The Association Between Tumor Radiomic Analysis and Peritumor Habitat-Derived Radiomic Analysis on Gadoxetate Disodium-Enhanced MRI With Microvascular Invasion in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment. PURPOSE: To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis. STUDY TYPE: Retrospective. SUBJECTS: Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94). FIELD STRENGTH/SEQUENCE: 1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence. ASSESSMENT: Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews. STATISTICAL TESTS: Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance. RESULTS: Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival. DATA CONCLUSION: The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

MR radiomics to predict microvascular invasion status and biological process in combined hepatocellular carcinoma-cholangiocarcinoma.

OBJECTIVES: To establish an MRI-based radiomics model for predicting the microvascular invasion (MVI) status of cHCC-CCA and to investigate biological processes underlying the radiomics model. METHODS: The study consisted of a retrospective dataset (82 in the training set, 36 in the validation set) and a prospective dataset (25 patients in the test set) from two hospitals. Based on the training set, logistic regression analyses were employed to develop the clinical-imaging model, while radiomic features were extracted to construct a radiomics model. The diagnosis performance was further validated in the validation and test sets. Prognostic aspects of the radiomics model were investigated using the Kaplan-Meier method and log-rank test. Differential gene expression analysis and gene ontology (GO) analysis were conducted to explore biological processes underlying the radiomics model based on RNA sequencing data. RESULTS: One hundred forty-three patients (mean age, 56.4 ± 10.5; 114 men) were enrolled, in which 73 (51.0%) were confirmed as MVI-positive. The radiomics model exhibited good performance in predicting MVI status, with the area under the curve of 0.935, 0.873, and 0.779 in training, validation, and test sets, respectively. Overall survival (OS) was significantly different between the predicted MVI-negative and MVI-positive groups (median OS: 25 vs 18 months, p = 0.008). Radiogenomic analysis revealed associations between the radiomics model and biological processes involved in regulating the immune response. CONCLUSION: A robust MRI-based radiomics model was established for predicting MVI status in cHCC-CCA, in which potential prognostic value and underlying biological processes that regulate immune response were demonstrated. CRITICAL RELEVANCE STATEMENT: MVI is a significant manifestation of tumor invasiveness, and the MR-based radiomics model established in our study will facilitate risk stratification. Furthermore, underlying biological processes demonstrated in the radiomics model will offer valuable insights for guiding immunotherapy strategies. KEY POINTS: MVI is of prognostic significance in cHCC-CCA, but lacks reliable preoperative assessment. The MRI-based radiomics model predicts MVI status effectively in cHCC-CCA. The MRI-based radiomics model demonstrated prognostic value and underlying biological processes. The radiomics model could guide immunotherapy and risk stratification in cHCC-CCA.

Correlation between clinical characteristics and epicardial adipose tissue features in acute myocarditis patients using coronary computed tomography (CT) vascular imaging: a case-control study with retrospective data collection.

Background: Epicardial adipose tissue (EAT) is unique type of visceral adipose tissue, sharing the same microcirculation with myocardium. This study aimed to assess the imaging features of EAT in patients with acute myocarditis (AM) and explore the relationships with clinical characteristics. Methods: For this retrospective case-control study, totally 38 AM patients and 52 controls were screened retrospectively from January 2019 to December 2022, and the EAT volume was measured from coronary computed tomography (CT) angiography imaging. Histogram analysis was performed to calculate parameters like the mean, standard deviation, interquartile range and percentiles of EAT attenuation. Whether EAT features change was assessed when clinical characteristics including symptoms, T wave abnormalities, pericardial effusion (PE), impairment of systolic function, and the need for intensive care presented. Results: The EAT volume (75.2±22.8 mL) and mean EAT attenuation [-75.8±4.4 Hounsfield units (HU)] of the AM group was significantly larger than the control group (64.7±26.0 mL, P=0.049; -77.9±5.0 HU, P=0.044). Among the clinical characteristics, only the presence of PE was associated with changes in EAT features. Patients with PE showed significantly changes in EAT attenuation including mean attenuation [analysis of variance (ANOVA) P=0.001] and quantitative histogram parameters. The mean attenuation of patients with PE (-71.9±4.0 HU) was significantly larger than controls (-77.9±5.0 HU, Bonferroni corrected P<0.001) and patients without PE (-77.4±3.5 HU, Bonferroni corrected P=0.003). Observed in histogram, the overall increase in EAT attenuation could lead to decrease in EAT volume, which resulted in no statistically significant difference in EAT volume between the AM patients with PE and controls (64.7±26.0 vs. 72.2±28.3 mL, Bonferroni corrected P>0.99). Conclusions: Compared to controls, EAT volume was significantly larger in AM, and EAT attenuation increased notably in the presence of PE. We recommend evaluating EAT volume and attenuation simultaneously when quantifying EAT using CT attenuation thresholds.

An unenhanced 3D-FLAIR sequence using long repetition time and constant flip angle to image endolymphatic hydrops.

OBJECTIVES: To evaluate a three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) sequence using a long repetition time (TR) and constant flip angle (CFA) in differentiating between perilymph and endolymph in a phantom study, and unenhanced endolymphatic hydrops (EH) imaging in a patient study. METHODS: Three solutions in similar ion and protein concentrations with endolymph, perilymph, and cerebrospinal fluid were prepared for variable flip angle (VFA) 3D-FLAIR (TR 10,000 ms) and CFA (120°) 3D-FLAIR using different TR (10,000, 16,000, and 20,000 ms). Fifty-two patients with probable or definite Meniere's disease received unenhanced CFA (120°) 3D-FLAIR using a long TR (20,000 ms) and 4-h-delay enhanced CFA (120°) 3D-FLAIR (TR 16,000 ms). Image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of them were compared. Agreement in the evaluation of the EH degree between them was analyzed. RESULTS: In the phantom study, CNRs between perilymphatic and endolymphatic samples of VFA 3D-FLAIR (TR 10,000 ms) and CFA 3D-FLAIR (TR 10,000, 16,000, and 20,000 ms) were 6.66 ± 1.30, 17.90 ± 2.76, 23.87 ± 3.09, and 28.22 ± 3.15 (p < 0.001). In patient study, average score (3.65 ± 0.48 vs. 4.19 ± 0.40), SNR (34.56 ± 9.80 vs. 51.40 ± 11.27), and CNR (30.66 ± 10.55 vs. 45.08 ± 12.27) of unenhanced 3D-FLAIR were lower than enhanced 3D-FLAIR (p < 0.001). Evaluations of the two sequences showed excellent agreement in the cochlear and vestibule (Kappa value: 0.898 and 0.909). CONCLUSIONS: The CFA 3D-FLAIR sequence using a long TR could be used in unenhanced EH imaging with high accuracy. CLINICAL RELEVANCE STATEMENT: Unenhanced imaging of endolymphatic hydrops is valuable in the diagnosis and follow-up of patients, especially those who cannot receive contrast-enhanced MRI. KEY POINTS: Ion and protein concentration differences can be utilized in differentiating endolymph and perilymph on MRI. Endolymphatic and perilymphatic samples could be differentiated in vitro on this 3D-FLAIR sequence. This unenhanced 3D-FLAIR sequence is in excellent agreement with the enhanced constant flip angle 3D-FLAIR sequence.

A scoring system for stratifying the risk of postoperative bone metastases in colorectal cancer.

BACKGROUND: Surveillance recommendations for postoperative high-risk colorectal bone metastases patients remain in a gray area of guidelines. We aimed to develop a risk stratification system to select ideal candidates for follow-up of colorectal bone metastases status. METHODS: Postoperative colorectal cancer patients were included to develop a risk-scoring system to predict bone metastases. Risk scores were calculated based on the predictive factors for bone metastases, which were identified using the Cox proportional hazard regression model. Kaplan-Meier curves visualize the differences between risk groups. RESULTS: Eight risk factors (age, lymph node metastasis, pathologic tumor deposit, KRAS mutation status, suspicious retroperitoneal lymph node metastasis, lung metastasis status, largest thickness of colorectal cancer lesion, largest short diameter of lymph node) were predictors of colorectal bone metastases and incorporated into the risk scoring system, and the patients were categorized into 2 risk groups. In the low-risk group, the 1, 3, and 5-year colorectal bone metastases rates were 2.4%, 4.6%, and 3.7%, respectively, whereas in the high-risk group, the 1, 3, and 5-year colorectal bone metastases rates were 15.6%, 29.9%, and 44.4%, respectively. The risk scoring system exhibited a C-index of 0.706, 0.795, and 0.841 in 1, 3, and 5 years, respectively. The Kaplan-Meier curve demonstrates that the incidence of colorectal bone metastases was higher in the high-risk group than in the low-risk group (50.5% vs 11.4%, P < .001). CONCLUSION: This risk-scoring system may be valuable in predicting colorectal bone metastases in colorectal cancer patients, and we suggest that colorectal bone metastases status surveillance be added in the high-risk group.

Value of magnetic resonance imaging for diagnosis of LR­3 and LR-4 lesions coexisting with hepatocellular carcinoma.

PURPOSE: To explore which preoperative clinical data and conventional magnetic resonance imaging (MRI) features may indicate the presence of hepatocellular carcinoma (HCC) in HCC patients coexisting with LR-3 and LR-4 lesions. METHODS: HCC Patients coexisting with LR-3 and LR-4 lesions who participated in a prospective clinical trial (XX) were included in this study. Two radiologists independently assessed the preoperative MRI features and each lesion was assigned according to the liver imaging reporting and data system (LI-RADS). The preoperative clinical data were also evaluated. The relative values of these parameters were assessed as potential predictors of HCC for coexisting LR-3 and LR-4 lesions. RESULTS: We enrolled 102 HCC patients (58.1 ± 11.5 years; 84.3% males) coexisting with 110 LR-3 and LR-4 lesions (HCCs group [n = 66]; non-HCCs group [n = 44]). The presence of restricted diffusion (OR: 18.590, p < 0.001), delayed enhancement (OR: 0.113, p < 0.001), and mild-moderate T2 hyperintensity (OR: 3.084, p = 0.048) were found to be independent predictors of HCC diagnosis. The sensitivity and specificity of the above independent variables for the diagnosis of HCC ranged from 66.7 to 80.3% and 56.8 to 88.6%, respectively. ROC analysis showed that, in discriminating HCC, the AUCs of the above factors were 0.777, 0.686, and 0.670, respectively. Combining these three findings for the prediction of HCC resulted in a specificity greater than 97%, and the AUC further increased to 0.874. CONCLUSION: The presence of restricted diffusion, delayed enhancement, and mild-moderate T2 hyperintensity can be useful features for risk stratification of coexisting LR-3 and LR-4 lesions in HCC patients. Trial registration a prospective clinical trial (ChiCTR2000036201).

Association Between MRI-Based Radiomics Features and Regional Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma and Its Clinical Outcome.

BACKGROUND: Regional lymph node metastasis (LNM) assessment is crucial for predicting intrahepatic cholangiocarcinoma (iCCA) prognosis. However, imaging assessment has limitations for identifying LNM. PURPOSE: To investigate the association between MRI radiomics features, regional LNM status, and prognosis in iCCA. STUDY TYPE: Retrospective. SUBJECTS: Two hundred ninety-six patients (male = 197) with surgically confirmed iCCA. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T. DWI, T2WI, and contrast-enhanced T1WI. ASSESSMENT: Clinical information, radiologic, and MRI-based radiomics features associated with LNM status were collected to establish models. Performance of MRI, PET/CT, and the combined LNM models were compared in training (N = 207) and test (N = 89) datasets. Overall survival (OS) was compared based on LNM status. STATISTICAL TESTS: The independent features were selected by 5-fold cross-validation. The performance of MRI, PET/CT, and the models was evaluated using the area under receiver operating characteristic curve (AUC). Univariable and multivariable Cox regression were used to identify independent variables for OS. Kaplan-Meier curves were compared with the log-rank test between LNM positive and negative groups. P < 0.05 was considered statistically significant. RESULTS: Intrahepatic duct dilatation, enhancement pattern, and CA19-9 were independent clinicoradiologic features. The radiomics model was constructed by the independent radiomics features extracted from T2WI and delay phase T1WI. The combined LNM model showed AUC of 0.888, 0.884, and 0.811 in training, validation, and test cohorts with a positive net benefit. PET/CT exhibited similar sensitivity to the combined LNM model (0.750 vs. 0.733, P > 0.999) while the combined LNM model showed higher specificity (0.703 vs. 0.630, P = 0.039) in the test cohort. High risk of regional LNM was significantly associated with worse OS (median: 24 months) than low risk (median: 30 months, P < 0.0001). DATA CONCLUSIONS: The combined LNM model has the strongest correlation with LNM status for mass-forming iCCA patients. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI.

The study aims to evaluate multiparametric magnetic resonance imaging (MRI) for differentiating Follicular thyroid neoplasm (FTN) from non-FTN and malignant FTN (MFTN) from benign FTN (BFTN). We retrospectively analyzed 702 postoperatively confirmed thyroid nodules, and divided them into training (n = 482) and validation (n = 220) cohorts. The 133 FTNs were further split into BFTN (n = 116) and MFTN (n = 17) groups. Employing univariate and multivariate logistic regression, we identified independent predictors of FTN and MFTN, and subsequently develop a nomogram for FTN and a risk score system (RSS) for MFTN prediction. We assessed performance of nomogram through its discrimination, calibration, and clinical utility. The diagnostic performance of the RSS for MFTN was further compared with the performance of the Thyroid Imaging Reporting and Data System (TIRADS). The nomogram, integrating independent predictors, demonstrated robust discrimination and calibration in differentiating FTN from non-FTN in both training cohort (AUC = 0.947, Hosmer-Lemeshow P = 0.698) and validation cohort (AUC = 0.927, Hosmer-Lemeshow P = 0.088). Key risk factors for differentiating MFTN from BFTN included tumor size, restricted diffusion, and cystic degeneration. The AUC of the RSS for MFTN prediction was 0.902 (95% CI 0.798-0.971), outperforming five TIRADS with a sensitivity of 73.3%, specificity of 95.1%, accuracy of 92.4%, and positive and negative predictive values of 68.8% and 96.1%, respectively, at the optimal cutoff. MRI-based models demonstrate excellent diagnostic performance for preoperative predicting of FTN and MFTN, potentially guiding clinicians in optimizing therapeutic decision-making.

Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

BACKGROUND: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. PURPOSE: To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. STUDY TYPE: Retrospective. SUBJECTS: 395 patients with 1149 FLLs. FIELD STRENGTH/SEQUENCE: The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. STATISTICAL TESTS: McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. RESULTS: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). DATA CONCLUSION: AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. TECHNICAL EFFICACY: Stage 2.

Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.

Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Estimating Efficacy of Conversion Therapy on Patients with Initially Unresectable Colorectal Cancer Liver Metastases by using MRI: Development of a Predictive Score.

RATIONALE AND OBJECTIVES: The conversion success rate (CSR) has crucial implication for clinical outcomes of initially unresectable colorectal liver metastases (CRLM) following conversion therapy. This study aimed to develop a simple predictive scoring model for identifying CSR according to baseline magnetic resonance imaging (MRI) features, and confirm its performance and prognostic significance in a validation cohort. METHODS: A total of 155 consecutive patients with initially unresectable CRLM were retrospectively reviewed in the study. A simple MRI-based predictive scoring model for identifying CSR was developed in the development cohort (n = 104) by using multivariable logistic regression analyzes. The diagnostic performance was evaluated for the predictive score. Thereafter, patients in the validation cohort (n = 51) were stratified into groups with predicted high CSR or low CSR according to the score. The progression-free survival (PFS) and overall survival (OS) were compared between two groups using the log-rank test. RESULTS: The predictive score of CSR, named mrNISE, incorporated the number of CRLM ≥ 10, the largest size ≥ 50 mm, poorly defined tumor-liver interface, and peritumoral enhancement. The AUC of the mrNISE score was 0.845 for the development cohort and 0.776 for the validation cohort. According to the score, patients with predicted high CSR had better PFS and OS than those with low CSR in both development and validation cohorts. CONCLUSION: The predictive score demonstrated great performance for identifying CSR of initially unresectable CRLM. Stratifying patients by the score, personalized treatment goals can be formulated before conversion therapy to improve clinical prognosis and reduce adverse events caused by ineffective treatment.

Association of Lipoprotein(a) Levels With Myocardial Infarction in Patients With Low-Attenuation Plaque.

BACKGROUND: Lipoprotein(a) (Lp[a]) is associated with an increased risk of myocardial infarction (MI). However, the mechanism underlying this association has yet to be fully elucidated. OBJECTIVES: This multicenter study aimed to investigate whether association between Lp(a) and MI risk is reinforced by the presence of low-attenuation plaque (LAP) identified by coronary computed tomography angiography (CCTA). METHODS: In a derivation cohort, a total of 5,607 patients with stable chest pain suspected of coronary artery disease who underwent CCTA and Lp(a) measurement were prospectively enrolled. In validation cohort, 1,122 patients were retrospectively collected during the same period. High Lp(a) was defined as Lp(a) ≥50 mg/dL. The primary endpoint was a composite of time to fatal or nonfatal MI. Associations were estimated using multivariable Cox proportional hazard models. RESULTS: During a median follow-up of 8.2 years (Q1-Q3: 7.2-9.3 years), the elevated Lp(a) levels were associated with MI risk (adjusted HR [aHR]: 1.91; 95% CI: 1.46-2.49; P < 0.001). There was a significant interaction between Lp(a) and LAP (Pinteraction <0.001) in relation to MI risk. When stratified by the presence or absence of LAP, Lp(a) was associated with MI in patients with LAP (aHR: 3.03; 95% CI: 1.92-4.76; P < 0.001). Mediation analysis revealed that LAP mediated 73.3% (P < 0.001) for the relationship between Lp(a) and MI. The principal findings remained unchanged in the validation cohort. CONCLUSIONS: Elevated Lp(a) augmented the risk of MI during 8 years of follow-up, especially in patients with LAP identified by CCTA. The presence of LAP could reinforce the relationship between Lp(a) and future MI occurrence.

Imaging Findings of Calcifying Nested Stromal-Epithelial Tumour of the Liver: A Case Report and Literature Review.

BACKGROUND: A calcifying nested stromal-epithelial tumour (CNSET) is an erratic primary liver malignant tumour frequently discovered in young girls and females. Neither its pathogenesis nor its nosogenesis is clearly known. While principally indolent, infrequent tumours with aggressive clinical progression have been defined. This paper describes a CNSET case with rare clinical and imaging features. CASE PRESENTATION: A 17-year-old girl initially presented with enlarged lymph nodes near the main portal vein of the liver and a large liver tumour. Lesions were identified on the imaging findings obtained via positron emission tomography-computed tomography (CT) scanning, including an abnormal increase of heterogeneous glucose metabolism in the intrahepatic mass, with a maximum standardised uptake value of around 3.2. The CT imaging showed multiple dense shadows in the lesion, while the magnetic resonance imaging indicated a long T1 and a slightly longer T2. CONCLUSIONS: This study summarises the imaging features of CNSETs to provide a reference for diagnosing liver tumours. In addition, the literature on the topics covered was systematically reviewed.

Detecting microvascular invasion in hepatocellular carcinoma using the impeded diffusion fraction technique to sense macromolecular coordinated water.

OBJECTIVES: Impeded diffusion fraction (IDF) is a novel and promising diffusion-weighted imaging (DWI) technique that allows for the detection of various diffusion compartments, including macromolecular coordinated water, free diffusion, perfusion, and cellular free water. This study aims to investigate the clinical potential of IDF-DWI in detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS: 66 patients were prospectively included. Metrics derived from IDF-DWI and the apparent diffusion coefficient (ADC) were calculated. Multivariate logistic regression was employed to identify clinical risk factors. Diagnostic performance was evaluated using the area under the receiver operating characteristics curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and the calibration error (cal-error). Additionally, a power analysis was conducted to determine the required sample size. RESULTS: The results suggested a significantly higher fraction of impeded diffusion (FID) originating from IDF-DWI in MVI-positive HCCs (p < 0.001). Moreover, the ADC was found to be significantly lower in MVI-positive HCCs (p = 0.019). Independent risk factors of MVI included larger tumor size and elevated alpha-fetoprotein (AFP) levels. The nomogram model incorporating ADC, FID, tumor size, and AFP level yielded the highest diagnostic accuracy for MVI (AUC-PR = 0.804, AUC-ROC = 0.783, cal-error = 0.044), followed by FID (AUC-PR = 0.693, AUC-ROC = 0.760, cal-error = 0.060) and ADC (AUC-PR = 0.570, AUC-ROC = 0.651, cal-error = 0.164). CONCLUSION: IDF-DWI shows great potential in noninvasively, accurately, and preoperatively detecting MVI in HCC and may offer clinical benefits for prognostic prediction and determination of treatment strategy.

Mucin-producing tumors of the ovary--preoperative differentiation between metastatic ovarian mucinous carcinoma and primary mucinous malignant tumors.

OBJECTIVE: To investigate the clinical and magnetic resonance imaging (MRI) features for preoperatively discriminating  primary ovarian mucinous malignant tumors (POMTs) and metastatic mucinous carcinomas involving the ovary (MOMCs). METHODS: This retrospective multicenter study enrolled 61 patients with 22 POMTs and 49 MOMCs, which were pathologically proved between November 2014 to Jane 2023. The clinical and MRI features were evaluated and compared between POMTs and MOMCs. Univariate and multivariate analyses were performed to identify the significant variables between the two groups, which were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: 35.9% patients with MOMCs were discovered synchronously with the primary carcinomas; 25.6% patients with MOMCs were bilateral, and all of the patients with POMTs were unilateral. The biomarker CEA was significantly different between the two groups (p = 0.002). There were significant differences in the following MRI features: tumor size, configuration, enhanced pattern, the number of cysts, honeycomb sign, stained-glass appearance, ascites, size diversity ratio, signal diversity ratio. The locular size diversity ratio (p = 0.005, OR = 1.31), and signal intensity diversity ratio (p = 0.10, OR = 4.01) were independent predictors for MOMCs. The combination of above independent criteria yielded the largest area under curve of 0.922 with a sensitivity of 82.3% and specificity of 88.9%. CONCLUSIONS: Patients with MOMCs were more commonly bilaterally and having higher levels of CEA, but did not always had a malignant tumor history. For ovarian mucin-producing tumors, the uniform locular sizes and signal intensities were more predict MOMCs.

Feasibility of the application of deep learning-reconstructed ultra-fast respiratory-triggered T2-weighted imaging at 3 T in liver imaging.

OBJECTIVE: The evaluate the feasibility of a novel deep learning-reconstructed ultra-fast respiratory-triggered T2WI sequence (DL-RT-T2WI) In liver imaging, compared with respiratory-triggered Arms-T2WI (Arms-RT-T2WI) and respiratory-triggered FSE-T2WI (FSE-RT-T2WI) sequences. METHODS: 71 patients with liver lesions underwent 3-T MRI and were prospectively enrolled. Two readers independently analyzed images acquired with DL-RT-T2WI, Arms-RT-T2WI, and FSE-RT-T2WI. The qualitative evaluation indicators, including overall image quality (OIQ), sharpness, noise, artifacts, lesion detectability (LC), lesion characterization (LD), cardiacmotion-related signal loss (CSL), and diagnostic confidence (DC), were evaluated in two readers, and further statistically compared using paired Wilcoxon rank-sum test among three sequences. RESULTS: 176 lesions were detected in DL-RT-T2W and Arms-RT-T2WI, and 175 were detected in FSE-RT-T2WI. The acquisition time of DL-RT-T2WI was improved by 4.8-7.9 folds compared to the other two sequences. The OIQ was scored highest for DL-RT-T2WI (R1, 4.61 ± 0.52 and R2, 4.62 ± 0.49), was significantly superior to Arms-RT-T2WI (R1, 4.30 ± 0.66 and R2, 4.34 ± 0.69) and FSE-RT-T2WI (R1, 3.65 ± 1.08 and R2, 3.75 ± 1.01). Artifacts and sharpness scored highest for DL-RT-T2WI, followed by Arms-RT-T2WI, and were lowest for FSE-RT-T2WI in both two readers. Noise and CSL for DL-RT-T2WI scored similar to Arms-RT-T2WI (P > 0.05) and were significantly superior to FSE-RT-T2WI (P < 0.001). Both LD and LC for DL-RT-T2WI were significantly superior to Arms-RT-T2WI and FSE-RT-T2WI in two readers (P < 0.001). DC for DL-RT-T2WI scored best, significantly superior to Arms-RT-T2WI (P < 0.010) and FSE-RT-T2WI (P < 0.001). CONCLUSIONS: The novel ultra-fast DL-RT-T2WI is feasible for liver imaging and lesion characterization and diagnosis, not only offers a significant improvement in acquisition time but also outperforms Arms-RT-T2WI and FSE-RT-T2WI concerning image quality and DC.

How to differentiate between combined hepatocellular carcinoma-cholangiocarcinoma and intrahepatic cholangiocarcinoma with rim arterial phase hyperenhancement?

PURPOSE: To analyze and compare the differences in MRI features between combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) and intrahepatic cholangiocarcinoma (iCCA) with arterial phase peripheral enhancement, so as to provide valuable references for preoperative differential diagnosis. METHODS: Seventy cHCC-CCA patients and 74 iCCA patients confirmed by pathology were included in this study. Their contrast-enhanced MRI showed rim arterial phase hyperenhancement (Rim APHE). The differences of clinicopathological data and MRI features between cHCC-CCA and iCCA were compared. Then, the sensitivity, specificity, and area under curve (AUC) were also analyzed and compared. RESULTS: Seventy cHCC-CCA patients (mean age, 55.7 ± 10.6 years) and 74 iCCA patients (mean age, 61.1 ± 10.5 years) were evaluated. In this study, univariable and multivariable regression analysis showed that AFP > 20 ng/ml (OR = 5.824, p = 0.006), enhancing capsule (OR = 7.252, p = 0.001), and mosaic architecture (OR = 32.732, p < 0.001) were independent risk factors of cHCC-CCA with Rim APHE. However, only hepatic capsule retraction (OR = 0.091, p < 0.001) was an independent predictor of iCCA. In addition, combining AFP > 20 ng/ml with enhancing capsule (96.7% vs. 79.2%, p < 0.001) and/or mosaic architecture (96.4% vs. 94.7%, p < 0.001) can improve the sensitivity of differentiating cHCC-CCA (vs. iCCA) with Rim APHE. CONCLUSION: The combination of elevated AFP and MRI features, such as enhancing capsule and mosaic architecture, will help in preoperative differential diagnosis of cHCC-CCA and iCCA with Rim APHE.

The value of varying diffusion curvature MRI for assessing the microvascular invasion of hepatocellular carcinoma.

PURPOSE: Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS: 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power. RESULTS: MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D1 (0.680 ± 0.100 × 10-3 vs 0.572 ± 0.148 × 10-3 mm2/s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10-3 vs 1.471 ± 0.322 × 10-3 mm2/s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D1, ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve. CONCLUSION: VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making.

Component prediction in combined hepatocellular carcinoma-cholangiocarcinoma: habitat imaging and its biologic underpinnings.

PURPOSE: To construct an MRI-based habitat imaging model to help predict component percentage in combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) preoperatively, and investigate the biologic underpinnings of habitat imaging in cHCC-CCA. METHODS: The study consisted of one retrospective model-building dataset and one prospective validation dataset from two hospitals. All voxels were assigned into different clusters according to the similarity of enhancement pattern by using K-means clustering method, and each habitat's volume fraction in each lesion was calculated. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select optimal predictors, and then to establish an MRI-based habitat imaging model. R-squared was calculated to evaluate performance of the prediction models. Model performance was also verified in the prospective dataset with RNA sequencing data, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was then applied to investigate the biologic underpinnings of habitat imaging. RESULTS: A total of 129 patients were enrolled (mean age, 56.1 ± 10.4, 102 man), among which 104 patients were in the retrospective model-building set, while 25 patients in the prospective validation set. Three habitats, habitat1 (HCC-alike habitat), habitat2 (iCCA-alike habitat), and habitat3 (in-between habitat), were identified. Habitat 1's volume fraction, habitat 3's volume fraction, nonrim APHE, nonperipheral washout, and LI-RADS categorization were selected to develop an HCC percentage prediction model with R-squared of 0.611 in the model-building set and 0.541 in the validation set. Habitat 1's volume fraction was correlated with genes involved in regulation of actin cytoskeleton and Rap1 signaling pathway, which regulate cell migration and tumor metastasis. CONCLUSION: Preoperative prediction of HCC percentage in patients with cHCC-CCA was achieved using an MRI-based habitat imaging model, which may correlate with signaling pathways regulating cell migration and tumor metastasis.

Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.

OBJECTIVES: To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis. METHODS: Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. RESULTS: The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01). CONCLUSION: ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis. CLINICAL RELEVANCE STATEMENT: ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other. KEY POINTS: • Both machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.