Role of Additional MRI-Based Morphologic Measurements on the Performance of VI-RADS for Muscle-Invasive Bladder Cancer.

BACKGROUND: Vesical Imaging-Reporting and Data System (VI-RADS) is a pathway for the standardized imaging and reporting of bladder cancer staging using multiparametric (mp) MRI. PURPOSE: To investigate additional role of morphological (MOR) measurements to VI-RADS for the detection of muscle-invasive bladder cancer (MIBC) with mpMRI. STUDY TYPE: Retrospective. POPULATION: A total of 198 patients (72 MIBC and 126 NMIBC) underwent bladder mpMRI was included. FIELD STRENGTH/SEQUENCE: 3.0 T/T2-weighted imaging with fast-spin-echo sequence, spin-echo-planar diffusion-weighted imaging and dynamic contrast-enhanced imaging with fast 3D gradient-echo sequence. ASSESSMENT: VI-RADS score and MOR measurement including tumor location, number, stalk, cauliflower-like surface, type of tumor growth, tumor-muscle contact margin (TCM), tumor-longitudinal length (TLL), and tumor cellularity index (TCI) were analyzed by three uroradiologists (3-year, 8-year, and 15-year experience of bladder MRI, respectively) who were blinded to histopathology. STATISTICAL TESTS: Significant MOR measurements associated with MIBC were tested by univariable and multivariable logistic regression (LR) analysis with odds ratio (OR). Area under receiver operating characteristic curve (AUC) with DeLong's test and decision curve analysis (DCA) were used to compared the performance of unadjusted vs. adjusted VI-RADS. A P-value <0.05 was considered statistically significant. RESULTS: TCM (OR 9.98; 95% confidence interval [CI] 4.77-20.8), TCI (OR 5.72; 95% CI 2.37-13.8), and TLL (OR 3.35; 95% CI 1.40-8.03) were independently associated with MIBC at multivariable LR analysis. VI-RADS adjusted by three MORs achieved significantly higher AUC (reader 1 0.908 vs. 0.798; reader 2 0.906 vs. 0.855; reader 3 0.907 vs. 0.831) and better clinical benefits than unadjusted VI-RADS at DCA. Specially in VI-RADS-defined equivocal lesions, MOR-based adjustment resulted in 55.5% (25/45), 70.4% (38/54), and 46.4% (26/56) improvement in accuracy for discriminating MIBC in three readers, respectively. DATA CONCLUSION: MOR measurements improved the performance of VI-RADS in detecting MIBC with mpMRI, especially for equivocal lesions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence.

BACKGROUND: To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy. METHODS: Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction. RESULTS: RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score. CONCLUSIONS: AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.

Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study.

BACKGROUND: The high level of expertise required for accurate interpretation of prostate MRI. PURPOSE: To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI. STUDY TYPE: Retrospective. SUBJECTS: One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U-Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test). FIELD STRENGTH/SEQUENCE: 3.0T/scanners, T2 -weighted imaging (T2 WI), diffusion-weighted imaging, and apparent diffusion coefficient map. ASSESSMENT: Close-loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology. STATISTICAL TESTS: Area under the receiver operating characteristic curve (AUC-ROC); Delong test; Meta-regression I2 analysis. RESULTS: In average, for internal test, AI had lower AUC-ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel-Haenszel I2  = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI-RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05). DATA CONCLUSION: Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

[Gonadoblastoma: Clinicopathological study and literature review of 3 cases].

OBJECTIVE: To investigate the clinical feature, pathological morphology, special histopathological subtype and immunohistochemical characteristic of gonadoblastoma. METHODS: Three patients of gonadoblastoma treated from 2014 to 2020 were enrolled, and the clinical characteristics, histological morphology and immunophenotype were analyzed, and the literatures were also reviewed. RESULT: Three phenotypical females were 14,17 and 27 years old. Case 1 was 46,XX with normal gonadal development. Case 2 was 46,XY and case 3 was chromosomal chimeric type (46, XY 90%/45,X 10%), both with dysgenetic gonads. Microscopically, the morphology of classic type was observed in all cases more or less, manifesting small nests of primitive germ cells and surrounding clustered sex cord-like cells, usually with Call-Exner like bodies and calcification. In additon, the morphology of special subtype can be seen in case 1,exhibiting cord-like tumor cells, which was segmentated by cellular fibrous stroma. Cases 2 and 3 were accompanied by dysgerminoma components. Immunohistochemically,all the primal germ cells were positive for OCT3/4, PLAP and CDll7 , and sexcord-like cells were positive for inhibin, SF-1, SOX9 and FOXL2 . Patients were followed up for 10 years, 6 years and 4 years respectively without recurrence. CONCLUSION: Gonadoblastoma is a rare germ cell-sex cord stromal tumor, which is usually accompanied by gonadal hypoplasia. As a special subtype, dissecting gonadoblastoma will be easily confused with dysgerminoma/seminoma, but the prognosis is better. So we should improve the understanding of this subtype and avoid overdiagnosis.

Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study.

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.

Longitudinal Multiparametric MRI Assessment of Irradiated Salivary Gland in a Rat Model: Correlated With Histological Findings.

BACKGROUND: Several magnetic resonance imaging (MRI) sequences have been applied to assess injured glands but without histological validation. PURPOSE: To evaluate longitudinal changes in multiparametric MRI (mp-MRI) of irradiated salivary glands in a rat model and investigate correlations between mp-MRI and histological findings. STUDY TYPE: Prospective. ANIMAL MODEL: Submandibular glands of 36 rats were radiated using a single dose of 15 Gy X-ray (irradiation [IR] group), and 6 other rats were enrolled into sham-IR group. mp-MRI were scanned 1 day after sham-IR (n = 6), or 1, 2, 4, 8, 12, 24 weeks after IR (n = 36, 6 per subgroup). FIELD STRENGTH/SEQUENCE: A 3.0-T/Diffusion-weighted imaging (DWI), readout-segmented echo-planar imaging (EPI) sequence; intravoxel incoherent motion DWI, single-shot EPI sequence; T1 mapping, dual-flip-angle gradient-echo sequence with volumetric interpolated breath-hold examination; T2 mapping, turbo spin-echo sequence. ASSESSMENT: Parameters including apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D* ), perfusion fraction (f), T1 and T2 value were obtained. Histological examinations, including hematoxylin and eosin staining (for acinar cell fraction [AC%] detection), Masson's trichrome staining (for degree of fibrosis [F%] determination) and CD34-immunohistochemical staining (for microvessel density [MVD] calculation), were performed at corresponding time points. STATISTICAL TESTS: One-way analysis of variance was used to compare the mp-MRI and histological parameters among different groups. Spearman correlation analysis was applied to determine the correlation between mp-MRI and histological parameters. Two-sided P ≤ 0.05 was considered statistically significant. RESULTS: Changes of mp-MRI parameters (ADC, D, D* , f, T1, T2) and histological results (AC%, F%, MVD) among the seven groups were all significant. ADC, D, and T2 values negatively correlated with AC% (ADC, r = -0.728; D, r = -0.773; T2, r = -0.600), f positively correlated with MVD (r = 0.496), and T1 values positively correlated with F% (r = 0.714). DATA CONCLUSION: mp-MRI might be able to noninvasively and quantitatively evaluate the dynamic pathological changes within the irradiated salivary glands. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

[Clinical characteristics and treatment strategies of prostatic mucinous adenocarcinoma: A report of 10 cases and literature review].

OBJECTIVE: To investigate the clinical characteristics and treatment strategies of prostatic mucinous adenocarcinoma (PMAC). METHODS: We retrospectively analyzed the clinical data on 10 cases of PMAC treated in the First Affiliated Hospital of Nanjing Medical University from January 2014 to June 2018. The patients were aged 51－79 (65 ± 14) years, with a medium PSA level of 89 (14.63－128.05) µg/L and Gleason scores of 3 + 3 in 1 case, 3 + 4 in 2, 4 + 3 in 1 and 8 in 6 cases preoperatively, 1 treated by robot-assisted radical prostatectomy and the other 9 by laparoscopic radical prostatectomy. We conducted pelvic cavity lymph node dissection for all the patients and analyzed their prognosis and survival. RESULTS: Operations were successfully completed in all the cases. Pathological examination revealed 2 cases of mucinous adenocarcinoma with signet ring cell carcinoma in the 10 PMAC patients, 2 at stage ≤T2b, 5 at stage ≥T2c, 3 positive at pelvic lymph node dissection and 5 positive at the incision margin. The patients were followed up for 6－48 (median 26) months. Four of the patients were found with biochemical recurrence within 2 years after operation and treated by androgen-deprivation therapy, radiotherapy and chemotherapy, which reduced the PSA level to <1.0 µg/ml in all the 4 cases. CONCLUSIONS: PMAC has a good prognosis. Radical surgery is recommended for moderate and low-risk PMAC and the patients with postoperative biochemical recurrence can benefit from comprehensive treatment of total androgen blockade.

A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection.

OBJECTIVES: To develop a machine learning (ML)-assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate cancer by integrating clinical, biopsy, and precisely defined magnetic resonance imaging (MRI) findings. PATIENTS AND METHODS: In all, 248 patients treated with radical prostatectomy and ePLND or PLND were included. ML-assisted models were developed from 18 integrated features using logistic regression (LR), support vector machine (SVM), and random forests (RFs). The models were compared to the Memorial SloanKettering Cancer Center (MSKCC) nomogram using receiver operating characteristic-derived area under the curve (AUC) calibration plots and decision curve analysis (DCA). RESULTS: A total of 59/248 (23.8%) lymph node invasions (LNIs) were identified at surgery. The predictive accuracy of the ML-based models, with (+) or without (-) MRI-reported LNI, yielded similar AUCs (RFs+ /RFs- : 0.906/0.885; SVM+ /SVM- : 0.891/0.868; LR+ /LR- : 0.886/0.882) and were higher than the MSKCC nomogram (0.816; P < 0.001). The calibration of the MSKCC nomogram tended to underestimate LNI risk across the entire range of predicted probabilities compared to the ML-assisted models. The DCA showed that the ML-assisted models significantly improved risk prediction at a risk threshold of ≤80% compared to the MSKCC nomogram. If ePLNDs missed was controlled at <3%, both RFs+ and RFs- resulted in a higher positive predictive value (51.4%/49.6% vs 40.3%), similar negative predictive value (97.2%/97.8% vs 97.2%), and higher number of ePLNDs spared (56.9%/54.4% vs 43.9%) compared to the MSKCC nomogram. CONCLUSIONS: Our ML-based model, with a 5-15% cutoff, is superior to the MSKCC nomogram, sparing ≥50% of ePLNDs with a risk of missing <3% of LNIs.

Using support vector machine analysis to assess PartinMR: A new prediction model for organ-confined prostate cancer.

BACKGROUND: Partin tables represent the most widely used predictive tool for prostate cancer stage at prostatectomy but with potential limitations. PURPOSE: To develop a new PartinMR model for organ-confined prostate cancer (OCPCA) by incorporating Partin table and mp-MRI with a support vector machine (SVM) analysis. STUDY TYPE: Retrospective. POPULATION: In all, 541 patients with biopsy-confirmed prostate cancer underwent mp-MRI. FIELD STRENGTH: T2 -weighted, diffusion-weighted imaging with a 3.0T MR scanner. ASSESSMENT: Candidate predictors included age, prostate-specific antigen, clinical stage, biopsy Gleason score (GS), and mp-MRI findings, ie, tumor location, Prostate Imaging and Reporting and Data System (PI-RADS) score, diameter (D-max), and 6-point MR stage. The PartinMR model with combination of a Partin table and mp-MRI findings was developed using SVM and 5-fold crossvalidation analysis. STATISTICAL TESTS: The predicted ability of the PartinMR model was compared with a standard Partin and a modified Partin table (mPartin) which used for mp-MRI staging. Statistical tests were made by area under receiver operating characteristic curve (AUC), adjusted proportional hazard ratio (HR), and a cost-effective benefit analysis. RESULTS: The rate of OCPCA at prostatectomy was 46.4% (251/541). Using MR staging, mPartin table (AUC, 0.814, 95% confidence interval [CI]: 0.779-0.846, P = 0.001) is appreciably better than the Partin table (AUC, 0.730, 95% CI: 0.690-0.767). Contrarily, adding all MR variables, the PartinMR model (AUC, 0.891, 95% CI: 0.884-0.899, P < 0.001) outperformed any other scheme, with 79.3% sensitivity, 75.7% specificity, 79% positive predictive value, and 76.0% negative predictive value for OCPCA. MR stage represented the most influential predictor of extracapsular extension (HR, 2.77, 95% CI: 1.54-3.33), followed by D-max (2.01, 95% CI: 1.31-2.68), biopsy GS (1.64, 95% CI: 1.35-2.12), and PI-RADS score (1.21, 95% CI: 1.01-1.98). DATA CONCLUSION: The new PartinMR model is superior to the conventional Partin table for OCPCA. Clinical implications of mp-MRI for prostate cancer stage must be confirmed in further trials. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018;48:499-506.

Construction of a Preoperative Radiologic-Risk Signature for Predicting the Pathologic Status of Prostate Cancer at Radical Prostatectomy.

OBJECTIVE: We developed a radiologic-risk signature (RRS) that serves as a surrogate for the pathologic status of prostate cancer (PCA) and investigated its ability to predict disease-free survival. MATERIALS AND METHODS: This study included 631 patients with localized PCA who underwent prostatic multiparametric MRI before prostatectomy. Images from 426 training datasets were structurally interpreted and correlated to a postoperative Memorial Sloan Kettering Cancer Center (MSKCC) score by a stepwise partial least-squares regression analysis. The developed RRS, compared with a preoperative Kattan nomogram, was validated in a cohort of 205 patients with 3-year follow-up data after prostatectomy in terms of calibration, discrimination, and clinical usefulness. Statistical tests were performed by AUC analysis, Kaplan-Meier test, and decision curve analysis. RESULTS: The RRS, which consists of 12 preoperative variables, faithfully represented postoperative MSKCC score in 426 training (r = 0.75; p < 0.001) and 205 validation (r = 0.79; p < 0.001) datasets. For patients in the validation group, RRS showed better discriminative power (C-index, 0.859; 95% CI, 0.779-0.939; p = 0.013) than did the preoperative Kattan nomogram (C-index, 0.780; 95% CI, 0.701-0.859) for predicting 3-year biochemical recurrence and showed higher net benefits for a probability threshold of greater than 10%. CONCLUSION: Characteristics of RRS can faithfully represent the tumor pathologic status and predict accurately the disease postoperative outcome before prostatectomy.