Clinical-Radiologic Morphology-Radiomics Model on Gadobenate Dimeglumine-Enhanced MRI for Identification of Highly Aggressive Hepatocellular Carcinoma: Temporal Validation and Multiscanner Validation.

BACKGROUND: Highly aggressive hepatocellular carcinoma (HCC) is characterized by high tumor recurrence and poor outcomes, but its definition and imaging characteristics have not been clearly described. PURPOSE: To develop and validate a fusion model on gadobenate dimeglumine-enhanced MRI for identifying highly aggressive HCC. STUDY TYPE: Retrospective. POPULATION: 341 patients (M/F = 294/47) with surgically resected HCC, divided into a training cohort (n = 177), temporal validation cohort (n = 77), and multiscanner validation cohort (n = 87). FIELD STRENGTH/SEQUENCE: 3T, dynamic contrast-enhanced MRI with T1-weighted volumetric interpolated breath-hold examination gradient-echo sequences, especially arterial phase (AP) and hepatobiliary phase (HBP, 80-100 min). ASSESSMENT: Clinical factors and diagnosis assessment based on radiologic morphology characteristics associated with highly aggressive HCCs were evaluated. The radiomics signatures were extracted from AP and HBP. Multivariable logistic regression was performed to construct clinical-radiologic morphology (CR) model and clinical-radiologic morphology-radiomics (CRR) model. A nomogram based on the optimal model was established. Early recurrence-free survival (RFS) was evaluated in actual groups and risk groups calculated by the nomogram. STATISTICAL TESTS: The performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curves analysis, and decision curves. Early RFS was evaluated by using Kaplan-Meier analysis. A P value <0.05 was considered statistically significant. RESULTS: The CRR model incorporating corona enhancement, cloud-like hyperintensity on HBP, and radiomics signatures showed the highest diagnostic performance. The area under the curves (AUCs) of CRR were significantly higher than those of the CR model (AUC = 0.883 vs. 0.815, respectively, for the training cohort), 0.874 vs. 0.769 for temporal validation, and 0.892 vs. 0.792 for multiscanner validation. In both actual and risk groups, highly and low aggressive HCCs showed statistically significant differences in early recurrence. DATA CONCLUSION: The clinical-radiologic morphology-radiomics model on gadobenate dimeglumine-enhanced MRI has potential to identify highly aggressive HCCs and non-invasively obtain prognostic information. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Magnetic resonance imaging radiomics-based prediction of clinically significant prostate cancer in equivocal PI-RADS 3 lesions in the transitional zone.

Purpose: This bi-institutional study aimed to establish a robust model for predicting clinically significant prostate cancer (csPCa) (pathological grade group ≥ 2) in PI-RADS 3 lesions in the transition zone by comparing the performance of combination models. Materials and methods: This study included 243 consecutive men who underwent 3-Tesla magnetic resonance imaging (MRI) and ultrasound-guided transrectal biopsy from January 2020 and April 2022 which is divided into a training cohort of 170 patients and a separate testing cohort of 73 patients. T2WI and DWI images were manually segmented for PI-RADS 3 lesions for the mean ADC and radiomic analysis. Predictive clinical factors were identified using both univariate and multivariate logistic models. The least absolute shrinkage and selection operator (LASSO) regression models were deployed for feature selection and for constructing radiomic signatures. We developed nine models utilizing clinical factors, radiological features, and radiomics, leveraging logistic and XGboost methods. The performances of these models was subsequently compared using Receiver Operating Characteristic (ROC) analysis and the Delong test. Results: Out of the 243 participants with a median age of 70 years, 30 were diagnosed with csPCa, leaving 213 without a csPCa diagnosis. Prostate-specific antigen density (PSAD) stood out as the only significant clinical factor (odds ratio [OR], 1.068; 95% confidence interval [CI], 1.029-1.115), discovered through the univariate and multivariate logistic models. Seven radiomic features correlated with csPCa prediction. Notably, the XGboost model outperformed eight other models (AUC of the training cohort: 0.949, and validation cohort: 0.913). However, it did not surpass the PSAD+MADC model (P > 0.05) in the training and testing cohorts (AUC, 0.949 vs. 0.888 and 0.913 vs. 0.854, respectively). Conclusion: The machine learning XGboost model presented the best performance in predicting csPCa in PI-RADS 3 lesions within the transitional zone. However, the addition of radiomic classifiers did not display any significant enhancement over the compound model of clinical and radiological findings. The most exemplary and generalized option for quantitative prostate evaluation was Mean ADC+PSAD.

Clinic-radiological features and radiomics signatures based on Gd-BOPTA-enhanced MRI for predicting advanced liver fibrosis.

OBJECTIVES: To develop and validate a combined model based on Gd-BOPTA-enhanced MRI to identify advanced liver fibrosis. METHODS: A total of 102 patients with chronic HBV infection were divided into a training cohort (n = 80) and a time-independent testing cohort 1 (n = 22). In the training cohort, radiomics signatures were extracted from the hepatobiliary phase. Model 1 was constructed with clinic-radiological factors using multivariable logistic regression to predict advanced liver fibrosis, and model 2 incorporated radiomics signatures based on model 1. The diagnostic performances were compared with serum fibrosis tests and FibroScan tests using area under curve (AUC) in testing cohort 1. Another 45 patients with other causes were collected in testing cohort 2 for further validation. RESULTS: Model 1 showed age (OR = 1.079) and periportal space widening (OR = 7.838) were the independent factors for predicting advanced fibrosis. After integrating radiomics signatures, model 2 enabled more accurately than model 1 in training cohort (0.940 vs. 0.802, p = 0.003). In testing cohort 1, model 2 demonstrated a superior AUC compared with model 1 (0.900 vs. 0.813，p = 0.131), FibroScan test (0.900 vs. 0.733, p = 0.193), and serum fibrosis tests (APRI and Fib-4 was 0.667 and 0.791). In testing cohort 2, model 2 incorporating radiomics signatures showed satisfactory performance (0.874 vs. 0.757，p = 0.010) compared with model 1. CONCLUSIONS: Radiomics signatures derived from Gd-BOPTA-enhanced HBP images may offer complementary information to the clinic-radiological model for predicting advanced liver fibrosis. KEY POINTS: • Linear or reticular hyperintensity on T2WI, periportal space widening, and diffuse periportal enhancement on HBP can be useful for predicting advanced liver fibrosis. • Clinic-radiological features such as patient age and periportal space widening are the two independent factors predicting advanced fibrosis. • Radiomics signatures derived from Gd-BOPTA-enhanced HBP images offer complementary information to the clinic-radiological model for predicting advanced liver fibrosis.

Multi-Region Radiomic Analysis Based on Multi-Sequence MRI Can Preoperatively Predict Microvascular Invasion in Hepatocellular Carcinoma.

Objectives: Microvascular invasion (MVI) affects the postoperative prognosis in hepatocellular carcinoma (HCC) patients; however, there remains a lack of reliable and effective tools for preoperative prediction of MVI. Radiomics has shown great potential in providing valuable information for tumor pathophysiology. We constructed and validated radiomics models with and without clinico-radiological factors to predict MVI. Methods: One hundred and fifteen patients with pathologically confirmed HCC (training set: n = 80; validation set: n = 35) who underwent preoperative MRI were retrospectively recruited. Radiomics models based on multi-sequence MRI across various regions (including intratumoral and/or peritumoral areas) were built using four classification algorithms. A clinico-radiological model was constructed individually and combined with a radiomics model to generate a fusion model by multivariable logistic regression. Results: Among the radiomics models, the model based on T2WI and arterial phase (T2WI-AP model) in the volume of the liver-HCC interface (VOIinterface) exhibited the best predictive power, with AUCs of 0.866 in the training group and 0.855 in the validation group. The clinico-radiological model exhibited good efficacy (AUC: 0.819 and 0.717, respectively). The fusion model showed excellent predictive ability (AUC: 0.915 and 0.868, respectively), outperforming both the clinico-radiological and the T2WI-AP models in the training and validation sets. Conclusion: The fusion model of multi-region radiomics achieves an enhanced prediction of the individualized risk estimation of MVI in HCC patients. This may be a beneficial tool for clinicians to improve decision-making in personalized medicine.

Added-value of ancillary imaging features for differentiating hepatocellular carcinoma from intrahepatic mass-forming cholangiocarcinoma on Gd-BOPTA-enhanced MRI in LI-RADS M.

OBJECTIVE: To identify the reliable imaging features and added-value of ancillary imaging features for differentiating hepatocellular carcinoma (HCC) and intrahepatic mass-forming cholangiocarcinoma (IMCC) assigned to LI-RADS M on Gd-BOPTA-enhanced MRI. METHODS: This retrospective study included 116 liver observations assigned to LI-RADS M, including 82 HCC and 34 IMCC histologically confirmed. Before and after adding ancillary imaging features, all variables with a p-value of < 0.05 in univariable analysis were entered into a multivariable logistic regression analysis to build diagnostic model 1 and model 2 to find reliable predictors of HCC diagnosis. Receiver operating characteristic (ROC) analysis and the DeLong test were used to compare the two models. RESULTS: Forty-nine of 82(59.8%) HCCs had a considerably higher frequency of enhancing "capsule" compared with IMCCs (p < 0.001). Based on LI-RADS major and LR-M features and clinical-pathologic factors, an elevated AFP level (OR = 10.676, 95%CI = 2.125-4.470, p = 0.004) and enhancing "capsule" (OR = 20.558, 95%CI = 4.470-94.550, p < 0.001) were extracted as independent risk factors in Model 1. After adding ancillary imaging features, Male (OR = 23.452, 95%CI = 1.465-375.404, p = 0.026), enhancing "capsule" (OR = 13.161, 95%CI = 1.725-100.400, p = 0.013), septum (OR = 17.983, 95%CI = 1.049-308.181, p = 0.046), small-scale central HBP hyperintensity (OR = 44.386, 95%CI = 1.610-1223.484, p = 0.025) were confirmed as independent significant variables associated with HCC. Model 2 demonstrated significantly superior AUC (0.918 vs 0.845, p = 0.021) compared with Model 1. When any two or more predictors in model 2 were satisfied, sensitivity was 91.46%, and accuracy was at the top (87.93%). CONCLUSION: Enhancing "capsule" was a reliable imaging feature to help identify HCC. Adding ancillary imaging features improved sensitivity and accuracy for HCC diagnosis with differentiation from IMCC in LR-M.

Ultrasound Versus Contrast-Enhanced Magnetic Resonance Imaging for Subclinical Synovitis and Tenosynovitis: A Diagnostic Performance Study.

OBJECTIVES: Radiographic manifestations of synovitis (e.g., erosions) can be observed only in the late stage of rheumatoid arthritis. Ultrasound is a noninvasive, cheap, and widely available technique that enables the evaluation of inflammatory changes in the peripheral joint. In the same way, dynamic contrast-enhanced magnetic resonance imaging (MRI) enables qualitative and quantitative measurements. The objectives of the study were to compare the sensitivity and accuracy of ultrasound in detecting subclinical synovitis and tenosynovitis with those of contrast-enhanced MRI. METHODS: The ultrasonography and contrast-enhanced MRI findings of the wrist, metacarpophalangeal, and proximal interphalangeal joints (n=450) of 75 patients with a history of joint pain and morning stiffness between 6 weeks and 2 years were reviewed. The benefits score was evaluated for each modality. RESULTS: The ultrasonic findings showed inflammation in 346 (77%) joints, while contrast-enhanced MRI found signs of early rheumatoid arthritis in 372 (83%) joints. The sensitivities of ultrasound and contrast-enhanced MRI were 0.795 and 0.855, respectively, and the accuracies were 0.769 and 0.823, respectively. Contrast-enhanced MRI had a likelihood of 0-0.83 and ultrasound had a likelihood of 0-0.77 for detecting synovitis and tenosynovitis at one time. The two imaging modalities were equally competitive for detecting synovitis and tenosynovitis (p=0.055). CONCLUSION: Ultrasound could be as sensitive and specific as contrast-enhanced MRI for the diagnosis of subclinical synovitis and tenosynovitis.

Ultrasound versus contrast-enhanced magnetic resonance imaging for subclinical synovitis and tenosynovitis: a diagnostic performance study

OBJECTIVES: Radiographic manifestations of synovitis (e.g., erosions) can be observed only in the late stage of rheumatoid arthritis. Ultrasound is a noninvasive, cheap, and widely available technique that enables the evaluation of inflammatory changes in the peripheral joint. In the same way, dynamic contrast-enhanced magnetic resonance imaging (MRI) enables qualitative and quantitative measurements. The objectives of the study were to compare the sensitivity and accuracy of ultrasound in detecting subclinical synovitis and tenosynovitis with those of contrast-enhanced MRI. METHODS: The ultrasonography and contrast-enhanced MRI findings of the wrist, metacarpophalangeal, and proximal interphalangeal joints (n=450) of 75 patients with a history of joint pain and morning stiffness between 6 weeks and 2 years were reviewed. The benefits score was evaluated for each modality. RESULTS: The ultrasonic findings showed inflammation in 346 (77%) joints, while contrast-enhanced MRI found signs of early rheumatoid arthritis in 372 (83%) joints. The sensitivities of ultrasound and contrast-enhanced MRI were 0.795 and 0.855, respectively, and the accuracies were 0.769 and 0.823, respectively. Contrast-enhanced MRI had a likelihood of 0-0.83 and ultrasound had a likelihood of 0-0.77 for detecting synovitis and tenosynovitis at one time. The two imaging modalities were equally competitive for detecting synovitis and tenosynovitis (p=0.055). CONCLUSION: Ultrasound could be as sensitive and specific as contrast-enhanced MRI for the diagnosis of subclinical synovitis and tenosynovitis.

Hepatocellular Carcinoma: Retrospective Evaluation of the Correlation Between Gadobenate Dimeglumine-Enhanced Magnetic Resonance Imaging and Pathologic Grade.

OBJECTIVE: The aim of this study was to evaluate the usefulness of gadobenate dimeglumine-enhanced magnetic resonance imaging in characterizing the grade of hepatocellular carcinoma (HCC) using the signal intensity (SI) of the erector spinae as internal reference. MATERIALS AND METHODS: Clinical data of 40 patients (a total of 44 lesions) confirmed by pathology for HCC were retrospectively reviewed. Gadobenate dimeglumine-enhanced magnetic resonance imaging was performed in all patients, and SI of lesions (SIles), liver parenchyma around the lesions (SIhep), erector spinae (SImus) and standard deviation of SI of the surrounding noise (SDnoi) on nonenhanced T2WI, nonenhanced T1WI, and contrast-enhanced T1WI (in both arterial and hepatobiliary phase [AP and HBP]) were measured, respectively. Contrast-to-noise ratio (CNR) were separately defined as CNR1 ([SIles - SIhep]/SDnoi) and CNR2 ([SIles - SImus]/SDnoi). Statistical analyses were performed using one-way analysis of variance, least significant difference test, logistic regression analysis, Spearman rank correlation, and receiver operating characteristic curves analysis. RESULTS: Forty-four HCCs, including 3 well-differentiated HCCs, 26 moderately differentiated HCCs, and 15 poorly differentiated (PD) HCCs, were confirmed. On logistic regression analysis, only CNR2 in the HBP was predictor of PD HCCs (P = 0.015, odds ratio = 1.040). The size of lesions, CNR1 in the AP, CNR2 in the AP, and CNR2 in the HBP, showed significant correlations with the degree of differentiation (correlation coefficients = -0.371, 0.435, 0.503, and 0.512, respectively; P = 0.013, 0.003, 0.001, and 0.000, respectively). Contrast-to-noise ratio 2 in the HBP with the cutoff of less than 4.56 could distinguish moderately differentiated HCCs from PD HCC with the sensitivity and specificity of 84.6% and 60.0%, respectively. CONCLUSIONS: Relatively low arterial enhancement and low CNR2 value in the HBP are predictive for poor histological grade of HCCs.