Risk Stratification of Prostate Cancer Using the Combination of Histogram Analysis of Apparent Diffusion Coefficient Across Tumor Diffusion Volume and Clinical Information: A Pilot Study.

BACKGROUND: The effectiveness of quantitative MRI and clinical information in the risk stratification of prostate cancer (PCa) patients was evaluated separately in previous research; however, the differentiation power of combining quantitative MRI and clinical information has yet to be investigated. PURPOSE: To investigate the power of combining histogram analysis of apparent diffusion coefficient (ADC) of tumor diffusion volume (tDv) with clinical information for the differentiation of low-grade (Gleason score [GS] ≤6) and high-grade (GS ≥7) PCa. STUDY TYPE: Retrospective. POPULATION: Fifty-nine PCa patients who underwent preoperative diffusion-weighted imaging (DWI) (acquired with b = 0, 1000 mm2 /s) and followed by radical prostatectomy within 6 months. SEQUENCES: T2 -weighted, DWI, and ADC images at 3.0T. ASSESSMENT: tDv defined with different ADC thresholds were analyzed for each patient and combined with age and prostate-specific antigen (PSA) level. Binary logistic regression with backward feature selection was applied to determine the best discrimination and corresponding combination of parameters. STATISTICAL TESTS: Kolmogorov-Smirnov test; independent samples t-test; Mann-Whitney U-test; Spearman's rank correlation; receiver operating characteristic (ROC) analysis; binary logistical regression. RESULTS: PSA and the 10th percentile ADC value of tDv defined with different diffusion thresholds were significantly different between low-grade and high-grade PCa groups (P < 0.05 for all). Median ADC of tDv based on a threshold of 1.008 × 10-3 mm2 /s exhibited the best performance (AUC = 0.86, 95% confidence interval [CI]: 0.75-0.94), whereas binary logistic regression with backward feature selection achieved 97.20% accuracy with AUC = 0.978 (95% CI: 0.929-0.997). DATA CONCLUSION: The discriminatory power of a single histogram variable of ADC in tDv was not significantly superior to that of a single clinical parameter. The combination of histogram analysis of ADC of tDv and clinical information using logistic regression might significantly improve the risk stratification of PCa and achieve reasonably high accuracy. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:556-564.

Feasibility and Diagnostic Performance of Voxelwise Computed Diffusion-Weighted Imaging in Breast Cancer.

BACKGROUND: Conventional diffusion-weighted imaging (DWI) with high b-values may improve lesion conspicuity, but with a low signal intensity and thus a low signal-to-noise ratio (SNR). The voxelwise computed DWI (vcDWI) may generate high-quality images with a strong lesion signal and low background. PURPOSE: To evaluate the feasibility and diagnostic performance of vcDWI. STUDY TYPE: Retrospective. POPULATION: In all, 67 patients with 72 lesions, 33 malignant and 39 benign. FIELD STRENGTH/SEQUENCE: 3T, including T2 /T1 , DWI with two b-values, and dynamic contrast-enhanced MRI (DCE-MRI). ASSESSMENT: Computed DWI (cDWI) with high b-values of 1500, 2000, 2500 s/mm2 (cDWI1500 , cDWI2000 , cDWI2500 ) and vcDWI were generated from measured DWI (mDWI). The mDWI, cDWIs and vcDWI were evaluated by three readers independently to determine lesion conspicuity, background signal suppression, overall image quality using 1-5 rating scales, as well as to give BI-RADS scores. The mean apparent diffusion coefficient (ADC) value for each lesion was measured. STATISTICAL TESTS: Agreement among the three readers was evaluated by the intraclass correlation coefficient. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic performance based on reading of mDWI, cDWIs, vcDWI, and the measured ADC values. RESULTS: vcDWI provided the best lesion conspicuity compared with mDWI and cDWIs (P < 0.005). For overall image quality, vcDWI was significantly better than cDWI (P < 0.005), but not significantly better compared with mDWI for two readers (P = 0.037 and P = 0.013) and significantly worse for the third reader (P < 0.005). Background signal suppression was the best on cDWI2500 , and better on vcDWI than on mDWI, cDWI1500 , and cDWI2000 . The AUC value for differential diagnosis was 0.868 for mDWI, 0.862 for cDWI1500 , 0.781 for cDWI2000 , 0.704 for cDWI2500 , 0.946 for vcDWI, 0.704 for ADC value, and 0.961 for DCE-MRI. DATA CONCLUSION: vcDWI was implemented without increasing scanning time, and it provided excellent lesion conspicuity for detection of breast lesions and assisted in differentiating malignant from benign breast lesions. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

[Optimizing scan protocol in whole-brain vessel one-stop examination with 640-multislice computed tomography].

OBJECTIVE: To explore the feasibility of optimized scan protocol in whole-brain vessel one-stop examination with 640-multislice computed tomography (640-MSCT) scanner. METHODS: A total of 28 patients undergoing whole-brain vessel examination but showing no obvious cerebral disease with 640-MSCT scanner between September 2012 and May 2013 were collected and divided into two groups of A (n = 14) and B (n = 14) . The recommended scan protocol (protocol 1: collecting 19 volumes) was applied in A group while the optimized scan protocol (protocol 2: collecting 15 volumes) formulated by reducing scanning phases reasonably and changing collection intervals in B group. The dose length product (DLP) was recorded automatically and effective dose (E) measured. The CT perfusion (CTP) values and computed tomographic angiography (CTA) images were analyzed for both groups. The regions of interest (ROI) of CTP images with area (20 ± 2) mm² were located in bilateral frontal white matter, parietal white matter, centrum semiovate, basal ganglia, occipital lobe and cerebellum. The image quality of CTA was evaluated by two experienced radiologists using double-blind method. The results were analyzed by statistics. RESULTS: Dose length product (DLP) in B group decreased 19.23% versus A group (3 419.40 vs 4 233.50 mGy·cm) .Every relative perfusion value of both sides from both groups were not statistically significant (P > 0.05) .Every relative perfusion parameter from individual territory in both groups showed no significant differences (P > 0.05) . The quality of CTA images between groups A and B were not statistically significant (P > 0.05) . CONCLUSION: On the premise that the accuracy of perfusion parameters and the quality of CTA images, the optimized scan protocol in whole-brain vessel one-stop examination can obviously reduce radiation dose and it has important clinical significance.

Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study.

PURPOSE: To assess the performance of Support Vector Machines (SVM) classification to stratify the Gleason Score (GS) of prostate cancer (PCa) in the central gland (CG) based on image features across multiparametric magnetic resonance imaging (mpMRI). MATERIALS AND METHODS: This retrospective study was approved by the institutional review board, and informed consent was waived. One hundred fifty-two CG cancerous ROIs were identified through radiological-pathological correlation. Eleven parameters were derived from the mpMRI and histogram analysis, including mean, median, the 10th percentile, skewness and kurtosis, was performed for each parameter. In total, fifty-five variables were calculated and processed in the SVM classification. The classification model was developed with 10-fold cross-validation and was further validated mutually across two separated datasets. RESULTS: With six variables selected by a feature-selection and variation test, the prediction model yielded an area under the receiver operating characteristics curve (AUC) of 0.99 (95% CI: 0.98, 1.00) when trained in dataset A2 and 0.91 (95% CI: 0.85, 0.95) for the validation in dataset B2. When the data sets were reversed, an AUC of 0.99 (95% CI: 0.99, 1.00) was obtained when the model was trained in dataset B2 and 0.90 (95% CI: 0.85, 0.95) for the validation in dataset A2. CONCLUSION: The SVM classification based on mpMRI derived image features obtains consistently accurate classification of the GS of PCa in the CG.