Optimized MRI Assessment for Clinically Significant Prostate Cancer: A STARD-Compliant Two-Center Study.

BACKGROUND: There is a requirement for a personalized strategy to make MRI more accessible to men with suspicion of clinically significant prostate cancer (CSPC). PURPOSE: To evaluate an optimized (Op)-MRI compared with biparametric (Bp)-MRI and multiparametric (Mp)-MRI for the diagnosis of CSPC. STUDY TYPE: Two-center, retrospective. SUBJECTS: A total of 346 patients from center 1 and 292 patients from center 2. FIELD STRENGTH/SEQUENCE: 3.0T scanners, T2 -weighted imaging (T2 WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. ASSESSMENT: Four radiologists interpreted the Bp-MRI (T2 WI and DWI) and Mp-MRI (T2 WI, DWI, and DCE) independently according to the Prostate Imaging Reporting and Data System (PI-RADS). For Op-MRI, two radiologists used an adjusted decision rule on Bp-MRI-assessed PI-RADS 3 lesions by determining early enhancement of DCE. Pathologies at biopsy and/or prostatectomy specimens were used as standard references. STATISTICAL TESTS: Performance was assessed using receiver operating characteristic (ROC) curves. Kappa statistics were used to assess interobserver variability. RESULTS: Interreader agreement was excellent for all three MRI assessments (all κ values >0.80). Op-MRI had comparable sensitivity (senior/junior: 90.9% [261/287] / 91.6% [263/287]) and higher specificity (78.1% [274/351] /74.4% [261/351]) compared with Mp-MRI (sensitivity: 92.3% [265/287] / 93.7% [269/287]; specificity: 67.8% [238/351] / 68.1% [239/351]) and Bp-MRI (sensitivity: 91.6% [263/287] / 93.4% [268/287]; specificity: 71.2% [250/351] / 70.1% [246/351]) for the diagnosis of CSPC. Compared to Mp-MRI, Op-MRI spared biopsy in 80.7% (515/638) of DCE scans with similar performance accuracy. Compared to Bp-MRI, Op-MRI downgraded 25.2% (31/123) of lesions at a cost of missing 6.5% (3/46) of malignancies, and upgraded 45.5% (56/123) of lesions with a positive predictive value of 62.5% (35/56) in 123 equivocal findings. DATA CONCLUSION: The Op-MRI, using an adjusted PI-RADS decision rule, did not compromise diagnostic accuracy with sparing biopsy in 80.7% of DCE scans compared to Mp-MRI, and outperformed Bp-MRI by regrading PI-RADS lesions. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Hypoxic conditioned medium derived from bone marrow mesenchymal stromal cells protects against ischemic stroke in rats.

In recent years, studies have shown that the secretome of bone marrow mesenchymal stromal cells (BMSCs) contains many growth factors, cytokines, and antioxidants, which may provide novel approaches to treat ischemic diseases. Furthermore, the secretome may be modulated by hypoxic preconditioning. We hypothesized that conditioned medium (CM) derived from BMSCs plays a crucial role in reducing tissue damage and improving neurological recovery after ischemic stroke and that hypoxic preconditioning of BMSCs robustly improves these activities. Rats were subjected to ischemic stroke by middle cerebral artery occlusion and then intravenously administered hypoxic CM, normoxic CM, or Dulbecco modified Eagle medium (DMEM, control). Cytokine antibody arrays and label-free quantitative proteomics analysis were used to compare the differences between hypoxic CM and normoxic CM. Injection of normoxic CM significantly reduced the infarct area and improved neurological recovery after stroke compared with administering DMEM. These outcomes may be associated with the attenuation of apoptosis and promotion of angiogenesis. Hypoxic preconditioning significantly enhanced these therapeutic effects. Fourteen proteins were significantly increased in hypoxic CM compared with normoxic CM as measured by cytokine arrays. The label-free quantitative proteomics analysis revealed 163 proteins that were differentially expressed between the two groups, including 107 upregulated proteins and 56 downregulated proteins. Collectively, our results demonstrate that hypoxic CM protected brain tissue from ischemic injury and promoted functional recovery after stroke in rats and that hypoxic CM may be the basis of a potential therapy for stroke patients.

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.

Acute kidney damage induced by low- and iso-osmolar contrast media in rats: Comparison study with physiologic MRI and histologic-gene examination.

PURPOSE: To investigate the physiopathological effects of low- and iso-osmolar contrast media (CM) on renal function with physiologic MRI and histologic-gene examination. MATERIALS AND METHODS: Forty-eight rats underwent time-course DWI and DCE-MRI at 3.0 Tesla (T) before and 5-15 min after exposure of CM or saline (Iop.370: 370 mgI/mL iopromide; Iod.320: 320 mgI/mL iodixanol; Iod.270: 270 mgI/mL iodixanol; 4 gI/kg body weight). Intrarenal viscosity was reflected by apparent diffusion coefficient (ADC). Renal physiologies were evaluated by DCE-derived glomerular filtration rate (GFR), renal blood flow (RBF), and renal blood volume (RBV). Potential acute kidney injury (AKI) was determined by histology and the expression of kidney injury molecule 1 (Kim-1). RESULTS: Iop.370 mainly increased ADC in inner-medulla (△ADCIM : 12.3 ± 11.1%; P < 0.001). Iod.320 and Iod.270 mainly decreased ADC in outer-medulla (△ADCIM ; Iod.320: 16.8 ± 7.5%; Iod.270: 18.1 ± 9.5%; P < 0.001) and inner-medulla (△ADCIM ; Iod.320: 28.4 ± 9.3%; Iod.270: 30.3 ± 6.3%; P < 0.001). GFR, RBF and RBV were significantly decreased by Iod.320 (△GFR: 45.5 ± 24.1%; △RBF: 44.6 ± 19.0%; △RBV: 35.2 ± 10.1%; P < 0.001) and Iod.270 (33.2 ± 19.0%; 38.1 ± 15.6%; 30.1 ± 10.1%; P < 0.001), while rarely changed by Iop.370 and saline. Formation of vacuoles and increase in Kim-1 expression was prominently detected in group of Iod.320, while rarely in Iod.270 and Iop.370. CONCLUSION: Iso-osmolar iodixanol, given at high-dose, produced prominent AKI in nonhydrated rats. This renal dysfunction could be assessed noninvasively by physiologic MRI. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:291-302.

MR-based prognostic nomogram for prostate cancer after radical prostatectomy.

PURPOSE: To assess a magnetic resonance imaging (MRI)-based nomogram in the prediction of prostate cancer (PCa) biochemical recurrence (BCR) within 3 years after prostatectomy. MATERIALS AND METHODS: Between 2009 and 2013, 205 patients with biopsy-confirmed PCa had MRI before prostatectomy. BCR was defined as a PSA failure (>0.2 ng/ml) after prostatectomy. MR features (cancer location, diameter, apparent diffusion coefficients [ADCs], PI-RADS v2 score, dynamic contrast-enhanced [DCE] type, and MR T-stage) were retrospectively evaluated for predicting 3-year BCR based on partial least square regression analysis. Second, imaging features were added to a popularized D'Amico and CAPRA scheme to determine imaging contribution to published nomograms. Lastly, a multivariable Cox regression analysis was employed to determine the independent risk factors of time to BCR. RESULTS: Three-year BCR rate (median follow-up of 44.9 mo) was 25.4% (52/205). The area under receiver operating characteristic (ROC) curve (Az) for MR nomogram (0.909, 95% confidence interval [CI]: 0.861-0.944) was higher than popularized D'Amico (0.793, 95% CI: 0.731-0.846, P = 0.001) and CAPRA (0.809, 95% CI: 0.748-0.860, P = 0.001). The performance of D'Amico (Az: 0.901, 95% CI: 0.852-0.938, P < 0.001) and CAPRA (Az: 0.894, 95% CI: 0.843-0.932, P = 0.004) was significantly improved by adding MR findings. Tumor ADCs (hazard ratio [HR] = 1.747; P = 0.011), PI-RADS score (HR = 4.123; P = 0.039), pathological Gleason score (HR = 3.701; P = 0.004), and surgical-T3b (HR = 6.341; P < 0.001) were independently associated with time to BCR. CONCLUSION: Multiparametric MRI, when converted into a prognostic nomogram, can predict the clinical outcome in patients with PCa after prostatectomy. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2017;45:586-596.

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

OBJECTIVE: To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). METHODS: This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. RESULTS: For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). CONCLUSION: Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. KEY POINTS: • Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.

Diffusion Kurtosis Imaging Helps to Predict Upgrading in Biopsy-Proven Prostate Cancer With a Gleason Score of 6.

OBJECTIVE: The purpose of this study was to investigate whether diffusion kurtosis imaging (DKI) is useful for predicting upgrades in Gleason score (GS) in biopsy-proven prostate cancer with a GS of 6. MATERIALS AND METHODS: A total of 46 patients with biopsy-proven GS 6 prostate cancer, 3-T DWI results, and surgical pathologic results were retrospectively included in the study. DWI data were postprocessed with monoexponential and DK models to quantify the apparent diffusion coefficient (ADC), apparent diffusion for gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). The volume of the lesions, prostate-specific antigen (PSA) level, and diffusion variables (ADCmin, Dappmin, Kappmax, ADCmean, Dappmean, and Kappmean) were evaluated. PSA and DKI were combined as a parameter in a logistic regression model. The utility of these parameters in predicting an upgrade in GS was analyzed with ROC regression. RESULTS: The rate of GS upgrade was 50.0% (23/46). The GS upgrade group had significantly lower ADCmin (p = 0.007), ADC mean (p = 0.003), D appmin (p < 0.001), and Dappmean (p = 0.001) values and significantly higher Kappmax (p = 0.003), Kappmean (p = 0.005), and PSA (p = 0.004) values than the group that did not have an upgrade. Among single parameters, Kappmax had the highest ROC AUC value (0.819, p < 0.05), and among all the parameters and models, PSA-Kappmax had the highest AUC (0.868, p < 0.05) and Youden index (0.6522). CONCLUSION: The results showed that DKI may help in prediction of GS upgrade in biopsy-proven GS 6 prostate cancer. The comprehensive consideration of DKI and PSA may be a promising approach to predicting GS upgrade.

Classifying CT/MR findings in patients with suspicion of hepatocellular carcinoma: Comparison of liver imaging reporting and data system and criteria-free Likert scale reporting models.

PURPOSE: To compare the Liver Imaging Reporting and Data System (LI-RADS) and a criteria-free Likert scale (LS) reporting models for classifying computed tomography/magnetic resonance imaging (CT/MR) findings of suspicious hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Imaging data of 281 hepatic nodules in 203 patients were retrospectively included. Imaging characteristics including diameter, arterial hyperenhancement, washout, and capsule were reviewed independently by two groups of readers using LI-RADS and LS (range, score 1-5). LS is primarily based on the overall impression of image findings without using fixed criteria. Interreader agreement (IRA), intraclass agreement (ICA), and diagnostic performance were determined by Fleiss, Cohen's kappa (κ), and logistic regression, respectively. RESULTS: There were 167 contrast-enhanced CT (CECT) versus 114 MR data. Overall, IRA was moderate (κ = 0.47, 0.52); IRA was moderate-to-good for arterial hyperenhancement, washout, and capsule (κ = 0.56-0.69); excellent for diameter and tumor embolus (κ = 0.99). Overall, ICA between LI-RADS and LS was moderate (κ = 0.44-0.50); ICA was good for scores 1-2 (κ = 0.71-0.90), moderate for scores 3 and 5 (κ = 0.41-0.52), but very poor for score 4 (κ = 0.11-0.19). LI-RADS produced significantly lower accuracy (78.6% vs. 87.2%) and sensitivity (72.1% vs. 92.8%), higher specificity (97.3% vs. 71.2%) and positive likelihood ratio (+LR: 26.32 vs. 3.23) in diagnosis of HCC. CECT produced relatively low IRA, ICA, and diagnostic ability against MR. CONCLUSION: There were substantial variations in liver observations between LI-RADS and LS. Further study is needed to investigate ICA between CECT and MR.

New RESOLVE-Based Diffusional Kurtosis Imaging in MRI-Visible Prostate Cancer: Effect of Reduced b Value on Image Quality and Diagnostic Effectiveness.

OBJECTIVE: The purpose of this article was to investigate whether a new readout segmentation of long variable echo-trains (RESOLVE)-based diffusional kurtosis imaging (DKI) with reduced b value technique can affect image quality and diagnostic effectiveness in MRI-visible prostate cancer (PCA). SUBJECTS AND METHODS: Prostatic RESOLVE DKI (0-1400 s/mm2) was prospectively performed for 12 volunteers. The optimal protocol was then performed in 108 MRI-visible PCAs to determine whether it can compete against a preferred b-value set (0-2000 s/mm(2)) regarding image quality and diagnostic effectiveness. Images were interpreted by two independent radiologists using the prostate imaging reporting and data system (PI-RADS). Readers' concordance and diagnostic effectiveness were tested with the Fleiss kappa and area under the ROC curve (Az) analyses. RESULTS: A b value of 1400 s/mm(2) generated a larger apparent diffusion coefficient of gaussian distribution (Dapp) (1.35 ± 0.31 vs 1.30 ± 0.30 mm(2)/s; p < 0.001) and apparent kurtosis coefficient (Kapp) (1.11 ± 0.26 vs 1.00 ± 0.21; p < 0.001) in PCA than did a b value of 2000 s/mm(2). Interreader agreement using PI-RADS was relatively low when Dapp and Kapp maps were excluded from image interpretations (κ = 0.39-0.41 vs κ = 0.66-0.68 with Dapp and Kapp maps). Interreader agreement in staging PCA was relatively high (κ > 0.80) and was not influenced by reducing the b value. The power of Dapp and Kapp to differentiate PCA from normal tissue (Az = 0.97-0.98), tissue with a Gleason score less than or equal to 3 + 4 from tissue with a Gleason score greater than 3 + 4 (Az = 0.77-0.82), and PCA stage lower than pT3 from stage pT3 and higher PCA (Az = 0.70-0.75) was not significantly degraded by reducing the b value. CONCLUSION: We found that b values significantly influenced image quality, PI-RADS score, and DKI outputs but did not degrade the diagnostic effectiveness of DKI parameters to detect and classify PCA.

The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer.

OBJECTIVE: To evaluate histogram analysis of intravoxel incoherent motion (IVIM) for discriminating the Gleason grade of prostate cancer (PCa). METHODS: A total of 48 patients pathologically confirmed as having clinically significant PCa (size > 0.5 cm) underwent preoperative DW-MRI (b of 0-900 s/mm(2)). Data was post-processed by monoexponential and IVIM model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analysis was performed by outlining entire-tumour regions of interest (ROIs) from histological-radiological correlation. The ability of imaging indices to differentiate low-grade (LG, Gleason score (GS) ≤6) from intermediate/high-grade (HG, GS > 6) PCa was analysed by ROC regression. RESULTS: Eleven patients had LG tumours (18 foci) and 37 patients had HG tumours (42 foci) on pathology examination. HG tumours had significantly lower ADCs and D in terms of mean, median, 10th and 75th percentiles, combined with higher histogram kurtosis and skewness for ADCs, D and f, than LG PCa (p < 0.05). Histogram D showed relatively higher correlations (ñ = 0.641-0.668 vs. ADCs: 0.544-0.574) with ordinal GS of PCa; and its mean, median and 10th percentile performed better than ADCs did in distinguishing LG from HG PCa. CONCLUSION: It is feasible to stratify the pathological grade of PCa by IVIM with histogram metrics. D performed better in distinguishing LG from HG tumour than conventional ADCs. KEY POINTS: • GS had relatively higher correlation with tumour D than ADCs. • Difference of histogram D among two-grade tumours was statistically significant. • D yielded better individual features in demonstrating tumour grade than ADC. • D* and f failed to determine tumour grade of PCa.

Comparison of Utility of Histogram Apparent Diffusion Coefficient and R2* for Differentiation of Low-Grade From High-Grade Clear Cell Renal Cell Carcinoma.

OBJECTIVE: The purpose of this study was to compare histogram analysis of apparent diffusion coefficient (ADC) and R2* for differentiating low-grade from high-grade clear cell renal cell carcinoma (RCC). MATERIALS AND METHODS: Forty-six patients with pathologically confirmed clear cell RCC underwent preoperative BOLD and DWI MRI of the kidneys. ADCs based on the entire tumor volume were calculated with b value combinations of 0 and 800 s/mm(2). ROI-based R2* was calculated with eight TE combinations of 6.7-22.8 milliseconds. Histogram analysis of tumor ADCs and R2* values was performed to obtain mean; median; width; and fifth, 10th, 90th, and 95th percentiles and histogram inhomogeneity, kurtosis, and skewness for all lesions. RESULTS: Thirty-three low-grade and 13 high-grade clear cell RCCs were found at pathologic examination. The TNM classification and tumor volume of clear cell RCC significantly correlated with histogram ADC and R2* (ρ = -0.317 to 0.506; p < 0.05). High-grade clear cell RCC had significantly lower mean, median, and 10th percentile ADCs but higher inhomogeneity and median R2* than low-grade clear cell RCC (all p < 0.05). Compared with other histogram ADC and R2* indexes, 10th percentile ADC had the highest accuracy (91.3%) in discriminating low- from high-grade clear cell RCC. R2* in discriminating hemorrhage was achieved with a threshold of 68.95 Hz. At this threshold, high-grade clear cell RCC had a significantly higher prevalence of intratumor hemorrhage (high-grade, 76.9%; low-grade, 45.4%; p < 0.05) and larger hemorrhagic area than low-grade clear cell RCC (high-grade, 34.9% ± 31.6%; low-grade, 8.9 ± 16.8%; p < 0.05). CONCLUSION: A close relation was found between MRI indexes and pathologic findings. Histogram analysis of ADC and R2* allows differentiation of low- from high-grade clear cell RCC with high accuracy.

DWI-associated entire-tumor histogram analysis for the differentiation of low-grade prostate cancer from intermediate-high-grade prostate cancer.

PURPOSE: To investigate diagnostic efficiency of DWI using entire-tumor histogram analysis in differentiating the low-grade (LG) prostate cancer (PCa) from intermediate-high-grade (HG) PCa in comparison with conventional ROI-based measurement. METHODS: DW images (b of 0-1400 s/mm(2)) from 126 pathology-confirmed PCa (diameter >0.5 cm) in 110 patients were retrospectively collected and processed by mono-exponential model. The measurement of tumor apparent diffusion coefficients (ADCs) was performed with using histogram-based and ROI-based approach, respectively. The diagnostic ability of ADCs from two methods for differentiating LG-PCa (Gleason score, GS ≤ 6) from HG-PCa (GS > 6) was determined by ROC regression, and compared by McNemar's test. RESULTS: There were 49 LG-tumor and 77 HG-tumor at pathologic findings. Histogram-based ADCs (mean, median, 10th and 90th) and ROI-based ADCs (mean) showed dominant relationships with ordinal GS of Pca (ρ = -0.225 to -0.406, p < 0.05). All above imaging indices reflected significant difference between LG-PCa and HG-PCa (all p values <0.01). Histogram 10th ADCs had dominantly high Az (0.738), Youden index (0.415), and positive likelihood ratio (LR+, 2.45) in stratifying tumor GS against mean, median and 90th ADCs, and ROI-based ADCs. Histogram mean, median, and 10th ADCs showed higher specificity (65.3%-74.1% vs. 44.9%, p < 0.01), but lower sensitivity (57.1%-71.3% vs. 84.4%, p < 0.05) than ROI-based ADCs in differentiating LG-PCa from HG-PCa. CONCLUSIONS: DWI-associated histogram analysis had higher specificity, Az, Youden index, and LR+ for differentiation of PCa Gleason grade than ROI-based approach.

Effect of LY294002 on adriamycin-induced epithelial-mesenchymal transition in human breast carcinoma cells.

OBJECTIVE: To study the effect of LY294002 on the adriamycin- induced epithelial-mesenchymal transition in human breast carcinoma cells. METHODS: Human breast carcinoma cells MCF-7 was cultured in vitro and then exposed to adriamycin with or without LY294002. The protein expression levels of Akt, phosphorylated-Akt (p-Akt), Snail, and E-cadherin was detected by Western blot analysis. The mRNA expressions of Snail and E-cadherin were determined by RT-PCR. RESULTS: Adriamycin significantly increased the protein expression of Snail and depressed the protein expression of E-cadherin (P<0.05). The pre-treatment with LY294002 significantly reversed the changes of activities and levels of the above proteins (P<0.05). CONCLUSION: LY294002 could reverse the adriamycin-induced epithelial-mesenchymal transition in human breast carcinoma cells by regulating the expressions of Snail and E-cadherin through suppressing PI3K/Akt signaling pathway.

A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions.

PURPOSE: PI-RADS score 3 is recognized as equivocal likelihood of clinically significant prostate cancer (csPCa) occurrence. We aimed to develop a Radiomics machine learning (RML)-based redefining score to screen out csPCa in equivocal PI-RADS score 3 category. METHODS: Total of 263 patients with the dominant index lesion scored PI-RADS 3 who underwent biopsy and/or follow-up formed the primary cohort. One-step RML (RML-i) model integrated radiomic features of T2WI, DWI, and ADC images all together, and two-step RML (RML-ii) model integrated the three independent radiomic signatures from T2WI (T2WIRS), DWI (DWIRS), and ADC (ADCRS) separately into a regression model. The two RML models, as well as T2WIRS, DWIRS, and ADCRS, were compared using the receiver operating characteristic-derived area under the curve (AUC), calibration plot, and decision-curve analysis (DCA). Two radiologists were asked to give a subjective binary assessment, and Cohen's kappa statistics were calculated. RESULTS: A total of 59/263 (22.4%) csPCa were identified. Inter-reader agreement was moderate (Kappa = 0.435). The AUC of RML-i (0.89; 95% CI 0.88-0.90) is higher (p = 0.003) than that of RML-ii (0.87; 95% CI 0.86-0.88). The DCA demonstrated that the RML-i and RML-ii significantly improved risk prediction at threshold probabilities of csPCa at 20% to 80% compared with doing-none or doing-all by PI-RADS score 3 or stratifying by separated DWIRS, ADCRS, or T2WIRS. CONCLUSION: Our RML models have the potential to predict csPCa in PI-RADS score 3 lesions, thus can inform the decision making process of biopsy.

Short-term Reproducibility of Pulmonary Nodule and Mass Detection in Chest Radiographs: Comparison among Radiologists and Four Different Computer-Aided Detections with Convolutional Neural Net.

To investigate the reproducibility of computer-aided detection (CAD) for detection of pulmonary nodules and masses for consecutive chest radiographies (CXRs) of the same patient within a short-term period. A total of 944 CXRs (Chest PA) with nodules and masses, recorded between January 2010 and November 2016 at the Asan Medical Center, were obtained. In all, 1092 regions of interest for the nodules and mass were delineated using an in-house software. All CXRs were randomly split into 6:2:2 sets for training, development, and validation. Furthermore, paired follow-up CXRs (n = 121) acquired within one week in the validation set, in which expert thoracic radiologists confirmed no changes, were used to evaluate the reproducibility of CAD by two radiologists (R1 and R2). The reproducibility comparison of four different convolutional neural net algorithms and two chest radiologists (with 13- and 14-years' experience) was conducted. Model performances were evaluated by figure-of-merit (FOM) analysis of the jackknife free-response receiver operating curve and reproducibility rates were evaluated in terms of percent positive agreement (PPA) and Chamberlain's percent positive agreement (CPPA). Reproducibility analysis of the four CADs and R1 and R2 showed variations in the PPA and CPPA. Model performance of YOLO (You Only Look Once) v2 based eDenseYOLO showed a higher FOM (0.89; 0.85-0.93) than RetinaNet (0.89; 0.85-0.93) and atrous spatial pyramid pooling U-Net (0.85; 0.80-0.89). eDenseYOLO showed higher PPAs (97.87%) and CPPAs (95.80%) than Mask R-CNN, RetinaNet, ASSP U-Net, R1, and R2 (PPA: 96.52%, 94.23%, 95.04%, 96.55%, and 94.98%; CPPA: 93.18%, 89.09%, 90.57%, 93.33%, and 90.43%). There were moderate variations in the reproducibility of CAD with different algorithms, which likely indicates that measurement of reproducibility is necessary for evaluating CAD performance in actual clinical environments.

The CD40/CD40L system regulates rat cerebral microvasculature after focal ischemia/reperfusion via the mTOR/S6K signaling pathway.

OBJECTIVE: The role of CD40/CD40 ligand (CD40L) in microvascular thrombosis is now widely accepted. However, the exact mechanisms linking the CD40/CD40L system and the soluble form of CD40L (sCD40L) with microvascular thrombosis are currently a topic of intensive research. The objective of this study was to assess the potential mechanisms in CD40/CD40L system-regulated microvascular thrombosis after focal ischemia/reperfusion (I/R). METHODS: Rats were subjected to 60-min transient middle cerebral artery occlusion (MCAO). The experiments were divided into three groups: sham operation, MCAO, and MCAO + CD40 antagonist. Dynamic changes of serum-free sCD40L levels for 0, 1, 3, 5, 6, and 12 h by ELISA detecting kit after focal I/R were observed, and the CD40 expression levels in both platelet surface and vascular endothelial cell surface were measured by flow cytometry and immunofluorescence, respectively. Cerebral infarct volume was analyzed 12 h after reperfusion. mTOR/S6K signaling was determined by Western blot. RESULTS: A comparison of thrombus formation between MCAO and CD40 antagonist treatment rats revealed a role for CD40 and/or CD40L in the inflammation-enhanced thrombosis responses in both of the platelet and vascular endothelial cell. MCAO rats yielded an acceleration of thrombus formation that was accompanied by increased CD40 levels in serum. The brain infarction was significantly decreased in CD40 antagonist treatment group compared to MCAO model group. The mTOR/S6K signaling was activated in MACO model than that of CD40 antagonist treatment group. CONCLUSIONS: Our findings indicate that CD40/CD40L system contributes to microvascular thrombosis and brain infarction induced by MCAO and reperfusion. The mTOR/S6K signaling pathway is involved in the regulation of cerebral microvasculature after focal I/R by CD40/CD40L. ABBREVIATIONS: AKT: protein kinase B; CD40L: CD40 ligand; CSF: cerebrospinal fluid; FITC: fluorescein isothiocyanate; I/R: ischemia/reperfusion; MCAO: middle cerebral artery occlusion; mTOR: mechanistic target of rapamycin; PE: P-phycoerythrin; sCD40L: soluble form of CD40L; TNF-a: tumor necrosis factor-alpha; WT: wild type.

Feasibility of triphasic CT with a modified two-point Patlak plot to determine spit kidney glomerular filtration rate in clinical practice.

PURPOSE: To investigate whether triphasic CT with a simplified Patlak plot can be used in clinical practice for the estimate of split kidney glomerular filtration rate (SKGFR). MATERIALS AND METHODS: The animal experiment included 15 rabbits that underwent 40 dynamic contrast-enhanced CT scans of the kidneys with 1.5 s time interval. Patlak-derived SKGFR was obtained using standard forty-point, two-point (unenhanced phase, arterial phase t α, and portovenous phase t ß), and a modified two-point (MTP) (unenhanced, t α, t ß, and a virtual t τ [t τ = (t α + t ß)/2]) image data, respectively. The MTP-Patlak plot approach was then validated in 13 patients who underwent a triphasic renal contrast-enhanced CT examination. SKGFR measured by 99mTc-DTPA clearance was as a standard reference. RESULTS: MTP-Patlak significantly reduced input function errors than two-point Patlak (21.1 ± 16.2 % vs 30.8 ± 15.2 %, p < 0.01) and showed good concordance with standard Patlak for measurement of SKGFR in animal experiment (1.20 ± 0.38 mL/g/min vs 1.51 ± 0.43 mL/g/min; linear correlation coefficient r = 0.87, p < 0.001). Human study showed that mean SKGFR was 45.7 mL/min (range, 26.5-86.2 mL/min) obtained from 99mTc-DTPA, and 38.2 mL/min (range, 18.6-79.3 mL/min) obtained from triphasic CT using MTP-Patlak plot. Linear correlation between the two methods was r = 0.75 (p < 0.01). The mean difference between SKGFRs as determined with the two methods was 7.4 ± 9.0 mL/min. CONCLUSION: The MTP-Patlak approach, featured with simplicity, is feasible in a clinically indicated CT examination for the evaluation of split renal function.