A novel stratification scheme combined with internal arteries in CT imaging for guiding postoperative adjuvant transarterial chemoembolization in hepatocellular carcinoma: a retrospective cohort study.

BACKGROUND: Although postoperative adjuvant transarterial chemoembolization (PA-TACE) improves survival outcomes in a subset of patients with resected hepatocellular carcinoma (HCC), the lack of reliable biomarkers for patient selection remains a significant challenge. The present study aimed to evaluate whether computed tomography imaging can provide more value for predicting benefits from PA-TACE and to establish a new scheme for guiding PA-TACE benefits. METHODS: In this retrospective study, patients with HCC who had undergone preoperative contrast-enhanced computed tomography and curative hepatectomy were evaluated. Inverse probability of treatment weight was performed to balance the difference of baseline characteristics. Cox models were used to test the interaction among PA-TACE, imaging features, and pathological indicators. An HCC imaging and pathological classification (HIPC) scheme incorporating these imaging and pathological indicators was established. RESULTS: This study included 1488 patients [median age, 52 years (IQR, 45-61 years); 1309 male]. Microvascular invasion (MVI) positive, and diameter >5 cm tumors achieved a higher recurrence-free survival (RFS), and overall survival (OS) benefit, respectively, from PA-TACE than MVI negative, and diameter ≤5 cm tumors. Patients with internal arteries (IA) positive benefited more than those with IA-negative in terms of RFS ( P =0.016) and OS ( P =0.018). PA-TACE achieved significant RFS and OS improvements in HIPC3 (IA present and diameter >5 cm, or two or three tumors) patients but not in HIPC1 (diameter ≤5 cm, MVI negative) and HIPC2 (other single tumor) patients. Our scheme may decrease the number of patients receiving PA-TACE by ~36.5% compared to the previous suggestion. CONCLUSIONS: IA can provide more value for predicting the benefit of PA-TACE treatment. The proposed HIPC scheme can be used to stratify patients with and without survival benefits from PA-TACE.

Perfusion of hepatocellular carcinomas measured by diffusion-derived vessel density biomarker: Higher hepatocellular carcinoma perfusion than earlier intravoxel incoherent motion reports.

Diffusion-derived vessel density (DVDD) is a physiological surrogate of the area of microvessels per unit tissue area. DDVD is calculated according to DDVD(b0b2) = Sb0/ROIarea0 - Sb2/ROIarea2, where Sb0 and Sb2 refer to the liver signal when b is 0 or 2 s/mm2. Pathohistological studies and contrast-enhanced CT/MRI data showed higher blood volume in hepatocellular carcinoma (HCC) relative to native liver tissue. With intravoxel incoherent motion (IVIM) imaging, most authors paradoxically reported a decreased perfusion fraction of HCC relative to the adjacent liver. This study applied DDVD to assess the perfusion of HCC. MRI was performed with a 3.0-T magnet. Diffusion-weighted images with b-values of 0 and 2 s/mm2 were acquired in 72 HCC patients. Thirty-two patients had microvascular invasion (MVI(+)) and 40 patients did not have microvascular invasion (MVI(-)). Fifty-eight patients had Edmondson-Steiner grade I or II HCC, and 14 patients had Edmondson-Steiner grade III or IV HCC. DDVD measurement was conducted on the axial slice that showed the largest HCC size. DDVD(b0b2) T/L = HCC DDVD(b0b2)/liver DDVD(b0b2). DDVD(b0b2) T/L median (95% confidence interval) of all HCCs was 2.942 (2.419-3.522), of MVI(-) HCCs was 2.699 (2.030-3.522), of MVI(+) HCCs was 2.988 (2.423-3.990), of Edmondson-Steiner grade I/II HCCs was 2.873 (2.277-3.465), and of Edmondson-Steiner grade III/IV HCCs was 3.403 (2.008-4.485). DDVD(b0b2) T/L approximately agrees with contrast agent dynamically enhanced CT/MRI literature data, whereas it differs from earlier IVIM study results, where HCC perfusion fraction was paradoxically lower relative to native liver tissue. A weak trend was noted with MIV(+) HCCs had a higher DDVD(b0b2) T/L than that of MVI(-) HCCs, and a weak trend was noted with the poorly differentiated group of HCCs (Edmondson-Steiner grade III and IV) had a higher DDVD(b0b2) T/L than that of the better differentiated group of HCCs (Edmondson-Steiner grade I and II).

Repeatability and reproducibility comparisons of liver IVIM imaging with free-breathing or respiratory-triggered sequences.

For liver intravoxel incoherent motion (IVIM) data acquisition, respiratory-triggering (RT) MRI is commonly used, and there are strong motivations to shorten the scan duration. For the same scan duration, more b values or higher numbers of excitations can be allowed for free-breathing (FB) imaging than for RT. We studied whether FB can be used to replace RT when careful IVIM image acquisition and image processing are conducted. MRI data of 22 healthy participants were acquired using a 3.0 T scanner. Diffusion imaging was based on a single-shot spin-echo-type echo-planar sequence and 16 b values of 0, 2, 4, 7, 10, 15, 20, 30, 46, 60, 72, 100, 150, 200, 400, and 600 s/mm2 . Each subject attended two scan sessions with an interval of 10-20 days. For each scan session, a subject was scanned twice, first with RT and then with FB. The mean image acquisition time was 5.4 min for FB and 10.8 min for RT. IVIM parameters were calculated with bi-exponential model segmented fitting with a threshold b value of 60 s/mm2 , and fitting started from b = 2 s/mm2 . There was no statistically significant difference between IVIM parameters measured with FB imaging or RT imaging. Perfusion fraction ICC (intraclass correlation coefficient) for FB imaging and RT imaging in the same scan session was 0.824. For perfusion fraction, wSD (within-subject standard deviation), BA (Bland-Altman) difference, BA 95% limit, and ICC were 0.022, 0.0001, -0.0635~0.0637, and 0.687 for FB and 0.031, 0.0122, -0.0723~0.0967, and 0.611 for RT. For Dslow (×10-3  s/mm2 ), wSD, BA difference, BA 95% limit, and ICC were 0.057, 0.0268, -0.1258~0.1793, and 0.471 for FB and 0.073, -0.0078, -0.2170-0.2014, and <0.4 for RT. The Dfast coefficient of variation was 0.20 for FB imaging and 0.28 for RT imaging. All reproducibility indicators slightly favored FB imaging.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy.

OBJECTIVES: The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence. METHODS: In this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction. RESULTS: MVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram. CONCLUSION: The proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy. KEY POINTS: • Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.

Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs.

OBJECTIVES: We aimed to develop and validate a deep convolutional neural network (DCNN) model for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and its clinical outcomes using contrast-enhanced computed tomography (CECT) in a large population of candidates for surgery. METHODS: This retrospective study included 1116 patients with HCC who had undergone preoperative CECT and curative hepatectomy. Radiological (R), DCNN, and combined nomograms were constructed in a training cohort (n = 892) respectively based on clinicoradiological factors, DCNN probabilities, and all factors; the performance of each model was confirmed in a validation cohort (n = 244). Accuracy and the AUC to predict MVI were calculated. Disease-free survival (DFS) and overall survival (OS) after surgery were recorded. RESULTS: The proportion of MVI-positive patients was respectively 38.8% (346/892) and 35.7 % (87/244) in the training and validation cohorts. The AUCs of the R, DCNN, and combined nomograms were respectively 0.809, 0.929, and 0.940 in the training cohorts and 0.837, 0.865, and 0.897 in the validation cohort. The combined nomogram outperformed the R nomogram in the training (p < 0.001) and validation (p = 0.009) cohorts. There was a significant difference in DFS and OS between the R, DCNN, and combined nomogram-predicted groups with and without MVI (p < 0.001). CONCLUSIONS: The combined nomogram based on preoperative CECT performs well for preoperative prediction of MVI and outcome. KEY POINTS: • A combined nomogram based on clinical information, preoperative CECT, and DCNN can predict MVI and clinical outcomes of patients with HCC. • DCNN provides added diagnostic ability to predict MVI. • The AUCs of the combined nomogram are 0.940 and 0.897 in the training and validation cohorts, respectively.

Two-Trait Predictor of Venous Invasion on Contrast-Enhanced CT as a Preoperative Predictor of Outcomes for Early-Stage Hepatocellular Carcinoma After Hepatectomy.

OBJECTIVES: This study aimed to assess the effectiveness of the two-trait predictor of venous invasion (TTPVI) on contrast-enhanced computed tomography (CECT) for the preoperative prediction of clinical outcomes in patients with early-stage hepatocellular carcinoma (HCC) after hepatectomy. METHODS: This retrospective study included 280 patients with surgically resected HCC who underwent preoperative CECT between 2012 and 2013. CT imaging features of HCC were assessed, and univariate and multivariate Cox regression analyses were used to evaluate the CT features associated with disease-free survival (DFS) and overall survival (OS). Subgroup analyses were used to summarized the hazard ratios (HRs) between patients in whom TTPVI was present and those in whom TTPVI was absent using a forest plot. RESULTS: Capsule appearance [HR, 0.504; 95% confidence interval (CI), 0.341-0.745; p < 0.001], TTPVI (HR, 1.842; 95% CI, 1.319-2.572; p < 0.001) and high level of alanine aminotransferase (HR, 1.620; 95% CI, 1.180-2.225, p = 0.003) were independent risk factors for DFS, and TTPVI (HR, 2.509; 95% CI, 1.518-4.147; p < 0.001), high level of alpha-fetoprotein (HR, 1.722; 95% CI, 1.067-2.788; p = 0.026), and gamma-glutamyl transpeptidase (HR, 1.787; 95% CI, 1.134-2.814; p = 0.026) were independent risk factors for OS. A forest plot revealed that the TTPVI present group had lower DFS and OS rates in most subgroups. Patients in whom TTPVI was present in stages I and II had a lower DFS and OS than those in whom TTPVI was absent. Moreover, there were significant differences in DFS (p < 0.001) and OS (p < 0.001) between patients classified as Barcelona Clinic Liver Cancer stage A in whom TTPVI was absent and in whom TTPVI was present. CONCLUSIONS: TTPVI may be used as a preoperative biomarker for predicting postoperative outcomes for patients with early-stage HCC.

A Nomogram Based on CT Deep Learning Signature: A Potential Tool for the Prediction of Overall Survival in Resected Non-Small Cell Lung Cancer Patients.

PURPOSE: To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients. PATIENTS AND METHODS: A total of 1792 deep learning features were extracted from non-enhanced and venous-phase CT images for each NSCLC patient in training cohort (n=231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness. In addition, in order to quantify the improvement in performance added by deep learning signature, the net reclassification improvement (NRI) was calculated. The results were validated in external validation cohort (n=77). RESULTS: A deep learning signature with 9 selected features was significantly associated with OS in both training cohort (hazard ratio [HR]=5.455, 95% CI: 3.393-8.769, P<0.001) and external validation cohort (HR=3.029, 95% CI: 1.673-5.485, P=0.004). The nomogram combining deep learning signature with clinical risk factors of TNM stage, lymphatic vessel invasion and differentiation grade showed favorable discriminative ability with C-index of 0.800 as well as a good calibration, which was validated in external validation cohort (C-index=0.723). Additional value of deep learning signature to the nomogram was statistically significant (NRI=0.093, P=0.027 for training cohort; NRI=0.106, P=0.040 for validation cohort). Decision curve analysis confirmed the clinical usefulness of this nomogram in predicting OS. CONCLUSION: The deep learning signature-based nomogram is a robust tool for prognostic prediction in resected NSCLC patients.

MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study.

OBJECTIVE: To develop and validate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for preoperative prediction of pathologic response to neoadjuvant chemotherapy (NAC) in patients with osteosarcoma. METHODS: We retrospectively enrolled 102 patients with histologically confirmed osteosarcoma who received chemotherapy before treatment from 4 hospitals (68 in the primary cohort and 34 in the external validation cohort). Quantitative imaging features were extracted from contrast-enhanced fat-suppressed T1-weighted images (CE FS T1WI). Four classification methods, i.e., the least absolute shrinkage and selection operator logistic regression (LASSO-LR), support vector machine (SVM), Gaussian process (GP), and Naive Bayes (NB) algorithm, were compared for feature selection and radiomics signature construction. The predictive performance of the radiomics signatures was assessed with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: Thirteen radiomics features selected based on the LASSO-LR classifier were adopted to construct the radiomics signature, which was significantly associated with the pathologic response. The prediction model achieved the best performance between good and poor responders with an AUC of 0.882 (95% CI, 0.837-0.918) in the primary cohort. Calibration curves showed good agreement. Similarly, findings were validated in the external validation cohort with good performance (AUC, 0.842 [95% CI, 0.793-0.883]) and good calibration. DCA analysis confirmed the clinical utility of the selected radiomics signature. CONCLUSION: The constructed CE FS T1WI-radiomics signature with excellent performance could provide a potential tool to predict pathologic response to NAC in patients with osteosarcoma. KEY POINTS: • The radiomics signature based on multicenter contrast-enhanced MRI was useful to predict response to NAC. • The prediction model obtained with the LASSO-LR classifier achieved the best performance. • The baseline clinical characteristics were not associated with response to NAC.

Pretreatment Prediction of Relapse Risk in Patients with Osteosarcoma Using Radiomics Nomogram Based on CT: A Retrospective Multicenter Study.

OBJECTIVE: To develop and externally validate a CT-based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year. MATERIALS AND METHODS: In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical-based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness. RESULTS: Kaplan-Meier curves based on the radiomics signature showed a significant difference between the high-risk and the low-risk groups in both training and validation cohorts (P < 0.001 and P = 0.015, respectively). The radiomics nomogram achieved good discriminant results in the training cohort (C-index: 0.779) and the validation cohort (C-index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical-based nomogram (P < 0.001). CONCLUSIONS: This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical-based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.

Altered Spontaneous Brain Activity and Functional Integration in Hemodialysis Patients With End-Stage Renal Disease.

PURPOSE: Using the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) algorithm to study the alteration of brain function in hemodialysis patients with end-stage renal disease (ESRD). PATIENTS AND METHODS: We recruited 20 patients with ESRD on regular hemodialysis and 17 healthy controls (HCs). All of the participants underwent resting-state fMRI (rs-fMRI), neuropsychological tests, and blood biochemical examination. The individual ALFF values between the two groups were tested by an independent sample t-test. Then, we set the altered ALFF brain areas as seed regions of interest (ROIs), and FC analysis was used to investigate the functional integration patterns between the seed ROI and the voxels within the whole brain. RESULTS: The ALFF values of the right precuneus and angular gyrus (RAG) in the ESRD group were lower than those in the HC subjects, but the right precentral gyrus showed higher ALFF values in patients. Hemoglobin (Hb) was negatively correlated with the ALFF values of the right precentral gyrus, and the ALFF values of the right precuneus were negatively correlated with line-tracing test (LTT) scores in patients with ESRD. Patients with ESRD show decreased connectivity between the RAG and the left precuneus, right superior frontal gyrus (RSFG), and the connectivity within the RAG was weak. In addition, FC in the RAG-right cuneus, right precuneus-left supramarginal gyrus was enhanced in the patient group. CONCLUSION: Our research suggested that, in hemodialysis patients with ESRD, the brain areas with abnormal spontaneous brain activity and FC are mainly located in the default mode network (DMN) regions. Hb and the LTT results were correlated with abnormal spontaneous brain activity. These findings provide additional evidence to understand the possible underlying neuropathological mechanisms in patients with ESRD.

Evaluation of Mesorectal Microcirculation With Quantitative Dynamic Contrast-Enhanced MRI.

OBJECTIVE. The purpose of this study was to use quantitative dynamic contrast-enhanced MRI (DCE-MRI) to evaluate mesorectal microcirculation in patients with rectal cancer. MATERIALS AND METHODS. A total of 53 patients with semicircular rectal tumors underwent DCE-MRI with a 3-T MRI system before surgery. ROIs were manually delineated in the mesorectum that surrounded the tumor and the mesorectum that surrounded the normal rectal wall. DCE-MRI parameters including forward volume transfer constant (Ktrans), reverse volume transfer constant (kep), and fractional extravascular extracellular space volume (Ve) were estimated using computer software. Histopathologic analysis served as the standard reference. RESULTS. Mesorectum that surrounded the tumor showed significantly higher Ktrans val ues than mesorectum that surrounded normal rectal wall (mean, 0.069 ± 0.035 [SD] vs 0.039 ± 0.020 min-1; p < 0.001). The tumor-surrounding mesorectum also showed higher Ve values than normal mesorectum (p < 0.001). An opposite trend was observed for kep, but this was not significant (p = 0.077). A lower Ktrans of the tumor-surrounding mesorectum was observed in patients with malignant lymph nodes compared with those with benign lymph nodes (mean, 0.054 ± 0.027 vs 0.076 ± 0.036 min-1; p = 0.034). Although kep values for the tumor-surrounding mesorectum were higher in patients with tumors categorized as pathologic Tis (pTis) to pT2 than in those with pT3 tumors, the p value was close to 0.05 (p = 0.047). The tumor-surrounding mesorectum showed no significant differences in the aforementioned parameters between patients with positive MRI-detected extramural vascular invasion (mrEMVI) and those with negative mrEMVI. CONCLUSION. Mesorectum that surrounded rectal tumor had a higher blood flow than that close to the normal rectal wall. The blood flow decreased in the tumor-surrounding mesorectum when there was nodal involvement.

Renal Cell Carcinoma: Predicting DNA Methylation Subtyping and Its Consequences on Overall Survival With Computed Tomography Imaging Characteristics.

PURPOSE: The aim of the study was to investigate associations between computed tomography (CT) imaging characteristics, DNA methylation subtyping, and overall survival in renal cell carcinomas. METHODS: Survival curves were calculated using the Kaplan-Meier analysis. The CT data from 212 patients generated with The Cancer Imaging Archive (TCIA) were reviewed. Identified were 70 (33.0%) M1 subtype, 17 (8.0%) M2 subtype, and 125 (59.0%) M3 subtype. Univariate and multivariate analyses were performed using the logistic regression model. RESULTS: Patients with M1 subtype had the shortest median overall survival (P < 0.001). On univariate analysis, long axis of 70 mm, intratumoral calcifications, enhancement, long axis > median, short axis > median, and intratumoral vascularity were associated with a significantly higher incidence of M1 subtype (P < 0.05). Short axis ≤ median, absence of necrosis, absence of intratumoral vascularity, and nodular enhancement were associated with M2 subtype (P < 0.05). Short axis ≤ median, long axis ≤ median, long axis of less than 70 mm, and necrosis were associated with a significantly higher incidence of M3 subtype (P < 0.05). On multivariate logistic regression analysis, long axis of greater than 70 mm (odds ratio [OR] = 2.452, P = 0.004; 95% confidence interval [CI] = 1.332-4.514) and necrosis (OR = 4.758, P = 0.041, 95% CI = 1.065-21.250) were associated with M1 subtype (area under the curve [AUC] = 0. 664). Necrosis (OR = 0.047, P < 0.001, 95% CI = 0.012-0.178) and enhancement (OR = 0.083, P = 0.024, 95% CI = 0.010-0.716) were associated with M2 subtype (AUC = 0.909). Long axis > median (OR = 0.303, P < 0.001, 95% CI = 0.164-0.561) and necrosis (OR = 3.256, P = 0.003, 95% CI = 1.617-10.303) were associated with M3 subtype (AUC = 0. 664). CONCLUSIONS: The shortest survival was observed in patients with M1 subtype. This preliminary radiogenomics analysis of renal cell carcinoma demonstrated associations between CT imaging characteristic and DNA methylation subtyping.

Evaluation of Placental Perfusion Based on Intravoxel Incoherent Motion Diffusion Weighted Imaging (IVIM-DWI) and Its Predictive Value for Late-Onset Fetal Growth Restriction.

Objective The aim of this study was to investigate placental blood perfusion in middle and late pregnancy and explore its predictive value for fetal growth restriction (FGR). Methods All pregnant women included in the study were examined using placental intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). Three IVIM parameters (D, f, D*) were obtained for each pregnant woman and analyzed using Image J software. Perfusion fraction f is a radiological marker of placental perfusion. The pulsatility index (PI) of the uterine artery is used to indirectly evaluate placental function. Results f-values were significantly lower in the late-onset FGR group compared to the normal late pregnancy group (19.07 vs. 27.78%). In addition, uterine artery PI values were markedly increased in the late-onset FGR group compared to the normal late pregnancy group (1.96 vs. 1.03), and neonatal weight was significantly lower in the late-onset FGR group (2.75 vs. 3.18 kg). There was a significant positive correlation between f-value, uterine artery PI and neonatal weight (r = 0.968, p < 0.01; r = 0.959, p < 0.01). There was a significant negative correlation between f-value and age of gestation (r = - 0.534, p < 0.01). Conclusion Perfusion fraction f was strongly correlated with uterine artery blood flow resistance as measured by color Doppler and had a certain predictive value for late-onset FGR.

Effectiveness of High-Speed T2-Corrected Multiecho MR Spectroscopic Method for Quantifying Thigh Muscle Fat Content in Boys With Duchenne Muscular Dystrophy.

OBJECTIVE. The purpose of this study was to investigate the potential of high-speed T2-corrected multiecho (HISTO) MR spectroscopy (MRS) for rapidly quantifying the fat content of thigh muscles in children with Duchenne muscular dystrophy (DMD). SUBJECTS AND METHODS. This study prospectively enrolled 58 boys with DMD (mean age, 7.5 years; range, 4-11 years) and 30 age-matched healthy boys (mean age, 7.2 years; range, 4-11 years) at one institution over a 1-year period. T1- and T2-weighted, multiecho Dixon, and HISTO sequences were performed on the right adductor magnus and vastus lateralis muscles. The fat fractions of these muscles were acquired from HISTO and multiecho Dixon images. An experienced radiologist graded the degree of fat infiltration of the adductor magnus and vastus lateralis muscles on axial T1-weighted images. The Bland-Altman method was used to assess the consistency and repeatability of the HISTO sequence. Pearson linear correlation analysis was used to determine the correlation coefficient relating HISTO fat fraction to multiecho Dixon fat fraction values. Spearman rank correlation analysis was used to assess the relation between the HISTO fat fraction values and T1-weighted image fat infiltration grades. The independent t test was used to compare the HISTO fat fraction values of the boys with DMD with those of the healthy control subjects. RESULTS. Bland-Altman analysis showed that 95.5% of the HISTO fat fraction values of the adductor magnus were within the 95% CI. HISTO fat fraction and multiecho Dixon fat fraction values of the adductor magnus and vastus lateralis muscles were highly positively correlated (adductor magnus, r = 0.983; vastus lateralis, r = 0.967; p < 0.0001). HISTO fat fraction values were also highly positively correlated with the grades of fat infiltration on T1-weighted images (adductor magnus, r = 0.911; vastus lateralis, r = 0.937; p < 0.0001). The HISTO fat fraction of the adductor magnus muscle was 33.3% ± 22.6% and of the vastus lateralis muscle was 25.6% ± 20.3% in patients with DMD. The corresponding values were 2.9% ± 2.1% and 2.3% ± 1.9% in the control group. The differences were statistically significant (p < 0.0001). CONCLUSION. The HISTO sequence is a rapid and feasible noninvasive MRS technique for quantifying the fat infiltration of thigh muscles in children with known or suspected DMD. It is useful for diagnosis and for assessment of disease activity and prognosis.

Pelvic Ultrasound in Diagnosing and Evaluating the Efficacy of Gonadotropin-Releasing Hormone Agonist Therapy in Girls With Idiopathic Central Precocious Puberty.

Background and Objective: Idiopathic central precocious puberty (ICPP) is characterized by early pubertal changes, the acceleration of growth velocity, and rapid bone maturation that often results in reduced adult height. Gonadotrophin-releasing hormone agonist (GnRHa) is currently considered to be an effective therapeutic agent. At present, GnRH stimulation test is adopted as a gold standard for the diagnosis of ICPP and the efficacy evaluation of GnRHa therapy. However, it is difficult to operate in practice due to the cumbersome procedures and multiple blood samples required. This study was conducted to establish the value of pelvic ultrasound in diagnosing ICPP and evaluating the efficacy of GnRHa therapy. Materials and Methods: One hundred and twenty-two girls with ICPP (ICPP group) were enrolled in the study. Pelvic ultrasound and levels of luteinizing hormone (LH) and follicle-stimulating hormone (FSH) were examined before and after GnRHa therapy for 3 months. Eighty normal prepubertal girls were enrolled as the control group. The difference in pelvic ultrasound parameters between the ICPP group before GnRHa therapy and the control group was compared by independent-sample t-test, while paired t-test for ICPP group before and after GnRHa therapy. Receiver operating characteristic (ROC) curve was used to explore the optimal pelvic ultrasound parameters for diagnosing ICPP. Pearson correlation analysis was performed between the pelvic ultrasound parameters and serum sexual hormone level. Results: The pelvic ultrasound parameters (length of the uterine body, anteroposterior diameter of the uterine body, transverse diameter of the uterine body, volume of the uterine body, uterine body-cervix ratio, length of the ovary, transverse diameter of the ovary, anteroposterior diameter of the ovary, volume of the ovary, number of increased follicles and maximum diameter of the follicle) in the ICPP group before GnRHa therapy were significantly larger than those of the control group (P < 0.05). All the above pelvic ultrasound parameters in the ICPP group were significantly decreased after GnRHa therapy compared with those before treatment (P < 0.05). The volume of the uterine body had the largest area under the ROC curve in differentiating between patients with ICCP and the control group. Pelvic ultrasound parameters were significantly correlated with serum sexual hormone levels (P < 0.05). Conclusion: This study indicates pelvic ultrasound is a simple and reliable tool to diagnose ICPP and evaluates the efficacy of GnRHa therapy by dynamically observing the morphology of internal genitalia. The volume of uterine body was the best ultrasound parameter to distinguish patients with ICPP from normal girls.

Comparison of T1 Mapping and T1rho Values with Conventional Diffusion-weighted Imaging to Assess Fibrosis in a Rat Model of Unilateral Ureteral Obstruction.

RATIONALE AND OBJECTIVES: The aim of this study was to investigate the potential of magnetic resonance imaging (MRI) T1 mapping and T1 relaxation time in the rotating frame (T1rho) for assessment of renal fibrosis in a rat model of unilateral ureteral obstruction (UUO). MATERIALS AND METHODS: UUO was created in 36 rats. Six rats were scanned at each of the six time points (on days 0, 1, 3, 5, 10, and 15 after UUO). The contralateral kidneys were examined as controls. Hematoxylin-eosin, Masson's trichrome, and alpha-smooth muscle actin (α-SMA) antibody staining assays were performed. MRI data obtained with a 3.0T scanner were analyzed with α-SMA expression and Masson's staining. RESULTS: The T1 relaxation times and T1rho values increased, and the mean apparent diffusion coefficient (ADC) values decreased with time after UUO. Simple regression analysis indicated that the mean ADCs, T1 relaxation times, and T1rho values had strong correlations with the α-SMA expression levels (R2 = 0.34, R2 = 0.66, R2 = 0.71, respectively; P< .001) and positive Masson's staining (R2 = 0.38, R2 = 0.67, R2 = 0.65, respectively; P< .001). CONCLUSIONS: The T1 mapping and T1rho parameters had better correlations with α-SMA expression and Masson's staining than ADC values.

Intravoxel Incoherent Motion and Arterial Spin Labeling MRI Analysis of Reversible Unilateral Ureteral Obstruction in Rats.

BACKGROUND: Renal fibrosis is a common consequence of chronic kidney disease (CKD) and is the mechanism by which various forms of CKD progress to endstage renal failure. Accurate assessment of renal fibrosis is important for treatment. PURPOSE: To measure longitudinal changes of intravoxel incoherent motion (IVIM) and arterial spin labeling (ASL) before and after reversible unilateral ureteral obstruction in an animal model. STUDY TYPE: Self-controlled animal study. ANIMAL MODEL: Surgical obstruction of the ureters was performed and then removed after 5 days. Rats were scanned on Days 0, 1, 3, and 5 after creating the obstruction and on Days 4, 7, and 12 after releasing the obstruction. FIELD STRENGTH/SEQUENCE: 3.0T/IVIM/ASL. ASSESSMENT: The apparent diffusion coefficient (ADC), pure molecular diffusion (D), perfusion fraction (f), pseudodiffusion (D*), and renal blood flow (RBF) obtained from the ASL were measured. STATISTICAL TESTS: Using SPSS v. 20.0 software, P < 0.05 were considered statistically significant. The data from each timepoint were compared using one-way analysis of variance and correlation analysis was applied to various parameters. RESULTS: The postobstruction kidneys showed renal tubule swelling and increased collagen fiber content. Renal tubule swelling was relieved after reversing the obstruction, but Masson staining and cell density analysis revealed progressive changes that were primarily localized to the medulla. In general, ADC, D, f, D*, and RBF decreased with time during the 5 days of obstruction, and increased after release of the obstruction. ADC positively correlated with D, f, D*, and RBF (r = 0.415, r = 0.634, r = 0.465 r = 0.586, P < 0.001, respectively) in the cortex in this study. Also, ADC showed a positive correlation with D, f, and D* (r = 0.724, r = 0.749, r = 0.151, P < 0.001, respectively) in the medulla. DATA CONCLUSION: Kidney perfusion was the major factor affecting ADC. Functional imaging may be useful for following progression of CKD. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:288-296.

Using Three-Dimensional Printing to Create Individualized Cranial Nerve Models for Skull Base Tumor Surgery.

OBJECTIVE: Using three-dimensional (3D) printing to create individualized patient models of the skull base, the optic chiasm and facial nerve can be previsualized to help identify and protect these structures during tumor removal surgery. METHODS: Preoperative imaging data for 2 cases of sellar tumor and 1 case of acoustic neuroma were obtained. Based on these data, the cranial nerves were visualized using 3D T1-weighted turbo field echo sequence and diffusion tensor imaging-based fiber tracking. Mimics software was used to create 3D reconstructions of the skull base regions surrounding the tumors, and 3D solid models were printed for use in simulation of the basic surgical steps. RESULTS: The 3D printed personalized skull base tumor solid models contained information regarding the skull, brain tissue, blood vessels, cranial nerves, tumors, and other associated structures. The sphenoid sinus anatomy, saddle area, and cerebellopontine angle region could be visually displayed, and the spatial relationship between the tumor and the cranial nerves and important blood vessels was clearly defined. The models allowed for simulation of the operation, prediction of operative details, and verification of accuracy of cranial nerve reconstruction during the operation. Questionnaire assessment showed that neurosurgeons highly valued the accuracy and usefulness of these skull base tumor models. CONCLUSIONS: 3D printed models of skull base tumors and nearby cranial nerves, by allowing for the surgical procedure to be simulated beforehand, facilitate preoperative planning and help prevent cranial nerve injury.