Tumor Stiffness Measurement at Multifrequency MR Elastography to Predict Lymphovascular Space Invasion in Endometrial Cancer.

Background Pathologic lymphovascular space invasion (LVSI) is associated with poor outcome in endometrial cancer. Its relationship with tumor stiffness, which can be measured with use of MR elastography, has not been extensively explored. Purpose To assess whether MR elastography-based mechanical characteristics can aid in the noninvasive prediction of LVSI in patients with endometrial cancer. Materials and Methods This prospective study included consecutive adult patients with a suspected uterine tumor who underwent MRI and MR elastography between October 2022 and July 2023. A region of interest delineated on T2-weighted magnitude images was duplicated on MR elastography images and used to calculate c (stiffness in meters per second) and φ (viscosity in radians) values. Pathologic assessment of hysterectomy specimens for LVSI served as the reference standard. Data were compared between LVSI-positive and -negative groups with use of the Mann-Whitney U test. Multivariable logistic regression was used to determine variables associated with LVSI positivity and develop diagnostic models for predicting LVSI. Model performance was assessed with use of area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results A total of 101 participants were included, 72 who were LVSI-negative (median age, 53 years [IQR, 48-62 years]) and 29 who were LVSI-positive (median age, 54 years [IQR, 49-60 years]). The tumor stiffness in the LVSI-positive group was higher than in the LVSI-negative group (median, 4.1 m/sec [IQR, 3.2-4.6 m/sec] vs 2.2 m/sec [IQR, 2.0-2.8 m/sec]; P < .001). Tumor volume, cancer antigen 125 level, and tumor stiffness were associated with LVSI positivity (adjusted odds ratio range, 1.01-9.06; P range, <.001-.04). The combined model (AUC, 0.93) showed better performance for predicting LVSI compared with clinical-radiologic model (AUC, 0.77; P = .003) and similar performance to the MR elastography-based model (AUC, 0.89; P = .06). Conclusion The addition of tumor stiffness as measured at MR elastography into a clinical-radiologic model improved prediction of LVSI in patients with endometrial cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ehman in this issue.

Altered gray matter volume and functional connectivity in lung cancer patients with bone metastasis pain.

Bone metastasis pain (BMP) is a severe chronic pain condition. Our previous studies on BMP revealed functional brain abnormalities. However, the potential effect of BMP on brain structure and function, especially gray matter volume (GMV) and related functional networks, have not yet been clearly illustrated. Voxel-based morphometry and functional connectivity (FC) analysis methods were used to investigate GMV and intrinsic FC differences in 45 right-handed lung cancer patients with BMP(+), 37 lung cancer patients without BMP(-), and 45 healthy controls (HCs). Correlation analysis was performed thereafter with all clinical variables by Pearson correlation. Compared to HCs, BMP(+) group exhibited decreased GMV in medial frontal gyrus (MFG) and right middle temporal gyrus (MTG). Compared with BMP(-) group, BMP(+) group exhibited reduced GMV in cerebelum_6_L and left lingual gyrus. However, no regions with significant GMV differences were found between BMP(-) and HCs groups. Receiver operating characteristic analysis indicated the potential classification power of these aberrant regions. Correlation analysis revealed that GMV in the right MTG was positively associated with anxiety in BMP(+) group. Further FC analysis demonstrated enhanced interactions between MFG/right MTG and cerebellum in BMP(+) patients compared with HCs. These results showed that BMP was closely associated with cerebral alterations, which may induce the impairment of pain moderation circuit, deficits in cognitive function, dysfunction of emotional control, and sensorimotor processing. These findings may provide a fresh perspective and further neuroimaging evidence for the possible mechanisms of BMP. Furthermore, the role of the cerebellum in pain processing needs to be further investigated.

Quantification of Hepatic Steatosis on Dual-Energy CT in Comparison With MRI mDIXON-Quant Sequence in Breast Cancer.

OBJECTIVE: The study aimed to evaluate the correlation and diagnostic value of liver fat quantification in unenhanced dual-energy CT (DECT) using quantitative magnetic resonance imaging (MRI) mDIXON-Quant sequence as reference standard in patients with breast cancer. METHODS: Patients with breast cancer were prospectively recruited between June 2018 and April 2020. Each patient underwent liver DECT and MRI mDIXON-Quant examination. The DECT-fat volume fraction (FVF) and liver-spleen attenuation differences were compared with the MRI-proton density fat fraction using scatterplots, Bland-Altman plots, and concordance correlation coefficient. Receiver operating characteristic curves were established to determine the diagnostic accuracy of hepatic steatosis by DECT. RESULTS: A total of 216 patients with breast cancer (mean age, 50.08 ± 9.33 years) were evaluated. The DECT-FVF correlated well with MRI-proton density fat fraction ( r2 = 0.902; P < 0.001), which was higher than the difference in liver-spleen attenuation ( r2 = 0.728; P < 0.001). Bland-Altman analysis revealed slight positive bias; the mean difference was 3.986. The DECT-FVF yielded an average concordance correlation coefficient of 0.677, which was higher than the difference of liver-spleen attenuation (-0.544). The DECT-FVF and the difference in liver-spleen attenuation both lead to mild overestimation of hepatic steatosis. The areas under the curve of DECT-FVF (0.956) were higher than the difference in liver-spleen attenuation (0.807) in identifying hepatic steatosis ( P < 0.001). CONCLUSIONS: Dual-energy CT-FVF may serve as a reliable screening and quantitative tool for hepatic steatosis in patients with breast cancer.

Brain network deficits in breast cancer patients after early neoadjuvant chemotherapy: A longitudinal MRI study.

Breast cancer (BC) patients who undergo chemotherapy are likely to develop chemotherapy-related cognitive impairment (CRCI). Recent studies of BC patients after chemotherapy have used graph theory to investigate the topological properties of the brain functional connectome. However, little is known about structural morphological networks in BC patients after early neoadjuvant chemotherapy (NAC). Brain morphological network organization in 47 female participants with BC was investigated before and after NAC. Topological properties of brain networks were ascertained based on morphological similarities in regional gray matter using a graph theory approach based on 3D T1-weighted MRI data. Nonparametric permutation testing was used to assess longitudinal-group differences in topological metrics. Compared with BC patients before NAC, BC patients after early NAC showed significantly increased global efficiency (p = .048), decreased path length (p = .033), and abnormal nodal properties and connectivity, mainly located in the central executive network (CEN). The change in the network efficiency of the right caudate was negatively correlated with the change in the Self-Rating Anxiety Scale score (r = -.435, p = .008), and the change in the nodal degree of the left superior frontal gyrus (dorsolateral part) was positively correlated with the change in the Functional Assessment of Cancer Therapy score (r = .547, p = .002). BC participants showed randomization in global properties and dysconnectivity in the CEN after early NAC. NAC may disrupt the cognitive balance of the brain morphological network in individuals with BC.

Inversion-Recovery-Prepared Oscillating Gradient Sequence Improves Diffusion-Time Dependency Measurements in the Human Brain.

BACKGROUND: Oscillating gradient diffusion MRI (dMRI) enables measurements at a short diffusion-time (td ), but it is challenging for clinical systems. Particularly, the low b-value and low resolution may give rise to cerebrospinal fluid (CSF) contamination. PURPOSE: To assess the effect of CSF partial volume on td -dMRI measurements and efficacy of inversion-recovery (IR) prepared oscillating and pulsed gradient dMRI sequence to improve td -dMRI measurements in the human brain. STUDY TYPE: Prospective. SUBJECTS: Ten normal volunteers and six glioma patients. FIELD STRENGTH/SEQUENCE: A 3 T; three-dimensional (3D) IR-prepared oscillating gradient-prepared gradient spin-echo (GRASE) and two-dimensional (2D) IR-prepared oscillating gradient echo-planar imaging (EPI) sequences. ASSESSMENT: We assessed the td -dependent patterns of apparent diffusion coefficient (ADC) in several gray and white matter structures, including the hippocampal subfields (head, body, and tail), cortical gray matter, thalamus, and posterior white matter in normal volunteers. Pulsed gradient (0 Hz) and oscillating gradients at frequencies of 20 Hz, 40 Hz, and 60 Hz dMRI were acquired with GRASE and EPI sequences with or without the IR module. We also tested the td -dependency patterns in glioma patients using the EPI sequence with or without the IR module. STATISTICAL TESTS: The differences in ADC across the different td s were compared by one-way ANOVA followed by post hoc pairwise t-tests with Bonferroni correction. RESULTS: In the healthy subjects, brain regions that were possibly contaminated by CSF signals, such as the hippocampus (head, body, and tail) and cortical gray matter, td -dependent ADC changes were only significant with the IR-prepared 2D and 3D sequences but not with the non-IR sequences. In brain glioblastomas patients, significantly higher td -dependence was observed in the tumor region with the IR module than that without IR (slope = 0.0196 µm2 /msec2 vs. 0.0034 µm2 /msec2 ). CONCLUSION: The IR-prepared sequence effectively suppressed the CSF partial volume effect and significantly improved the td -dependent measurements in the human brain. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE-MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study.

BACKGROUND: Siamese network (SN) using longitudinal DCE-MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection. PURPOSE: To identify pCR in early-stage NAC, using SN with longitudinal DCE-MRI and introducing IPS for phases selection. STUDY TYPE: Multicenter, longitudinal. POPULATION: Center A: 162 female patients (50.63 ± 8.41 years) divided 7:3 into training and internal validation cohorts. Center B: 61 female patients (50.08 ± 7.82 years) were used as an external validation cohort. FIELD STRENGTH/SEQUENCE: Center A: single vendor 3.0 T with a compressed-sensing volume interpolated breath-hold examination sequence. Center B: single vendor 1.5 T with volume interpolated breath-hold examination sequence. ASSESSMENT: Patients underwent DCE-MRI before and after two NAC cycles, with tumor regions of interest (ROI) manually delineated. Histopathology was the reference for pCR identification. Models developed included a clinical one, four SN models based on IPS-selected phases, and integrated models combining clinical and SN features. STATISTICAL TESTS: Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare AUCs. Net reclassification improvement and integrated discrimination improvement (IDI) tests were employed for performance comparison. P < 0.05 was considered significant. RESULTS: In internal and external validation cohorts, the clinical model showed AUCs of 0.760 and 0.718. SN and integrated models, with increasing phases via IPS, achieved AUCs ranging from 0.813 to 0.951 and 0.818 to 0.922. Notably, SN-3 and integrated-3 and integrated-4 outperformed the clinical model. However, input phases beyond 20% did not significantly enhance performance (IDI test: SN-4 vs. SN-3, P = 0.314 and 0.630; integrated-4 vs. integrated-3, P = 0.785 and 0.709). DATA CONCLUSION: The longitudinal multiphase DCE-MRI based on the SN demonstrates promise for identifying pCR in breast cancer. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 4.

Diffusion-time dependent diffusion MRI: effect of diffusion-time on microstructural mapping and prediction of prognostic features in breast cancer.

OBJECTIVES: This study aimed to evaluate the effect of achievable td on the accuracy of microstructural mapping based on simulation and patient experiments, and investigate the feasibility of td-dMRI in distinguishing prognostic factors in breast cancer patients. METHODS: Simulation was performed using different td settings. Patients with breast cancer were enrolled prospectively between November 2020 and January 2021, who underwent oscillating and pulsed gradient encoded dMRI on a 3-T scanner using short-/long-td protocol with oscillating frequency up to 50/33 Hz. Data were fitted with a two-compartment model to estimate cell diameter (d), intracellular fraction (fin), and diffusivities. Estimated microstructural markers were used to differentiate immunohistochemical receptor status and the presence of lymph node (LN), which were correlated with histopathological measurements. RESULTS: Simulation results showed that d fitted from the short-td protocol significantly reduced estimation error than those from long-td (2.07 ± 1.51% versus 3.05 ± 1.92%, p < 0.0001) while the estimation error of fin was robust to different protocols. Among a total of 37 breast cancer patients, the estimated d was significantly higher in HER2-positive and LN-positive (p < 0.05) groups compared to their negative counterparts only using the short-td protocol. Histopathological validation in a subset of 6 patients with whole slide images showed the estimated d was highly correlated with measurements from H&E staining (r = 0.84, p = 0.03) only using the short-td protocol. CONCLUSIONS: The results indicated the necessity of short-td for accurate microstructural mapping in breast cancer. The current td-dMRI with a total acquisition time of 4.5 min showed its potential in the diagnosis of breast cancer. KEY POINTS: • Short td is important for accurate microstructural mapping in breast cancer using the td-dMRI technique, based on simulation and histological validation. • The 4.5-min td-dMRI protocol showed potential clinical value for breast cancer, given the difference in cell diameter between HER2/LN positive and negative groups.

Extracellular volume fraction determined by equilibrium contrast-enhanced CT for the prediction of the pathological complete response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer.

OBJECTIVES: To determine the extracellular volume (ECV) fraction derived from equilibrium contrast-enhanced CT for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC). METHODS: The ECV fraction before NCRT (ECVpre) and/or ECV after NCRT (ECVpost) of rectal tumors was assessed, and ECVΔ was calculated as ECVpost - ECVpre. The histopathologic tumor regression grading (TRG) was assessed. pCR (TRG 0 grade) was defined as the absence of viable tumor cells in the primary tumor and lymph nodes. Demographic and clinicopathological characteristics and ECV fraction were compared between the pCR and non-pCR groups. A mixed model was constructed by logistic regression. The performance for predicting pCR was assessed with the area under the receiver-operator curve (AUC). The AUCs of the different methods were compared by the method proposed by DeLong et al. RESULTS: Seventy-five patients were included; 17 achieved pCR, and 58 achieved non-pCR. The ECVpost (17.05 ± 2.36% vs. 29.94 ± 1.20%; p < 0.001) and ECVΔ (- 17.01 ± 3.01% vs. 0.44 ± 1.45%; p < 0.001) values in the pCR group were significantly lower than those in the non-pCR group. The mixed model that combined ECVpost with ECVΔ achieved an AUC of 0.92 (95% confidence interval (CI) = 0.81-0.98), which was higher than that of ECVpost (AUC, 0.91 (95% CI = 0.80-0.97); p = 0.60) or ECVΔ (AUC, 0.90 (95% CI = 0.79-0.97); p = 0.61). CONCLUSIONS: ECVpost and ECVΔ determined by using equilibrium contrast-enhanced CT were useful in distinguishing between pCR and non-pCR patients with LARC who received NCRT. KEY POINTS: • ECVpost and ECVΔ (ECVpost - ECVpre) differed significantly between the non-pCR and pCR groups. • ECVpre cannot be used to predict the efficacy of neoadjuvant chemoradiotherapy. • ECVpost combined with ECVΔ had the best performance with an AUC of 0.92 for predicting pCR after NCRT in LARC.

XGBoost-based multiparameters from dual-energy computed tomography for the differentiation of multiple myeloma of the spine from vertebral osteolytic metastases.

OBJECTIVES: To evaluate the performance of extreme gradient boosting (XGBoost) combined with multiparameters from dual-energy computed tomography (mpDECT) to differentiate between multiple myeloma (MM) of the spine and vertebral osteolytic metastases (VOM). METHODS: For this retrospective study, 28 patients (83 lesions) with MM of the spine and 23 patients (54 lesions) with VOM who underwent DECT were included. The mpDECT for each lesion, including normalized effective atomic number, slope of the spectral Hounsfield unit curve, CT attenuation, and virtual noncalcium (VNCa), was obtained. Boruta was used to select the key parameters, and then subsequently merged with XGBoost to yield a prediction model. The lesions were divided into the training and testing group in a 3:1 ratio. The highest performance of the univariate analysis was compared with XGBoost using the Delong test. RESULTS: The mpDECT of MM was significantly lower than that of VOM (all p < 0.05). In univariate analysis, VNCa had the highest area under the receiver operating characteristic curve (AUC) in the training group (0.81) and testing group (0.87). Based on Boruta, 6 parameters of DECT were selected for XGBoost model construction. The XGBoost model achieved an excellent and stable diagnostic performance, as shown in the training group (AUC of 1.0) and testing group (AUC of 0.97), with a sensitivity of 80%, a specificity of 95%, and an accuracy of 88%, which was superior to VNCa (p < 0.05). CONCLUSIONS: XGBoost combined with mpDECT yielded promising performance in differentiating between MM of the spine and VOM. KEY POINTS: • The multiparameters obtained from dual-energy CT of multiple myeloma differed significantly from those of vertebral osteolytic metastases. • The virtual noncalcium offered the highest AUC in the univariate analysis to distinguish multiple myeloma from vertebral osteolytic metastases. • Extreme gradient boosting combined with multiparameters from dual-energy CT had a promising performance to distinguish multiple myeloma from vertebral osteolytic metastases.

Quantitative parameters derived from dual-energy computed tomography for the preoperative prediction of early recurrence in patients with esophageal squamous cell carcinoma.

OBJECTIVES: This study aimed to assess the efficacy of quantitative parameters derived from dual-energy computed tomography (DECT) for the preoperative prediction of early recurrence (ER) in patients with esophageal squamous cell carcinoma (ESCC). METHODS: In total, 78 patients with ESCC who underwent radical esophagectomy and DECT from June 2019 to August 2020 were enrolled in this study. Normalized iodine concentration (NIC) and electron density (Rho) in tumors were measured using arterial and venous phase images, whereas unenhanced images were used to determine the effective atomic number (Zeff). Univariate and multivariate Cox proportional hazards models were used to identify independent risk predictors of ER. Receiver operating characteristic curve analysis was performed using the independent risk predictors. ER-free survival curves were constructed using the Kaplan-Meier method. RESULTS: NIC in the arterial phase (A-NIC; hazards ratio [HR], 3.91; 95% confidence interval [CI], 1.79-8.56; p = 0.001) and pathological grade (PG; HR, 2.69; 95% CI, 1.32-5.49; p = 0.007) were identified as significant risk predictors of ER. The area under the curve of A-NIC for predicting ER in patients with ESCC was not significantly higher than that of PG (0.72 vs. 0.66, p = 0.441). In a stratified survival analysis, patients with high A-NIC or poorly differentiated ESCC had a higher rate of ER than those with low A-NIC or highly/moderately differentiated ESCC. CONCLUSIONS: A-NIC derived from DECT can be used to noninvasively predict preoperative ER in patients with ESCC, with an efficacy comparable to that of pathological grade. CLINICAL RELEVANCE STATEMENT: Preoperative quantitative measurement of dual-energy CT parameters can predict the early recurrence of esophageal squamous cell carcinoma and serve as an independent prognostic factor to guide clinical designation of personalized treatment. KEY POINTS: • Normalized iodine concentration in the arterial phase and pathological grade were independent risk predictors of early recurrence in patients with esophageal squamous cell carcinoma. • Normalized iodine concentration in the arterial phase may be a noninvasive imaging marker for preoperatively predicting early recurrence in patients with esophageal squamous cell carcinoma. • The efficacy of normalized iodine concentration in the arterial phase derived from dual-energy computed tomography for predicting early recurrence is comparable to that of pathological grade.

Early alterations in cortical morphology after neoadjuvant chemotherapy in breast cancer patients: A longitudinal magnetic resonance imaging study.

There is growing evidence that chemotherapy may have a significant impact on the brains of breast cancer patients, causing changes in cortical morphology. However, early morphological alterations induced by chemotherapy in breast cancer patients are unclear. To investigate the patterns of those alterations, we compared female breast cancer patients (n = 45) longitudinally before (time point 0, TP0) and after (time point 1, TP1) the first cycle of neoadjuvant chemotherapy, using voxel-based morphometry (VBM) and surface-based morphometry (SBM). VBM and SBM alteration data underwent correlation analysis. We also compared cognition-related neuropsychological tests in the breast cancer patients between TP0 and TP1. Reductions in gray matter volume, cortical thickness, sulcal depth, and gyrification index were found in most brain areas, while increments were found to be mainly concentrated in and around the hippocampus. Reductions of fractal dimension mainly occurred in the limbic and occipital lobes, while increments mainly occurred in the anterior and posterior central gyrus. Significant correlations were found between altered VBM and altered SBM mainly in the bilateral superior frontal gyrus. We found no significant differences in the cognition-related neuropsychological tests before and after chemotherapy. The altered brain regions are in line with those associated with impaired cognitive domains in previous studies. We conclude that breast cancer patients showed widespread morphological alterations soon after neoadjuvant chemotherapy, despite an absence of cognitive impairments. The affected brain regions may indicate major targets of early brain damage after chemotherapy.

Nomogram based on CT-derived extracellular volume for the prediction of post-hepatectomy liver failure in patients with resectable hepatocellular carcinoma.

OBJECTIVES: This study aimed to develop and validate a nomogram based on extracellular volume (ECV) derived from computed tomography (CT) for predicting post-hepatectomy liver failure (PHLF) in patients with resectable hepatocellular carcinoma (HCC). METHODS: A total of 202 patients with resectable HCC from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. One hundred twenty-one patients from our hospital and 81 patients from another hospital were assigned to the training cohort and the validation cohort, respectively. CT-derived ECV was measured using nonenhanced and equilibrium-phase-enhanced CT images. The nomogram was developed with independent predictors of PHLF. Predictive performance and calibration were assessed by receiver operator characteristic (ROC) analysis and Hosmer-Lemeshow test, respectively. The Delong test was used to compare the areas under the curve (AUCs). RESULTS: CT-derived ECV had a strong correlation with the postoperative pathological fibrosis stage of the background liver (p < 0.001, r = 0.591). The nomogram combining CT-derived ECV, serum albumin (Alb), and serum total bilirubin (Tbil) obtained higher AUCs than the albumin-bilirubin (ALBI) score for predicting PHLF in both the training cohort (0.828 vs. 0.708; p = 0.004) and the validation cohort (0.821 vs. 0.630; p < 0.001). The nomogram showed satisfactory goodness of fit for PHLF prediction in the training and validation cohorts (p = 0.621 and 0.697, respectively). CONCLUSIONS: The nomogram contributes to the preoperative prediction of PHLF in patients with resectable HCC. KEY POINTS: • CT-derived ECV had a strong correlation with the postoperative pathological fibrosis stage of the background liver. • CT-derived ECV was an independent predictor of PHLF in patients with resectable HCC. • The nomogram based on CT-derived ECV showed a superior prediction efficacy than that of clinical models (including Child-Pugh stage, MELD score, and ALBI score).

Chemotherapy-associated steatohepatitis was concomitant with epicardial adipose tissue volume increasing in breast cancer patients who received neoadjuvant chemotherapy.

OBJECTIVES: To investigate the prevalence of chemotherapy-associated steatohepatitis, quantitate the epicardial adipose tissue (EAT) volume in breast cancer patients, and explore the mediating effect of liver fat content on EAT volume in breast cancer patients who received neoadjuvant chemotherapy (NAC). METHODS: From October 2018 to April 2020, patients were retrospectively reviewed and divided into breast cancer non-NAC and NAC groups. The prevalence of chemotherapy-associated steatohepatitis was evaluated through quantitative MRI mDIXON-Quant examinations by using defined proton density fat fraction cutoffs of liver fat. The EAT volume was quantified on chest CT by semi-automatic volume analysis software. Bootstrap analysis was used in the breast cancer NAC group to test the significance of the mediating effect of liver fat content on EAT volume. RESULTS: A total of 662 breast cancer patients (non-NAC group: 445 patients; NAC group: 217 patients) were included. The prevalence of chemotherapy-associated steatohepatitis in the NAC group was significantly higher than the prevalence of hepatic steatosis in the non-NAC group (42.8% vs. 33.3%, p < 0.001). EAT volume was measured in 561 of 662 breast cancer patients, and was significantly higher in the NAC group than in the non-NAC group (137.26 ± 53.48 mL vs. 125.14 ± 58.77 mL, p = 0.020). In the breast cancer NAC group, the indirect effect of liver fat content on EAT volume was 2.545 (p < 0.001), and the contribution rate to the effect was 69.1%. CONCLUSIONS: EAT volume was significantly higher in the BC-NAC group than in the BC-non-NAC group. KEY POINTS: • The prevalence of CASH was as high as 42.8% in BC patients. • NAC significantly increased the EAT volume in BC patients. • The liver fat content caused the change of EAT volume through mediating effect.

Initial Investigation of Clinical Value of Noise-Optimized Virtual Monoenergetic Images Derived From Dual-Energy Computed Tomography Angiography in Preoperative Perforator Planning of Anterolateral Thigh Flap Transplantation.

OBJECTIVE: To objectively and subjectively assess the image characteristics of noise-optimized virtual monoenergetic images [MEI (+)] and polyenergetic images (PEIs) from dual-energy computed tomography angiography and then to explore the clinical value of the optimal MEI (+) in preoperative perforator planning of anterolateral thigh (ALT) flap transplantation. METHODS: Sixteen patients (32 thighs) who underwent lower extremity run-off dual-energy computed tomography angiography for planning ALT flap transplantation were enrolled. One standard PEI and 5 MEI (+) in 10-keV intervals (range, 40-80 keV) were reconstructed. First, we compared the image quality subjectively (branch order, image quality, and vascular network continuity) and objectively (vascular attenuation, image noise, signal-to-noise ratio, and the contrast-to-noise ratio). Then, we compared the clinical value (number, type, source artery, pedicle length, caliber, and location of all sizable perforators) between the optimal MEI (+) and PEI groups. RESULTS: The 40-keV MEI (+) was rated superior subjective and objective image quality metrics to PEI (all P < 0.001). Compared with PEI, 40 keV MEI (+) increased the number of visible perforators, the percentage of perforators with identifiable types, and the measurable length of perforator pedicle (all P < 0.001). CONCLUSIONS: We recommend 40 keV MEI (+) for the visualization of perforators and their contribution to the selection and location of suitable perforators in preoperative planning for ALT flaps.

A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) is a heterogeneous group of kidney cancers. Renal capsule invasion is an essential factor for RCC staging. To develop radiomics models from CT images for the preoperative prediction of capsule invasion in RCC patients. METHODS: This retrospective study included patients with RCC admitted to the Chongqing University Cancer Hospital (01/2011-05/2019). We built a radiomics model to distinguish patients grouped as capsule invasion versus non-capsule invasion, using preoperative CT scans. We evaluated effects of three imaging phases, i.e., unenhanced phases (UP), corticomedullary phases (CMP), and nephrographic phases (NP). Five different machine learning classifiers were compared. The effects of tumor and tumor margins are also compared. Five-fold cross-validation and the area under the receiver operating characteristic curve (AUC) are used to evaluate model performance. RESULTS: This study included 126 RCC patients, including 46 (36.5%) with capsule invasion. CMP exhibited the highest AUC (AUC = 0.81) compared to UP and NP, when using the forward neural network (FNN) classifier. The AUCs using features extracted from the tumor region were generally higher than those of the marginal regions in the CMP (0.81 vs. 0.73) and NP phase (AUC = 0.77 vs. 0.76). For UP, the best result was obtained from the marginal region (AUC = 0.80). The robustness analysis on the UP, CMP, and NP achieved the AUC of 0.76, 0.79, and 0.77, respectively. CONCLUSIONS: Radiomics features in renal CT imaging are associated with the renal capsule invasion in RCC patients. Further evaluation of the models is warranted.

Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI.

BACKGROUND: The number of metastatic axillary lymph nodes (ALNs) play a crucial role in the staging, prognosis and therapy of patients with breast cancer. PURPOSE: To predict the number of metastatic ALNs in breast cancer via radiomics. MATERIAL AND METHODS: We enrolled 197 patients with breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A total of 3386 radiomic features were extracted from the early- and delayed-phase subtraction images. To classify the number of metastatic ALNs, logistic regression was used to develop a radiomic signature and nomogram. RESULTS: The radiomic signature were constructed to distinguish the N0 group from the N+ (metastatic ALNs ≥ 1) group, which yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and test group, respectively. Based on the radiomic signature and BI-RADS category, a nomogram was further developed and showed excellent predictive performance with AUC values of 0.85 and 0.89 in the training and test groups, respectively. Another radiomic signature was constructed to distinguish the N1 (1-3 ALNs) group from the N2-3 (≥4 metastatic ALNs) group and showed encouraging performance with AUC values of 0.94 and 0.84 in training and test group, respectively. CONCLUSIONS: We developed a nomogram and a radiomic signature that can be used to predict ALN metastasis and distinguish the N1 from the N2-3 group. Both nomogram and radiomic signature may be potential tools to assist clinicians in assessing ALN metastasis in patients with breast cancer.

Structural and functional underpinnings of precentral abnormalities in amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the loss of both upper and lower motor neurons. Studies using various magnetic resonance imaging (MRI) analytical approaches have consistently identified significant precentral abnormalities in ALS, whereas their structural and functional underpinnings remain poorly understood. METHODS: Using cortical thickness, fractional anisotropy (FA), and effective connectivity, we performed a multimodal MRI study to examine the structural and functional alterations associated with precentral abnormalities in patients with ALS (n = 60) compared with healthy controls (n = 60). RESULTS: Cortical thickness analysis revealed significant cortical thinning in the right precentral gyrus (PCG), superior frontal gyrus, and superior temporal gyrus in patients with ALS. Tractwise white matter microstructure analyses revealed decreased FA in the tracts connected to the PCG cluster in patients with ALS involving the right corticospinal tract and the middle posterior body of the corpus callosum. Additionally, the cortical thickness of the PCG cluster was found to be positively correlated with FA of the tracts connected to the PCG cluster, suggesting that these two structural features are tightly coupled. Using spectral dynamic causal modelling, effective connectivity analysis among the three regions with cortical thinning revealed decreased self-inhibitory influence in the PCG cluster in patients with ALS, which might be an endophenotypic manifestation of an imbalance in inhibitory and excitatory neurotransmitters in this region. CONCLUSIONS: The present data shed new light on the structural and functional underpinnings of precentral abnormalities in ALS.

Diagnostic performance of diffusion-weighted imaging combined with dynamic contrast-enhanced magnetic resonance imaging for prostate cancer: a systematic review and meta-analysis.

BACKGROUND: The diagnostic performance of diffusion-weighted imaging (DWI) combined with dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for the detection of prostate cancer (PCa) has not been studied systematically to date. PURPOSE: To investigate the value of DWI combined with DCE-MRI quantitative analysis in the diagnosis of PCa. MATERIAL AND METHODS: A systematic search was conducted through PubMed, MEDLINE, the Cochrane Library, and EMBASE databases without any restriction to language up to 10 December 2019. Studies that used a combination of DWI and DCE-MRI for diagnosing PCa were included. RESULTS: Nine studies with 778 participants were included. The combination of DWI and DCE-MRI provide accurate performance in diagnosing PCa with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratios of 0.79 (95% confidence interval [CI] = 0.76-0.81), 0.85 (95% CI = 0.83-0.86), 6.58 (95% CI = 3.93-11.00), 0.24 (95% CI = 0.17-0.34), and 36.43 (95% CI = 14.41-92.12), respectively. The pooled area under the summary receiver operating characteristic curve was 0.9268. Moreover, 1.5-T MR scanners demonstrated a slightly better performance than 3.0-T scanners. CONCLUSION: Combined DCE-MRI and DWI could demonstrate a highly accurate area under the curve, sensitivity, and specificity for detecting PCa. More studies with large sample sizes are warranted to confirm these results.

Evaluating the Image Quality of Monoenergetic Images From Dual-Energy Computed Tomography With Low-Concentration and Low-Flow-Rate Contrast Media for the Arterials Supply to the Nipple-Areola Complex in Breast Cancer Compared With Conventional Computed Tomography Angiography.

OBJECTIVE: The objective of this study was to evaluate the image quality of monoenergetic images (MEIs (+)) acquired from dual-energy computed tomography with low-concentration and low-flow-rate contrast media for the arterial supply to the nipple-areola complex (NAC) in breast cancer compared with conventional computed tomography angiography (CTA). METHODS: We enrolled 25 patients (MEI (+)300 group, 300 mg/mL and 2.5 mL/s of contrast media) and 23 patients (CTA370 group, 370 mg/mL and 3.5 mL/s of contrast media) for assessing NAC blood supply angiography. The image quality of the 2 groups was evaluated objectively and subjectively. RESULTS: The 40 keV MEI (+)300 demonstrated higher attenuation and contrast-to-noise ratio than CTA370 group (P < 0.001). The subjective image quality and visualization of the arteries were comparable between 2 groups. CONCLUSIONS: The 40 keV MEI (+)300 acquired from dual-energy computed tomography can achieve comparable image quality of arterial supply to NAC with low-concentration and low-flow-rate contrast media in breast cancer compared with CTA370.

Precentral degeneration and cerebellar compensation in amyotrophic lateral sclerosis: A multimodal MRI analysis.

Amyotrophic lateral sclerosis (ALS) is a progressive and intractable neurodegenerative disease of human motor system characterized by progressive muscular weakness and atrophy. A considerable body of research has demonstrated significant structural and functional abnormalities of the primary motor cortex in patients with ALS. In contrast, much less attention has been paid to the abnormalities of cerebellum in this disease. Using multimodal magnetic resonance imagining data of 60 patients with ALS and 60 healthy controls, we examined changes in gray matter volume (GMV), white matter (WM) fractional anisotropy (FA), and functional connectivity (FC) in patients with ALS. Compared with healthy controls, patients with ALS showed decreased GMV in the left precentral gyrus and increased GMV in bilateral cerebellum, decreased FA in the left corticospinal tract and body of corpus callosum, and decreased FC in multiple brain regions, involving bilateral postcentral gyrus, precentral gyrus and cerebellum anterior lobe, among others. Meanwhile, we found significant intermodal correlations among GMV of left precentral gyrus, FA of altered WM tracts, and FC of left precentral gyrus, and that WM microstructural alterations seem to play important roles in mediating the relationship between GMV and FC of the precentral gyrus, as well as the relationship between GMVs of the precentral gyrus and cerebellum. These findings provided evidence for the precentral degeneration and cerebellar compensation in ALS, and the involvement of WM alterations in mediating the relationship between pathologies of the primary motor cortex and cerebellum, which may contribute to a better understanding of the pathophysiology of ALS.