE at multiparametric magnetic resonance imaging (mp-MRI). MethodsSeventy-nine patients who underwent 3-T MRI and were histopathologically confirmed by microblocks were enrolled in this retrospective study. The index lesions were interpreted by two experienced radiologists. Apparent diffusion coefficient (ADC) values were also noted. Weighted κ statistics were used to compare interreader agreement. Univariate logistic regression analysis was performed to define independent predictors of EPE status. Multivariable logistic regression and receiver operating characteristic (ROC) analysis were performed to compare the MRI-based methods and clinical variables (ISUP grade, prostate volume and PSA density) + MRI-based methods for pathologic EPE prediction by using the area under the curve (AUC) value. ResultsThe mean age was 64.5 years ± 6.2. 33/79 (41.8%) patients had pathologic EPE. As ESUR score showed weak interreader agreement (κ = 0.537), Likert scale, TCL, and EPE grade showed moderate agreement (κ = 0.608, κ = 0.747, κ = 0.647 respectively). Univariate ROC analysis result showed that all MRI-based score systems, mean ADC value, the ISUP grade, prostate volume, PSA density were the best variables in predicting EPE. ROC analysis results of four MRI-based methods showed good diagnostic performance. At multivariate analysis, all clinical models showed excellent diagnostic performance. ConclusionAll four MRI-based methods had good diagnostic performance. Furthermore, consisting of both qualitative and quantitative parameters and being less reader experience dependent, EPE grade was a promising method in predicting EPE. All clinical models showed excellent diagnostic performance.

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer.

Simple SummaryFor patients with newly diagnosed prostate cancer, it is important to detect tumor growth beyond the prostate, as this can affect a patient’s prognosis, influence management decisions, and alter treatment strategies. It is recognized that on prostate MRI, some instances of extraprostatic tumor growth can be missed. In this study, we merged patient data from multiple hospitals in different countries and developed a type of mathematical formula called “nomogram” that combines MRI findings with other available patient data. The results of our study allow physicians to more accurately diagnose extraprostatic tumor growth by combining clinical, biopsy, and MRI-derived information according to their relative statistical importance.Background: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. Methods: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings. Results: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all). Conclusions: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.

The role of T2-weighted images in assessing the grade of extraprostatic extension of the prostate carcinoma.

Extraprostatic extension (EPE) is an unfavorable prognostic factor and the grade of EPE is also shown to be correlated with the prognosis of prostate cancer. The current study assessed the value of prostate magnetic resonance imaging (MRI) in measuring the radial distance (RD) of EPE and the role of T2 WI signs in predicting the grade of EPE. A total of 110 patients who underwent prostate MRI before radical prostatectomy are enrolled in this retrospective study. Eighty-four patients have organ confined disease and the remaining twenty-six patients have EPE all verified by histopathology. Prostate MRI examinations were conducted with 3T MRI scanner and phased array coil with the following sequences: T2 WI, T1 WI, DCE, DWI with ADC mapping, and high b-value at b = 1500s/mm2. The likelihood of EPE with 5-point Likert scale was assigned, several MRI features were extracted for each dominant tumor identified by using T2 WI. Tumors with Likert scales 4-5 were evaluated further to obtain MRI-based RD. The relationship between pathological and MRI-determined RD was tested. Univariate and multivariate logistic regression models were developed to detect the grade of pathological EPE. The inputs were among the 2 clinical parameters and 4 MRI features. There is a moderate correlation between pathological RD and MRI-determined RD (ρ = 0.45, P < 0.01). In univariate and multivariate models, MRI features and clinical parameters possess varying significance levels (univariate models; P = 0.048-0.788, multivariate models; P = 0.173-0.769). Multivariate models perform better than the univariate models by offering fair to good performances (AUC = 0.69-0.85). The multivariate model that employs the MRI features offers better performance than the model employs clinical parameters (AUC = 0.81 versus 0.69). Co-existence of T2 WI signs provide higher diagnostic value even than clinical parameters in predicting the grade of EPE. Combined use of clinical parameters and MRI features deliver slightly superior performance than MRI features alone.

A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI.

Purpose To evaluate MRI features associated with pathologically defined extraprostatic extension (EPE) of prostate cancer and to propose an MRI grading system for pathologic EPE. Materials and Methods In this prospective study, consecutive male study participants underwent preoperative 3.0-T MRI from June 2007 to March 2017 followed by robotic-assisted laparoscopic radical prostatectomy. An MRI-based EPE grading system was defined as follows: curvilinear contact length of 1.5 cm or capsular bulge and irregularity were grade 1, both features were grade 2, and frank capsular breach were grade 3. Multivariable logistic regression and decision curve analyses were performed to compare the MRI grade model and clinical parameters (prostate-specific antigen, Gleason score) for pathologic EPE prediction by using the area under the receiver operating characteristic curve (AUC) value. Results Among 553 study participants, the mean age was 60 years ± 8 (standard deviation); the median prostate-specific antigen value was 6.3 ng/mL. A total of 125 of 553 (22%) participants had pathologic EPE at radical prostatectomy. Detection of pathologic EPE, defined as number of pathologic EPEs divided by number of participants with individual MRI features, was as follows: curvilinear contact length, 88 of 208 (42%); capsular bulge and irregularity, 78 of 175 (45%); and EPE visible at MRI, 37 of 56 (66%). For MRI, grades 1, 2, and 3 for detection of pathologic EPE were 18 of 74 (24%), 39 of 102 (38%), and 37 of 56 (66%), respectively. Clinical features plus the MRI-based EPE grading system (prostate-specific antigen, International Society of Urological Pathology stage, MRI grade) predicted pathologic EPE better than did MRI grade alone (AUC, 0.81 vs 0.77, respectively; P < .001). Conclusion Higher MRI-based extraprostatic extension (EPE) grading categories were associated with a greater risk of pathologic EPE. Clinical features plus MRI grading had the highest diagnostic performance for prediction of pathologic EPE. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Eberhardt in this issue.

MP20-09 MRI-BASED RISK ASSESSMENT FOR POSTOPERATIVE BIOCHEMICAL RECURRENCE USING THE PROSTATE IMAGING REPORTING AND DATA SYSTEM (PI-RADS) SCORES AND CAPSULAR CONTACT LENGTH

You have accessJournal of UrologyImaging/Radiology: Uroradiology II1 Apr 2018MP20-09 MRI-BASED RISK ASSESSMENT FOR POSTOPERATIVE BIOCHEMICAL RECURRENCE USING THE PROSTATE IMAGING REPORTING AND DATA SYSTEM (PI-RADS) SCORES AND CAPSULAR CONTACT LENGTH Yoh Matsuoka, Junichiro Ishioka, Hiroshi Tanaka, Tomo Kimura, Yuma Waseda, Sho Uehara, Yosuke Yasuda, Toshiki Kijima, Soichiro Yoshida, Minato Yokoyama, Kazutaka Saito, Kazunori Kihara, and Yasuhisa Fujii Yoh MatsuokaYoh Matsuoka More articles by this author , Junichiro IshiokaJunichiro Ishioka More articles by this author , Hiroshi TanakaHiroshi Tanaka More articles by this author , Tomo KimuraTomo Kimura More articles by this author , Yuma WasedaYuma Waseda More articles by this author , Sho UeharaSho Uehara More articles by this author , Yosuke YasudaYosuke Yasuda More articles by this author , Toshiki KijimaToshiki Kijima More articles by this author , Soichiro YoshidaSoichiro Yoshida More articles by this author , Minato YokoyamaMinato Yokoyama More articles by this author , Kazutaka SaitoKazutaka Saito More articles by this author , Kazunori KiharaKazunori Kihara More articles by this author , and Yasuhisa FujiiYasuhisa Fujii More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.679AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The latest tumor-node-metastasis staging of prostate cancer (PC) does not include MRI for clinical T categorization. However, PI-RADS version 2 might be useful for prognostic prediction as well as capsular contact length (CL) on MRI (AUA2013, 2016) because the PI-RADS scale for the likelihood of clinically significant PC is based on signal intensity, size, and invasive behavior. We aim to develop an MRI-based simple risk stratification of biochemical recurrence (BCR) after radical prostatectomy (RP). METHODS We enrolled 312 clinically localized PC patients who had undergone multiparametric MRI and RP without prior treatment. Index lesions were assessed by MRI for PI-RADS scores, size, location, extraprostatic involvement, and CL. BCR was defined as PSA >0.2 ng/ml. MRI findings were examined for associations with BCR by Cox regression analyses. BCR-free survival was estimated using the Kaplan-Meier method and compared by log-rank test. The predictive ability of models was evaluated. RESULTS A total of 85 men (27%) had pathological extraprostatic extension and 69 men (22%) experienced BCR at a median follow-up of 56 months. On MRI, 24/38/109/141 men (8/12/35/45%) were assigned to PI-RADS scores ≤2/3/4/5. Index lesions were located in anterior/posterior/both halves of the prostate in 116/139/37 men (37/45/12%). Median diameter and CL of index tumors were 13mm and 11mm, respectively, and 174 men (56%) met PI-RADS criteria for extraprostatic involvement. BCR-free survival was stratified according to PI-RADS scores overall (p <0.001), but no difference was found between scores 3 and 4 (p = 0.80). Multivariate analyses showed that PI-RADS score 5 (hazard ratio [HR] 9.5, p = 0.002), score 3-4 (HR 4.7, p = 0.05), and CL >15mm (HR 2.0, p = 0.02) were independent predictors of BCR. Patients were divided into three groups as follows: PI-RADS score ≤2 (n = 24); score 3-4 with CL ≤15mm (n = 131); and score 3-4 with CL >15mm or score 5 (n = 157); these had five-year BCR-free rates of 100%, 83%, and 57%, respectively (p <0.001). The concordance indexes for this model and PI-RADS local staging were 0.69 and 0.61, respectively. CONCLUSIONS The MRI-based assessment model consisting of PI-RADS scores and CL is simple and useful in risk stratification for postoperative BCR. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e255-e256 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Yoh Matsuoka More articles by this author Junichiro Ishioka More articles by this author Hiroshi Tanaka More articles by this author Tomo Kimura More articles by this author Yuma Waseda More articles by this author Sho Uehara More articles by this author Yosuke Yasuda More articles by this author Toshiki Kijima More articles by this author Soichiro Yoshida More articles by this author Minato Yokoyama More articles by this author Kazutaka Saito More articles by this author Kazunori Kihara More articles by this author Yasuhisa Fujii More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Tumor contact length: A novel multiparametric MRI predictor of prostate cancer outcomes.

61 Background: Multiparametric MRI (MP-MRI) can visualize prostate tumors. MP-MRI characteristics (extraprostatic extension (ECE), tumor volume, etc.) can be predictive of final pathologic findings such as lymph node (LN) involvement, pathological ECE (pECE), and biochemical recurrence (BCR). These are pivotal in the decision-making process regarding treatment. Tumor contact length (TCL) is defined as the length of cancer in contact with the prostatic capsule. We evaluated the ability of MP-MRI determined TCL in predicting pECE, BCR and LN in patients undergoing radical prostatectomy. Methods: All patients who underwent a 3T MP-MRI at the NCI from 2007 to 2015 were retrospectively classed into no ECE, suspicious ECE (sECE) and frank ECE (fECE) based on MP-MRI findings. sECE was defined as tumor with capsular bulge on MRI while fECE was clear capsular obliteration and tumor extension beyond the prostatic capsule. Demographic data was obtained on patients with fECE and sECE on MP-MRI with the presence of pECE, LN, and BCR status following radical prostatectomy from a single surgeon (PP) experience. Chi-Square and Wilcoxon rank sum test were used to compare proportions and continuous variables respectively. Logistic regression was used to determine the predictive ability of TCL. Statistical significance was defined as p-value ≤0.05. Results: Of all 1,260 patients who underwent MP-MRI, we focused on 146 who had sECE (68) or fECE (78) on MP-MRI. Mean age was 60 years and median prostate specific antigen was 11.7 ng/ml. Logistic regression analysis showed that MP-MRI determined TCL was predictive of ECE (p=0.01), LN status (p=0.0001) on final pathology and BCR (p=0.05) during follow up. Patients with pECE had a longer median MP-MRI TCL (2.8 cm) compared to those without pECE (2.4 cm), p=0.04. When analyzed individually, fECE correlated with pECE (p=0.05) while s ECE did not correlate with pECE (p=0.11). Although, not statistically significant, the median MP-MRI TCL for sECE with pECE was still longer than in sECE with no pECE in the sub-group analysis. Conclusions: Longer TCL on MP-MRI can indicate presence of ECE, LN involvement at final pathology as well as predict BCR on follow up.

Does Prostate HistoScanning™ accurately identify prostate cancer, measure tumour volume and assess pathological stage prior to radical prostatectomy?

Objective The objective of this paper is to assess the ability of Prostate HistoScanning™ (PHS) to accurately identify tumour volume, index lesion characteristics and pathological stage. PHS is a novel technology employing transrectal ultrasound scanning and software analysis of radiofrequency data to produce signatures for benign and cancerous tissues. Recent reports have suggested PHS is capable of characterising the index cancer lesion and disease multifocality and detecting extraprostatic extension (EPE). Materials and methods The index test was preoperative PHS on patients undergoing radical prostatectomy (RP). The reference test was the whole-mount pathological analysis of the RP specimen. PHS analysis estimated total tumour volumes, tumour volumes by prostate sextant, the locations and volumes of index lesions, and the presence and location of EPE. Results There was no correlation between PHS and histology total tumour volume estimates (Pearson coefficient –0.099), despite accounting for specimen fixation shrinkage (Pearson coefficient –0.070), nor among 144 prostate sextants in 24 patients (Pearson coefficient 0.14). Sensitivity and specificity of PHS in detecting foci &gt; 0.2 ml were 63% and 53%, respectively; and 37% and 71%, respectively, for foci &gt; 0.5 ml. Pearson correlation coefficient for index lesion volumes identified at pathology vs PHS was 0.065. PHS failed to locate accurately index lesion and pathological EPE. Conclusions PHS fails to identify total tumour volumes, tumour volumes prostate sextant, index lesion volumes and locations, and presence and location of EPE compared to RP pathology. PHS appears unsuitable for routine diagnostic clinical use in prostate cancer.

Study of testosterone as a predictor of tumor aggressiveness in patients with prostate cancer

A growing body of evidence suggests that low testosterone can be an independent predictor of adverse clinicopathological features and worse prognosis in prostate cancer (PCa) patients. However, this association is still incompletely understood and the results are divisive. The aim of this study was to analyze testosterone as a predictor of aggressive disease in subjects with clinically localized PCa. A cohort was conducted including the patients submitted to radical prostatectomy in our institution during a period of four years. The patients had clinically localized disease and their total testosterone (TT) was routinely measured preoperatively in the morning before surgery. They were stratified in groups with low (< 300 ng/dL) and normal TT (≥ 300 ng/dL). Tumor aggressiveness was inferred based on preoperative PSA levels, pathological Gleason score (lower, equal or greater than 7), TNM stage and surgical margins status. After analyzing 164 patients we found a significant association between mean preoperative TT and extraprostatic disease (379 for pT3 vs. 421 ng/for pT2 - p < 0.001, AUC > 0.99). Conversely, men with high Gleason score had similar mean TT compared to those with lower scores. Preoperative low TT (defined as TT < 300 ng/dL) could not be statistically correlated with either preoperative PSA levels, pathological Gleason score, extraprostatic extension, positive surgical margins or seminal vesicles involvement. This study indicates that testosterone may be a useful predictive tool once pathological extraprostatic extension was somewhat signaled by lower TT levels preoperatively. However, it does not consolidate a clear association between aggressive tumor biology and hypogonadism.

Endorectal magnetic resonance imaging for predicting pathologic T3 disease in Gleason score 7 prostate cancer: Implications for prostate brachytherapy

To determine the ability of endorectal magnetic resonance imaging (erMRI) and other pretreatment factors to predict the presence and extent of extraprostatic extension (EPE) in men with Gleason score (GS) 7 prostate cancer. We included patients with clinical stage T1c-T2c, GS=7 (3+4 or 4+3), and prostate-specific antigen (PSA) <10ng/mL who underwent pre-prostatectomy erMRI. We compared pathologic EPE findings with pretreatment factors. One hundred seventy-one men were eligible for inclusion. Pretreatment characteristics were: median age=60 years (42-76); median PSA 4.9ng/mL (0.4-9.9); GS 3+4=61%; T1c=51%; T2a=25%; T2b=21%; T2c=3%; ≥50% positive cores=46%; EPE-positive (EPE+) erMRI=28%. Thirty-three percent had pathologic EPE. Increasing T-stage (p<0.0001) and EPE+ erMRI (p<0.0001) were significant predictors of pathologic EPE, whereas GS (4+3 vs. 3+4) (p=0.14), percentage of positive core biopsies (p=0.15), and pretreatment PSA (p=0.41) were not. Median EPE distance was 1.75mm (range, <1-15mm). The rates of EPE >5mm and EPE >3mm were 11% and 15%, respectively. The odds ratios for erMRI detection of any EPE and of EPE >5mm were 3.06 and 3.75, respectively. T-stage and EPE+ erMRI predict pathologic EPE in men with GS 7 prostate cancer. The ability of erMRI to detect EPE increases with increasing EPE distance. These findings may be useful in patient selection for prostate brachytherapy monotherapy.