BackgroundWhether men with a prostate‐specific antigen (PSA) level of 4–10 ng/mL should be recommended for a biopsy is clinically challenging.PurposeTo develop and validate a radiomics model based on multiparametric MRI (mp‐MRI) in patients with PSA levels of 4–10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies.Study TypeRetrospective.SubjectsIn all, 199 patients with PSA levels of 4–10 ng/mL.Field Strength/Sequence3T, T2‐weighted, diffusion‐weighted, and dynamic contrast‐enhanced MRI.AssessmentLesion regions of interest (ROIs) from T2‐weighted, diffusion‐weighted, and dynamic contrast‐enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical‐radiological risk factors.Statistical TestsFor continuous variables, variance equality was assessed by Levene's test and Student's t‐test, and Welch's t‐test was used to assess between‐group differences. For categorical variables, Pearson's chi‐square test, Fisher's exact test, or the approximate chi‐square test was used to assess between‐group differences. P < 0.05 was considered statistically significant.ResultsThe combined model incorporating the multi‐imaging fusion model, age, PSA density (PSAD), and the PI‐RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical‐radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical‐radiological model by 18.4%.Data ConclusionThe combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4–10 ng/mL and might aid in treatment decision‐making and reduce unnecessary biopsies.Level of Evidence: 3Technical Efficacy Stage: 3J. Magn. Reson. Imaging 2020;51:1890–1899.

Accurate surface anatomy is essential for safe clinical practice. There are numerous inconsistencies in clinically important surface markings among and within contemporary anatomical reference texts. The aim of this study was to investigate key thoracic and abdominal surface anatomy landmarks in living Chinese adults using computed tomography (CT). A total of 100 thoracic and 100 abdominal CT scans were examined. Our results indicated that the following key surface landmarks differed from current commonly-accepted descriptions: the positions of the tracheal bifurcation, azygos vein termination, and pulmonary trunk bifurcation (all below the plane of the sternal angle at vertebral level T5-T6 in most individuals); the superior vena cava formation and junction with the right atrium (most often behind the 1st and 4th intercostal spaces, respectively); and the level at which the inferior vena cava and esophagus traverse the diaphragm (T10 and T11, respectively). The renal arteries were most commonly at L1; the midpoint of the renal hila was most frequently at L2; the 11th rib was posterior to the left kidney in only 29% of scans; and the spleen was most frequently located between the 10th and 12th ribs. A number of significant sex- and age-related differences were noted. The Chinese population was also compared with western populations on the basis of published reports. Reappraisal of surface anatomy using modern imaging tools in vivo will provide both quantitative and qualitative evidence to facilitate the clinical application of these key surface landmarks.

PurposeTo compare the diagnostic accuracy of biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) for prostate cancer (PCa) and clinically significant prostate cancer (csPCa) and to explore the application value of dynamic contrast-enhanced (DCE) MRI in prostate imaging.Methods and materialsThis study retrospectively enrolled 235 patients with suspected PCa in our hospital from January 2016 to December 2017, and all lesions were histopathologically confirmed. The lesions were scored according to the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2). The bpMRI (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI]/apparent diffusion coefficient [ADC]) and mpMRI (T2WI, DWI/ADC and DCE) scores were recorded to plot the receiver operating characteristic (ROC) curves. The area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for each method were calculated and compared. The patients were further stratified according to bpMRI scores (bpMRI ≥3, and bpMRI = 3, 4, 5) to analyse the difference in DCE MRI between PCa and non-PCa lesions (as well as between csPCa and non-csPCa).ResultsThe AUC values for the bpMRI and mpMRI protocols for PCa were comparable (0.790 [0.732–0.840] and 0.791 [0.733–0.841], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for PCa were 76.2, 79.5, 72.6, 75.8, and 76.6%, respectively, and the values for mpMRI were 77.4, 84.4, 69.9, 75.2, and 80.6%, respectively. The AUC values for the bpMRI and mpMRI protocols for the diagnosis of csPCa were similar (0.781 [0.722–0.832] and 0.779 [0.721–0.831], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for csPCa were 74.0, 83.8, 66.9, 64.8, and 85.0%, respectively; and 73.6, 87.9, 63.2, 63.2, and 87.8%, respectively, for mpMRI. For patients with bpMRI scores ≥3, positive DCE results were more common in PCa and csPCa lesions (both P = 0.001). Further stratification analysis showed that for patients with a bpMRI score = 4, PCa and csPCa lesions were more likely to have positive DCE results (P = 0.003 and P < 0.001, respectively).ConclusionThe diagnostic accuracy of bpMRI is comparable with that of mpMRI in the detection of PCa and the identification of csPCa. DCE MRI is helpful in further identifying PCa and csPCa lesions in patients with bpMRI ≥3, especially bpMRI = 4, which may be conducive to achieving a more accurate PCa risk stratification. Rather than omitting DCE, we think further comprehensive studies are required for prostate MRI.

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Background Biopsy Gleason score (GS) is crucial for prostate cancer (PCa) treatment decision‐making. Upgrading in GS from biopsy to radical prostatectomy (RP) puts a proportion of patients at risk of undertreatment. Purpose To develop and validate a radiomics model based on multiparametric magnetic resonance imaging (mp‐MRI) to predict PCa upgrading. Study Type Retrospective, radiomics. Population A total of 166 RP‐confirmed PCa patients (training cohort, n = 116; validation cohort, n = 50) were included. Field Strength/Sequence 3.0T/T2‐weighted (T2W), apparent diffusion coefficient (ADC), and dynamic contrast enhancement (DCE) sequences. Assessment PI‐RADSv2 score for each tumor was recorded. Radiomic features were extracted from T2W, ADC, and DCE sequences and Mutual Information Maximization criterion was used to identify the optimal features on each sequence. Multivariate logistic regression analysis was used to develop predictive models and a radiomics nomogram and their performance was evaluated. Statistical Tests Student's t or chi‐square were used to assess the differences in clinicopathologic data between the training and validation cohorts. Receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated. Results In PI‐RADSv2 assessment, 67 lesions scored 5, 70 lesions scored 4, and 29 lesions scored 3. For each sequence, 4404 features were extracted and the top 20 best features were selected. The radiomics model incorporating signatures from the three sequences achieved better performance than any single sequence (AUC: radiomics model 0.868, T2W 0.700, ADC 0.759, DCE 0.726). The combined mode incorporating radiomics signature, clinical stage, and time from biopsy to RP outperformed the clinical model and radiomics model (AUC: combined model 0.910, clinical model 0.646, radiomics model 0.868). The nomogram showed good performance (AUC 0.910) and calibration (P‐values: training cohort 0.624, validation cohort 0.294). Data Conclusion Radiomics based on mp‐MRI has potential to predict upgrading of PCa from biopsy to RP. Level of Evidence 3 Technical Efficacy Stage 5 J. Magn. Reson. Imaging 2020;52:1239–1248.

Background: To develop a radiomics model based on multiparametric MRI (mpMRI) for preoperative prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa).Methods: Ninety-five pathology-confirmed PCa patients with 115 lesions (49 positive and 66 negative) were retrospectively enrolled. A 3.0T MR scanner was used to perform T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE). Radiomics features extracted from T2WI, DWI, apparent diffusion coefficient (ADC), and DCE were used to build a radiomics model. Patients' clinical and pathological variables were also obtained to build a clinical model. The radiomics model and clinical model were further integrated to build a combined nomogram. All lesions were randomly divided into the training group (82 lesions) and the validation group (33 lesions). A least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to build the radiomics model. The diagnostic performance of different models was assessed by calculating the area under the curve (AUC) and compared using the Delong test. The calibration curve and decision curve analyses were used to assess the calibration and clinical usefulness of the radiomics model. Results:The AUC values for the radiomics model in the training and validation group were 0.919 and 0.865, respectively, with a good calibration performance. The decision curve analysis confirmed the clinical utility of the radiomics model. The accuracy, sensitivity, and specificity were 81.8, 71.4, and 89.5% in the validation group. In the validation group, the radiomics model outperformed the clinical model (AUC = 0.658, P = 0.020), and was comparable with the combined nomogram (AUC = 0.857, P = 0.644). Conclusion:The radiomics model based on mpMRI could different EPE and non-EPE lesions with satisfactory diagnostic performance, and this model might assist in predicting EPE before prostatectomy.

CTTA proved to be a feasible tool for differentiating LGUC from HGUC. MPP quantified from fine texture scale on unenhanced images was the optimal diagnostic parameter for estimating histologic grade of urothelial carcinoma.

Background Computed tomography texture analysis (CTTA) has gained an increasing role in oncology and has successfully demonstrated to reflect biological associations with glucose metabolism, hypoxia, angiogenesis, and even genetic variation. Purpose To determine whether quantitative CTTA can be used to differentiate metastases from benign adrenal masses on single energy CT images. Material and Methods A total of 225 patients with 265 histologically confirmed adrenal masses (101 metastases, 98 pheochromocytomas, and 66 lipid-poor adenomas) were included in this retrospective study. CTTA was performed and six texture parameters (including mean, SD of pixel distribution histogram, mean of positive pixels, entropy, kurtosis, skewness) across six spatial scaling factor (SSF) were recorded on both unenhanced and contrast-enhanced CT images. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was calculated using the significant texture parameters for the objective. Diagnostic performance was evaluated using the cut-off values of texture parameters by ROC analysis. The optimal discriminative texture parameters were used to produce support vector machine (SVM) classifiers. Diagnostic accuracy and 10-fold cross-validation was performed. Results Compared to benign adrenal masses, metastases had significantly lower mean gray-level intensity, SD, entropy, mean of positive pixels and kurtosis on unenhanced images (P < 0.0083). On contrast-enhanced CT images, except for skewness and kurtosis, the other four texture texture-quantifiers were lower in the metastatic compared to the other group (P < 0.0083). A model incorporating mean, SD, entropy and mean value of positive pixels produced an AUC of 0.85 ± 0.03 with an SVM accuracy of 77% to identify metastatic from benign adrenal masses. Conclusion Differentiation of metastatic and benign adrenal masses can be achieved accurately with CTTA on contrast-enhanced CT images.