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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

Radiation Dose and Image quality of coronary CT angiography performed with whole-heart coverage CT scanner with 0.25s rotation time in patients with irregular heart rhythm.

Objectives: To evaluate radiation exposure, image quality, and diagnostic performance of coronary CT angiography (CCTA) using the invasive coronary angiography (ICA) as the reference standard in patients with irregular heart rhythm on a 0.25 s rotation time, 16 cm coverage, single-beat, CT scanner with AI-assisted motion correction. Methods: CCTA data-sheets of 427 patients using a CT scanner with an ECG monitoring system and motion correction algorithm were collected retrospectively. All the patients were divided into two groups: regular heart rhythm (357 patients) and irregular heart rhythm (70 patients). 22 patients in irregular heart rhythm underwent ICA. Image quality and effective dose in both groups were evaluated and compared. Image quality was evaluated on 5-point scales. The diagnostic performance of CCTA in irregular heart rhythm group was compared with the results of ICA. Results: The image quality in both groups was similar (4.34 ± 0.47 vs 4.37 ± 0.48, p > 0.05). No significant difference was observed in effective dose between two groups (2.7 ± 0.7 vs 2.9 ± 1.3, p > 0.05). The diagnostic accuracy was 90.91% in a patient-based analysis, 96.97% in a vessel-based analysis, and 98.61% in a segment-based analysis. In irregular heart rhythm group, gender was an important factor affecting the number of CCTA scans in a single examination and the radiation dose exposed to the patient. Conclusions: For patients with irregular heart rhythm, a CT scanner with an ECG monitoring system and motion correction algorithm can not only reduce the radiation dose to the same level as patients with normal heart rhythms, but also ensure that the images with high diagnostic accuracy.

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.

A Transformer-Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation.

BACKGROUND & AIMS: We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA. METHODS: A Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI-based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180). RESULTS: Over 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence-free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a-fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02-2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40-5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95% CI: 1.40-3.29, p = .001) yielded a c-index of .731 (bootstrapped 95% CI: .667-.778) for evaluating RFS after RFA. Among the three risk factors, MVI was the most powerful predictor for intrahepatic distance recurrence. CONCLUSIONS: The proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early-stage HCC patients undergoing RFA.

Cardiac magnetic resonance-based layer-specific strain in immune checkpoint inhibitor-associated myocarditis.

AIMS: To assess the different imaging characteristics between corticosteroid-sensitive (CS) and corticosteroid-refractory (CR) immune checkpoint inhibitor-associated myocarditis (ICIaM) with cardiac magnetic resonance (CMR) and the potential CMR parameters in the early detection of CR ICIaM. METHODS AND RESULTS: Thirty-five patients diagnosed with ICIaM and 30 age and gender-matched cancer patients without a history of ICI treatment were enrolled. CMR with contrast was performed within 2 days of clinical suspicion. Left ventricular ejection fraction (LVEF) and late gadolinium enhancement (LGE) were assessed by CMR. LV sub-endocardial (GLSendo) and sub-epicardial (GLSepi) global longitudinal strains were quantified by offline feature tracking analysis. CS and CR ICIaM were defined based on the trend of Troponin I and clinical course during corticosteroid treatment. All 35 patients presented with non-fulminant symptoms upon initial assessment. Twenty patients (57.14%) were sensitive, and 15 (42.86%) were refractory to corticosteroids. Compared with controls, 22 patients (62.86%) with ICIaM developed LGE. LVEF decreased in CR ICIaM compared with the CS group and controls. GLSendo (-14.61 ± 2.67 vs. -18.50 ± 2.53, P < 0.001) and GLSepi (-14.75 ± 2.53 vs. -16.68 ± 2.05, P < 0.001) significantly increased in patients with CR ICIaM compared with the CS ICIaM. In patients with CS ICIaM, although GLSepi (-16.68 ± 2.05 vs. -19.31 ± 1.80, P < 0.001) was impaired compared with the controls, GLSendo was preserved. There was no difference in CMR parameters between LGE-positive and negative groups. LVEF, GLSendo, and GLSepi were predictors of CR ICIaM. When LVEF, GLSendo, and GLSepi were included in multivariate analysis, only GLSendo remained an independent predictor of CR ICIaM (OR: 2.170, 95% CI: 1.189-3.962, P = 0.012). A GLSendo of ≥-17.10% (sensitivity, 86.7%; specificity, 80.0%; AUC, 0.860; P < 0.001) could predict CR ICIaM in the ICIaM cohort. Kaplan-Meier analysis showed that in patients with impaired GLSendo of ≥-17.10%, cardiovascular adverse events (CAEs) occurred much earlier than in patients with preserved GLSendo of <-17.10% (Log-rank test P = 0.017). CONCLUSIONS: CR and CS ICIaM demonstrated different functional and morphological characteristics in different myocardial layers. An impaired GLSendo could be a helpful parameter in early identifying corticosteroid-refractory individuals in the ICIaM population.

MRI for Hepatitis B-Associated Intrahepatic Cholangiocarcinoma: A Multicenter Comparative Study.

BACKGROUND: The diagnosis of intrahepatic cholangiocarcinoma (iCCA) is challenging in hepatitis B virus (HBV)-infected patients, due to the overlapping clinical manifestations and atypical imaging patterns compared to patients without HBV. PURPOSE: To investigate the preoperative imaging characteristics of iCCA in patients with HBV in comparison to those without HBV. STUDY TYPE: Retrospective. SUBJECTS: 431 patients with histopathologically confirmed iCCA (143 HBV-positive and 288 HBV-negative patients) were retrospectively enrolled from three institutes, and patients were allocated to the training (n = 302) and validation (n = 129) cohorts from different institutes or time period; 100 matching HBV-positive hepatocellular carcinoma (HCC) patients were also enrolled. FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T, including T1- and T2-weighted, diffusion-weighted and dynamic gadopentetate dimeglumine-enhanced imaging. ASSESSMENT: Clinical and MRI features were analyzed and compared between HBV-positive and HBV-negative patients with iCCA, and between HBV-positive patients with iCCA and HCC. STATISTICAL TESTS: Univariate and multivariate logistic regression analyses with odds ratio (OR) to identify independent features for discriminating HBV-associated iCCA. Diagnostic model generation by incorporating independent features, and the performance for discrimination was evaluated by receiver operating characteristics with the area under the curve (AUC) and 95% confidence interval (CI). AUCs were compared by the DeLong's method. A P-value <0.05 was considered statistically significant. RESULTS: Compared to patients without HBV, washout or degressive enhancement pattern (OR = 51.837), well-defined tumor margin (OR = 8.758) and no peritumoral bile duct dilation (OR = 4.651) were independent significant features for discriminating HBV-associated iCCAs. All these features were also the predominant MRI manifestations for HBV-associated HCC. The combined index showed an AUC of 0.798 (95% CI 0.748-0.842) in the training cohort and an AUC of 0.789 (95% CI 0.708-0.856) in the validation cohort for discrimination. The sensitivity, specificity, and accuracy were all >70%, which was superior to each single feature alone in both cohorts. [Correction added after first online publication on 29 June 2023. The Field Strength/Sequence has been updated from 5-T to 1.5-T.] DATA CONCLUSION: Preoperative MRI may help to discriminate HBV-associated iCCA. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY STAGE: 2.

Cancer-associated fibroblasts in neoadjuvant setting for solid cancers.

Therapeutic approaches for cancer have become increasingly diverse in recent times. A comprehensive understanding of the tumor microenvironment (TME) holds great potential for enhancing the precision of tumor therapies. Neoadjuvant therapy offers the possibility of alleviating patient symptoms and improving overall quality of life. Additionally, it may facilitate the reduction of inoperable tumors and prevent potential preoperative micrometastases. Within the TME, cancer-associated fibroblasts (CAFs) play a prominent role as they generate various elements that contribute to tumor progression. Particularly, extracellular matrix (ECM) produced by CAFs prevents immune cell infiltration into the TME, hampers drug penetration, and diminishes therapeutic efficacy. Therefore, this review provides a summary of the heterogeneity and interactions of CAFs within the TME, with a specific focus on the influence of neoadjuvant therapy on the microenvironment, particularly CAFs. Finally, we propose several potential and promising therapeutic strategies targeting CAFs, which may efficiently eliminate CAFs to decrease stroma density and impair their functions.

Should the Baseline MRI Staging Criteria Differentiate Between Mucinous and Classical Rectal Adenocarcinoma?

RATIONALE AND OBJECTIVES: To compare baseline MR imaging features for pre-treatment staging between rectal mucinous adenocarcinoma (RMAC) and rectal classical adenocarcinoma (RCAC), and to investigate whether the subtype of mucinous carcinoma influences MRI evaluation criteria and high-risk tumors identifying. METHODS: A total of 306 patients who underwent surgical rectal cancer resection were retrospectively reviewed in the study. MR imaging parameters of the primary tumor and lymph nodes (LNs) were compared between two subtypes. Logistic regression and receiver operating characteristic analyses were performed to test significant associations between LN imaging parameters and malignant LN status in RMAC and RCAC, respectively. RESULTS: The length of mucinous tumors was larger than RCAC tumors in pT3 and pT4 stage. For pN0 patients, the long and short diameters of the largest LN on MRI were more likely to be larger in RCAC than RMAC. For pN+ patients, the proportion of LNs exhibiting internal heterogeneity in RMAC was obviously greater than that in RCAC. The best cut-off value of the largest short diameter of malignant LNs was 6.05 mm for RMAC and 8.05 mm for RCAC. And the highest AUC for predicting LNs metastases based on the largest short diameter was 0.794 for RMAC using 6 mm size cut-off, and 0.667 for RCAC using 8 mm cut-off. CONCLUSION: The imaging features that were associated with LN metastases were different between RMAC and RCAC, and different size criteria of LNs was suggested to distinguish high-risk tumors. Clinicians should stay vigilant of LN status and take histologic subtypes into consideration before assigning clinical strategies.