Contrast-Enhanced Ultrasound-Magnetic Resonance Imaging Radiomics Based Model for Predicting the Biochemical Recurrence of Prostate Cancer: A Feasibility Study.

This study was aimed at developing a model for predicting postoperative biochemical recurrence of prostate cancer (PCa) using clinical data-CEUS-MRI radiomics and at verifying its clinical effectiveness. The clinical imaging data of 159 patients pathologically confirmed with PCa and who underwent radical prostatectomy in Xiangyang No. 1 People's Hospital and Jiangsu Hospital of Chinese Medicine from March 2016 to December 2020 were retrospectively analyzed. According to the 2-5-year follow-up results, the patients were divided into the biochemical recurrence (BCR) group (n = 59) and the control group (n = 100). The training set and test set were established in the proportion of 7/3; 4 prediction models were established based on the clinical imaging data. In training set, the area under the curve (AUC) and decision curve analysis (DCA) by R was conducted to compare the efficiency of 4 prediction models, and then, external validation was performed using the test set. Finally, a nomogram tool for predicting BCR was developed. Univariate regression analysis confirmed that the SmallAreaHighGrayLevelEmphasis, RunVariance, Contrast, tumor diameter, clinical T stage, lymph node metastasis, distant metastasis, Gleason score, preoperative PSA, treatment method, CEUS-peak intensity (PI), time to peak (TTP), arrival time (AT), and elastography grade were the influencing factors for predicting BCR. In the training set, the AUC of combinatorial model demonstrated the highest efficiency in predicting BCR [AUC: 0.914 (OR 0.0305, 95% CI: 0.854-0.974)] vs. the general clinical data model, the CEUS model, and the MRI radiomics model. The DCA confirmed the largest net benefits of the combinatorial model. The test set validation gave consistent results. The nomogram tool has been well applied clinically. The previous clinical and imaging data alone did not perform well for predicting BCR. Our combinatorial model firstly using clinical data-CEUS-MRI radiomics provided an opportunity for clinical screening of BCR and help improve its prognosis.

Wang Y ，Feng G ，Wang J ，An P ，Duan P ，Hu Y ，Ye Y ，Li Y ，Qin P ，Song P ... -  《-》

Predicting Model of Biochemical Recurrence of Prostate Carcinoma (PCa-BCR) Using MR Perfusion-Weighted Imaging-Based Radiomics.

An P ，Lin Y ，Hu Y ，Qin P ，Ye Y ，Gu W ，Li X ，Song P ，Feng G ... -  《-》

Prediction of pelvic lymph node metastasis in prostate cancer using radiomics based on T(2)-weighted imaging.

Liu X ，Zhang Y ，Sun Z ，Wang X ，Zhang X ，Wang X ... -  《-》

Prognostic modeling for nasopharyngeal carcinoma (NC) undergoing concurrent chemoradiotherapy using clinical and enhanced MRI-Delta radiomics data: A preliminary study.

Wang Q ，An P ，Song L ，Liu J ，Liu J ... -  《-》

Prognostic Implications of Multiparametric Magnetic Resonance Imaging and Concomitant Systematic Biopsy in Predicting Biochemical Recurrence After Radical Prostatectomy in Prostate Cancer Patients Diagnosed with Magnetic Resonance Imaging-targeted Biopsy.

The prognostic role of multiparametric magnetic resonance imaging (mpMRI) and systematic biopsy in predicting biochemical recurrence (BCR) after radical prostatectomy (RP) in prostate cancer (PCa) patients has not been addressed yet. To develop a risk tool predicting BCR after RP in patients diagnosed with magnetic resonance imaging (MRI)-targeted biopsy. A total of 804 patients with a clinical suspicion of PCa and positive mpMRI diagnosed with MRI-targeted plus concomitant systematic biopsy treated with RP were identified. The outcome was represented by BCR defined as two prostate-specific antigen (PSA) values ≥0.2ng/ml after surgery. Multivariable Cox regression analyses assessed the predictors of BCR. A risk tool model based on imaging and biopsy parameters was developed and validated internally. The c-index, calibration plot, and decision curve analyses were used to assess discrimination, calibration, and the net benefit associated with its use in predicting BCR at 36 mo. Median (interquartile range) follow-up was 28 (25-29) mo, and 89 patients experienced BCR. The 36-mo BCR-free survival rate was 89%. The maximum diameter of the index lesion and seminal vesicle invasion (SVI) at mpMRI as well as the presence of clinically significant PCa at systematic biopsy (defined as a grade group of >2) were associated with BCR (all p≤0.03). A model based on PSA, Prostate Imaging Reporting and Data System score, SVI at mpMRI, diameter of the index lesion, grade group at MRI-targeted biopsy, and clinically significant PCa at systematic biopsy achieved the highest discrimination (77%) among all clinical models, as well as the European Association of Urology risk groups (62%) and the Cancer of the Prostate Risk Assessment (CAPRA) score (60%). This tool was characterized by excellent calibration at internal validation and the highest net benefit when predicting BCR for the threshold risk between 0% and 30%. The adoption of predictive models accounting for mpMRI and MRI-targeted biopsy-derived variables and concomitant systematic biopsy would improve clinicians' ability to identify patients at a higher risk of early recurrence after surgery. The use of information obtained at multiparametric magnetic resonance imaging (mpMRI), and MRI-targeted and concomitant systematic biopsy would improve clinicians' ability to identify prostate cancer patients at a higher risk of experiencing early biochemical recurrence after surgery.

Gandaglia G ，Ploussard G ，Valerio M ，Marra G ，Moschini M ，Martini A ，Roumiguié M ，Fossati N ，Stabile A ，Beauval JB ，Malavaud B ，Scuderi S ，Barletta F ，Afferi L ，Rakauskas A ，Gontero P ，Mattei A ，Montorsi F ，Briganti A ... -  《European Urology Oncology》