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OBJECTIVE: The objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)-generated cancer maps versus MRI and conventional nomograms. MATERIALS AND METHODS: We retrospectively analysed data from 147 patients who received MRI-targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI-based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI-visible lesions, PSMA T stage, Partin tables, and the PRedicting ExtraCapsular Extension nomogram were used for comparison.Postsurgical specimens were processed using whole-mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLongs test. RESULTS: Unfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient-level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (-60%) and posterior prostate (-40%). CONCLUSIONS: Unfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve-spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI-based cancer mapping may lead to better oncological and functional outcomes for patients.

Abstract Background No standard has been established for the clinical target volume (CTV) margins of lymph nodes (LNs) in patients with non-small-cell lung cancer (NSCLC) receiving chemotherapy or chemotherapy combined with immunotherapy followed by radiotherapy. This study aimed to discuss the CTV range of NSCLC after chemotherapy or chemotherapy combined with immunotherapy by observing the microscopic extent of tumor spread beyond the LN capsule. Methods We retrospectively analyzed the data of 240 patients with stage II and III NSCLC who underwent surgery without neoadjuvant therapy, with neoadjuvant chemotherapy (NAC), or with NAC combined with immunotherapy (NACI). We measured the maximal distance of extracapsular extension (ECE) using a digital microscope, analyzed the correlation between clinicopathological features and ECE distance, and determined the CTV margins of metastatic LN under different treatment methods. Results The ECE distance differed significantly among the three groups (p

Abstract Objective The objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)‐generated cancer maps versus MRI and conventional nomograms. Materials and methods We retrospectively analysed data from 147 patients who received MRI‐targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI‐based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI‐visible lesions, PSMA T stage, Partin tables, and the “PRedicting ExtraCapsular Extension” nomogram were used for comparison. Postsurgical specimens were processed using whole‐mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong's test. Results Unfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient‐level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (−60%) and posterior prostate (−40%). Conclusions Unfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve‐spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI‐based cancer mapping may lead to better oncological and functional outcomes for patients.

Abstract The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72–0.92 and 0.69–0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE. Critical relevant statement The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients. Protocol of systematic review registration PROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 . Key Points Radiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients. Graphical Abstract

The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72–0.92 and 0.69–0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE. Critical relevant statement: The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients. Protocol of systematic review registration: PROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342. Key Points: Radiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients. [ABSTRACT FROM AUTHOR]

Background/Objectives: The presence of seminal vesicle invasion (SVI) in prostate cancer (PCa) is associated with poorer postoperative outcomes. This study evaluates the predictive value of magnetic resonance imaging (MRI) and prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) for SVI in PCa. Methods: This cohort study included consecutive robotic prostatectomy patients for PCa at three Australian tertiary referral centres between April 2016 and September 2022. MRI and PSMA PET/CT results, clinicopathological variables, including age, BMI, prostate-specific antigen (PSA), PSA density, DRE, Biopsy Gleason score, Positive biopsy cores, PIRADS v2.1 score, MRI volume and MRI lesion size were extracted. The sensitivity, specificity, and accuracy of MRI and PSMA PET/CT for predicting SVI were compared with the histopathological results by receiver operating characteristic (ROC) analysis. Subgroup univariate and multivariate analysis was performed. Results: Of the 528 patients identified, 86 had SVI on final pathology. MRI had a low sensitivity of 0.162 (95% CI: 0.088–0.261) and a high specificity of 0.963 (95% CI: 0.940–0.979). The PSMA PET/CT had a low sensitivity of 0.439 (95% CI: 0.294–0591) and a high specificity of 0.933 (95% CI: 0.849–0.969). When MRI and PSMA PET/CT were used in combination, the sensitivity and specificity improved to 0.514 (95%CI: 0.356–0.670) and 0.880 (95% CI: 0.813–0.931). The multivariate regression showed a higher biopsy Gleason score (p = 0.033), higher PSA (p < 0.001), older age (p = 0.001), and right base lesions (p = 0.003) to be predictors of SVI. Conclusions: MRI and PSMA PET/CT independently underpredicted SVI. The sensitivity and AUC improved when they were used in combination. Multiple clinicopathological factors were associated with SVI on multivariate regression and predictive models incorporating this information may improve oncological outcomes. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the utility of magnetic resonance imaging (MRI) and MRI‐ultrasound fusion targeted biopsy (TB) for predicting unexpected extracapsular extension (ECE) in clinically localized prostate cancer (CLPC). Methods: This study enrolled 89 prostate cancer patients with one or more lesions showing a Prostate Imaging‐Reporting and Data System (PI‐RADS) score ≥3 but without morphological abnormality in the prostatic capsule on pre‐biopsy MRI. All patients underwent TB and systematic biopsy followed by radical prostatectomy (RP). Each lesion was examined by 3‐core TB, taking cores from each third of the lesion. The preoperative variables predictive of ECE were explored by referring to RP specimens in the lesion‐based analysis. Results: Overall, 186 lesions, including 81 (43.5%), 73 (39.2%), and 32 (17.2%) with PI‐RADS 3, 4, and 5, respectively, were analyzed. One hundred and twenty‐two lesions (65.6%) were diagnosed as cancer on TB, and ECE was identified in 33 (17.7%) on the RP specimens. The positive TB core number was ≤2 in 129 lesions (69.4%) and three in 57 lesions (30.6%). On the multivariate analysis, PI‐RADS ≥4 (p = 0.049, odds ratio [OR] = 2.39) and three positive cores on TB (p = 0.005, OR = 3.07) were independent predictors of ECE. Lesions with PI‐RADS ≥4 and a positive TB core number of 3 had a significantly higher rate of ECE than those with PI‐RADS 3 and a positive TB core number ≤2 (37.5% vs. 7.8%, p < 0.001). Conclusions: Positive TB core number in combination with PI‐RADS scores is helpful to predict unexpected ECE in CLPC. [ABSTRACT FROM AUTHOR]

Abstract Objective Identify correlations between lymph node characteristics and extranodal extension (ENE). Study Design Retrospective chart review. Setting Tertiary care center. Methods Patients who underwent neck dissection for oral cavity squamous cell carcinoma from 2004 to 2018 were included, with a starting sample of 496. The primary outcome was ENE in at least 1 lymph node. Additional variables included number of dissected nodes, positive nodes by level, positive lymph node ratio (LNR), and diameter of metastatic deposit and ENE focus. Univariate and multivariate binary logistic regression analyses were performed to determine correlations between included variables and ENE. Results Of the 496 patients, 233 had nodal metastasis (47.0%). 13,814 nodes were removed, with 714 (5.2%) containing metastasis. Of the positive nodes, 28.0% had ENE, 47.2% did not have ENE, and 24.8% were unknown. The mean ENE diameter was 5.1 mm (SD, 9.9). On univariate logistic regression analysis, ipsilateral neck LNR per 0.1 unit increase (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.02‐1.32, P = .02), metastatic deposit size per 1 mm increase (OR 1.06, CI 1.04‐1.08, P

Nerve-sparing radical prostatectomy (NSRP) for prostate cancer (PC) enables better postoperative recovery of continence and potency but may increase the risk of positive surgical margins. This study aimed to investigate preoperative predictive factors for extracapsular extension (ECE) of PC to select patients for NSRP. We retrospectively evaluated 288 patients with PC (576 lobes) diagnosed with 12-core transrectal ultrasound-guided biopsy and magnetic resonance imaging (MRI) who underwent laparoscopic or robot-assisted radical prostatectomy at our institution. Surgical specimens and preoperative parameters (prostate-specific antigen, prostate volume, biopsy and MRI findings, preoperative therapy) were analyzed. Of 576 prostate lobes, the incidence Ipsilateral ECE was identified in 97 (16.8%) lobes. The higher number of unilateral positive biopsy cores, the highest Gleason score 8 or more and positive unilateral findings on MRI are significant higher in prostate sides with ECE in univariate analysis. In multivariate analysis, positive unilateral MRI findings (odds ratio [OR], 2.86; p < 0.001) and unilateral biopsy positive core ≥ 3 (OR, 3.73; p < 0.001) were independent predictors of unilateral ECE. The detection rate of unilateral ECE in those cases with two factors (side-specific positive biopsy core 2 or less and side-specific MRI findings negative) was 7.1% (19/269). Patients with fewer unilateral positive biopsy cores and negative unilateral MRI findings might be good candidates for NSRP. [ABSTRACT FROM AUTHOR]

Abstract Background To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models. Methods This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models’ net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC). Results In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only. Conclusions The combined model based on clinical, semantic and radiomic features may be used to predict pECE + in patients with PCa and results in a positive net benefit when used to choose between prostatectomy with NNS or NNSS.

Background: To investigate the predictive capability of a new parameter, the distance between the fibromuscular capsule and the tumor as measured using a prostate biopsy core (referred to as “distance to the tumor” [DTT]), for the presence of extracapsular extension (ECE). Materials and methods: We analyzed specimens obtained from 246 patients diagnosed with prostate cancer. All patients underwent prebiopsy, prostate magnetic resonance imaging (MRI), and subsequent prostatectomy. DTT measurements were obtained for each prostate biopsy core, and the minimum (min) DTT was extracted. We assessed the relationship between min DTT, MRI-estimated ECE, and pathological ECE, considering factors such as the PI-RADS score and tumor location. Results: In this study of 246 patients, the mean age was 65.8 years, and the mean prostate-specific antigen (PSA) level was 18.9 ng/ml. Patients with suspicious lesions in the peripheral zone and pathological ECE displayed higher rates of positive digital rectal examination (DRE), elevated PSA levels, and shorter DTT values in the biopsy cores. DTT demonstrated an accurate estimation of the presence of ECE, similar to MRI findings. Min DTT exhibited higher accuracy for peripheral zone masses, with a cutoff value of 1.0 mm for min DTT predicting ECE (AUC: 0.84, sensitivity: 72.23%, specificity: 77.78%, P

Abstract Background In this study, we explored the diagnostic performances of multiparametric magnetic resonance imaging (mpMRI), 68 Ga-PSMA-11 PET/CT and combination of 68 Ga-PSMA-11 PET/CT and mpMRI (mpMRI + PET/CT) for extracapsular extension (ECE). Based on the analyses above, we tested the feasibility of using mpMRI + PET/CT results to predict T staging in prostate cancer patients. Methods By enrolling 75 patients of prostate cancer with mpMRI and 68 Ga-PSMA-11 PET/CT before radical prostatectomy, we analyzed the detection performances of ECE in mpMRI, 68 Ga-PSMA-11 PET/CT and mpMRI + PET/CT on their lesion images matched with their pathological sample images layer by layer through receiver operating characteristics (ROC) analysis. By inputting the lesion data into Prostate Imaging Reporting and Data System (PI-RADS), we divided the lesions into different PI-RADS scores. The improvement of detecting ECE was analyzed by net reclassification improvement (NRI). The predictors for T staging were evaluated by using univariate and multivariable analysis. The Kappa test was used to evaluate the prediction ability. Results One hundred three regions of lesion were identified from 75 patients. 50 of 103 regions were positive for ECE. The ECE diagnosis AUC of mpMRI + PET/CT is higher than that of mpMRI alone (ΔAUC = 0.101; 95% CI, 0.0148 to 0.1860; p

To explore the impact of different imaging classifications of prostate cancer (PCa) with extracapsular extension (EPE) on positive surgical margins (PSM) after laparoscopic radical prostatectomy. Methods: Clinical data were collected for 114 patients with stage PT3a PCa admitted to Ningbo Yinzhou No. 2 Hospital from September 2019 to August 2023. Radiologists classified the EPE imaging of PCa into Type I, Type II, and Type III. A chi-square test or t-test was employed to analyze the factors related to PSM. Multivariate regression analysis was conducted to determine the factors associated with PSM. Receiver operating characteristic curve analysis was used to calculate the area under the curve and evaluate the diagnostic performance of our model. Clinical decision curve analysis was performed to assess the clinical net benefit of EPE imaging classification, biopsy grade group (GG), and combined model. Results: Among the 114 patients, 58 had PSM, and 56 had negative surgical margins. Multivariate analysis showed that EPE imaging classification and biopsy GG were risk factors for PSM after laparoscopic radical prostatectomy. The areas under the curve for EPE imaging classification and biopsy GG were 0.677 and 0.712, respectively. The difference in predicting PSM between EPE imaging classification and biopsy GG was not statistically significant (P>0.05). However, when used in combination, the diagnostic efficiency significantly improved, with an increase in the area under the curve to 0.795 (P<0.05). The clinical decision curve analysis revealed that the clinical net benefit of the combined model was significantly higher than that of EPE imaging classification and biopsy GG. Conclusions: EPE imaging classification and biopsy GG were associated with PSM after laparoscopic radical prostatectomy, and their combination can significantly improve the accuracy of predicting PSM. [ABSTRACT FROM AUTHOR]

Background: Diagnosis and treatment management for head and neck squamous cell carcinoma (HNSCC) is guided by routine diagnostic head and neck computed tomography (CT) scans to identify tumor and lymph node features. The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with HNSCC. It is essential to detect the occurrence of ECE as it changes staging and treatment planning for patients. Current clinical ECE detection relies on visual identification and pathologic confirmation conducted by clinicians. However, manual annotation of the lymph node region is a required data preprocessing step in most of the current machine learning‐based ECE diagnosis studies. Purpose: In this paper, we propose a Gradient Mapping Guided Explainable Network (GMGENet) framework to perform ECE identification automatically without requiring annotated lymph node region information. Methods: The gradient‐weighted class activation mapping (Grad‐CAM) technique is applied to guide the deep learning algorithm to focus on the regions that are highly related to ECE. The proposed framework includes an extractor and a classifier. In a joint training process, informative volumes of interest (VOIs) are extracted by the extractor without labeled lymph node region information, and the classifier learns the pattern to classify the extracted VOIs into ECE positive and negative. Results: In evaluation, the proposed methods are well‐trained and tested using cross‐validation. GMGENet achieved test accuracy and area under the curve (AUC) of 92.2% and 89.3%, respectively. GMGENetV2 achieved 90.3% accuracy and 91.7% AUC in the test. The results were compared with different existing models and further confirmed and explained by generating ECE probability heatmaps via a Grad‐CAM technique. The presence or absence of ECE has been analyzed and correlated with ground truth histopathological findings. Conclusions: The proposed deep network can learn meaningful patterns to identify ECE without providing lymph node contours. The introduced ECE heatmaps will contribute to the clinical implementations of the proposed model and reveal unknown features to radiologists. The outcome of this study is expected to promote the implementation of explainable artificial intelligence‐assiste ECE detection. [ABSTRACT FROM AUTHOR]

Objectives To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE. Methods We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix. Results AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P  <  .05). Conclusion The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions. Advances in knowledge This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PCa) is a common solid organ male malignancy with high global mortality. In South Africa, one in 28 men develop PCa. Determining biochemical recurrence (BCR) factors after radical prostatectomy will enable close surveillance and early management. Aim: The study aims to determine pathological parameters associated with BCR post-radical prostatectomy at a tertiary hospital in the Free State, South Africa. Setting: All patients (N = 200) who underwent radical prostatectomy in 2013-2018 at the Department of Urology, Universitas Academic Hospital, Bloemfontein were included. Methods: This was a retrospective descriptive study. Relevant data were extracted from the hospital's electronic database. Results: The patients' median age was 66 years (range: 46-80). A total of 70 patients (35.0%) had BCR, of whom 31 (44.3%) had positive surgical margins (PSM) alone and recurrence occurred at various intervals in their follow-up period, from 9 to 48 months. Patients who had PSMs, extracapsular expansion (ECE), and seminal vesicle invasion (SVI) made up 10.0% (n = 7) of these and had a relapse within 18 months of follow-up. Patients with negative postoperative specimens experienced BCR, ranging from 15 to 48 months, made up 25.7% (n = 18). Extracapsular extension and PSM as combined parameters were 10.0% (n = 7). One patient had SVI as an independent parameter. Conclusion: These pathological parameters can be linked to BCR after radical prostatectomy. Positive surgical margins prove a strong predictor of BCR after radical prostatectomy. Contribution: Knowing the most sensitive predictive pathological parameters - either in isolation or combination - is essential to optimise patient management and tighten follow-up schedules. [ABSTRACT FROM AUTHOR]

Objectives: To identify the predictive factors of prostate cancer extracapsular extension (ECE) in an institutional cohort of patients who underwent multiparametric MRI of the prostate prior to radical prostatectomy (RP). Patients and methods: Overall, 126 patients met the selection criteria, and their medical records were retrospectively collected and analysed; 2 experienced radiologists reviewed the imaging studies. Logistic regression analysis was conducted to identify the variables associated to ECE at whole-mount histology of RP specimens; according to the statistically significant variables associated, a predictive model was developed and calibrated with the Hosmer–Lomeshow test. Results: The predictive ability to detect ECE with the generated model was 81.4% by including the length of capsular involvement (LCI) and intraprostatic perineural invasion (IPNI). The predictive accuracy of the model at the ROC curve analysis showed an area under the curve (AUC) of 0.83 [95% CI (0.76–0.90)], p < 0.001. Concordance between radiologists was substantial in all parameters examined (p < 0.001). Limitations include the retrospective design, limited number of cases, and MRI images reassessment according to PI-RADS v2.0. Conclusion: The LCI is the most robust MRI factor associated to ECE; in our series, we found a strong predictive accuracy when combined in a model with the IPNI presence. This outcome may prompt a change in the definition of PI-RADS score 5. [ABSTRACT FROM AUTHOR]

Background: Local staging of prostate cancer (PCa) is important for treatment planning. Radiologist interpretation using qualitative criteria is variable with high specificity but low sensitivity. Quantitative methods may be useful in the diagnosis of extracapsular extension (ECE). Purpose: To assess the performance of quantitative MRI markers for detecting ECE. Study Type: Systematic review and meta‐analysis. Subjects: 4800 patients from 28 studies with histopathologically confirmed PCa on radical prostatectomy were pooled for meta‐analysis. Patients from 46 studies were included for systematic review. Field Strength/Sequence: Diffusion‐weighted, T2‐weighted, and dynamic contrast‐enhanced MRI at 1.5 T or 3 T. Assessment: PubMed, Embase, Web of Science, Scopus, and Cochrane databases were searched to identify studies on diagnostic test accuracy or association of any quantitative MRI markers with ECE. Results extracted by two independent reviewers for tumor contact length (TCL) and mean apparent diffusion coefficient (ADC‐mean) were pooled for meta‐analysis, but not for other quantitative markers including radiomics due to low number of studies available. Statistical Tests: Hierarchical summary receiver operating characteristic (HSROC) curves were computed for both TCL and ADC‐mean, but summary operating points were computed for TCL only. Heterogeneity was investigated by meta‐regression. Results were significant if P ≤ 0.05. Results: At the 10 mm threshold for TCL, summary sensitivity and specificity were 0.76 [95% confidence interval (CI) 0.71–0.81] and 0.68 [95% CI 0.63–0.73], respectively. At the 15 mm threshold, summary sensitivity and specificity were 0.70 [95% CI 0.53–0.83] and 0.74 [95% CI 0.60–0.84] respectively. The area under the HSROC curves for TCL and ADC‐mean were 0.79 and 0.78, respectively. Significant sources of heterogeneity for TCL included timing of MRI relative to biopsy. Data Conclusion: Both 10 mm and 15 mm thresholds for TCL may be reasonable for clinical use. From comparison of the HSROC curves, ADC‐mean may be superior to TCL at higher sensitivities. Level of Evidence: 3 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Background: In this study, we explored the diagnostic performances of multiparametric magnetic resonance imaging (mpMRI), 68 Ga-PSMA-11 PET/CT and combination of 68 Ga-PSMA-11 PET/CT and mpMRI (mpMRI + PET/CT) for extracapsular extension (ECE). Based on the analyses above, we tested the feasibility of using mpMRI + PET/CT results to predict T staging in prostate cancer patients. Methods: By enrolling 75 patients of prostate cancer with mpMRI and 68 Ga-PSMA-11 PET/CT before radical prostatectomy, we analyzed the detection performances of ECE in mpMRI, 68 Ga-PSMA-11 PET/CT and mpMRI + PET/CT on their lesion images matched with their pathological sample images layer by layer through receiver operating characteristics (ROC) analysis. By inputting the lesion data into Prostate Imaging Reporting and Data System (PI-RADS), we divided the lesions into different PI-RADS scores. The improvement of detecting ECE was analyzed by net reclassification improvement (NRI). The predictors for T staging were evaluated by using univariate and multivariable analysis. The Kappa test was used to evaluate the prediction ability. Results: One hundred three regions of lesion were identified from 75 patients. 50 of 103 regions were positive for ECE. The ECE diagnosis AUC of mpMRI + PET/CT is higher than that of mpMRI alone (ΔAUC = 0.101; 95% CI, 0.0148 to 0.1860; p < 0.05, respectively). Compared to mpMRI, mpMRI + PET/CT has a significant improvement in detecting ECE in PI-RADS 4–5 (NRI 36.1%, p < 0.01). The diagnosis power of mpMRI + PET/CT was an independent predictor for T staging (p < 0.001) in logistic regression analysis. In patients with PI-RADS 4–5 lesions, 40 of 46 (87.0%) patients have correct T staging prediction from mpMRI + PET/CT (κ 0.70, p < 0.01). Conclusion: The prediction of T staging in PI-RADS 4–5 prostate cancer patients by mpMRI + PET/CT had a quite good performance. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the performance of risk calculators (RCs) predicting lymph node invasion (LNI) and extraprostatic extension (EPE) in men undergoing transperineal magnetic resonance imaging/transrectal ultrasound (TRUS)‐fusion template saturation biopsy (TTSB) and conventional systematic TRUS‐guided biopsy (SB). Patients and Methods: The RCs were tested in a consecutive cohort of 645 men undergoing radical prostatectomy with extended pelvic LN dissection between 2005 and 2019. TTSB was performed in 230 (35.7%) and SB in 415 (64.3%) men. Risk of LNI and EPE was calculated using the available RCs. Discrimination, calibration, and clinical usefulness stratified by different biopsy techniques were assessed. Results: Lymph node invasion was observed in 23 (10%) and EPE in 73 (31.8%) of cases with TTSB and 53 (12.8%) and 158 (38%) with SB, respectively. RCs showed an excellent discrimination and acceptable calibration for prediction of LNI based on TTSB (area under the curve [AUC]/risk estimation: Memorial Sloan Kettering Cancer Center [MSKCC]‐RC 0.79/−4%, Briganti (2012)‐RC 0.82/−4%, Gandaglia‐RC 0.81/+6%). These were comparable in SB (MSKCC‐RC 0.78/+2%; Briganti (2012)‐RC 0.77/−3%). Decision curve analysis (DCA) revealed a net benefit at threshold probabilities between 3% and 6% when TTSB was used. For prediction of EPE based on TTSB an inferior discrimination and variable calibration were observed (AUC/risk estimation: MSKCC‐RC 0.71/+8% and Martini (2018)‐RC 0.69/+2%) achieving a net benefit on DCA only at risk thresholds of >17%. Performance of RCs for prediction of LNI and EPE based on SB showed comparable results with a better performance in the DCA for LNI (risk thresholds 1–2%) and poorer performance for EPE (risk threshold >20%). This study is limited by its retrospective single‐institution design. Conclusions: The potentially more accurate grading ability of TTSB did not result in improved performance of preoperative RCs. Prediction tools for LNI proved clinical usefulness while RCs for EPE did not. [ABSTRACT FROM AUTHOR]

Simple Summary: Multiparametric magnetic resonance imaging (mpMRI) is now standard practice for suspected prostate cancer (PCa) patients, significantly enhancing risk assessment and PCa detection. Integrating MRI into clinical staging allows for more precise, personalized treatment planning in cases of extraprostatic cancer extension. Adverse MRI findings, such as a macroscopic extracapsular extension on MRI (mECE+), capsular disruption, extended tumor capsular contact length (TCCL), Grade Group (GG) ≥ 4, positive surgical margins (PSM), and pECE+ on pathology, were associated with higher biochemical recurrence (BCR) risk. Particularly in low/intermediate-risk patients (pECE− and GG < 4), adverse MRI characteristics correlated with elevated BCR risk. This feature highlights the importance of incorporating predictive MRI features pre-surgery to aid clinical decisions and enhance outcomes in prostate cancer. Adverse MRI features assist in identifying low/intermediate-risk patients needing closer monitoring. Objectives: This study aimed to assess the impact of the covariates derived from a predictive model for detecting extracapsular extension on pathology (pECE+) on biochemical recurrence-free survival (BCRFS) within 4 years after robotic-assisted radical prostatectomy (RARP). Methods: Retrospective data analysis was conducted from a single center between 2015 and 2022. Variables under consideration included prostate-specific antigen (PSA) levels, patient age, prostate volume, MRI semantic features, and Grade Group (GG). We also assessed the influence of pECE+ and positive surgical margins on BCRFS. To attain these goals, we used the Kaplan–Meier survival function and the multivariable Cox regression model. Additionally, we analyzed the MRI features on BCR (biochemical recurrence) in low/intermediate risk patients. Results: A total of 177 participants with a follow-up exceeding 6 months post-RARP were included. The 1-year, 2-year, and 4-year risks of BCR after radical prostatectomy were 5%, 13%, and 21%, respectively. The non-parametric approach for the survival analysis showed that adverse MRI features such as macroscopic ECE on MRI (mECE+), capsular disruption, high tumor capsular contact length (TCCL), GG ≥ 4, positive surgical margins (PSM), and pECE+ on pathology were risk factors for BCR. In low/intermediate-risk patients (pECE− and GG < 4), the presence of adverse MRI features has been shown to increase the risk of BCR. Conclusions: The study highlights the importance of incorporating predictive MRI features for detecting extracapsular extension pre-surgery in influencing early outcomes and clinical decision making; mECE+, TCCL, capsular disruption, and GG ≥ 4 based on pre-surgical biopsy were independent prognostic factors for early BCR. The presence of adverse features on MRI can assist in identifying low/intermediate-risk patients who will benefit from closer monitoring. [ABSTRACT FROM AUTHOR]

Abstract Recent advancements on nerve-sparing robotic prostatectomy allow fewer side effects such as urinary incontinence and sexual dysfunction. To perform such techniques, it is essential for the surgeon to know if the neurovascular bundle is involved. Despite being the gold-standard imaging method for Prostate Cancer (PCa) staging, Magnetic Resonance Imaging (MRI) lacks high specificity for detecting extracapsular extension (ECE). Therefore, it is essential to understand the pathologic aspects of ECE to better evaluate the MRI findings of PCa. We reviewed the normal MRI appearance of the prostate gland and the periprostatic space and correlated them to prostatectomy specimens. The different findings of ECE and neurovascular bundle invasion are exemplified with images of both MRI and histologic specimens. Graphical abstract

Background: Prediction of extracapsular extension (ECE) is essential to achieve a balance between oncologic resection and neural tissue preservation. Microultrasound (MUS) is an attractive alternative to multiparametric magnetic resonance imaging (mpMRI) in the staging scenario. Objective: To create a side-specific nomogram integrating clinicopathologic parameters and MUS findings to predict ipsilateral ECE and guide nerve sparing. Design, setting, and participants: Prospective data were collected from consecutive patients who underwent robotic-assisted radical prostatectomy from June 2021 to May 2022 and had preoperative MUS and mpMRI. A total of 391 patients and 612 lobes were included in the analysis. Outcome measurements and statistical analysis: ECE on surgical pathology was the primary outcome. Multivariate regression analyses were carried out to identify predictors for ECE. The resultant multivariable model's performance was visualized using the receiver-operating characteristic curve. A nomogram was developed based on the coefficients of the logit function for the MUS-based model. A decision curve analysis (DCA) was performed to assess clinical utility. Results and limitations: The areas under the receiver-operating characteristic curve (AUCs) of the MUS-based model were 81.4% and 80.9% (95% confidence interval [CI] 75.6, 84.6) after internal validation. The AUC of the mpMRI-model was also 80.9% (95% CI 77.2, 85.7). The DCA demonstrated the net clinical benefit of the MUS-based nomogram and its superiority compared with MUS and MRI alone for detecting ECE. Limitations of our study included its sample size and moderate inter-reader agreement. Conclusions: We developed a side-specific nomogram to predict ECE based on clinicopathologic variables and MUS findings. Its performance was comparable with that of a mpMRI-based model. External validation and prospective trials are required to corroborate our results. Patient summary: The integration of clinical parameters and microultrasound can predict extracapsular extension with similar results to models based on magnetic resonance imaging findings. This can be useful for tailoring the preservation of nerves during surgery.

Objective: There are many models to predict extracapsular extension (ECE) in patients with prostate cancer. We aimed to externally validate several models in a Japanese cohort. Methods: We included patients treated with robotic-assisted radical prostatectomy for prostate cancer. The risk of ECE was calculated for each patient in several models (prostate side-specific and non-side-specific). Model performance was assessed by calculating the receiver operating curve and the area under the curve (AUC), calibration plots, and decision curve analyses. Results: We identified ECE in 117 (32.9%) of the 356 prostate lobes included. Patients with ECE had a statistically significant higher prostate-specific antigen level, percentage of positive digital rectal examination, percentage of hypoechoic nodes, percentage of magnetic resonance imaging nodes or ECE suggestion, percentage of biopsy positive cores, International Society of Urological Pathology grade group, and percentage of core involvement. Among the side-specific models, the Soeterik, Patel, Sayyid, Martini, and Steuber models presented AUC of 0.81, 0.78, 0.77, 0.75, and 0.73, respectively. Among the non-side-specific models, the memorial Sloan Kettering Cancer Center web calculator, the Roach formula, the Partin tables of 2016, 2013, and 2007 presented AUC of 0.74, 0.72, 0.64, 0.61, and 0.60, respectively. However, the 95% confidence interval for most of these models overlapped. The side-specific models presented adequate calibration. In the decision curve analyses, most models showed net benefit, but it overlapped among them. Conclusion: Models predicting ECE were externally validated in Japanese men. The side-specific models predicted better than the non-side-specific models. The Soeterik and Patel models were the most accurate performing models.

The purpose of this study was to analyze the value of transrectal shear-wave elastography (SWE) in combination with multivariable tools for predicting adverse pathological features before radical prostatectomy (RP). Preoperative clinicopathological variables, multiparametric magnetic resonance imaging (mp-MRI) manifestations, and the maximum elastic value of the prostate (Emax) on SWE were retrospectively collected. The accuracy of SWE for predicting adverse pathological features was evaluated based on postoperative pathology, and parameters with statistical significance were selected. The diagnostic performance of various models, including preoperative clinicopathological variables (model 1), preoperative clinicopathological variables + mp-MRI (model 2), and preoperative clinicopathological variables + mp-MRI + SWE (model 3), was evaluated with area under the receiver operator characteristic curve (AUC) analysis. Emax was significantly higher in prostate cancer with extracapsular extension (ECE) or seminal vesicle invasion (SVI) with both P < 0.001. The optimal cutoff Emax values for ECE and SVI were 60.45 kPa and 81.55 kPa, respectively. Inclusion of mp-MRI and SWE improved discrimination by clinical models for ECE (model 2 vs model 1, P = 0.031; model 3 vs model 1, P = 0.002; model 3 vs model 2, P = 0.018) and SVI (model 2 vs model 1, P = 0.147; model 3 vs model 1, P = 0.037; model 3 vs model 2, P = 0.134). SWE is valuable for identifying patients at high risk of adverse pathology.

Background: We conducted a comparative analysis between low and high-dose postoperative radiotherapy in patients with hypopharyngeal squamous cell carcinoma (HPSCC) in stage III or IV without positive surgical margins and extracapsular extension (ECE). Propensity score matching (PSM) was used to eliminate confounding factors and reduce bias. Methods: The matched-pair analysis included 156 patients divided into two groups: the low-dose radiotherapy group (LD-RT 50 Gy, 78 cases) and the highdose radiotherapy group (HD-RT 60 Gy, 78 cases). Both cohorts were statistically comparable in terms of age, gender, subsite, and TNM classification. Results: The median follow-up time was 49 months (ranging from 5 to 100 months). The overall survival (OS) rate, progression-free survival (PFS) rate, locoregional control rate (87% vs. 85.7%; p = 0.754), distant metastases-free survival (79.2% vs. 76.6%; p = 0.506), and the occurrence of second primary tumors (96.1% vs. 93.5%; p = 0.347) showed no significant differences between the LD-RT group and the HD-RT group. The 3-year OS was 64.9% and 61% in the low-dose and high-dose group, respectively, and 63% in the entire group (p = 0.547). The 3-year PFS was 63.6% and 54.5% (p = 0.250), respectively, and the 3-year PFS of the entire group was 59.1%. Multivariate analyses revealed that pathological T and N classification, and pathological differentiation were associated with 3-year OS, PFS, and LRFS and were independent prognostic factors (p < 0.05). LD-RT was not associated with an increased risk of death and disease progression compared to HD-RT. Conclusion: The results of postoperative low-dose radiotherapy did not show inferiority to those of high-dose radiation for patients with advanced hypopharyngeal cancer without positive surgical margins and ECE in terms of OS, PFS, locoregional control, and metastases-free survival. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the independent clinical, pathological, and radiological factors associated with extracapsular extension in radical prostatectomy specimens and to improve the accuracy of predicting extracapsular extension of prostate cancer before surgery. Methods: From August 2018 to June 2023, the clinical and pathological data of 229 patients with confirmed prostate cancer underwent radical prostatectomy from The Second Hospital of Yinzhou. The patients' multiparametric magnetic resonance imaging data were graded using the Likert scale. The chi-square or independent-sample T-test was used to analyze the related factors for an extracapsular extension. Multivariate analysis was used to identify independent factors associated with extracapsular extension in prostate cancer. Additionally, receiver operating characteristic curve analysis was used to calculate the area under the curve and assess the diagnostic performance of our model. The clinical decision curve was used to analyze the clinical net income of Likert scale, biopsy positive rate, biopsy GG, and combined mode. Results: Of the 229 patients, 52 had an extracapsular extension, and 177 did not. Multivariate analysis showed that the Likert scale score, biopsy grade group and biopsy positive rate were independent risk factors for extracapsular extension in prostate cancer. The area under the curves for the Likert scale score, biopsy grade group, and biopsy positive rate were 0.802, 0.762, and 0.796, respectively. Furthermore, there was no significant difference in the diagnostic efficiency for extracapsular extension (P>0.05). However, when these three factors were combined, the diagnostic efficiency was significantly improved, and the area under the curve increased to 0.905 (P<0.05). In the analysis of the decision curve, The clinical net income of the combined model is obviously higher than that of Likert scale, biopsy positive rate, and biopsy GG. Conclusion: The Likert scale, biopsy grade group and biopsy positive rate are independent risk factors for extracapsular extension in prostate cancer, and their combination can significantly improve the diagnostic efficiency for an extracapsular extension. [ABSTRACT FROM AUTHOR]

Objectives: To demonstrate the importance of extracapsular extension (ECE) of transitional zone (TZ) prostate cancer (PCa), examine the causes of its missed detection by Mp-MRI, and develop a new predictive model by integrating multi-level clinical variables. Materials and methods: This retrospective study included 304 patients who underwent laparoscopic radical prostatectomy after 12 + X needle transperineal transrectal ultrasound (TRUS)-MRI-guided targeted prostate biopsy from 2018 to 2021 in our center was performed. Results: In this study, the incidence rates of ECE were similar in patients with MRI lesions in the peripheral zone (PZ) and TZ (P = 0.66). However, the missed detection rate was higher in patients with TZ lesions than in those with PZ lesions (P < 0.05). These missed detections result in a higher positive surgical margin rate (P < 0.05). In patients with TZ lesions, detected MP-MRI ECE may have grey areas: the longest diameters of the MRI lesions were 16.5–23.5 mm; MRI lesion volumes were 0.63–2.51 ml; MRI lesion volume ratios were 2.75–8.86%; PSA were 13.85–23.05 ng/ml. LASSO regression was used to construct a clinical prediction model for predicting the risk of ECE in TZ lesions from the perspective of MRI and clinical features, including four variables: the longest diameter of MRI lesions, TZ pseudocapsule invasion, ISUP grading of biopsy pathology, and number of positive biopsy needles. Conclusions: Patients with MRI lesions in the TZ have the same incidence of ECE as those with lesions in the PZ, but a higher missed detection rate. [ABSTRACT FROM AUTHOR]

Abstract Background To construct a model based on magnetic resonance imaging (MRI) features and histological and clinical variables for the prediction of pathology-detected extracapsular extension (pECE) in patients with prostate cancer (PCa). Methods We performed a prospective 3 T MRI study comparing the clinical and MRI data on pECE obtained from patients treated using robotic-assisted radical prostatectomy (RARP) at our institution. The covariates under consideration were prostate-specific antigen (PSA) levels, the patient’s age, prostate volume, and MRI interpretative features for predicting pECE based on the Prostate Imaging–Reporting and Data System (PI-RADS) version 2.0 (v2), as well as tumor capsular contact length (TCCL), length of the index lesion, and prostate biopsy Gleason score (GS). Univariable and multivariable logistic regression models were applied to explore the statistical associations and construct the model. We also recruited an additional set of participants—which included 59 patients from external institutions—to validate the model. Results The study participants included 185 patients who had undergone RARP at our institution, 26% of whom were pECE+ (i.e., pECE positive). Significant predictors of pECE+ were TCCL, capsular disruption, measurable ECE on MRI, and a GS of ≥7(4 + 3) on a prostate biopsy. The strongest predictor of pECE+ is measurable ECE on MRI, and in its absence, a combination of TCCL and prostate biopsy GS was significantly effective for detecting the patient’s risk of being pECE+. Our predictive model showed a satisfactory performance at distinguishing between patients with pECE+ and patients with pECE−, with an area under the ROC curve (AUC) of 0.90 (86.0–95.8%), high sensitivity (86%), and moderate specificity (70%). Conclusions Our predictive model, based on consistent MRI features (i.e., measurable ECE and TCCL) and a prostate biopsy GS, has satisfactory performance and sufficiently high sensitivity for predicting pECE+. Hence, the model could be a valuable tool for surgeons planning preoperative nerve sparing, as it would reduce positive surgical margins.

Introduction: The high incidence and increasing awareness of prostate cancer, along with ongoing development of new and improved treatment methods have generated considerable need for imaging techniques that allow for accurate detection and staging of tumour prior to treatment. Aim: To compare the findings of Transrectal Ultrasound (TRUS) and Magnetic Resonance Imaging (MRI) in the diagnosis and localisation of carcinoma prostate. Materials and Methods: This cross-sectional study was conducted in the Department of Radiodiagnosis, Mysore Medical College, Mysore, Karnataka, India from April 2018 to June 2019. This study included 43 male patients, with age ranging from 49 to 76 years. They underwent TRUS, MRI and TRUS guided 12-core biopsies after being suspected with prostate cancer based on high Prostate Specific Antigen (PSA) values (greater than 4.0 ng/mL) or abnormal Digital Rectal Examination (DRE) findings. A cross table was used to compare the histopathology results, TRUS and MRI findings, from which sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were calculated. Results: Total of 43 male patients were included with mean age of 64.8 years. The sensitivity, specificity, PPV and NPV of TRUS for detection of malignancy was 69.70, 80, 92 and 44.44 respectively and for MRI, it was 87.88%, 70%, 90.63% and 63.64% respectively. In addition, MRI detected lymphadenopathy in three patients and skeletal metastasis in four patients. Conclusion: MRI can improve the false negative biopsies resulting due to the inability of TRUS in the detection of abnormal areas, by showing the exact area of abnormality.

Abstract Objectives PSMA PET/MRI showed the potential to increase the sensitivity for extraprostatic disease (EPD) assessment over mpMRI; however, the interreader variability for EPD is still high. Therefore, we aimed to assess whether quantitative PSMA and mpMRI imaging parameters could yield a more robust EPD prediction. Methods We retrospectively evaluated PCa patients who underwent staging mpMRI and [68Ga]PSMA-PET, followed by radical prostatectomy at our institution between 01.02.2016 and 31.07.2019. Fifty-eight cases with PET/MRI and 15 cases with PET/CT were identified. EPD was determined on histopathology and correlated with quantitative PSMA and mpMRI parameters assessed by two readers: ADC (mm2/1000 s), longest capsular contact (LCC, mm), tumor volume (cm3), PSMA-SUVmax and volume-based parameters using a fixed threshold at SUV > 4 to delineate PSMAtotal (g/ml) and PSMAvol (cm3). The t test was used to compare means, Pearson’s test for categorical correlation, and ROC curve to determine the best cutoff. Interclass correlation (ICC) was performed for interreader agreement (95% CI). Results Seventy-three patients were included (64.5 ± 6.0 years; PSA 14.4 ± 17.1 ng/ml), and 31 had EPD (42.5%). From mpMRI, only LCC reached significance (p = 0.005), while both volume-based PET parameters PSMAtotal and PSMAvol were significantly associated with EPD (p = 0.008 and p = 0.004, respectively). On ROC analysis, LCC, PSMAtotal, and PSMAvol reached an AUC of 0.712 (p = 0.002), 0.709 (p = 0.002), and 0.718 (p = 0.002), respectively. ICC was moderate–good for LCC 0.727 (0.565–0.828) and excellent for PSMAtotal and PSMAvol with 0.944 (0.990–0.996) and 0.985 (0.976–0.991), respectively. Conclusions Quantitative PSMA parameters have a similar potential as mpMRI LCC to predict EPD of PCa, with a significantly higher interreader agreement.

Introduction: The PRECE is a model predicting the risk of extracapsular extension (ECE) of prostate cancer: it has been developed on more than 6000 patients who underwent robotic radical prostatectomy (RARP) at the Global Robotic Institute, FL, USA. Up to now, it is the single tool predicting either the side and the amount of ECE. The model has a free user-friendly interface and is made up from simple and available covariates, namely age, PSA, cT, GS and percent of positive core, the latter topographically distributed within the prostate gland. Despite the successful performance at internal validation, the model is still lacking an external validation (EV). The aim of the paper is to externally validate the PRECE model on an Italian cohort of patients elected to RARP. Methods: 269 prostatic lobes from 141 patients represented the validation dataset. The EV was performed with the receiver operating characteristics (ROC) curves and calibration, to address the ability of PRECE to discriminate between patients with or without ECE. Results: Overall, an ECE was found in 91 out of the 269 prostatic lobes (34%). Twenty-five patients out of pT3 had a bilateral ECE. The ROC curve showed an AUC of 0.80 (95% CI 0.74–0.85). Sensitivity and specificity were 77% and 69%, respectively. The model showed an acceptable calibration with tendency towards overestimation. Conclusions: From the current EV, the PRECE displays a good predictive performance to discriminate between cases with and without ECE; despite preliminary, outcomes may support the generalizability of the model in dataset other than the development one. [ABSTRACT FROM AUTHOR]

Objective: To define the clinicopathological and radiological factors independently associated with the existence of an extraprostatic extension in radical prostatectomy specimens. Methods: A total of 202 patients who underwent robotic prostatectomy following biparametric magnetic resonance imaging were assessed. We evaluated the clinicopathological and magnetic resonance imaging variables. We performed receiver‐operating characteristic curve analyses to identify factors associated with extraprostatic extension. We engaged in multivariate analysis to identify factors independently associated with such extension. Results: Extraprostatic extensions were apparent in the final prostatectomy specimens of 62 patients (31%). The areas under the curves of the prostate‐specific antigen level, the biopsy grade group, and the tumor‐capsular contact length on magnetic resonance imaging were 0.76, 0.71, and 0.70, respectively, in receiver‐operating characteristic analysis when used to predict extraprostatic extension; thus, higher than the areas under the curves of the other variables (0.61–0.68). The prostate‐specific antigen level (odds ratio 1.090, p = 0.004), the biopsy grade group (odds ratios 2.678 and 6.358, p = 0.017 and p < 0.001 for grade group 3–4 and 5), and the tumor‐capsular contact length (odds ratio 1.079, p = 0.001) were independently associated with extraprostatic extension. When the three factors were combined, the area under the receiver‐operator characteristic curve increased to 0.79. Conclusions: The prostate‐specific antigen level, the biopsy grade group, and the tumor‐capsular contact length on magnetic resonance imaging were independently associated with extracapsular extension. [ABSTRACT FROM AUTHOR]

Background: Presurgical assessment of hepatocellular carcinoma (HCC) aggressiveness can benefit patients' treatment options and prognosis. Purpose: To develop an artificial intelligence (AI) tool, namely, LiSNet, in the task of scoring and interpreting HCC aggressiveness with computed tomography (CT) imaging. Methods: A total of 358 patients with HCC undergoing curative liver resection were retrospectively included. Three subspecialists were recruited to pixel‐wise annotate and grade tumor aggressiveness based on CT imaging. LiSNet was trained and validated in 193 and 61 patients with a deep neural network to emulate the diagnostic acumen of subspecialists for staging HCC. The test set comprised 104 independent patients. We subsequently compared LiSNet with an experience‐based binary diagnosis scheme and human–AI partnership that combined binary diagnosis and LiSNet for assessing tumor aggressiveness. We also assessed the efficiency of LiSNet for predicting survival outcomes. Results: At the pixel‐wise level, the agreement rate of LiSNet with subspecialists was 0.658 (95% confidence interval [CI]: 0.490–0.779), 0.595 (95% CI: 0.406–0.734), and 0.369 (95% CI: 0.134–0.566), for scoring HCC aggressiveness grades I, II, and III, respectively. Additionally, LiSNet was comparable to subspecialists for predicting histopathological microvascular invasion (area under the curve: LiSNet: 0.668 [95% CI: 0.559–0.776] versus subspecialists: 0.699 [95% CI: 0.591–0.806], p > 0.05). In a human–AI partnered diagnosis, combining LiSNet and experience‐based binary diagnosis can achieve the best predictive ability for microvascular invasion (area under the curve: 0.705 [95% CI: 0.589–0.820]). Furthermore, LiSNet was able to indicate overall survival after surgery. Conclusion: The designed LiSNet tool warrants evaluation as an alternative tool for radiologists to conduct automatic staging of HCC aggressiveness at the pixel‐wise level with CT imaging. Its prognostic value might benefit patients' treatment options and survival prediction. [ABSTRACT FROM AUTHOR]

Abstract Purpose To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. Methods and materials In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets. Results The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively. Conclusions The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

Simple Summary: In this multicentric study, we tested the accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting extracapsular extension (ECE) out of the prostate in order to plan surgical sparing of neurovascular bundles in radical prostatectomy. Univariate and multivariate logistic regression analyses were performed to identify other risk factors for ECE. We found that it has a good ability to exclude extracapsular extension but a poor ability to identify it correctly. Risk factors other than mpMRI that predicted ECE were as follows: prostatic specific antigen, digital rectal examination, ratio of positive cores, and biopsy grade group. We suggest that using mpMRI exclusively should not be recommended to decide on surgical approaches. The correct identification of extracapsular extension (ECE) of prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI) is crucial for surgeons in order to plan the nerve-sparing approach in radical prostatectomy. Nerve-sparing strategies allow for better outcomes in preserving erectile function and urinary continence, notwithstanding this can be penalized with worse oncologic results. The aim of this study was to assess the ability of preoperative mpMRI to predict ECE in the final prostatic specimen (PS) and identify other possible preoperative predictive factors of ECE as a secondary end-point. We investigated a database of two high-volume hospitals to identify men who underwent a prostate biopsy with a pre-biopsy mpMRI and a subsequent RP. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mpMRI in predicting ECE were calculated. A univariate analysis was performed to find the association between image staging and pathological staging. A multivariate logistic regression was performed to investigate other preoperative predictive factors. A total of 1147 patients were selected, and 203 out of the 1147 (17.7%) patients were classified as ECE according to the mpMRI. ECE was reported by pathologists in 279 out of the 1147 PS (24.3%). The PPV was 0.58, the NPV was 0.72, the sensitivity was 0.32, and the specificity was 0.88. The multivariate analysis found that PSA (OR 1.057, C.I. 95%, 1.016–1.100, p = 0.006), digital rectal examination (OR 0.567, C.I. 95%, 0.417–0.770, p = 0.0001), ratio of positive cores (OR 9.687, C.I. 95%, 3.744–25.006, p = 0.0001), and biopsy grade in prostate biopsy (OR 1.394, C.I. 95%, 1.025–1.612, p = 0.0001) were independent factors of ECE. The mpMRI has a great ability to exclude ECE, notwithstanding that low sensitivity is still an important limitation of the technique. [ABSTRACT FROM AUTHOR]

ObjectiveTo determine whether there are subsets of men with pathological high grade prostate cancer (Gleason score 8-10) with particularly high or low 2-year biochemical recurrence (BCR) risk after radical prostatectomy (RP) when stratified into groups based on combinations of pathological features, such as surgical margin status, extracapsular extension (ECE) and seminal vesicle invasion (SVI).Materials and methodsWe identified 459 men treated with RP with pathological Gleason score 8-10 prostate cancer in the SEARCH database. The men were stratified into five groups based on pathological characteristics: group 1, men with negative surgical margins (NSMs) and no ECE; group 2, men with positive surgical margin (PSMs) and no ECE; group 3, men with NSMs and ECE; group 4, men with PSMs and ECE; and group 5, men with SVI. Cox proportional hazards models and the log-rank test were used to compare BCR among the groups.ResultsAt 2 years after RP, pathological group was significantly correlated with BCR (log-rank, P < 0.001) with patients in group 5 (+SVI) having the highest BCR risk (66%) and those in group 1 (NSMs and no ECE) having the lowest risk (14%). When we compared groups 2, 3, and 4, with each other, there was no significant difference in BCR among the groups (~50% 2-year BCR risk; log-rank P = 0.28). Results were similar when adjusting for prostate-specific antigen, age, pathological Gleason sum and clinical stage, or after excluding men who received adjuvant therapy.ConclusionsIn patients with high grade (Gleason score 8-10) prostate cancer after RP, the presence of either PSMs, ECE or SVI was associated with an increased risk of early BCR, with a 2-year BCR risk of ≥50%. Conversely, men with organ-confined margin-negative disease had a very low risk of early BCR despite Gleason score 8-10 disease.

Purpose: Fixed bulky nodal disease in patients with head and neck cancer of unknown primary (HNCUP) remains difficult to treat. This retrospective study evaluated the therapeutic efficacy of selective intra-arterial chemoradiotherapy with docetaxel and nedaplatin for fixed bulky nodal disease in HNCUP. Methods: Data from seven consecutive patients with fixed bulky nodal disease in HNCUP who had undergone selective intra-arterial chemoradiotherapy were analyzed. Whole pharyngeal mucosa and all bilateral nodal areas were irradiated (total dose 50 Gy), and bulky nodal lesions were provided an additional 20 Gy. Intra-arterial chemotherapy used a combination of nedaplatin (80 mg/m2) and docetaxel (60 mg/m2). Outcome measures were local control, disease-free survival, overall survival, and adverse events. Statistical analyses were performed using the Kaplan–Meier method. Results: Median follow-up period was 24 months (range 9–64). All patients had extracapsular extension (N3b) on imaging and clinical findings. Symptoms due to bulky disease were neck discomfort (100%), tumor bleeding (43%), tracheal obstruction (14%), and carotid sinus syndrome (28%). Median value for maximum diameter of cervical disease was 84 mm (range 70–107), and 3-year local control, disease-free survival, and overall survival rates were 100, 54, and 64%, respectively. Symptoms due to bulky disease disappeared in all patients after intra-arterial chemoradiotherapy. Grade 4 leukopenia occurred in two patients (28%) as an acute adverse event. No other serious acute adverse events were observed. Conclusion: Selective intra-arterial chemoradiotherapy with docetaxel and nedaplatin can potentially achieve both favorable local control and survival in in HNCUP with fixed bulky nodal disease. [ABSTRACT FROM AUTHOR]

Purpose: We investigated role of neutrophil-to-lymphocyte ratio (NLR) in the diagnosis and prediction of extracapsular extension (ECE) in clinically localized prostate cancer (PCa). Materials and methods: A total of 396 patients with clinically localized PCa who underwent open radical retropubic prostatectomy (RRP), and 260 patients with benign prostatic hyperplasia (BPH) who underwent suprapubic prostatectomy were included in the study. Preoperative NLR, prostate specific antigen (PSA), prostate specific antigen density (PSAD), free PSA, prostate volume (PV), free/total PSA (f/t PSA) in both groups, and relation of NLR with PSA, Gleason Score (GS), and pathologic stage in PCa group were investigated. Records of patients were analyzed retrospectively. Results: NLR, free PSA, f/t PSA, and PV were statistically higher in BHP, and PSAD was higher in PCa group. In PCa group, NLR was found to be higher in patients with PSA >10 ng/ml compared to those with less than ⩽10 ng/ml. NLR increases as the preoperative GS increases, and it was higher in pT3 patients than pT2 patients. NLR was statistically higher in those patients with positive lymph nodes than those without after RRP (p = 0.029). Conclusion: NLR is not a sufficient biomarker in differentiating clinically localized PCa from BPH. NLR increases as preoperative GS and pathologic stage increases. Lymph node involved patients after RRP have statistically higher NLR. NLR can be an indicator of ECE and lymph-node involvement in clinically localized PCa. [ABSTRACT FROM AUTHOR]

Objectives: PSMA PET/MRI showed the potential to increase the sensitivity for extraprostatic disease (EPD) assessment over mpMRI; however, the interreader variability for EPD is still high. Therefore, we aimed to assess whether quantitative PSMA and mpMRI imaging parameters could yield a more robust EPD prediction. Methods: We retrospectively evaluated PCa patients who underwent staging mpMRI and [68Ga]PSMA-PET, followed by radical prostatectomy at our institution between 01.02.2016 and 31.07.2019. Fifty-eight cases with PET/MRI and 15 cases with PET/CT were identified. EPD was determined on histopathology and correlated with quantitative PSMA and mpMRI parameters assessed by two readers: ADC (mm2/1000 s), longest capsular contact (LCC, mm), tumor volume (cm3), PSMA-SUVmax and volume-based parameters using a fixed threshold at SUV > 4 to delineate PSMAtotal (g/ml) and PSMAvol (cm3). The t test was used to compare means, Pearson's test for categorical correlation, and ROC curve to determine the best cutoff. Interclass correlation (ICC) was performed for interreader agreement (95% CI). Results: Seventy-three patients were included (64.5 ± 6.0 years; PSA 14.4 ± 17.1 ng/ml), and 31 had EPD (42.5%). From mpMRI, only LCC reached significance (p = 0.005), while both volume-based PET parameters PSMAtotal and PSMAvol were significantly associated with EPD (p = 0.008 and p = 0.004, respectively). On ROC analysis, LCC, PSMAtotal, and PSMAvol reached an AUC of 0.712 (p = 0.002), 0.709 (p = 0.002), and 0.718 (p = 0.002), respectively. ICC was moderate–good for LCC 0.727 (0.565–0.828) and excellent for PSMAtotal and PSMAvol with 0.944 (0.990–0.996) and 0.985 (0.976–0.991), respectively. Conclusions: Quantitative PSMA parameters have a similar potential as mpMRI LCC to predict EPD of PCa, with a significantly higher interreader agreement. [ABSTRACT FROM AUTHOR]

Hailang Liu,1 Kun Tang,1 Ding Xia,1 Xinguang Wang,1 Wei Zhu,1 Liang Wang,2 Weimin Yang,1 Ejun Peng,1 Zhiqiang Chen1 1Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, People’s Republic of China; 2Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, People’s Republic of ChinaCorrespondence: Ejun Peng; Zhiqiang Chen Email qingyangfeng60@gmail.com; d201981784@hust.edu.cnObjective: To develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsies.Patients and Methods: We retrospectively collected data from PCa patients who underwent standard (12-core) systematic biopsy and radical prostatectomy. To develop predictive models, the following variables were included in multivariable logistic regression analyses: total prostate-specific antigen (TPSA), central transition zone volume (CTZV), prostate-specific antigen (PSAD), maximum diameter of the index lesion at bp-MRI, EPE at bp-MRI, SVI at bp-MRI, biopsy Gleason grade group, and number of positive biopsy cores. Three risk calculators were built based on the coefficients of the logit function. The area under the curve (AUC) was applied to determine the models with the highest discrimination. Decision curve analyses (DCAs) were performed to evaluate the net benefit of each risk calculator.Results: A total of 222 patients were included in this study. Overall, 83 (37.4%), 75 (33.8%), and 107 (48.2%) patients had EPE, SVI, and upgrading at final pathology, respectively. The addition of bp-MRI data improved the discrimination of models for predicting SVI (0.807 vs 0.816) and upgrading (0.548 vs 0.625) but not EPE (0.766 vs 0.763). Similarly, models including clinical parameters, bp-MRI data, and information on systematic biopsies achieved the highest AUC in the prediction of EPE (0.842), SVI (0.913), and upgrading (0.794), and the three corresponding risk calculators yielded the highest net benefit.Conclusion: We developed three easy-to-use risk calculators for the prediction of adverse pathological features based on patient clinical parameters, bp-MRI data, and information on systematic biopsies. This may be greatly beneficial to urologists in the decision-making process for PCa patients.Keywords: prostate cancer, extracapsular extension, seminal vesicle invasion, upgrading, biparametric MRI, systematic biopsy, predictive model

ObjectiveTo evaluate the diagnostic performance of preoperative contrast-enhanced ultrasound (CEUS) in the detection of extracapsular extension (ECE) and cervical lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) and the added value of CEUS in the evaluation of PTC invasiveness to conventional ultrasound (US).Materials and MethodsA total of 62 patients were enrolled retrospectively, including 30 patients with invasive PTCs (Group A, ECE or LNM present) and 32 patients with non-invasive PTCs (Group B). All patients underwent US and CEUS examinations before surgery. US and CEUS features of PTCs and lymph nodes were compared between groups. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of US, CEUS, and the combination of the two in the detection of ECE and LNM of PTCs were calculated. Logistic regression was used to analyze relationships between variables.ResultsThe PTC size was larger in group A on both US and CEUS (P = 0.001, P = 0.003). More PTCs showed hyper-enhancement in group A (P = 0.013) than in group B. More PTCs had >25% contact between PTC and the thyroid capsule and discontinued capsule on US and CEUS (all P < 0.05) in group A than in group B. More absent hilum and calcification of lymph nodes were observed in group A (both P < 0.05) than in group B on US. More centripetal perfusion and enlarged lymph nodes were observed in group A (both P < 0.05) than in group B on CEUS. CEUS alone and US combined with CEUS manifested higher diagnostic accuracy (79.0%) than US alone (72.6%) in the detection of ECE. The combination of US and CEUS manifested the highest diagnostic accuracy (95.2%) than CEUS alone (90.3%) and US alone (82.2%) in the detection of LNM. Diagnoses of ECE and LNM by the combination of US and CEUS were independent risk factors for PTC invasiveness [odds ratio (OR) = 29.49 and 97.20, respectively; both P = 0.001].ConclusionCEUS or US combined with CEUS is recommended for the detection of PTC ECE, while the combination of US and CEUS is most recommended for LNM detection. CEUS plays an essential role in the preoperative evaluation of PTC invasiveness.

Objective: To evaluate the diagnostic performance of preoperative contrast-enhanced ultrasound (CEUS) in the detection of extracapsular extension (ECE) and cervical lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) and the added value of CEUS in the evaluation of PTC invasiveness to conventional ultrasound (US). Materials and Methods: A total of 62 patients were enrolled retrospectively, including 30 patients with invasive PTCs (Group A, ECE or LNM present) and 32 patients with non-invasive PTCs (Group B). All patients underwent US and CEUS examinations before surgery. US and CEUS features of PTCs and lymph nodes were compared between groups. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of US, CEUS, and the combination of the two in the detection of ECE and LNM of PTCs were calculated. Logistic regression was used to analyze relationships between variables. Results: The PTC size was larger in group A on both US and CEUS (P = 0.001, P = 0.003). More PTCs showed hyper-enhancement in group A (P = 0.013) than in group B. More PTCs had >25% contact between PTC and the thyroid capsule and discontinued capsule on US and CEUS (all P < 0.05) in group A than in group B. More absent hilum and calcification of lymph nodes were observed in group A (both P < 0.05) than in group B on US. More centripetal perfusion and enlarged lymph nodes were observed in group A (both P < 0.05) than in group B on CEUS. CEUS alone and US combined with CEUS manifested higher diagnostic accuracy (79.0%) than US alone (72.6%) in the detection of ECE. The combination of US and CEUS manifested the highest diagnostic accuracy (95.2%) than CEUS alone (90.3%) and US alone (82.2%) in the detection of LNM. Diagnoses of ECE and LNM by the combination of US and CEUS were independent risk factors for PTC invasiveness [odds ratio (OR) = 29.49 and 97.20, respectively; both P = 0.001]. Conclusion: CEUS or US combined with CEUS is recommended for the detection of PTC ECE, while the combination of US and CEUS is most recommended for LNM detection. CEUS plays an essential role in the preoperative evaluation of PTC invasiveness. [ABSTRACT FROM AUTHOR]

Purpose This study aimed to develop a model for predicting pathologic extracapsular extension (ECE) and seminal vesicle invasion (SVI) while integrating magnetic resonance imaging-based T-staging (cTMRI, cT1c-cT3b). Materials and Methods A total of 1,915 who underwent radical prostatectomy between 2006-2016 met the inclusion/exclusion criteria. We performed a multivariate logistic regression analysis as well as Bayesian network (BN) modeling based on possible confounding factors. The BN model was internally validated using 5-fold validation. Results According to the multivariate logistic regression analysis, initial prostate-specific antigen (iPSA) (ß=0.050, p < 0.001), percentage of positive biopsy cores (PPC) (ß=0.033, p < 0.001), both lobe involvement on biopsy (ß=0.359, p=0.009), Gleason score (ß=0.358, p < 0.001), and cTMRI (ß=0.259, p < 0.001) were significant factors for ECE. For SVI, iPSA (ß=0.037, p < 0.001), PPC (ß=0.024, p < 0.001), Gleason score (ß=0.753, p < 0.001), and cTMRI (ß=0.507, p < 0.001) showed statistical significance. BN models to predict ECE and SVI were also successfully established. The overall area under the receiver operating characteristic curve (AUC)/accuracy of the BN models were 0.76/73.0% and 0.88/89.6% for ECE and SVI, respectively. According to internal comparison between the BN model and Roach formula, BN model had improved AUC values for predicting ECE (0.76 vs. 0.74, p=0.060) and SVI (0.88 vs. 0.84, p < 0.001). Conclusion Two models to predict pathologic ECE and SVI integrating cTMRI were established and installed on a separate website for public access to guide radiation oncologists. [ABSTRACT FROM AUTHOR]

Purpose: A balance between preserving urinary continence as well as sexual potency and achieving negative surgical margins is of clinical relevance while implementary difficulty. Accurate detection of extracapsular extension (ECE) of prostate cancer (PCa) is thus crucial for determining appropriate treatment options. We aimed to develop and validate an artificial intelligence (AI)–based tool for detecting ECE of PCa using multiparametric magnetic resonance imaging (mpMRI). Methods: Eight hundred and forty nine consecutive PCa patients who underwent mpMRI and prostatectomy without previous radio- or hormonal therapy from two medical centers were retrospectively included. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts' prior knowledge (PAGNet) from 596 training patients. Model validation was performed in 150 internal and 103 external patients. Performance comparison was made between AI, two experts using a criteria-based ECE grading system, and expert-AI interaction. Results: An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% confidence interval [CI], 0.827–0.884), 0.807 (95% CI, 0.735–0.867), and 0.728 (95% CI, 0.631–0.811) in training, internal, and external validation data, respectively. The performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When experts' interpretations were adjusted by AI assessments, the performance of two experts was improved. Conclusion: Our AI tool, showing improved accuracy, offers a promising alternative to human experts for ECE staging using mpMRI. [ABSTRACT FROM AUTHOR]