The Value of Myocardial Fibrosis Parameters Derived from Cardiac Magnetic Resonance Imaging in Risk Stratification for Patients with Hypertrophic Cardiomyopathy

RATIONALE AND OBJECTIVES

The aim of the study was to determine whether myocardial fibrosis parameters of cardiac magnetic resonance imaging (MRI) has added value in the risk stratification of hypertrophic cardiomyopathy (HCM) patients.

MATERIALS AND METHODS

In this retrospective study, 108 patients with HCM (mean age ± standard deviation, 55.5 ± 13.4 years) were included from January 2019 to April 2022, and were followed up for 2 years to record sudden cardiac death (SCD) adverse events. All HCM patients underwent cardiac MRI and were divided into a training cohort (n = 81; mean age, 56.1 ± 13.0 years) and a validation cohort (n = 27; mean age, 57.8 ± 13.9 years). According to the presence of SCD risk factors defined by the 2020 AHA/ACC guidelines, HCM patients were classified into low-risk and high-risk groups. Cardiac MRI features, including late gadolinium enhancement (LGE), T1 mapping, and extracellular volume fraction (ECV), were assessed and compared between the two groups. Logistic regression analysis was used to select the optimal predictors of SCD from cardiac MRI features and HCM Risk-SCD score to construct prediction models. Receiver operating curve (ROC) analysis was used to assess the predictive performance of the constructed prediction model. Cox regression analysis was also used to determine the optimal predictors of SCD adverse events.

RESULTS

Multivariate logistic analysis showed that the global ECV was the single myocardial fibrosis parameter predictive of the risk of SCD (p < 0.001). The areas under the ROC curves (AUC) of global ECV were higher than those of LGE, global native T1, global postcontrast T1, and HCM Risk-SCD (AUC = 0.85 vs. 0.74, 0.77, 0.63, 0.78). An integrative risk stratification model combining global ECV (odds ratio, 1.36 [95% CI: 1.16-1.60]; p < 0.001) and HCM Risk-SCD score (odds ratio, 1.63 [95% CI: 1.08-2.47]; p < 0.001) achieved an AUC of 0.89 (95% CI: 0.81-0.96) in the training cohort, which was significantly higher than that of HCM Risk-SCD score alone (p = 0.03). The AUC of the integrative model was 0.93 (95% CI: 0.84-1.00) in the validation cohort. Multivariate Cox regression analysis also showed that the global ECV was an independent predictor of SCD adverse events (hazard ratio, 1.27 [95% CI: 1.10-1.47]).

CONCLUSION

The ECV derived from cardiac MRI is comparable to the HCM Risk-SCD scale in predicting the SCD risk stratification in patients with HCM.

Comparison of surface microscopy coil and ankle joint special phased array coil magnetic resonance imaging in assessing preoperative osteochondral lesions of the talus

Background

Lesion size is a major determinant of treatment strategies and predictor of clinical outcomes for osteochondral lesions of the talus (OLTs). Although magnetic resonance imaging (MRI) has been commonly used in the preoperative evaluation of OLTs, MRI has low reliability and usually overestimates or underestimates lesion size compared with intraoperative assessment. This study aims to determine whether the surface microscopy coil (SMC) can improve the accuracy of assessment of preoperative OLTs compared with conventional coil MRI, ankle joint special phased array coil (ASC).

Methods

A total of 43 patients diagnosed with OLTs undertook preoperative MRI examination with both SMC and ASC were included in this prospective study from 2019 to 2022. The diameter of the lesion was measured in sagittal plane and coronal plane at its widest point and then the lesion area was calculated. Then MRI measurements were compared with arthroscopy or open-surgery measurements.

Results

The mean lesion area measured with ASC was significantly greater than that measured intraoperatively (95.07±44.60 vs. 52.74±29.86 mm2, P<0.001), while there was no significant difference between lesion area measured in SMC and intraoperatively (55.28±36.06 vs. 52.74±29.86 mm2, P=0.576). Diameter measured in ASC was significantly greater than that measured intraoperatively in both coronal plane (8.95±2.48 vs. 6.67±1.81, P<0.001) and sagittal plane (13.12±3.76 vs. 9.58±3.98, P<0.001). No significant difference between lesion diameter measured in SMC and intraoperatively in both coronal plane (6.44±2.59 vs. 6.67±1.81, P=0.608) or sagittal plane (10.23±3.69 vs. 9.58±3.98, P=0.194). Compared with surgical assessment, 39 of 43 cases were consistent with SMC assessment while only 26 of 43 cases were consistent with ASC assessment (39/43 vs. 26/43, P=0.002).

Conclusions

Diameter measured with SMC was much more accurate than ASC MRI. Compared with ASC MRI, the SMC had a much higher concordance rate between preoperative assessment and surgical assessment.

Detecting lesion-specific ischemia in patients with coronary artery disease with computed tomography fractional flow reserve measured at different sites

OBJECTIVES

Whether a stenosis can cause hemodynamic lesion-specific ischemia is critical for the treatment decision in patients with coronary artery disease (CAD). Based on coronary computed tomography angiography (CCTA), CT fractional flow reserve (FFR_CT) can be used to assess lesion-specific ischemia. The selection of an appropriate site along the coronary artery tree is vital for measuring FFR_CT. However the optimal site to measure FFR_CT for a target stenosis remains to be adequately determined. The purpose of this study was to determine the optimal site to measure FFR_CT for a target lesion in detecting lesion-specific ischemia in CAD patients by evaluating the performance of FFR_CT measured at different sites distal to the target lesion in detecting lesion-specific ischemia with FFR measured with invasive coronary angiography (ICA) as reference standard.

METHODS

In this single-center retrospective cohort study, a total of 401 patients suspected of having CAD underwent invasive ICA and FFR between March 2017 and December 2021 were identified. 52 patients having both CCTA and invasive FFR within 90 days were enrolled. Patients with vessels 30%-90% diameter stenosis as determined by ICA were referred to invasive FFR evaluation, which was performed 2-3 cm distal to the stenosis under the condition of hyperemia. For each vessel with 30%-90% diameter stenosis, if only one stenosis was present, this stenosis was selected as the target lesion; if serial stenoses were present, the stenosis most distal to the vessel end was chosen as the target lesion. FFR_CT was measured at four sites: 1 cm, 2 cm, and 3 cm distal to the lower border of the target lesion (FFR_CT-1 cm, FFR_CT-2 cm, FFR_CT-3 cm), and the lowest FFR_CT at the distal vessel tip (FFR_CT-lowest). The normality of quantitative data was assessed using the Shapiro-Wilk test. Pearson's correlation analysis and Bland-Altman plots were used for assessing the correlation and difference between invasive FFR and FFR_CT. Correlation coefficients derived from Chi-suqare test were used to assess the correlation between invasive FFR and the cominbaiton of FFR_CT measred at four sites. The performances of significant obstruction stenosis (diameter stenosis ≥ 50%) at CCTA and FFR_CT measured at the four sites and their combinations in diagnosing lesion-specific ischemia were evaluated by receiver-operating characteristic (ROC) curves using invasive FFR as the reference standard. The areas under ROC curves (AUCs) of CCTA and FFR_CT were compared by the DeLong test.

RESULTS

A total of 72 coronary arteries in 52 patients were included for analysis. Twenty-five vessels (34.7%) had lesion-specific ischemia detected by invasive FFR and 47 vesseles (65.3%) had no lesion-spefifice ischemia. Good correlation was found between invasive FFR and FFR_CT-2 cm and FFR_CT-3 cm (r = 0.80, 95% CI, 0.70 to 0.87, p < 0.001; r = 0.82, 95% CI, 0.72 to 0.88, p < 0.001). Moderate correlation was found between invasive FFR and FFR_CT-1 cm and FFR_CT-lowest (r = 0.77, 95% CI, 0.65 to 0.85, p < 0.001; r = 0.78, 95% CI, 0.67 to 0.86, p < 0.001). FFR_CT-1 cm + FFR_CT-2 cm, FFR_CT-2 cm + FFR_CT-3 cm, FFR_CT-3 cm + FFR_CT-lowest, FFR_CT-1 cm + FFR_CT-2 cm + FFR_CT-3 cm, and FFR_CT-2 cm + FFR_CT-3 cm + FFR_CT-lowest were correatled with invasive FFR (r = 0.722; 0.722; 0.701; 0.722; and 0.722, respectively; p < 0.001 for all). Bland-Altman plots revealed a mild difference between invasive FFR and the four FFR_CT (invasive FFR vs. FFR_CT-1 cm, mean difference -0.0158, 95% limits of agreement: -0.1475 to 0.1159; invasive FFR vs. FFR_CT-2 cm, mean difference 0.0001, 95% limits of agreement: -0.1222 to 0.1220; invasive FFR vs. FFR_CT-3 cm, mean difference 0.0117, 95% limits of agreement: -0.1085 to 0.1318; and invasive FFR vs. FFR_CT-lowest, mean difference 0.0343, 95% limits of agreement: -0.1033 to 0.1720). AUCs of CCTA, FFR_CT-1 cm, FFR_CT-2 cm, FFR_CT-3 cm, and FFR_CT-lowest in detecting lesion-specific ischemia were 0.578, 0.768, 0.857, 0.856 and 0.770, respectively. All FFR_CT had a higher AUC than CCTA (all p < 0.05), FFR_CT-2 cm achieved the highest AUC at 0.857. The AUCs of FFR_CT-2 cm and FFR_CT-3 cm were comparable (p > 0.05). The AUCs were similar between FFR_CT-1 cm + FFR_CT-2 cm, FFR_CT-3 cm + FFR_CT-lowest and FFR_CT-2 cm alone (AUC = 0.857, 0.857, 0.857, respectively; p > 0.05 for all). The AUCs of FFR_CT-2 cm + FFR_CT-3 cm, FFR_CT-1 cm + FFR_CT-2 cm + FFR_CT-3 cm, FFR_CT-and 2 cm + FFR_CT-3 cm + FFR_CT-lowest (0.871, 0.871, 0.872, respectively) were slightly higher than that of FFR_CT-2 cm alone (0.857), but without significnacne differences (p > 0.05 for all).

CONCLUSIONS

FFR_CT measured at 2 cm distal to the lower border of the target lesion is the optimal measurement site for identifying lesion-specific ischemia in patients with CAD.

Preoperative Prediction of the Aggressiveness of Oral Tongue Squamous Cell Carcinoma with Quantitative Parameters from Dual-Energy Computed Tomography

Objectives

To determine whether quantitative parameters derived from dual-energy computed tomography (DECT) were predictive of the aggressiveness of oral tongue squamous cell carcinoma (OTSCC) including the pathologic stages, histologic differentiation, lymph node status, and perineural invasion (PNI).

Methods

Between August 2019 and March 2021, 93 patients (mean age, 54.6 ± 13.8 years; 66 men) with pathologically diagnosed OTSCC were enrolled in this prospective study. Preoperative DECT was performed and quantitative parameters (e.g., slope of the spectral Hounsfield unit curve [λ_Hu], normalized iodine concentration [nIC], normalized effective atomic number [nZ_eff], and normalized electron density [nRho]) were measured on arterial phase (AP) and venous phase (VP) DECT imaging. Quantitative parameters from DECT were compared between patients with different pathologic stages, histologic differentiation, lymph node statuses, and perineural invasion statuses. Logistic regression analysis was utilized to assess independent parameters and the diagnostic performance was analyzed by the receiver operating characteristic curves (ROC).

Results

λ_Hu and nIC in AP and λ_Hu, nZ_eff, and nIC in VP were significantly lower in stage III–IV lesions than in stage I–II lesions (p < 0.001 to 0.024). λ_Hu in VP was an independent predictor of tumor stage with an odds ratio (OR) of 0.29, and area under the curve (AUC) of 0.80. λ_Hu and nIC were higher in well-differentiated lesions than in poorly differentiated lesions (p < 0.001 to 0.021). The nIC in VP was an independent predictor of histologic differentiation with OR of 0.31, and AUC of 0.78. λ_Hu and nIC in VP were lower in OTSCCs with lymph node metastasis than those without metastasis (p < 0.001 to 0.005). λ_Hu in VP was the independent predictor of lymph node status with OR of 0.42, and AUC of 0.74. No significant difference was found between OTSCCs without PNI and those with PNI in terms of the quantitative DECT parameters.

Conclusion

DECT can be a complementary means for the preoperative prediction of the aggressiveness of OTSCC.

Co-existing Hypertension and Hyperhomocysteinemia Increases the Risk of Carotid Vulnerable Plaque and Subsequent Vascular Event: An MR Vessel Wall Imaging Study

Purpose

This study sought to determine the associations of co-existing hypertension and hyperhomocysteinemia (H-Hcy) with carotid vulnerable plaque features and subsequent vascular events.

Methods

Symptomatic patients with carotid atherosclerosis were enrolled and underwent carotid magnetic resonance (MR) vessel wall imaging. The patients were divided into the following groups: co-existing hypertension and H-Hcy group; isolated hypertension group; isolated H-Hcy group; and control group. The morphological and compositional characteristics of carotid plaques were assessed on MR images and compared among different groups. Univariate and multivariate cox regressions were used to calculate the hazard ratio (HR) and corresponding 95% confidence interval (CI) of co-existing hypertension and H-Hcy in predicting subsequent vascular events after at least 1-year followed-up.

Results

In total, 217 patients (mean age, 59.4 ± 11.9 years; 154 males) were recruited. Patients in co-existing hypertension and H-Hcy group had a significantly higher prevalence of carotid lipid-rich necrotic core (LRNC) than isolated H-Hcy and control group (73.2 vs. 43.3 vs. 50%, p = 0.015). During the median follow-up time of 12.2 ± 4.3 months, 61 (39.8%) patients experienced vascular events. After adjusting for baseline confounding factors, co-existing hypertension and H-Hcy (HR, 1.82; 95% CI, 1.01–3.27; p = 0.044), presence of carotid LRNC (HR, 2.25; 95% CI, 1.09–4.65; p = 0.029), and combination of co-existing hypertension and H-Hcy and carotid LRNC (HR, 2.39; 95% CI, 1.26–4.43; p = 0.007) were significantly associated with subsequent vascular events.

Conclusions

Co-existing hypertension and H-Hcy are associated with carotid vulnerable plaque features, such as LRNC. Combining co-existing hypertension and H-Hcy with carotid vulnerable plaque features has a stronger predictive value for subsequent vascular events than each measurement alone.

Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Purpose

To compare the performances of deep learning (DL) to radiomics analysis (RA) in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) based on pretreatment dynamic contrast-enhanced MRI (DCE-MRI) in breast cancer.

Materials and Methods

This retrospective study included 356 breast cancer patients who underwent DCE-MRI before NAC and underwent surgery after NAC. Image features and kinetic parameters of tumors were derived from DCE-MRI. Molecular information was assessed based on immunohistochemistry results. The image-based RA and DL models were constructed by adding kinetic parameters or molecular information to image-only linear discriminant analysis (LDA) and convolutional neural network (CNN) models. The predictive performances of developed models were assessed by receiver operating characteristic (ROC) curve analysis and compared with the DeLong method.

Results

The overall pCR rate was 23.3% (83/356). The area under the ROC (AUROC) of the image-kinetic-molecular RA model was 0.781 [95% confidence interval (CI): 0.735, 0.828], which was higher than that of the image-kinetic RA model (0.629, 95% CI: 0.595, 0.663; P &lt; 0.001) and comparable to that of the image-molecular RA model (0.755, 95% CI: 0.708, 0.802; P = 0.133). The AUROC of the image-kinetic-molecular DL model was 0.83 (95% CI: 0.816, 0.847), which was higher than that of the image-kinetic and image-molecular DL models (0.707, 95% CI: 0.654, 0.761; 0.79, 95% CI: 0.768, 0.812; P &lt; 0.001) and higher than that of the image-kinetic-molecular RA model (0.778, 95% CI: 0.735, 0.828; P &lt; 0.001).

Conclusions

The pretreatment DCE-MRI-based DL model is superior to the RA model in predicting pCR to NAC in breast cancer patients. The image-kinetic-molecular DL model has the best prediction performance.

MRI-Based Radiomics Nomogram: Prediction of Axillary Non-Sentinel Lymph Node Metastasis in Patients With Sentinel Lymph Node-Positive Breast Cancer

Background

Overtreatment of axillary lymph node dissection (ALND) may occur in patients with axillary positive sentinel lymph node (SLN) but negative non-SLN (NSLN). Developing a magnetic resonance imaging (MRI)-based radiomics nomogram to predict axillary NSLN metastasis in patients with SLN-positive breast cancer could effectively decrease the probability of overtreatment and optimize a personalized axillary surgical strategy.

Methods

This retrospective study included 285 patients with positive SLN breast cancer. Fifty five of them had metastatic NSLNs and 230 had non-metastatic NSLNs. MRI-based radiomic features of primary tumors were extracted and MRI morphologic findings of the primary tumor and axillary lymph nodes were assessed. Four models, namely, a radiomics signature, an MRI-clinical nomogram, and two MRI-clinical-radiomics nomograms were established based on MRI morphologic findings, clinicopathologic characteristics, and MRI-based radiomic features to predict the NSLN status. The optimal predictors in each model were selected using the 5-fold cross-validation (CV) method. Their predictive performances were determined by the receiver operating characteristic (ROC) curves analysis. The area under the curves (AUCs) of different models was compared by the Delong test. Their discrimination capability, calibration curve, and clinical usefulness were also assessed.

Results

The 5-fold CV analysis showed that the AUCs ranged from 0.770 to 0.847 for the radiomics signature, from 0.720 to 0.824 for the MRI-clinical nomogram, from 0.843 to 0.932 for the MRI-clinical-radiomics nomogram. The optimal predictive factors in the radiomics signature, MRI-clinical nomogram, and MRI-clinical-radiomics nomogram were one texture feature of diffusion-weighted imaging (DWI), two clinicopathologic features together with one MRI morphologic finding, and the DWI-based texture feature together with the two clinicopathologic features plus the one MRI morphologic finding, respectively. The MRI-clinical-radiomics nomogram with CA 15-3 included achieved the highest AUC compared with the radiomics signature (0.868 vs. 0.806, P <0.001) and MRI-clinical nomogram (0.868 vs. 0.761; P <0.001). In addition, the MRI-clinical-radiomics nomogram without CA 15-3 showed a higher performance than that of the radiomics signature (AUC, 0.852 vs. 0.806, P = 0.016) and the MRI-clinical nomogram (AUC, 0.852 vs. 0.761, P = 0.007). The MRI-clinical-radiomics nomograms showed good discrimination and good calibration. Decision curve analysis demonstrated that the MRI-clinical-radiomics nomograms were clinically useful.

Conclusion

The MRI-clinical-radiomics nomograms developed in our study showed high predictive performance, which can be used to predict the axillary NSLN status in SLN-positive breast cancer patients before surgery.

Quantitative Parameters of Diffusion Spectrum Imaging: HER2 Status Prediction in Patients With Breast Cancer

OBJECTIVE

To explore the value of quantitative parameters derived from diffusion spectrum imaging (DSI) in preoperatively predicting human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer.

METHODS

In this prospective study, 114 and 56 female patients with invasive ductal carcinoma were consecutively included in a derivation cohort and an independent validation cohort, respectively. Each patient was categorized into HER2-positive or HER2-negative groups based on the pathologic result. All patients underwent DSI and conventional MRI including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). The tumor size, type of the time-signal intensity curve (TIC) from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, and quantitative parameters derived from DSI, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) of primary tumors, were measured and compared between the HER2-positive and HER2-negative groups in the derivation cohort. Univariable and multivariable logistic regression analyses were used to determine the potential independent predictors of HER2 status. The discriminative ability of quantitative parameters was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort.

RESULTS

In the derivation cohort, the tumor size, TIC type, and ADC values did not differ between the HER2-positive and HER2-negative groups (p = 0.126-0.961). DSI quantitative parameters including axial kurtosis of DKI (DKI_AK), non-Gaussianity (MAP_NG), axial non-Gaussianity (MAP_NGAx), radial non-Gaussianity (MAP_NGRad), return-to-origin probability (MAP_RTOP), return-to-axis probability of MAP (MAP_RTAP), and intracellular volume fraction of NODDI (NODDI_ICVF) were lower in the HER2-positive group than in the HER2-negative group (p ≤ 0.001-0.035). DSI quantitative parameters including radial diffusivity (DTI_RD), mean diffusivity of DTI (DTI_MD), mean squared diffusion (MAP_MSD), and q-space inverse variance of MAP (MAP_QIV) were higher in the HER2-positive group than in the HER2-negative group (p = 0.016-0.049). The ROC analysis showed that the area under the curve (AUC) of ADC was 0.632 and 0.568, respectively, in the derivation and validation cohorts. The AUC values of DSI quantitative parameters ranged from 0.628 to 0.700 and from 0.673 to 0.721, respectively, in the derivation and validation cohorts. Logistic regression analysis showed that only NODDI_ICVF was an independent predictor of HER2 status (p = 0.001), with an AUC of 0.700 and 0.721, respectively, in the derivation and validation cohorts.

CONCLUSIONS

DSI could be helpful for preoperative prediction of HER2, but DSI alone may not be sufficient in predicting HER2 status preoperatively in patients with breast cancer.

Relationship between lung injury extent and phenotype manifested in non-contrast CT and cardiac injury during acute stage of COVID-19

PURPOSE

This study evaluated the diagnostic values of the extent of lung injury manifested in non-contrast enhanced CT (NCCT) images, the inflammatory and immunological biomarkers C-reactive protein (CRP) and lymphocyte for detecting acute cardiac injury (ACI) in patients with COVID-19. The correlations between the NCCT-derived parameters and arterial blood oxygen level were also investigated.

METHODS

NCCT lung images and blood tests were obtained in 143 patients with COVID-19 in approximately two weeks after symptom onset, and arterial blood gas measurement was also acquired in 113 (79%) patients. The diagnostic values of normal, moderately and severely abnormal lung parenchyma volume relative to the whole lungs (RVNP, RVMAP, RVSAP, respectively) measured from NCCT images for detecting the heart injury confirmed with high-sensitivity troponin I assay was determined.

RESULTS

RVNP, RVMAP and RVSAP exhibited similar accuracy for detecting ACI in COVID-19 patients. RVNP was significantly lower while both RVMAP and RVSAP were significantly higher in the patients with ACI. All of the NCCT-derived parameters exhibited poor linear and non-linear correlations with PaO2 and SaO2. The patients with ACI had a significantly higher CRP level but a lower lymphocyte level compared to the patients without ACI. Combining one of these two biomarkers with any of the three NCCT-derived parameter further improved the accuracy for predicting ACI in patients with COVID-19.

CONCLUSION

The NCCT-delineated normal and abnormal lung parenchmyma tissues were statistically significant predictors of ACI in patients with COVID-19, but both exhibited poor correlations with the arterial blood oxygen level. The incremental diagnostic values of lymphocyte and CRP suggested viral infection and inflammation were closely related to the heart injury during the acute stage of COVID-19.

A coarse-refine segmentation network for COVID-19 CT images

The rapid spread of the novel coronavirus disease 2019 (COVID-19) causes a significant impact on public health. It is critical to diagnose COVID-19 patients so that they can receive reasonable treatments quickly. The doctors can obtain a precise estimate of the infection's progression and decide more effective treatment options by segmenting the CT images of COVID-19 patients. However, it is challenging to segment infected regions in CT slices because the infected regions are multi-scale, and the boundary is not clear due to the low contrast between the infected area and the normal area. In this paper, a coarse-refine segmentation network is proposed to address these challenges. The coarse-refine architecture and hybrid loss is used to guide the model to predict the delicate structures with clear boundaries to address the problem of unclear boundaries. The atrous spatial pyramid pooling module in the network is added to improve the performance in detecting infected regions with different scales. Experimental results show that the model in the segmentation of COVID-19 CT images outperforms other familiar medical segmentation models, enabling the doctor to get a more accurate estimate on the progression of the infection and thus can provide more reasonable treatment options.

The ratio of the max-to-mean coronary artery calcium score in the most calcified vessel is associated with the presence of coronary artery disease

PURPOSE

This study aimed to clarify the relationship between the severity of the calcium burden in the most calcified coronary vessel and coronary artery disease (CAD).

METHOD

Of 2150 patients, 376 examined by both coronary computed tomographic angiography and invasive coronary angiography (ICA) within 30 days at Sun Yat-sen Memorial Hospital between March 2011 and July 2020 were included. Three coronary artery calcium scores (CACSs), including the Agatston score, volume score, and mass score, and other clinical variables were recorded. The ratio of max-to-mean CACS in the most calcified vessel (CACS_max:mean) was defined as the CACS in the most calcified vessel/average CACS of the four major epicardial coronary arteries. Logistic regression and least absolute shrinkage and selection operator (LASSO) analyses were performed to assess the relationship between CACS_max:mean and CAD.

RESULTS

CACS_max:mean was higher in 81.1% of subjects diagnosed with CAD than in subjects without CAD. In multivariate logistic regression analysis, CACS_max:mean determined by the Agatston score, volumetric score, and mass score was associated with CAD. In LASSO analysis, Agatston score_max:mean (not the total Agatston score or other CACS_max:mean) had the strongest correlation with CAD (β = 0.125). AUCs in the training set and the validation set were 0.811 and 0.789, respectively. Increased age, diabetes and hypertension correlated with higher Agatston score_max:mean.

CONCLUSIONS

In addition to total CACS, CACS_max:mean may be a novel diagnostic parameter for CAD, showing the calcium burden severity.

Dynamic network topological properties for classifying primary dysmenorrhoea in the pain-free phase

BACKGROUND

Primary dysmenorrhoea (PDM) is known to alter brain static functional activity. This study aimed to explore the dynamic topological properties (DTP) of dynamic brain functional network in women with PDM in the pain-free phase and their performance in distinguishing PDM in the pain-free phase from healthy controls.

METHODS

Thirty-five women with PDM and 38 healthy women without PDM were included. A dynamic brain functional network was constructed using the slide-window approach. The stability (TP-Stab) and variability (TP-Var) of the DTP of the dynamic functional network were computed using the graph-theory method. A support vector machine (SVM) was used to evaluate the performance of DTP in identifying PDM in the pain-free phase.

RESULTS

Compared with healthy controls, women with PDM had not only lower TP-Stab in global DTP, which included cluster clustering coefficient (C_p ), characteristic path length (L_p ), global efficiency (E_g ) and local efficiency (E_loc ), but also lower TP-Stab and higher TP-Var in nodal DTP (nodal efficiency, E_nod ), mainly in the prefrontal cortex, anterior cingulate cortex, parahippocampal regions and insula. The TP-Stab and TP-Var were significantly correlated with psychological variables, that is positive emotions, sense of control and meaningful existence. SVM analysis showed that the DTP could identify PDM in the pain-free phase from healthy controls with an accuracy of 79.31%, sensitivity of 82.61% and specificity of 76%.

CONCLUSIONS

Women with PDM in the pain-free phase have altered global DTP and nodal DTP, mainly involving pain-related neurocircuits. The highly variable brain network is helpful for identifying PDM in the pain-free phase.

SIGNIFICANCE

This study shows that women with primary dysmenorrhoea (PDM) have decreased stability of dynamic network topological properties (DTP) and increased DTP variability in the pain-free phase. The altered DTP can be used to identify PDM in the pain-free phase. These findings demonstrate the presence of unstable characteristics in the whole network and disrupted pain-related neurocircuits, which might be used as potential classifiers for PDM in the pain-free phase. This study improves our knowledge of the brain mechanisms underlying PDM.

R2* value derived from multi-echo Dixon technique can aid discrimination between benign and malignant focal liver lesions

BACKGROUND

R2* estimation reflects the paramagnetism of the tumor tissue, which may be used to differentiate between benign and malignant liver lesions when contrast agents are contraindicated.

AIM

To investigate whether R2* derived from multi-echo Dixon imaging can aid differentiating benign from malignant focal liver lesions (FLLs) and the impact of 2D region of interest (2D-ROI) and volume of interest (VOI) on the outcomes.

METHODS

We retrospectively enrolled 73 patients with 108 benign or malignant FLLs. All patients underwent conventional abdominal magnetic resonance imaging and multi-echo Dixon imaging. Two radiologists independently measured the mean R2* values of lesions using 2D-ROI and VOI approaches. The Bland–Altman plot was used to determine the interobserver agreement between R2* measurements. Intraclass correlation coefficient (ICC) was used to determine the reliability between the two readers. Mean R2* values were compared between benign and malignant FFLs using the nonparametric Mann–Whitney test. Receiver operating characteristic curve analysis was used to determine the diagnostic performance of R2* in differentiation between benign and malignant FFLs. We compared the diagnostic performance of R2* measured by 2D-ROI and VOI approaches.

RESULTS

This study included 30 benign and 78 malignant FLLs. The interobserver reproducibility of R2* measurements was excellent for the 2D-ROI (ICC = 0.994) and VOI (ICC = 0.998) methods. Bland–Altman analysis also demonstrated excellent agreement. Mean R2* was significantly higher for malignant than benign FFLs as measured by 2D-ROI (P < 0.001) and VOI (P < 0.001). The area under the curve (AUC) of R2* measured by 2D-ROI was 0.884 at a cut-off of 25.2/s, with a sensitivity of 84.6% and specificity of 80.0% for differentiating benign from malignant FFLs. R2* measured by VOI yielded an AUC of 0.875 at a cut-off of 26.7/s in distinguishing benign from malignant FFLs, with a sensitivity of 85.9% and specificity of 76.7%. The AUCs of R2* were not significantly different between the 2D-ROI and VOI methods.

CONCLUSION

R2* derived from multi-echo Dixon imaging whether by 2D-ROI or VOI can aid in differentiation between benign and malignant FLLs.

Quantitative kinetic parameters of primary tumor can be used to predict pelvic lymph node metastasis in early-stage cervical cancer

PURPOSE

To investigate the role of kinetic parameters of primary tumor derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer.

METHODS

66 women with newly diagnosed cervical cancer were included between July 2017 and August 2019. All patients had a FIGO stage IB-IIA cancer and treated with hysterectomy and bilateral lymphadenectomy. Kinetic parameters of the primary tumor were derived from DCE-MRI data. The tumor diameter, ADC value, kinetic parameters, and nodal short-axis diameter were compared between patients with or without PLNM. Logistic regression analysis was used to determine the independent predictors for PLNM and receiver operator characteristic curve was used to evaluate the predictive performance.

RESULTS

There were 20 patients with PLNM and 46 patients without PLNM. Tumor diameter, the efflux rate constant (K_ep), and nodal short-axis diameter were significantly higher in patients with PLNM (P < 0.01). Multivariate logistic regression analysis showed that K_ep and short-axis diameter were independent predictors for PLNM. Combining K_ep and nodal short-axis diameter yielded the highest area under the curve (AUC) of 0.839. Combined with K_ep, the sensitivity, specificity, negative predictive value, and positive predictive value of nodal short-axis diameter increased from 0.500, 0.957, 0.815, and 0.833 to 0.600, 0.978, 0.923, and 0.849, respectively. With 1.113 min^-1 as threshold, the sensitivity and specificity values of K_ep in predicting PLNM in patients with normal-sized lymph nodes were 0.909 and 0.667, respectively.

CONCLUSIONS

K_ep of primary tumor can be used as a surrogate marker to predict PLNM in cervical cancer.

New Year's greeting and overview of Artificial Intelligence in Medical Imaging in 2021

As editors of Artificial Intelligence in Medical Imaging (AIMI), it is our great pleasure to take this opportunity to wish all of our authors, subscribers, readers, Editorial Board members, independent expert referees, and staff of the Editorial Office a Very Happy New Year. On behalf of the Editorial Team, we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts, our independent referees, and our subscribers and readers for their continuous support, dedication, and encouragement. Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office, AIMI advanced in 2020 and we look forward to greater achievements in 2021.

Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning

Abstract

Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification.

Graphic Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12539-021-00420-z.

Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer

OBJECTIVES

To develop and validate a multiparametric MRI-based radiomics nomogram for pretreatment predicting the axillary sentinel lymph node (SLN) burden in early-stage breast cancer.

METHODS

A total of 230 women with early-stage invasive breast cancer were retrospectively analyzed. A radiomics signature was constructed based on preoperative multiparametric MRI from the training dataset (n = 126) of center 1, then tested in the validation cohort (n = 42) from center 1 and an external test cohort (n = 62) from center 2. Multivariable logistic regression was applied to develop a radiomics nomogram incorporating radiomics signature and predictive clinical and radiological features. The radiomics nomogram's performance was evaluated by its discrimination, calibration, and clinical use and was compared with MRI-based descriptors of primary breast tumor.

RESULTS

The constructed radiomics nomogram incorporating radiomics signature and MRI-determined axillary lymph node (ALN) burden showed a good calibration and outperformed the MRI-determined ALN burden alone for predicting SLN burden (area under the curve [AUC]: 0.82 vs. 0.68 [p < 0.001] in training cohort; 0.81 vs. 0.68 in validation cohort [p = 0.04]; and 0.81 vs. 0.58 [p = 0.001] in test cohort). Compared with the MRI-based breast tumor combined descriptors, the radiomics nomogram achieved a higher AUC in test cohort (0.81 vs. 0.58, p = 0.005) and a comparable AUC in training (0.82 vs. 0.73, p = 0.15) and validation (0.81 vs. 0.65, p = 0.31) cohorts.

CONCLUSION

A multiparametric MRI-based radiomics nomogram can be used for preoperative prediction of the SLN burden in early-stage breast cancer.

KEY POINTS

• Radiomics nomogram incorporating radiomics signature and MRI-determined ALN burden outperforms the MRI-determined ALN burden alone for predicting SLN burden in early-stage breast cancer. • Radiomics nomogram might have a better predictive ability than the MRI-based breast tumor combined descriptors. • Multiparametric MRI-based radiomics nomogram can be used as a non-invasive tool for preoperative predicting of SLN burden in patients with early-stage breast cancer.

The Usefulness of Bayesian Network in Assessing the Risk of Triple-Negative Breast Cancer

RATIONALE AND OBJECTIVES

To evaluate a Bayesian network (BN) model learned from epidemiological and clinical information, and various MRI parameters for predicting the risk of triple-negative breast cancer (TNBC).

MATERIALS AND METHODS

For this retrospective study, 214 women (mean age ± standard deviation, 50.5±10.6 years) with breast cancer were included between April 2016 and April 2018. All patients underwent MRI, including dynamic contrast-enhanced (DCE)-MRI. The morphologic MRI features, the pattern of the time-signal intensity curve (TIC) and the kinetic parameters were obtained for each lesion. The epidemiological and clinical parameters and those imaging parameters were used to construct BN model to estimate TNBC risk. ROC curves upon probability estimates were used to determine the performance of the BN using area under the ROC curves (A_z), sensitivity, specificity, and accuracy.

RESULTS

A BN model consisted of 16 epidemiological and clinical characteristics, morphologic MRI features, and quantitative DCE-MRI parameters were established. The posttest probability table showed that patients with age <35 years, mass-like lesions, type I TIC, and MaxCon ≥ 0.186 were at the highest risk of TNBC. The constructed BN model had an A_z of 0.663 (95% confidence interval [CI]: 0.654, 0.672), sensitivity of 0.660 (95% CI: 0.644, 0.675), specificity of 0.740 (95% CI: 0.726, 0.753) and accuracy of 0.724 (95% CI: 0.714, 0.733) in classifying TNBC.

CONCLUSION

The BN model integrating epidemiological and clinical characteristics, morphologic and kinetic MRI parameters provide a noninvasive analytical approach for preoperative prediction of the risk of TNBC.

Theranostic Nanomedicine for Synergistic Chemodynamic Therapy and Chemotherapy of Orthotopic Glioma

Glioma is a common primary brain malignancy with a poor prognosis. Chemotherapy is the first-line treatment for brain tumors but low efficiency of drugs in crossing the blood-brain barrier (BBB) and drug resistance related to tumor hypoxia thwart its efficacy. Herein, a theranostic nanodrug (iRPPA@TMZ/MnO) is developed by incorporating oleic acid-modified manganese oxide (MnO) and temozolomide (TMZ) into a polyethylene glycol-poly(2-(diisopropylamino)ethyl methacrylate-based polymeric micelle containing internalizing arginine-glycine-aspartic acid (iRGD). The presence of iRGD provides the nanodrug with a high capacity of crossing the BBB and penetrating the tumor tissue. After accumulation in glioma, the nanodrug responds to the tumor microenvironment to simultaneously release TMZ, Mn2+, and O2. The released TMZ induces tumor cell apoptosis and the released Mn2+ causes intracellular oxidative stress that kill tumor cells via a Fenton-like reaction. The O2 produced in situ alleviates tumor hypoxia and enhances the chemotherapy/chemodynamic therapeutic effects against glioma. The Mn2+ can also serve as a magnetic resonance imaging (MRI) contrast agent for tumor imaging during therapy. The study demonstrates the great potential of this multifunctional nanodrug for MRI-visible therapy of brain glioma.

Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models

Background

To compare the diagnostic performance of neurite orientation dispersion and density imaging (NODDI), mean apparent propagator magnetic resonance imaging (MAP-MRI), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs).

Methods

Patients with previously untreated, histopathologically confirmed HGGs (n = 20) or SBMs (n = 21) appearing as a solitary and contrast-enhancing lesion on structural MRI were prospectively recruited to undergo diffusion-weighted MRI. DWI data were obtained using a q-space Cartesian grid sampling procedure and were processed to generate parametric maps by fitting the NODDI, MAP-MRI, DKI, DTI and DWI models. The diffusion metrics of the contrast-enhancing tumor and peritumoral edema were measured. Differences in the diffusion metrics were compared between HGGs and SBMs, followed by receiver operating characteristic (ROC) analysis and the Hanley and McNeill test to determine their diagnostic performances.

Results

NODDI-based isotropic volume fraction (V_iso) and orientation dispersion index (ODI); MAP-MRI-based mean-squared displacement (MSD) and q-space inverse variance (QIV); DKI-generated radial, mean diffusivity and fractional anisotropy (RD_k, MD_k and FA_k); and DTI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA) of the contrast-enhancing tumor were significantly different between HGGs and SBMs (p < 0.05). The best single discriminative parameters of each model were V_iso, MSD, RD_k and RD for NODDI, MAP-MRI, DKI and DTI, respectively. The AUC of V_iso (0.871) was significantly higher than that of MSD (0.736), RD_k (0.760) and RD (0.733) (p < 0.05).

Conclusion

NODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based V_iso has the highest performance.

Peritumoral administration of IFNβ upregulated mesenchymal stem cells inhibits tumor growth in an orthotopic, immunocompetent rat glioma model

Background

Immunotherapy with IFNβ is a promising strategy for treating malignant glioma. However, systemic administration of IFNβ is inadequate because of low intratumoral concentration and major adverse effects. This study aimed to determine whether mesenchymal stem cells (MSCs) can be used as cellular vehicles to locally deliver IFNβ for glioma therapy by using in vivo MRI tracking.

Methods

A recombinant lentiviral vector encoding IFNβ and ferritin heavy chain (FTH) reporter genes was constructed to transduce MSCs. The effectiveness and safety of transduction were assessed. After the IFNβ and FTH overexpressed MSCs (IFNβ-FTH-MSCs) were transplanted into intracranial orthotopic rat F98 gliomas via peritumoral, intracerebral, intratumoral or intra-arterial injection, MRI was performed to track IFNβ-FTH-MSCs and to evaluate their therapeutic effect on glioma in vivo, as validated by histologic analysis, quantitative PCR and ELISA assays.

Results

MSCs were efficiently and safely transduced to upregulate their IFNβ secretion and FTH expression by the constructed lentivirus. After peritumoral injection, IFNβ-FTH-MSCs appeared as hypointense signals on MRI, which gradually diminished but remained visible until 11 days. Compared with other administration routes, only peritumoral injection of IFNβ-FTH-MSCs showed a remarkable inhibition on the glioma growth. Nearly 30% of IFNβ-FTH-MSCs survived up to 11 days after peritumoral injection, while most of IFNβ-FTH-MSCs injected via other routes died within 11 days. IFNβ-FTH-MSCs grafted peritumorally secreted IFNβ persistently, leading to pronounced Batf3⁺ dendritic cells and CD8⁺ T lymphocyte infiltration within the glioma.

Conclusions

MSCs can be used as cellular vehicles of IFNβ to treat malignant glioma effectively via peritumoral injection.

Quantitative dynamic contrast-enhanced MR imaging can be used to predict the pathologic stages of oral tongue squamous cell carcinoma

Background

To investigate whether quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic parameters can be used to predict the pathologic stages of oral tongue squamous cell carcinoma (OTSCC).

Methods

For this prospective study, DCE-MRI was performed in participants with OTSCC from May 2016 to June 2017. The pharmacokinetic parameters, including K^trans, K_ep, V_e, and V_p, were derived from DCE-MRI by utilizing a two-compartment extended Tofts model and a three-dimensional volume of interest. The postoperative pathologic stage was determined in each patient based on the 8th AJCC cancer staging manual. The quantitative DCE-MRI parameters were compared between stage I–II and stage III–IV lesions. Logistic regression analysis was used to determine independent predictors of tumor stages, followed by receiver operating characteristic (ROC) analysis to evaluate the predictive performance.

Results

The mean K^trans, K_ep and V_p values were significantly lower in stage III–IV lesions compared with stage I–II lesions (p = 0.013, 0.005 and 0.011, respectively). K_ep was an independent predictor for the advanced stages as determined by univariate and multivariate logistic analysis. ROC analysis showed that K_ep had the highest predictive capability, with a sensitivity of 64.3%, a specificity of 82.6%, a positive predictive value of 81.8%, a negative predictive value of 65.5%, and an accuracy of 72.5%.

Conclusion

The quantitative DCE-MRI parameter K_ep can be used as a biomarker for predicting pathologic stages of OTSCC.

Histological validation of simultaneous non-contrast angiography and intraplaque hemorrhage imaging (SNAP) for characterizing carotid intraplaque hemorrhage

OBJECTIVES

This study sought to validate the performance of simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP) imaging in characterizing carotid IPH by histology.

METHODS

Thirty-five patients with carotid atherosclerotic disease (symptomatic 50-70% stenosis or > 70% stenosis) scheduled for carotid endarterectomy underwent 3.0-T carotid MR imaging by acquiring SNAP and magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) sequences. Presence and area of IPH were separately evaluated on SNAP and MP-RAGE images. Presence and area of IPH were also assessed on histology. Agreement between SNAP/MP-RAGE and histology was determined in identify and quantify IPH using Cohen kappa, Spearman correlation, and Bland-Altman analyses.

RESULTS

Of all 35 patients (mean age: 63.1 ± 8.8 years; 27 males), 128 slices with successful registration were eligible for analysis. The accuracy, sensitivity, specificity, and positive and negative predictive values were 86.7%, 85%, 89.6%, 93.2%, and 78.2% for SNAP, and 76.6%, 75%, 79.2%, 85.7%, and 65.5% for MP-RAGE in identification of IPH, respectively. In identification of IPH, the kappa value between SNAP and histology and between MP-RAGE and histology was 0.725 and 0.520, respectively. The correlation between SNAP and histology (r = 0.805, p < 0.001) was stronger than that between MP-RAGE and histology (r = 0.637, p < 0.001) in measuring IPH area. Bland-Altman analysis showed that, in measuring IPH area, the bias of SNAP (1.4 mm², 95% CI: - 0.016 to 2.883) was smaller than that of MP-RAGE (1.7 mm², 95% CI: - 0.039 to 3.430) compared with histology.

CONCLUSIONS

This validation study by histology demonstrates that SNAP sequence better identifies and quantifies carotid intraplaque hemorrhage compared with traditional MP-RAGE sequence.

KEY POINTS

• SNAP imaging showed better agreement with histology compared with MP-RAGE imaging, especially for the IPHs with small size. • SNAP sequence is a more effective tool to identify and quantify carotid IPH than traditional sequence of MP-RAGE that can help clinicians to optimizing the treatment strategy. • The plaque components of rich lipid pools or loose matrix and chronic/old IPH (cholesterol crystals) can lead to false positive and false negative results in SNAP and MP-RAGE imaging for identifying IPH.

Mean Apparent Propagator MRI Is Better Than Conventional Diffusion Tensor Imaging for the Evaluation of Parkinson's Disease: A Prospective Pilot Study

Background and Purpose

Mean apparent propagator (MAP) MRI is a novel diffusion imaging method to map tissue microstructure. The purpose of this study was to evaluate the diagnostic value of the MAP MRI in Parkinson’s disease (PD) in comparison with conventional diffusion tensor imaging (DTI).

Methods

23 PD patients and 22 age- and gender-matched healthy controls were included. MAP MRI and DTI were performed on a 3T MR scanner with a 20-channel head coil. The MAP metrics including mean square displacement (MSD), return to the origin probability (RTOP), return to the axis probability (RTAP), and return to the plane probability (RTPP), and DTI metrics including fractional anisotropy (FA), and mean diffusivity (MD), were measured in subcortical gray matter and compared between the two groups. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic performance of all the metrics. The association between the diffusion metrics and disease severity was assessed by Pearson correlation analysis.

Results

For MAP MRI, the mean values of MSD in the bilateral caudate, pallidum, putamen, thalamus and substantia nigra (SN) were higher in PD patients than in healthy controls (p_FDR ≤ 0.001); the mean values of the zero displacement probabilities (RTOP, RTAP, and RTPP) in the bilateral caudate, pallidum, putamen and thalamus were lower in PD patients (p_FDR < 0.001). For DTI, only FA in the bilateral SN was significantly higher in PD patients than those in the controls (p_FDR < 0.001). ROC analysis showed that the areas under the curves of MAP MRI metrics (MSD, RTOP, RTAP, and RTPP) in the bilateral caudate, pallidum, putamen and thalamus (range, 0.85–0.94) were greater than those of FA and MD of DTI (range, 0.55–0.69) in discriminating between PD patients and healthy controls. RTAP in the ipsilateral pallidum (r = −0.56, p_FDR = 0.027), RTOP in the bilateral and contralateral putamen (r = −0.58, p_FDR = 0.019; r = −0.57, p_FDR = 0.024) were negatively correlated with UPDRS III motor scores.

Conclusion

MAP MRI outperformed the conventional DTI in the diagnosis of PD and evaluation of the disease severity.

Liver fat accumulation measured by high-speed T2-corrected multi-echo magnetic resonance spectroscopy can predict risk of cholelithiasis

BACKGROUND

Liver fat accumulation is associated with increased cholesterol synthesis and hypersecretion of biliary cholesterol, which may be related to the development of cholelithiasis.

AIM

To investigate whether liver fat accumulation measured by high-speed T2-corrected multi-echo magnetic resonance spectroscopy (MRS) is a risk factor for cholelithiasis.

METHODS

Forty patients with cholelithiasis and thirty-one healthy controls were retrospectively enrolled. The participants underwent high-speed T2-corrected multi-echo single-voxel MRS of the liver at a 3T MR scanner. The proton density fat fraction (PDFF) and R2 value were calculated. Serum parameters and waist circumference (WC) were recorded. Spearman’s correlation analysis was used to analyze the relationship between PDFF, R2, and WC values. Multivariate logistic regression analysis was carried out to determine the significant predictors of the risk of cholelithiasis. Receiver operating characteristic curve (ROC) analysis was used to evaluate the discriminative performance of significant predictors.

RESULTS

Patients with cholelithiasis had higher PDFF, R2, and WC values compared with healthy controls (5.8% ± 4.2% vs 3.3% ± 2.4%, P = 0.001; 50.4 ± 24.8/s vs 38.3 ± 8.8/s, P = 0.034; 85.3 ± 9.0 cm vs 81.0 ± 6.9 cm, P = 0.030; respectively). Liver iron concentration extrapolated from R2 values was significantly higher in the cholelithiasis group (2.21 ± 2.17 mg/g dry tissue vs 1.22 ± 0.49 mg/g dry tissue, P = 0.034) than in the healthy group. PDFF was positively correlated with WC (r = 0.502, P < 0.001) and R2 (r = 0.425, P < 0.001). Multivariate logistic regression analysis showed that only PDFF was an independent risk factor for cholelithiasis (odds ratio = 1.79, 95%CI: 1.22-2.62, P = 0.003). ROC analysis showed that the area under the curve of PDFF was 0.723 for discriminating cholelithiasis from healthy controls, with a sensitivity of 55.0% and specificity of 83.9% when the cut-off value of PDFF was 4.4%.

CONCLUSION

PDFF derived from high speed T2-corrected multi-echo MRS can predict the risk of cholelithiasis.