Thoracic aorta injury detected by 4D flow MRI predicts subsequent main adverse cardiovascular events in breast cancer patients receiving anthracyclines: A longitudinal study.

PURPOSE: To investigate longitudinal thoracic aorta injury using 3-dimensional phase-contrast magnetic resonance imaging (4D flow MRI) parameters and to evaluate their value for predicting the subsequent main adverse cardiovascular events (MACEs) in breast cancer patients receiving anthracyclines. METHODS: Between July 2020 and July 2021, eighty-eight female participants with breast cancer scheduled to receive anthracyclines with or without trastuzumab prospectively enrolled. Each subjects underwent 4D flow MRI at baseline, 3 and 6 months in relation to baseline. The diameter, peak velocity (Vpeak), wall shear stress (WSS), pulse wave velocity (PWV), energy loss (EL) and pressure gradient (PG) of thoracic aorta were measured. The association between these parameters and subsequent MACEs was performed by Cox proportional hazard models. RESULTS: Ten participants had subsequently MACEs. The Vpeak and PG gradually decreased and the WSS, PWV and EL progressively increased at 3 and 6 months compared with baseline. Adjusted multivariable analysis showed that the WSS of the proximal, mid- and distal ascending aorta [HR, 1.314 (95% confidence interval (CI): 1.003, 1.898)], [HR, 1.320 (95% CI: 1.002, 1.801)] and [HR, 1.322 (95% CI: 1.001, 1.805)] and PWV of ascending aorta [HR, 2.223 (95% CI: 1.010, 4.653)] at 3 months were associated with subsequent MACEs. Combined WSS and PWV of ascending aorta at 3 months yielded the highest AUC (0.912) for predicting subsequent MACEs. CONCLUSION: Combined WSS and PWV of ascending aorta at 3 months is helpful for predicting the subsequent MACEs in breast cancer patients treated by anthracyclines.

CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma.

OBJECTIVES: Posthepatectomy liver failure (PHLF) is a severe complication of liver resection. We aimed to develop and validate a model based on extracellular volume (ECV) and liver volumetry derived from computed tomography (CT) for preoperative predicting PHLF in resectable hepatocellular carcinoma (HCC) patients. METHODS: A total of 393 resectable HCC patients from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. A total of 281 patients from our hospital were randomly divided into a training cohort (n = 181) and an internal validation cohort (n = 100), and 112 patients from another hospital formed the external validation cohort. CT-derived ECV was measured on nonenhanced and equilibrium phase images, and liver volumetry was measured on portal phase images. The model is composed of independent predictors of PHLF. The under the receiver operator characteristic curve (AUC) and calibration curve were used to reflect the predictive performance and calibration of the model. Comparison of AUCs used the DeLong test. RESULTS: CT-derived ECV, measured future liver remnant (mFLR) ratio, and serum albumin were independent predictors for PHLF in resectable HCC patients. The AUC of the model was significantly higher than that of the ALBI score in the training cohort, internal validation cohort, and external validation cohort (all p < 0.001). The calibration curve of the model showed good consistency in the training cohort and the internal and external validation cohorts. CONCLUSIONS: The novel model contributes to the preoperative prediction of PHLF in resectable HCC patients. CRITICAL RELEVANCE STATEMENT: The novel model combined CT-derived extracellular volume, measured future liver remnant ratio, and serum albumin outperforms the albumin-bilirubin score for predicting posthepatectomy liver failure in patients with resectable hepatocellular carcinoma. KEY POINTS: • CT-derived ECV correlated well with the fibrosis stage of the background liver. • CT-derived ECV and mFLR ratio were independent predictors for PHLF in HCC. • The AUC of the model was higher than the CT-derived ECV and mFLR ratio. • The model showed a superior predictive performance than that of the ALBI score.

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.

Cardiovascular Risk Stratification by Automatic Coronary Artery Calcium Scoring on Pretreatment Chest Computed Tomography in Diffuse Large B-Cell Lymphoma Receiving Anthracycline-Based Chemotherapy: A Multicenter Study.

BACKGROUND: Balancing the cardiovascular risk and benefit of anthracycline-based chemotherapy in patients with diffuse large B-cell lymphoma is an important clinical issue. We aimed to evaluate whether the pretreatment coronary artery calcium score (CACS) can stratify the risk of cancer therapy-related cardiac dysfunction (CTRCD) and major adverse cardiovascular events (MACEs) in patients with diffuse large B-cell lymphoma receiving anthracycline-based chemotherapy. METHODS: The patients with diffuse large B-cell lymphoma from 4 hospitals were retrospectively enrolled. The CACS was automatically calculated on nongated chest computed tomography before treatment using artificial intelligence-CACS software and divided into 3 categories (0, 1-100, and >100). The associations between the CACS and CTRCD and between the CACS and MACEs were assessed by logistic regression and Fine-Gray competing-risk regression model. Nelson-Aalen cumulative risk curve was performed to assess the cumulative incidence of MACEs. RESULTS: A total of 1468 patients (785 men and 683 women; 100% Asian) were enrolled, and 362 and 185 patients developed CTRCD and MACEs, respectively. Compared with a CACS of 0 (n=826), there was stepwise higher odds of CTRCD with a CACS between 1 and 100 (n=356; odds ratio, 2.587) and a CACS >100 (n=286; odds ratio, 5.239). The CACS was associated with MACEs (1-100 versus 0: subdistribution hazard ratio 3.726; >100 versus 0: subdistribution hazard ratio 7.858; all P<0.001). Competing risk-adjusted MACEs rates for patients with a CACS of 0, 1 to 100, and >100 were 1.21%, 8.43%, and 11.19%, respectively, at 3 years, and 3.27%, 16.01%, 31.12%, respectively, at 5 years. CONCLUSIONS: The automatic CACS derived from chest computed tomography before treatment was helpful to identify high-risk patients of CTRCD and MACE and guide clinicians to implement cardiovascular protection strategies in patients with diffuse large B-cell lymphoma who received anthracycline-based chemotherapy.

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.

Predicting treatment response to concurrent chemoradiotherapy in squamous cell carcinoma of the cervix using amide proton transfer imaging and intravoxel incoherent motion imaging.

PURPOSE: The purpose of this study was to investigate whether amide proton transfer (APT) imaging and intravoxel incoherent motion (IVIM) imaging can predict tumor response to concurrent chemoradiotherapy (CCRT) in patients with squamous cell carcinoma of the cervix (SCCC). MATERIAL AND METHODS: Fifty-nine women (mean age, 54 years ± 10 [standard deviation] years; age range: 32-81 years) with pathologically confirmed SCCC underwent magnetic resonance imaging examination of the pelvis including APT and IVIM before concurrent chemoradiotherapy. They were divided into complete remission (CR) and non-CR groups according to therapeutic effect. APT values and IVIM-derived parameters were measured. Intra- and interobserver agreement for IVIM and APT parameters was assessed using intraclass correlation coefficient (ICC) The independent samples t-test was performed to compare the evaluated parameters between the two groups. Predictive performance for treatment response was evaluated by receiver operator characteristic (ROC) curve analysis. RESULTS: There were 38 and 21 patients in the non-CR and CR groups, respectively. Excellent interobserver and intraobserver agreement were obtained for all IVIM and APT parameters, with ICCs ranging from 0.844 to 0.962. Perfusion fraction (f) and APT values were lower in the CR group compared with the non-CR group (both P < 0.05). The combination of f and APT values showed good diagnostic performances in predicting response to concurrent chemoradiotherapy, with an area under the ROC curve of 0.852 (95% CI: 0.744-0.961), 79% sensitivity (95% CI: 63-90%), 90% specificity (95% CI: 70-99%) and 83% accuracy (95% CI: 71-92%). CONCLUSION: APT and IVIM imaging may serve as noninvasive tools for predicting response to concurrent chemoradiotherapy in patients with SCCC.

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).

Myocardial extracellular volume derived from contrast-enhanced chest computed tomography for longitudinal evaluation of cardiotoxicity in patients with breast cancer treated with anthracyclines.

OBJECTIVES: To assess the value of myocardial extracellular volume (ECV) derived from contrast-enhanced chest computed tomography (CT) for longitudinal evaluation of cardiotoxicity in patients with breast cancer (BC) treated with anthracycline (AC). MATERIALS AND METHODS: A total of 1151 patients with BC treated with anthracyclines, who underwent at least baseline, and first follow-up contrast-enhanced chest CT were evaluated. ECV and left ventricular ejection fraction (LVEF) were measured before (ECV0, LVEF0), during ((ECV1, LVEF1) and (ECV2, LVEF2)), and after (ECV3, LVEF3) AC treatment. ECV values were evaluated at the middle of left ventricular septum on venous phase images. Cancer therapy-related cardiac dysfunction (CTRCD) was recorded. RESULTS: Mean baseline LVEF values were 65.85% ± 2.72% and 102 patients developed CTRCD. The mean ECV0 was 26.76% ± 3.03% (N0 = 1151). ECV1, ECV2, and ECV3 (median interval: 61 (IQR, 46-75), 180 (IQR, 170-190), 350 (IQR, 341-360) days from baseline) were 31.32% ± 3.10%, 29.60% ± 3.24%, and 32.05% ± 3.58% (N1 = 1151, N2 = 841, N3 = 511). ECV1, ECV2, and ECV3 were significantly higher than ECV0 (p < 0.001). ECV0 and ECV1 showed no difference between CTRCD (+) and CTRCD (-) group (p1 = 0.150; p2 = 0.216). However, ECV2 and ECV3 showed significant differences between the two groups (p3 < 0.001; p4 < 0.001). CONCLUSION: CT-derived ECV is a potential biomarker for dynamic monitoring AC cardiotoxicity in patients with BC.

MRI-based radiomics to compare the survival benefit of induction chemotherapy plus concurrent chemoradiotherapy versus concurrent chemoradiotherapy plus adjuvant chemotherapy in locoregionally advanced nasopharyngeal carcinoma: A multicenter study.

BACKGROUND AND PURPOSE: It remains uncertain whether induction chemotherapy (IC) plus concurrent chemoradiotherapy (CCRT) or CCRT plus adjuvant chemotherapy (AC) is more effective in patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC). This study aimed to develop and validate a joint radiomic and clinical signature (RCS) for the prognostic stratification of LA-NPCs and to identify patients who might benefit more from IC + CCRT or CCRT + AC. MATERIALS AND METHODS: Overall, 893 LA-NPC patients who received IC + CCRT or CCRT + AC were enrolled from four hospitals. RCS based on pretreatment magnetic resonance images and clinical data was constructed for predicting 5-year progression-free survival (PFS). The predictive ability of the RCS and TNM staging system for 5-year PFS, locoregional relapse-free survival (LRRFS), distant metastasis-free survival (DMFS) and overall survival (OS) were compared by Harrell's concordance indices (C-indices). Patients were divided into high- and low-risk subgroups based on RCS scores. The survival benefit of IC + CCRT vs. CCRT + AC in different subgroups was compared by Kaplan-Meier survival curves. RESULTS: The RCS combining the radiomic signature, TNM stage and EBV DNA demonstrated significantly higher C-indices than TNM stage for predicting 5-year PFS, LRRFS, DMFS and OS in the training and validation cohorts. In the high-risk group (RCS score ≥ 0.25), CCRT + AC achieved significantly better PFS, LRRFS, DMFS and OS than IC + CCRT. In the low-risk group (RCS score < 0.25), IC + CCRT yielded significantly better outcomes than CCRT + AC. CONCLUSION: The RCS provides a noninvasive way to predict the outcomes of LA-NPC and helps identify patients who may benefit more from IC + CCRT vs. CCRT + AC.

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.

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.

Longitudinal pericardial adipose tissue changes in patients with breast cancer receiving anthracycline-based chemotherapy: a retrospective cohort study.

Background: Pericardial adipose tissue (PAT) has been associated with adverse cardiac events. In this study, we evaluated changes of PAT in patients with breast cancer during and after anthracycline-based chemotherapy and explored the clinical variables associated with increases in PAT volume at the completion of chemotherapy. Methods: A total of 278 breast cancer patients who were receiving anthracycline-based chemotherapy were retrospectively enrolled. Their PAT volumes were measured using non-contrast chest computed tomography (CT) images from a dedicated workstation. We compared these volumes to their measurements at baseline, during different chemotherapy cycles, at and after chemotherapy completion. We identified the clinical variables associated with increases in PAT volume at chemotherapy completion using logistic regression analyses. Results: At the completion of chemotherapy, PAT volumes were shown to have increased compared to baseline measurements (87.67±45.09 vs. 104.25±47.74 cm3; P=0.00). After 4, 6, and 8 chemotherapy cycles, PAT volumes increased from the baseline measurement by 9.48% [95% confidence interval (CI): -2.30% to 21.27%], 14.75% (95% CI: 4.68% to 24.82%), and 20.02% (95% CI: 11.38% to 28.66%), respectively. Compared to volumes measured at chemotherapy completion (104.25±47.74 cm3), PAT volumes at 6 and 12 months after chemotherapy completion were 105.23±49.27 and 107.56±46.34 cm3, respectively. The differences between chemotherapy completion and follow-up PAT volumes were not statistically significant. A variable associated with an increase in PAT from baseline to chemotherapy completion was the number of chemotherapy cycles (8 vs. 4) [odds ratio (OR) =3.850; 95% CI: 1.751 to 8.488]. Conclusions: Patients with breast cancer who undergo anthracycline-based chemotherapy can experience unfavorable PAT volume increases, which are maintained after the completion of treatment. Patients at risk of increases in PAT volume at chemotherapy completion can be identified based on clinical risk factors and targeted for interventions.

Using quantitative parameters derived from pretreatment dual-energy computed tomography to predict histopathologic features in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) patients with a high tumor grade, lymphovascular invasion (LVI), or perineural invasion (PNI) tend to demonstrate a poor prognosis in clinical series. Thus, the identification of histopathological features, including tumor grade, LVI, and PNI, before treatment could be used to stratify the prognosis of patients with HNSCC. This study aimed to assess whether quantitative parameters derived from pretreatment dual-energy computed tomography (DECT) can predict the histopathological features of patients with HNSCC. METHODS: In this study, 72 consecutive patients with pathologically confirmed HNSCC were enrolled and underwent dual-phase (noncontrast-enhanced phase and contrast-enhanced phase) DECT examinations. Normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λHU), and normalized effective atomic number (NZeff) were calculated. The attenuation values on 40-140 keV noise-optimized virtual monoenergetic images [VMIs (+)] in the contrast-enhanced phase were recorded. The diagnostic performance of the quantitative parameters for predicting histopathological features, including tumor grade, LVI, and PNI, was assessed by receiver operating characteristic curves. RESULTS: The NIC, λHU, NZeff, and attenuation value on the VMIs (+) at 40 keV (A40) in the grade III group, LVI-positive group, and PNI-positive group were significantly higher than those in the grade I and II groups, the LVI-negative group, and the PNI-negative group (all P values <0.05). A multivariate logistic regression model combining these 4 quantitative parameters improved the diagnostic performance of the model in predicting tumor grade, LVI, and PNI (areas under the curve: 0.969, 0.944, and 0.931, respectively). CONCLUSIONS: Quantitative parameters derived from pretreatment DECT, including NIC, λHU, NZeff, and A4,0 were found to be imaging markers for predicting the histopathological characteristics of HNSCC. Combining all these characteristics improved the predictive performance of the model.

Low-dose CT with tin filter combined with iterative metal artefact reduction for guiding lung biopsy.

BACKGROUND: Computed tomography (CT) is currently the imaging modality of choice for guiding pulmonary percutaneous procedures. The use of a tin filter allows low-energy photons to be absorbed which contribute little to image quality but increases the radiation dose that a patient receives. Iterative metal artefact reduction (iMAR) was developed to diminish metal artefacts. This study investigated the impact of using tin filtration combined with an iMAR algorithm on dose reduction and image quality in CT-guided lung biopsy. METHODS: Ninety-nine consecutive patients undergoing CT-guided lung biopsy were randomly assigned to routine-dose CT protocols (groups A and B; without and with iMAR, respectively) or tin filter CT protocols (groups C and D; without or with iMAR, respectively). Subjective image quality was analysed using a 5-point Likert scale. Objective image quality was assessed, and the noise, contrast-to-noise ratio, and figure of merit were compared among the four groups. Metal artefacts were quantified using CT number reduction and metal diameter blurring. The radiation doses, diagnostic performance, and complication rates were also estimated. RESULTS: The subjective image quality of the two scan types was compared. Images with iMAR reconstruction were superior to those without iMAR reconstruction (group A: 3.49±0.65 vs. group B: 4.63±0.57; P<0.001, and group C: 3.88±0.66 vs. group D: 4.82±0.39; P<0.001). Images taken with a tin filter were found to have a significantly higher figure-of-merit than those taken without a tin filter (group A: 14,041±7,230 vs. group C: 21,866±10,656; P=0.001, and group B: 13,836±6,849 vs. group D: 21,639±9,964; P=0.001). In terms of metal artefact reduction, tin filtration combined with iMAR showed the lowest CT number reduction (116.62±103.48 HU) and metal diameter blurring (0.85±0.30) among the protocols. The effective radiation dose in the tin filter groups was 73.2% lower than that in the routine-dose groups. The complication rate and diagnostic performance (sensitivity, specificity, and overall accuracy) did not differ significantly between the tin filter and routine-dose groups (all P>0.05). CONCLUSIONS: Tin filtration combined with an iMAR algorithm may reduce the radiation dose compared to the routine-dose CT protocol, while maintaining comparable diagnostic accuracy and image quality and producing fewer metal artefacts.

Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions.

BACKGROUND: Multiparametric dual-energy computed tomography (mpDECT) is widely used to differentiate various kinds of tumors; however, the data regarding its diagnostic performance with machine learning to diagnose breast tumors is limited. We evaluated univariate analysis and machine learning performance with mpDECT to distinguish between benign and malignant breast lesions. METHODS: In total, 172 patients with 214 breast lesions (55 benign and 159 malignant) who underwent preoperative dual-phase contrast-enhanced DECT were included in this retrospective study. Twelve quantitative features were extracted for each lesion, including CT attenuation (precontrast, arterial, and venous phases), the arterial-venous phase difference in normalized effective atomic number (nZeff), normalized iodine concentration (NIC), and slope of the spectral Hounsfield unit (HU) curve (λHu). Predictive models were developed using univariate analysis and eight machine learning methods [logistic regression, extreme gradient boosting (XGBoost), stochastic gradient descent (SGD), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), random forest (RF), decision tree, and linear support vector machine (SVM)]. Classification performances were assessed based on the area under the receiver operating characteristic curve (AUROC). The best performances of the conventional univariate analysis and machine learning methods were compared using the Delong test. RESULTS: The univariate analysis showed that the venous phase λHu had the highest AUROC (0.88). Machine learning with mpDECT achieved an excellent and stable diagnostic performance, as shown by the mean classification performances in the training dataset (AUROC, 0.88-0.99) and testing (AUROC, 0.83-0.96) datasets. The performance of the AdaBoost model based on mpDECT was more stable than the other machine learning models and superior to the univariate analysis (AUROC, 0.96 vs. 0.88; P<0.001). CONCLUSIONS: The performance of the AdaBoost classifier based on mpDECT data achieved the highest mean accuracy compared to the other machine learning models and univariate analysis in differentiating between benign and malignant breast lesions.

Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia.

BACKGROUND: Stage T1 nasopharyngeal carcinoma (NPCT1) and benign hyperplasia (BH) are 2 common causes of nasopharyngeal mucosa/submucosa thickening without specific clinical symptoms. The treatment management of these 2 entities is significantly different. Reliable differentiation between the 2 entities is critical for the treatment decision and prognosis of patients. Therefore, our study aims to explore the optimal energy level of noise-optimized virtual monoenergetic images [VMI (+)] derived from dual-energy computed tomography (DECT) to display NPCT1 and BH and to explore the clinical value of DECT for differentiating these 2 diseases. METHODS: A total of 91 patients (44 NPCT1, 47 BH) were enrolled. The demarcation of the lesion margins and overall image quality, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were evaluated for 40-80 kiloelectron volts (keV) VMIs (+) and polyenergetic images in the contrast-enhanced phase. Image features were assessed in the contrast-enhanced images with optimal visualization of NPCT1 and BH. The demarcation of NPCT1 and BH in iodine-water maps was also assessed. The contrast-enhanced images were used to calculate the slope of the spectral Hounsfield unit curve (λHU) and normalized iodine concentration (NIC). The nonenhanced phase images were used to calculate the normalized effective atomic number (NZeff). The attenuation values on 40-80 keV VMIs (+) in the contrast-enhanced phase were recorded. The diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. RESULTS: The 40 keV VMI (+) in the enhanced phase yielded higher demarcation of the lesion margins scores, overall image quality scores, noise, SNR, and CNR values than 50-80 keV VMIs (+) and polyenergetic images. NPCT1 yielded higher attenuation values on VMI (+) at 40 keV (A40), NIC, λHU, and NZeff values than BH. The multivariate logistic regression model combining image features (tumor symmetry) with quantitative parameters (A40, NIC, λHU, and NZeff) yielded the best performance for differentiating the 2 diseases (AUC: 0.963, sensitivity: 89.4%, specificity: 93.2%). CONCLUSIONS: The combination of DECT-derived image features and quantitative parameters contributed to the differentiation between NPCT1 and BH.

Model-free adaptive sliding mode control with adjustable funnel boundary for robot manipulators with uncertainties.

Considering that the nominal dynamics model or numerous parameters of robotics are usually unsuitable for real applications, a model-free adaptive sliding mode control with an adjustable funnel boundary is proposed for robot manipulators with uncertainties. First, time delay estimation (TDE) technique is utilized to estimate the unknown dynamics of the control system, which ensures an attractive model-free advantage. Furthermore, a modified funnel function is introduced to transform the trajectory tracking error fall within an adjustable funnel boundary strictly. Then, based on the transformed error variable, a novel funnel nonsingular fast terminal sliding mode control scheme is developed to enhance the transient and steady-state tracking performance of the closed-loop control system. To cope with the TDE error, an adaptive update method is designed with only one adaptive parameter, which is adaptively tuned according to the sliding surface. Finally, the simulation and experimental results are presented to illustrate the superiority and high-precision tracking performance of the proposed approach.

Simultaneous multi-slice readout-segmentation of long variable echo-trains for accelerated diffusion-weighted imaging of nasopharyngeal carcinoma: A feasibility and optimization study.

OBJECTIVES: We aimed to evaluate the feasibility of simultaneous multi-slice readout segmentation of long variable echo-trains (SMS-RESOLVE) for diffusion-weighted imaging (DWI) of nasopharyngeal carcinoma (NPC) and determine the optimal acceleration factor of SMS-RESOLVE DWI compared with conventional RESOLVE DWI. MATERIALS AND METHODS: One hundred and five patients with histologically confirmed NPC were enrolled. DWIs including conventional RESOLVE DWI, SMS-RESOLVE DWI with acceleration factors of 2 and 3 (2 × SMS-RESOLVE and 3 × SMS-RESOLVE) were acquired at b-values of 0 and 800 s/mm2. The subjective image quality, including overall image quality, lesion conspicuity, and lesion distortion scores, and objective image quality, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast in these three protocols, were assessed and compared. The apparent diffusion coefficients (ADCs) of the primary tumor and right masseter muscle and normalized ADCs in the three protocols were measured and compared. RESULTS: The 2 × SMS-RESOLVE provided comparable image quality and ADCs with a 48.3% reduction in scan time compared with conventional RESOLVE DWI. The 3 × SMS-RESOLVE showed significantly poorer image quality and the highest ADCs with a 56.9% reduction in scan times compared with conventional RESOLVE DWI. CONCLUSION: The SMS-RESOLVE DWI with an acceleration factor of 2 yielded the best compromise between image quality and scan time reduction and could be an alternative to conventional RESOLVE DWI for nasopharyngeal carcinoma.

Multiparametric dual-energy CT for distinguishing nasopharyngeal carcinoma from nasopharyngeal lymphoma.

OBJECTIVES: To determine the optimal kiloelectron volt of noise-optimized virtual monoenergetic images [VMI (+)] for visualization of nasopharyngeal carcinoma (NPC) and nasopharyngeal lymphoma (NPL), and to explore the clinical value of quantitative parameters derived from dual-energy computed tomography (DECT) for distinguishing the two entities. MATERIALS AND METHODS: Eighty patients including 51 with NPC and 29 with NPL were enrolled. The VMIs (+) at 40-80 keV with an interval of 10 keV were reconstructed by contrast enhanced images. The overall image quality and demarcation of lesion margins, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed in VMIs (+) and polyenergetic images (PEI). Normalized iodine concentration (NIC), slope of the spectral Hounsfield unit curve (λHU) and effective atomic number (Zeff) were calculated. Diagnostic performance was assessed by receiver operating characteristic (ROC) curve. RESULTS: The 40 keV VMI (+) yielded highest overall image quality scores, demarcation of lesion margins scores, SNR and CNR. The values of NIC, λHU and Zeff in NPL were higher than those in NPC (P <  0.001). Multivariate logistic regression model combining NIC, λHU and Zeff showed the best performance for distinguishing NPC from NPL (AUC: 0.947, sensitivity: 93.1 % and specificity: 92.2 %). CONCLUSION: VMI (+) reconstruction at 40 keV was optimal for visualizing NPC and NPL. Quantitative parameters derived from DECT were helpful for differentiating NPC from NPL.

Predicting Progression-Free Survival Using MRI-Based Radiomics for Patients With Nonmetastatic Nasopharyngeal Carcinoma.

Objectives: This study aimed to explore the predictive value of MRI-based radiomic model for progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC). Methods: A total of 327 nonmetastatic NPC patients [training cohort (n = 230) and validation cohort (n = 97)] were enrolled. The clinical and MRI data were collected. The least absolute shrinkage selection operator (LASSO) and recursive feature elimination (RFE) were used to select radiomic features. Five models [Model 1: clinical data, Model 2: overall stage, Model 3: radiomics, Model 4: radiomics + overall stage, Model 5: radiomics + overall stage + Epstein-Barr virus (EBV) DNA] were constructed. The prognostic performances of these models were evaluated by Harrell's concordance index (C-index). The Kaplan-Meier method was applied for the survival analysis. Results: Model 5 incorporating radiomics, overall stage, and EBV DNA yielded the highest C-indices for predicting PFS in comparison with Model 1, Model 2, Model 3, and Model 4 (training cohorts: 0.805 vs. 0.766 vs. 0.749 vs. 0.641 vs. 0.563, validation cohorts: 0.874 vs. 0.839 vs. 836 vs. 0.689 vs. 0.456). The survival curve showed that the high-risk group yielded a lower PFS than the low-risk group. Conclusions: The model incorporating radiomics, overall stage, and EBV DNA showed better performance for predicting PFS in nonmetastatic NPC patients.