Background: Accurate evaluation of adnexal masses allows correct surgical procedure, avoiding unnecessary surgery. Purpose: To evaluate the accuracy of magnetic resonance imaging (MRI) in the diagnosis of malignancy of adnexal lesions. Material and Methods: We retrospectively reviewed the pelvic MRI scans of 161 patients with 199 surgically confirmed adnexal masses, between November 1998 and June 2005. The criteria for adnexal malignancy were contrast-enhanced solid lesions, contrast-enhanced solid components in mixed lesions (except those with low-signal-intensity solid components on T2-weighted imaging [T2WI]), contrast-enhanced papillary projections in cystic lesions (except those with low-signal-intensity papillary projections on T2WI), or septal thickness ≥3 mm. Ascites, peritoneal metastasis, and pelvic adenopathy were also regarded as criteria for malignancy. Results: On MRI evaluation, 97 adnexal lesions were malignant and 102 were non-malignant. Thirty-two percent of patients with ascites had benign lesions. Histopathologic evaluation of the adnexal lesions showed that 83 were malignant (true positives), 100 were non-malignant (true negatives), and seven were uncertain malignant potential tumors; two were false negative and seven were false positive. The MRI sensitivity and specificity for malignancy were 98% and 93%, respectively. MRI reached an accuracy of 95%, with a positive predictive value of 0.92 and a negative predictive value of 0.98 for malignant adnexal lesions. The kappa coefficient was 0.906, indicating almost perfect agreement between MRI and histological results. Conclusion: MRI is an accurate method for evaluating the malignancy of adnexal lesions.

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

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 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.

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.