Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features

Funding This work is supported by a PhD student scholarship (Adalgisa Guerra) it was granted as a scientifc project by Hospital da Luz (ID LH.INV.F2019027). Helena Mouriño was supported by CEAUL (funded by FCT, Portugal, through the project UIDB/00006/2020).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 184 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.publishersversionpublishe

Endovascular embolization in gastrointestinal stromal tumor (GIST)

Os autores descrevem um caso clínico de uma doente com hemorragia digestiva baixa submetida a embolização endovascular com sucesso. O diagnóstico etiológico da lesão sangrante foi tumor do estroma gastrointestinal (GIST). A propósito deste caso, os autores fazem uma breve revisão teórica dos GISTs.The authors report a clinical case of a patient with lower gastrointestinal bleeding treated with endovascular embolization. The etiology of the hemorrhage was GIST. A review of this entity is performed

Miradas desde la historia social y la historia intelectual: América Latina en sus culturas: de los procesos independistas a la globalización

Fil: Benito Moya, Silvano G. A. Universidad Católica de Córdoba. Facultad de Filosofía y Humanidades; Argentina.Fil: Universidad Católica de Córdoba. Facultad de Filosofía y Humanidades; Argentina

MRI for adenomyosis: a pictorial review

Abstract Adenomyosis is defined as the presence of ectopic endometrial glands and stroma within the myometrium. It is a disease of the inner myometrium and results from infiltration of the basal endometrium into the underlying myometrium. Transvaginal ultrasonography (TVUS) and magnetic resonance imaging (MRI) are the main radiologic tools for this condition. A thickness of the junctional zone of at least 12 mm is the most frequent MRI criterion in establishing the presence of adenomyosis. Adenomyosis can appear as a diffuse or focal form. Adenomyosis is often associated with hormone-dependent lesions such as leiomyoma, deep pelvic endometriosis and endometrial hyperplasia/polyps. Herein, we illustrate the MRI findings of adenomyosis and associated conditions, focusing on their imaging pitfalls. Teaching points • Adenomyosis is defined as the presence of ectopic endometrium within the myometrium. • MRI is an accurate tool for the diagnosis of adenomyosis and associated conditions. • Adenomyosis can be diffuse or focal. • The most established MRI finding is thickening of junctional zone exceeding 12 mm. • High-signal intensity myometrial foci on T2- or T1-weighted images are also characteristic

Risk Biomarkers for Biochemical Recurrence after Radical Prostatectomy for Prostate Cancer Using Clinical and MRI-Derived Semantic Features

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

Correction: Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features

publishersversionpublishe

Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features

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

Clinical application of machine learning models in patients with prostate cancer before prostatectomy

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

Additional file 1 of Clinical application of machine learning models in patients with prostate cancer before prostatectomy

Supplementary Material 1: Table S1. Standardised institutional MR image sequence parameters for Prostate Protocol at 3T. Table S2. The inter-reader agreement for MRI semantic features. Table S3. The inter-observer variability for radiomics feature