Extracellular volume fraction determined by equilibrium contrast-enhanced dual-energy CT as a prognostic factor in patients with stage IV pancreatic ductal adenocarcinoma

The clinical applications of dual-layer spectral detector CT in digestive system diseases

OBJECTIVE

Dual-layer spectral detector CT (DLCT) has several advantages in clinical practice, this study aims to reveal the clinical applications of DLCT in digestive system diseases.

MATERIALS AND METHODS

We searched PubMed and Cochrane Reviews for articles published from January 1, 2010 to May 31, 2024, using the terms "dual-layer spectral detector CT" or "dual-layer CT" combined with "hepatic fat" or "hepatic fibrosis" "hepatocellular carcinoma" or "pancreatic ductal adenocarcinoma" or "pancreatic neuroendocrine tumors" or "gastric cancer" or "colorectal cancer" or "Crohn's disease" or "bowel ischemia" or "acute abdominal conditions".

RESULTS

DLCT consists of a top layer sensitive to lower-energy photons and a bottom layer sensitive to higher-energy photons. This configuration enables simultaneous acquisition of two energy spectra from a single X-ray beam ensuring consistent spatial alignment and temporal resolution. Spectral raw images allow image post-processing to improve image quality, reduce radiation doses and contrast media doses, and generate multiple quantitative parameters. It has broad potential for early detection, accurate staging, efficacy assessment, and prognosis prediction of liver, pancreatic, and gastrointestinal diseases, as well as for the assessment of digestive system vasculature.

CONCLUSIONS

DLCT not only provides valuable information for the clinical diagnosis and therapeutic effect evaluation of digestive system diseases but also may play a more important role in the overall management of digestive diseases and in the decision-making of individualized medicine.

KEY POINTS

Question What are the advantages of DLCT compared to traditional single-energy CT in the early detection, staging, and therapeutic evaluation of digestive system diseases? Findings DLCT enhances image quality, improves tissue characterization, and allows for multi-parametric analysis, making it superior in detecting and evaluating liver, pancreatic, and gastrointestinal diseases. Clinical relevance DLCT provides high-quality, multi-parametric imaging that improves the accuracy of diagnosing digestive diseases, facilitates more precise treatment planning, and enhances monitoring of treatment response, ultimately contributing to better patient management and prognosis.

Dual energy CT-derived quantitative parameters and hematological characteristics predict pathological complete response in neoadjuvant chemoradiotherapy esophageal squamous cell carcinoma patients

PURPOSE

There is no gold standard method to predict pathological complete response (pCR) in esophageal squamous cell carcinoma (ESCC) patients before surgery after neoadjuvant chemoradiotherapy (nCRT). This study aims to investigate whether dual layer detector dual energy CT (DECT) quantitative parameters and clinical features could predict pCR for ESCC patients after nCRT.

PATIENTS AND METHODS

This study retrospective recruited local advanced ESCC patients who underwent nCRT followed by surgical treatment from December 2019 to May 2023. According to pCR status (no visible cancer cells in primary cancer lesion and lymph nodes), patients were categorized into pCR group (N = 25) and non-pCR group (N = 28). DECT quantitative parameters were derived from conventional CT images, different monoenergetic (MonoE) images, virtual non-contrast (VNC) images, Z-effective (Zeff) images, iodine concentration (IC) images and electron density (ED) images. Slope of spectral curve (λHU), normalized iodine concentration (NIC), arterial enhancement fraction (AEF) and extracellular volume (ECV) were calculated. Difference tests and spearman correlation were used to select quantitative parameters for DECT model building. Multivariate logistic analysis was used to build clinical model, DECT model and combined model.

RESULTS

A total of 53 patients with locally advanced ESCC were enrolled in this study who received nCRT combined with surgery and underwent DECT examination before treatment. After spearman correlation analysis and multivariate logistic analysis, AEF and ECV showed significant roles between pCR and non-pCR groups. These two quantitative parameters were selected for DECT model. Multivariate logistic analysis revealed that LMR and RBC were also independent predictors in clinical model. The combined model showed the highest sensitivity, specificity, PPV and NPV compared to the clinical and DECT model. The AUC of the combined model is 0.893 (95%CI: 0.802-0.983). Delong's test revealed the combined model significantly different from clinical model (Z =-2.741, P = 0.006).

CONCLUSION

Dual-layer DECT derived ECV fraction and AEF are valuable predictors for pCR in ESCC patients after nCRT. The model combined DECT quantitative parameters and clinical features might be used as a non-invasive tool for individualized treatment decision of those ESCC patients. This study validates the role of DECT in pCR assessment for ESCC and a large external cohort is warranted to ensure the robustness of the proposed DECT evaluation criteria.

Dual-energy computed tomography for predicting histological grading and survival in patients with pancreatic ductal adenocarcinoma

OBJECTIVES

We evaluated the value of dual-energy computed tomography (DECT) parameters derived from pancreatic ductal adenocarcinoma (PDAC) to discriminate between high- and low-grade tumors and predict overall survival (OS) in patients.

METHODS

Data were retrospectively collected from 169 consecutive patients with pathologically confirmed PDAC who underwent third-generation dual-source DECT enhanced dual-phase scanning before surgery between January 2017 and March 2023. Patients with prior treatments, other malignancies, small tumors, or poor-quality scans were excluded. Two radiologists evaluated three clinical and seven radiological features and measured sixteen DECT-derived parameters. Univariate and multivariate analyses were applied to select independent predictors. A prediction model and a corresponding nomogram were developed, and the area under the curve (AUC), calibration, and clinical applicability were assessed. The correlations between factors and OS were evaluated using Kaplan-Meier survival and Cox regression analyses.

RESULTS

One hundred sixty-nine patients were randomly divided into training (n = 118) and validation (n = 51) cohorts, among which 43 (36.4%) and 19 (37.3%) had high-grade PDAC confirmed by pathology, respectively. The vascular invasion, normalized iodine concentration in the venous phase, and effective atomic number in the venous phase were independent predictors for histological grading. A nomogram was constructed to predict the risk of high-grade tumors in PDAC, with AUCs of 0.887 and 0.844 in the training and validation cohorts, respectively. The nomogram exhibited good calibration and was more beneficial than a single parameter in both cohorts. Pathological- and nomoscore-predicted high-grade PDACs were associated with poor OS (all p < 0.05).

CONCLUSIONS

The nomogram, which combines DECT parameters and radiological features, can predict the histological grade and OS in patients with PDAC before surgery.

KEY POINTS

Question Preoperative determination of histological grade in PDAC is crucial for guiding treatment, yet current methods are invasive and limited. Findings A DECT-based nomogram combining vascular invasion, normalized iodine concentration, and effective atomic number accurately predicts histological grade and OS in PDAC patients. Clinical relevance The DECT-based nomogram is a reliable, non-invasive tool for predicting histological grade and OS in PDAC. It provides essential information to guide personalized treatment strategies, potentially improving patient management and outcomes.

Prognostic impact of extracellular volume fraction derived from equilibrium contrast-enhanced CT in HCC patients receiving immune checkpoint inhibitors

This study aimed to investigate whether extracellular volume (ECV) fraction derived from equilibration contrast-enhanced computed tomography (CECT) affects prognosis in HCC patients receiving ICIs. This retrospective study ultimately included 211 HCC patients undergoing ICIs, of whom 60 were included in an internal validation to assess the reproducibility of the results. Baseline unenhanced and equilibrated CECT were used to measure CT values of the tumor, liver and aorta, which were combined with hematocrit to calculate the ECV fraction. Correlation analysis was used to investigate the association between tumor ECV and liver ECV fractions. The effects of clinical variables and ECV fraction on progression-free survival (PFS) and overall survival (OS) were evaluated using Cox proportional hazards models and Kaplan-Meier curves. Of these 151 patients, tumor ECV fraction positively correlated with liver ECV fraction. In the Lower tumor ECV group, PFS (5.6 vs. 7.6 months) and OS (10.5 vs. 15.5 months) were notably shorter than in the Higher tumor ECV group, while no significant differences were found between the Higher and Lower liver ECV groups. Furthermore, the multivariable Cox regression model demonstrated that higher tumor ECV fraction level was an independent protective factor for PFS and OS (all P < 0.001). Internal validation cohort preliminary demonstrated reproducibility of results. The tumor ECV fraction is expected to become a routine indicator before ICIs therapy for HCC patients in contrast to liver ECV fraction, contributing to their subsequent management.

Predictive value of extracellular volume fraction determined using enhanced computed tomography for pathological grading of clear cell renal cell carcinoma: a preliminary study

OBJECTIVE

To explore the potential of using the extracellular volume fraction (ECV), measured through enhanced computed tomography (CT), as a tool for determining the pathological grade of clear cell renal cell carcinoma (ccRCC).

METHODS

This retrospective study, approved by the institutional review board, included 65 patients (median age: 58.40 ± 10.84 years) who were diagnosed with ccRCC based on the nucleolar grading of the International Society of Urological Pathology (ISUP). All patients underwent preoperative abdominal enhanced CT between January 2022 and August 2024. CT features from the unenhanced, corticomedullary, nephrographic, and delayed phases were analyzed, and the extracellular volume fraction (ECV) of ccRCC was calculated by measuring CT values from regions of interest in both the unenhanced and nephrographic phases. Statistical significance was evaluated for differences in these parameters across the four ISUP grades. Additionally, diagnostic efficiency was assessed using receiver operating characteristic (ROC) curve analysis.

RESULTS

The ECV showed significant differences across the four ISUP grades of ccRCC, its potential as an important predictor of high-grade ccRCC (P = 0.035). The ROC curve analysis indicated that ECV exhibited the highest diagnostic efficacy for assessing the lower- and higher- pathological grade of ccRCC, with an area under the ROC curve of 0.976. The optimal diagnostic threshold for ECV was determined to be 41.64%, with a sensitivity of 91.31% and a specificity of 97.62%.

CONCLUSIONS

ECV derived from enhanced CT has the potential to function as an in vivo biomarker for distinguishing between lower- and higher-grade ccRCC. This quantitative measure provides diagnostic value that extends beyond traditional qualitative CT features, offering a more precise and objective assessment of tumor grade.

Pancreatic changes in patients with visceral fat obesity: an evaluation with contrast-enhanced dual-energy computed tomography with automated three-dimensional volumetry

PURPOSE

To investigate pancreatic changes associated with visceral fat obesity (VFO) and their clinical relevance using contrast-enhanced dual-energy CT (DE-CT) with automated 3D volumetry.

METHODS

This retrospective study included patients who underwent triple-phase contrast-enhanced dynamic abdominal DE-CT. The patients were divided into two groups based on the measured visceral fat area: the VFO group (≥ 100 cm2) and the non-VFO group (< 100 cm2). Pancreatic changes in 3D CT volumetric measurement parameters were evaluated.

RESULTS

In total, 119 patients were evaluated (mean age, 67.6 ± 12.9 years old; 80 men). The extracellular volume fraction calculated from iodine maps (ECV-ID) (r = -0.683, p < 0.001) was most strongly associated with the visceral fat area, followed by the fat volume fraction (FVF) of the pancreas (r = 0.582, p < 0.001) with a statistically moderate correlation. The pancreatic volume and FVF of the pancreas were significantly higher in the VFO group than in the non-VFO group (volume: 84.9 ± 22.9 vs. 76.5 ± 25.8, p = 0.025, FVF: 15.5 ± 7.7 vs. 8.7 ± 9.5, p < 0.001). Conversely, the pancreatic CT attenuation value on unenhanced CT (19.9 ± 12.0 vs. 29.6 ± 13.8, p < 0.001), pancreatic iodine concentration in the equilibrium phase (EP) (18.4 ± 5.7 vs. 19.8 ± 4.7, p = 0.003), contrast enhancement (CE) value of pancreas (32.2 ± 5.3 vs. 34.5 ± 8.5, p = 0.005), and ECV-ID (26.7 ± 5.4 vs. 34.1 ± 7.4, p < 0.001) in the VFO group were significantly lower than those in the non-VFO group.

CONCLUSION

An increase in the pancreatic volume and FVF of the pancreas, as well as a reduction in the ECV fraction and the CE value in EP of the pancreas measured by automated 3D DE-CT volumetry, were the characteristic pancreatic changes in patients with VFO.

Dual-energy CT extracellular volume fraction predicts tumor collagen ratio and possibly survival for inoperable pancreatic cancer patients

OBJECTIVES

Tumor collagen is vital in chemotherapy resistance of pancreatic cancer (PC), but its non-invasive evaluation remains challenging. This study aims to investigate the association of variables derived from dual-energy CT with the collagen ratio (CR) of PC and to determine the prognostic value of CR in unresectable diseases.

MATERIALS AND METHODS

A total of 83 patients with resected PC and 71 patients with unresectable PC were enrolled. In the resected group, the correlation between the tumor CR and variables of dual-energy CT was analyzed. In the unresectable group, Cox regression analyses were conducted to investigate the prognostic value of dual-energy CT-predicted CR and other clinicoradiological indicators.

RESULTS

The patients with resected PC were divided into low and high-CR sets with a threshold of 55%. In the resected group, the extracellular volume fraction calculated by the iodine concentration (ECV_IC) was the only predictor of tumor CR according to univariate and multivariate analysis (hazard ratio [HR] (95% confidence interval [CI]):1.19 [1.03-1.37]). The correlation coefficient r was 0.26 (p = 0.02) between ECV_IC and specific CR values. In the training set of unresectable PC group, ECV_IC (HR (95% CI): 0.94 (0.89-0.99), p = 0.03) and contrast-enhanced pattern (CEP) (HR (95% CI): 3.20 (1.41-7.27), p = 0.01) were independent prognostic factors for overall survival. The nomogram model was constructed and showed a good performance.

CONCLUSION

The ECV_IC is a non-invasive indicator of tumor CR in PC. The ECV_IC and CEP have the potential to predict the prognosis of unresectable PC.

KEY POINTS

Question Non-invasive evaluation of tumor collagen, a vital determinant of chemotherapy resistance of pancreatic cancer, remains challenging. Findings Tumor collagen ratio can be noninvasively predicted by extracellular volume fraction based on iodine concentration. Clinical relevance The nomogram model composed of extracellular volume fraction and contrast-enhanced pattern can serve as an effective and convenient tool for stratifying the prognosis of patients with unresectable pancreatic cancer.

Spectral CT radiomics features of the tumor and perigastric adipose tissue can predict lymph node metastasis in gastric cancer

OBJECTIVES

To develop a nomogram based on the radiomics features of tumour and perigastric adipose tissue adjacent to the tumor in dual-layer spectral detector computed tomography (DLCT) for lymph node metastasis (LNM) prediction in gastric cancer (GC).

METHODS

A retrospective analysis was conducted on 175 patients with gastric adenocarcinoma. They were divided into training cohort (n = 125) and validation cohort (n = 50). The radiomics features from the tumour and perigastric fat based on DLCT spectral images were extracted to construct radiomics models for LNM prediction using Lasso-GLM method. Preoperative clinicopathological features, DLCT routine parameters, and the optimal radiomics models were analyzed to establish the clinical-DLCT model, clinical-DLCT-radiomics model and a nomogram. All models were internally validated using the Bootstrap method and evaluated using receiver operating characteristic (ROC) curve.

RESULTS

The area under the ROC curve (AUC) values of optimal radiomics models based on tumour (Model 1) and perigastric fat (Model 2) were 0.923 and 0.822 in training cohort, 0.821 and 0.767 in validation cohort. The clinical-DLCT model based on Nct and ECVID demonstrated an AUC value of 0.728 in training cohort and 0.657 in validation cohort. The clinical-DLCT-radiomics model and the nomogram were established by incorporating Nct, ECVID and the linear predictive values of Models 1 and 2, exhibiting superior predictive efficacy with an AUC value of 0.935 in training cohort and 0.876 invalidation cohort.

CONCLUSIONS

The nomogram based on Nct, ECVID, and the radiomics features of tumour and perigastric fat in DLCT demonstrates potential for predicting LNM in GC. This approach may contribute to the development of treatment strategies and improve the clinical outcomes for GC patients.

Extracellular Volume Derived from Equilibrium CT for the Prediction of Survival Outcomes in Patients with Pancreatic Ductal Adenocarcinoma

ObjectiveTo assess the efficiency of extracellular volume (ECV) derived from equilibrium computed tomography (CT) in predicting recurrence-free survival (RFS) and overall survival (OS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC).MethodsThis retrospective study included 83 patients who underwent CT and R0 resection between January 2016 and September 2023. The pattern of tumor recurrence and prognosis were recorded for each patient. Tumor recurrence was classified into three groups: isolated local recurrence group, distant recurrence group and censored group. The associations between the CT-ECV and clinicopathological features and recurrence pattern of PDAC were evaluated by chi-squared test. Multivariable Cox proportional-hazards models were conducted to evaluate the effects of clinical factors, CT features and CT-ECV on RFS and OS.ResultsThe median RFS and OS were 10.7 and 17.1 months, respectively. On multivariate analysis, the CT-ECV and adjacent organ invasion were found to be associated with RFS (HR, 0.968, P = .017; HR, 0.453; P = .006), and only the CT-ECV was an independent prognostic factor for OS (HR, 0.968; P = .022). Low CT-ECV group was significantly associated with elevated CA19-9, larger tumor size, G3 (tumor grade) and II/III (AJCC tumor stage) (P < .05). In the recurrence pattern analysis, the CT-ECV did not exhibit an association between local recurrence and non-local recurrence groups (P = .455), while patients in the low CT-ECV group were more inclined to experience distant recurrence after curative surgery (P = .037).ConclusionsCT-ECV determined by equilibrium contrast-enhanced CT was a useful imaging biomarker for predicting distant recurrence and survival in resectable PDAC patients, which may facilitate further risk stratification and personalized care.

Prediction of tumor regression grade in far-advanced gastric cancer after preoperative immuno-chemotherapy using dual-energy CT-derived extracellular volume fraction

OBJECTIVES

This study examines the effectiveness of dual-energy CT (DECT) delayed-phase extracellular volume (ECV) fraction in predicting tumor regression grade (TRG) in far-advanced gastric cancer (FAGC) patients receiving preoperative immuno-chemotherapy.

MATERIALS AND METHODS

A retrospective analysis was performed on far-advanced gastric adenocarcinoma patients treated with preoperative immuno-chemotherapy at our institution from August 2019 to March 2023. Patients were categorized based on their TRG into pathological complete response (pCR) and non-pCR groups. ECV was determined using the delayed-phase iodine maps. In addition, tumor iodine densities and standardized iodine ratios were meticulously analyzed using the triple-phase enhanced iodine maps. Univariate analysis with five-fold cross-validation and Spearman correlation determined DECT parameters and clinical indicators association with pCR. The predictive accuracy of these parameters for pCR was evaluated using a weighted logistic regression model with five-fold cross-validation.

RESULTS

Of the 88 patients enrolled (mean age 60.8 ± 11.1 years, 63 males), 21 (23.9%) achieved pCR. Univariate analysis indicated ECV's significant role in differentiating between pCR and non-pCR groups (average p value = 0.021). In the logistic regression model, ECV independently predicted pCR with an average odds ratio of 0.911 (95% confidence interval, 0.798-0.994). The model, incorporating ECV, tumor area, and IDAV (the relative change rate of iodine density from venous phase to arterial phase), showed an average area under curves (AUCs) of 0.780 (0.770-0.791) and 0.766 (0.731-0.800) for the training and validation sets, respectively, in predicting pCR.

CONCLUSION

DECT-derived ECV fraction is a valuable predictor of TRG in FAGC patients undergoing preoperative immuno-chemotherapy.

CLINICAL RELEVANCE STATEMENT

This study demonstrates that DECT-derived extracellular volume fraction is a reliable predictor for pathological complete response in far-advanced gastric cancer patients receiving preoperative immuno-chemotherapy, offering a noninvasive tool for identifying potential treatment beneficiaries.

Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type I and type II epithelial ovarian carcinoma

OBJECTIVES

To investigate the feasibility of using the iodine concentration (IC) parameter and extracellular volume (ECV) fraction derived from dual-energy CT for distinguishing between type I and type II epithelial ovarian carcinoma (EOC).

METHODS

This study retrospectively included 172 patients with EOC preoperatively underwent dual-energy CT scans. Patients were grouped as type I and type II EOC according to postoperatively pathologic results. Normalized IC (NIC, %) values from arterial-phase (AP), venous-phase (VP) and delay-phase (DP) were measured by two observers. ECV fraction (%) was calculated by DP-NIC and hematocrit. Intra-observer correlation coefficient (ICC) was used to assess the agreement between measurements made by two observers. The differences of imaging parameters between the two groups were compared. Logistic regression was used to select independent predictive factors and establish combined parameter. Receiver operating characteristic curve was used to analyze performance of all parameters.

RESULTS

The ICCs for all parameters exceeded 0.75. All parameters in type II EOC were all significantly higher than those in type I EOC (all P < 0.05). VP-NIC exhibited the highest Area under the curve (AUC) of 0.804, along with 80.39% sensitivity and 71.43% specificity. VP-NIC was identified as the independent factor. The sensitivity and specificity of ECV fraction were 78.43% and 71.43%, respectively. The combined parameter consisting of AP-NIC, VP-NIC, DP-NIC, and ECV fraction yielded an AUC of 0.823, with sensitivity of 76.47% and specificity of 77.14%. The sensitivity of the combined parameter was significantly higher than that of AP-NIC (P = 0.049).

CONCLUSION

It is valuable for dual-energy CT IC-based parameters and ECV fraction in preoperatively identifying type I and type II EOC.

CRITICAL RELEVANCE STATEMENT

Dual-energy CT-normalized iodine concentration and extracellular volume fraction achieved satisfactory discriminative efficacy, distinguishing between type I and type II epithelial ovarian carcinoma.

Extracellular volume fraction derived from dual-energy CT: a potential predictor for acute pancreatitis after pancreatoduodenectomy

OBJECTIVES

To investigate the value of extracellular volume (ECV) fraction and fat fraction (FF) derived from dual- energy CT (DECT) for predicting postpancreatectomy acute pancreatitis (PPAP) after pancreatoduodenectomy (PD).

METHODS

This retrospective study included patients who underwent DECT and PD between April 2022 and September 2022. PPAP was determined according to the International Study Group for Pancreatic Surgery (ISGPS) definition. Iodine concentration (IC) and FF of the pancreatic parenchyma were measured on preoperative DECT. The ECV fraction was calculated from iodine map images of the equilibrium phase. The independent predictors for PPAP were assessed by univariate and multivariable logistic regression analysis and receiver operating characteristic (ROC) curve analysis.

RESULTS

Sixty-nine patients were retrospectively enrolled (median age, 60 years; interquartile range, 55-70 years; 47 men). Of these, nine patients (13.0%) developed PPAP. These patients had lower portal venous phase IC, equilibrium phase IC, FF, and ECV fraction, and higher pancreatic parenchymal-to-portal venous phase IC ratio and pancreatic parenchymal-to-equilibrium phase IC ratio, compared with patients without PPAP. After multivariable analysis, ECV fraction was independently associated with PPAP (odd ratio [OR], 0.87; 95% confidence interval [CI]: 0.79, 0.96; p < 0.001), with an area under the curve (AUC) of 0.839 (sensitivity 100.0%, specificity 58.3%).

CONCLUSIONS

A lower ECV fraction is independently associated with the occurrence of PPAP after PD. ECV fraction may serve as a potential predictor for PPAP after PD.

CLINICAL RELEVANCE STATEMENT

DECT-derived ECV fraction of pancreatic parenchyma is a promising biomarker for surgeons to preoperatively identify patients with higher risk for postpancreatectomy acute pancreatitis after PD and offer selective perioperative management.

KEY POINTS

PPAP is a complication of pancreatic surgery, early identification of higher-risk patients allows for risk mitigation. Lower DECT-derived ECV fraction was independently associated with the occurrence of PPAP after PD. DECT aids in preoperative PAPP risk stratification, allowing for appropriate treatment to minimize complications.

Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma

PURPOSE

To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).

METHODS

The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.

RESULTS

166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000-1.000) in the training set and 0.927 (95%CI: 0.837-0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837-0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.

CONCLUSION

This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.

Comparison of preoperative diagnostic performance between dual-energy CT, conventional CT, and MRI in endometrial cancer

Purpose

To compare the diagnostic performance of virtual monoenergetic imaging (VMI), computed tomography (CT), and magnetic resonance imaging (MRI) in patients with endometrial cancer (EC).

Material and methods

This retrospective study analysed 45 EC patients (mean age: 62 years, range: 44-84 years) undergoing contrast-enhanced CT with dual-energy CT (DECT) and MRI between September 2021 and October 2022. Dual-energy CT generated conventional CT (C-CT) and 40 keV VMI. Quantitative analysis compared contrast-to-noise ratio (CNR) of tumour to myometrium between C-CT and VMI. Qualitative assessment by 5 radiologists compared C-CT, VMI, and MRI for myometrial invasion (MI), cervical invasion, and lymph node metastasis. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated and compared for each diagnostic parameter.

Results

Virtual monoenergetic imaging showed significantly higher CNR than C-CT (p < 0.001) and a higher sensitivity for MI than C-CT (p = 0.027) and MRI (p = 0.011) but lower specificity than MRI (p = 0.018). C-CT had a higher sensitivity and AUC for cervical invasion than MRI (p = 0.018 and 0.004, respectively).

Conclusions

The study found no significant superiority of MRI over CT across all diagnostic parameters. VMI demonstrated heightened sensitivity for MI, and C-CT showed greater sensitivity and AUC for cervical invasion than MRI. This suggests that combining VMI with C-CT holds promise as a comprehensive preoperative staging tool for EC when MRI cannot be performed.

Differentiating between benign and malignant breast lesions using dual-energy CT-based model: development and validation

OBJECTIVES

To develop and validate a dual-energy CT (DECT)-based model for noninvasively differentiating between benign and malignant breast lesions detected on DECT.

MATERIALS AND METHODS

This study prospectively enrolled patients with suspected breast cancer who underwent dual-phase contrast-enhanced DECT from July 2022 to July 2023. Breast lesions were randomly divided into the training and test cohorts at a ratio of 7:3. Clinical characteristics, DECT-based morphological features, and DECT quantitative parameters were collected. Univariate analyses and multivariate logistic regression were performed to determine independent predictors of benign and malignant breast lesions. An individualized model was constructed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic ability of the model, whose calibration and clinical usefulness were assessed by calibration curve and decision curve analysis.

RESULTS

This study included 200 patients (mean age, 49.9 ± 11.9 years; age range, 22-83 years) with 222 breast lesions. Age, lesion shape, and the effective atomic number (Zeff) in the venous phase were significant independent predictors of breast lesions (all p < 0.05). The discriminative power of the model incorporating these three factors was high, with AUCs of 0.844 (95%CI 0.764-0.925) and 0.791 (95% CI 0.647-0.935) in the training and test cohorts, respectively. The constructed model showed a preferable fitting (all p > 0.05 by the Hosmer-Lemeshow test) and provided enhanced net benefits than simple default strategies within a wide range of threshold probabilities in both cohorts.

CONCLUSION

The DECT-based model showed a favorable diagnostic performance for noninvasive differentiation between benign and malignant breast lesions detected on DECT.

CRITICAL RELEVANCE STATEMENT

The combination of clinical and morphological characteristics and DECT-derived parameter have the potential to identify benign and malignant breast lesions and it may be useful for incidental breast lesions on DECT to decide if further work-up is needed.

KEY POINTS

It is important to characterize incidental breast lesions on DECT for patient management. DECT-based model can differentiate benign and malignant breast lesions with good performance. DECT-based model is a potential tool for distinguishing breast lesions detected on DECT.

The value of preoperative diagnosis of colorectal adenocarcinoma pathological T staging based on dual-layer spectral-detector computed tomography extracellular volume fraction: a preliminary study

PURPOSE

To investigate the value of preoperative diagnosis of colorectal adenocarcinoma (CRAC) pathological T staging based on dual-layer spectral-detector computed tomography (DLCT) extracellular volume fraction (ECV) of CRAC lesions.

METHODS

We prospectively collected clinical and DLCT imaging data from 165 patients with CRAC who attended two hospitals from June 2022 to April 2023. The enrolled patients were divided into a training group (n = 110, from Hospital 1) and an external validation group (n = 55, from Hospital 2). Measuring and calculating DLCT parameters of lesions, including CT values of 40 and 100 keV virtual mono-energetic images (VMI), iodine concentration (IC) and effective atomic number (Eff-Z) in the arterial phases (AP) and venous phases (VP), and ECV in the delayed phase (DP). The differences in clinical characteristics and DLCT parameters were compared between different pT subgroups. The correlation between DLCT parameters and pT stages were evaluated by Spearman correlation analysis. A multifactorial binary logistic stepwise forward regression analysis was performed to obtain independent influences associated with pT stage. Receiver operating characteristic curves (ROCs) were used to assess diagnostic efficacy and were expressed as area under the curve (AUC).

RESULTS

Each DLCT parameter was higher in pT3 stage tumors than in pT1-2 stage tumors (all P < 0.05). The highest correlation was found between ECV and pT stage (r = 0.637). ECV were independent influences associated with pT stage. ECV had excellent diagnostic efficacy for CRAC pT staging in both the training and external validation groups (AUC = 0.919 and 0.892).

CONCLUSION

ECV based on DLCT measurement can be used for preoperative noninvasive diagnosis of CRAC pT staging with excellent diagnostic efficacy. It can provide a new imaging marker for the preoperative evaluation of CRAC and help clinicians formulate individualized treatment earlier. However, it needs to be confirmed with a larger sample size.

Extracellular volume fraction can predict the treatment response and survival outcome of colorectal cancer liver metastases

OBJECTIVE

To assess the prognostic value of pre- and post-therapeutic changes in extracellular volume (ECV) fraction of liver metastases (LMs) for treatment response (TR) and survival outcomes in colorectal cancer liver metastases (CRLM).

METHODS

186 LMs were confirmed by pathology or follow-up (Training: 130; Test: 56). We analyzed the changes in ECV fraction of LMs before and after 2 cycles of chemotherapy combined with bevacizumab. After 12 cycles, we evaluated the TR on LMs based on the RECIST v1.1. Relative changes in ECV fraction and Hounsfield Units (HU), defined as ΔECV and ΔHU, were associated with progression-free survival (PFS), overall survival (OS), and TR. We identified TR predictors with multivariate logistic regression and PFS, OS risk factors with COX analysis.

RESULTS

186 LMs were classified as TR lesions (TR+: 84) and non-TR lesions (TR-:102). ΔECV, ΔHUA-E, and texture could distinguish the TR of LMs in training and test set (P < 0.05). ΔECV [Odds ratio (OR): 1.03; 95% Confidence interval (CI): 1.02-1.05, P < 0.01] was an independent predictor of TR-. Area under the curve (AUC), sensitivity and specificity of TR model in training and test set were 0.87, 0.84, 90.14%, 90.32%, 72.88%, 64.00%, respectively. High CRD_score indicates that patients have shorter PFS [Hazard ratio (HR): 2.01; 95%CI: 1.02-3.98, P = 0.045)] and OS (HR: 1.89, 95%CI: 1.04-3.42, P = 0.038).

CONCLUSION

ΔECV can be used as an independent predictor of TR of CRLM chemotherapy combined with bevacizumab.

Comparing extracellular volume fraction with apparent diffusion coefficient for the characterization of breast tumors

PURPOSE

To investigate the feasibility of dual-energy CT (DECT)-derived extracellular volume (ECV) fraction for characterization of breast tumors, compared to apparent diffusion coefficient (ADC) and validated against histopathological findings.

MATERIAL AND METHODS

The ECV fraction and ADC were prospectively assessed in patients with breast tumors using chest DECT and breast MRI. The diagnostic performance of ECV fraction and ADC was accessed in predicting breast histopathological subtypes and pathological complete response (pCR) status. Histopathological sections were analyzed by digital image analysis. Pearson's correlation analysis was used to correlate between DECT and histopathological ECV fractions.

RESULTS

This study included 271 patients, with 314 breast lesions (61 benign and 253 malignant). The ECV fraction and ADC showed comparable area under the curve (AUC) for distinguishing benign from malignant lesions (p = 0.123) and invasive carcinoma from ductal carcinoma in situ (p = 0.115). There were significant differences in ECV fraction between different hormone receptors and Ki67 states (p = 0.001 ∼ 0.014), while ADC values only differed among various Ki67 states (p < 0.001). The ECV fraction was lower (p = 0.007), ADC was higher (p = 0.013) in pCR than in non-pCR group, with an AUC of 0.748 and 0.730 (p = 0.887), respectively. There was a positive correlation between DECT and histopathological ECV fractions (r = 0.615, p < 0.01).

CONCLUSIONS

Routine chest DECT-derived ECV fraction is a viable quantitative imaging biomarker for predicting histopathological subtypes and pCR in patient with breast tumors, and correlated well with histopathology finding.

Dual-Layer Spectral Detector Computed Tomography Quantitative Parameters: A Potential Tool for Lymph Node Activity Determination in Lymphoma Patients

Dual-energy CT has shown promising results in determining tumor characteristics and treatment effectiveness through spectral data by assessing normalized iodine concentration (nIC), normalized effective atomic number (nZeff), normalized electron density (nED), and extracellular volume (ECV). This study explores the value of quantitative parameters in contrast-enhanced dual-layer spectral detector CT (SDCT) as a potential tool for detecting lymph node activity in lymphoma patients. A retrospective analysis of 55 lymphoma patients with 289 lymph nodes, assessed through 18FDG-PET/CT and the Deauville five-point scale, revealed significantly higher values of nIC, nZeff, nED, and ECV in active lymph nodes compared to inactive ones (p < 0.001). Generalized linear mixed models showed statistically significant fixed-effect parameters for nIC, nZeff, and ECV (p < 0.05). The area under the receiver operating characteristic curve (AUROC) values of nIC, nZeff, and ECV reached 0.822, 0.845, and 0.811 for diagnosing lymph node activity. In conclusion, the use of g nIC, nZeff, and ECV as alternative imaging biomarkers to PET/CT for identifying lymph node activity in lymphoma holds potential as a reliable diagnostic tool that can guide treatment decisions.

Prediction of pancreatic fibrosis by dual-energy CT-derived extracellular volume fraction: Comparison with MRI

OBJECTIVES

To investigate the correlation between dual-energy CT (DECT) and MRI measurements of the extracellular volume fraction (ECV) and to assess the accuracy of both methods in predicting pancreatic fibrosis (PF).

METHODS

We retrospectively analyzed 43 patients who underwent pancreatectomy and preoperative pancreatic DECT and MRI between November 2018 and May 2022. The ECV was calculated using the T1 relaxation time (for MR-ECV) or absolute enhancement (for DECT-ECV) at equilibrium phase (180 s after contrast injection in our study). Pearson coefficient and Bland-Altman analysis were used to compare the correlation between the two ECVs, Spearman correlations were used to investigate the association between imaging parameters and PF, Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the ECVs for advanced fibrosis (F2-F3), and multivariate logistic regression analysis was used to examine the relationship between PF and imaging parameters.

RESULTS

There was a strong correlation between DECT- and MR-derived ECVs (r = 0.948; p < 0.001). The two ECVs were positively correlated with PF (DECT: r = 0.647, p < 0.001; MR: r = 0.614, p < 0.001), and the mean values were 0.34 ± 0.08 (range: 0.22-0.62) and 0.35 ± 0.09 (range: 0.24-0.66), respectively. The area under the operating characteristic curve (AUC) for subjects with advanced fibrosis diagnosed by ECV was 0.86 for DECT-ECV and 0.87 for MR-ECV. Multivariate logistic regression analysis showed that the DECT-ECV was an independent predictor of PF.

CONCLUSIONS

The ECV could be an effective predictor of histological fibrosis, and DECT is equivalent to MRI for characterizing pancreatic ECV changes.

Correlations between contrast-enhanced CT-measured extracellular volume fraction, histopathological features, and MYCN amplification status in abdominal neuroblastoma: a retrospective study

PURPOSE

To retrospectively investigate the correlations between contrast-enhanced CT (CECT)-measured extracellular volume fraction (fECV) and histopathological features, as well as MYCN amplification status, in abdominal neuroblastoma.

MATERIALS AND METHODS

One hundred and forty-one patients with abdominal neuroblastoma who underwent CECT scanning were retrospectively enrolled. Calculation of fECV involved the measurement of CT values within regions of interest located within the neuroblastoma and aorta on both non-contrast-enhanced CT and equilibrium CECT. The correlations between fECV and various factors, including pathological subtype, mitosis karyorrhexis index (MKI), Shimada classification, MYCN amplification status, International Neuroblastoma Risk Group (INRG) stage, and risk group were analyzed using either the Mann-Whitney U test or Kruskal-Wallis test.

RESULTS

Neuroblastoma and ganglioneuroblastoma exhibited fECV values of 0.349 (0.252, 0.424) and 0.438 (0.327, 0.508), respectively, indicating a statistically significant difference (Z = 2.200, P = 0.028). Additionally, the fECV decreased significantly in neuroblastoma with high MKI (H = 8.314, P = 0.016) or unfavorable histology (Z = 3.880, P < 0.001), as well as in those with MYCN amplification (Z = 5.486, P < 0.001). Notably, a significant variation in fECV was observed among different INRG stages (H = 16.881, P <0.001) and risk groups (H = 29.014, P < 0.001).

CONCLUSION

CECT-derived fECV is associated with histopathological features, MYCN amplification status, INRG stage, and risk stratification of abdominal neuroblastoma, reflecting a potential correlation between the extracellular matrix and the biological behavior of neuroblastoma.

Progress in the application of dual-energy CT in pancreatic diseases

Pancreatic diseases are difficult to diagnose due to their insidious onset and complex pathophysiological developmental characteristics. In recent years, dual-energy computed tomography (DECT) imaging technology has rapidly advanced. DECT can quantitatively extract and analyze medical imaging features and establish a correlation between these features and clinical results. This feature enables the adoption of more modern and accurate clinical diagnosis and treatment strategies for patients with pancreatic diseases so as to achieve the goal of non-invasive, low-cost, and personalized treatment. The purpose of this review is to elaborate on the application of DECT for the diagnosis, biological characterization, and prediction of the survival of patients with pancreatic diseases (including pancreatitis, pancreatic cancer, pancreatic cystic tumor, pancreatic neuroendocrine tumor, and pancreatic injury) and to summarize its current limitations and future research prospects.

Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects

Gastrointestinal (GI) cancer is the leading cause of cancer-related deaths worldwide. Computed tomography (CT) is an important auxiliary tool for the diagnosis, evaluation, and prognosis prediction of gastrointestinal tumors. Spectral CT is another major CT revolution after spiral CT and multidetector CT. Compared to traditional CT which only provides single-parameter anatomical diagnostic mode imaging, spectral CT can achieve multi-parameter imaging and provide a wealth of image information to optimize disease diagnosis. In recent years, with the rapid development and application of spectral CT, more and more studies on the application of spectral CT in the characterization of GI tumors have been published. For this review, we obtained a substantial volume of literature, focusing on spectral CT imaging of gastrointestinal cancers, including esophageal, stomach, colorectal, liver, and pancreatic cancers. We found that spectral CT can not only accurately stage gastrointestinal tumors before operation but also distinguish benign and malignant GI tumors with improved image quality, and effectively evaluate the therapeutic response and prognosis of the lesions. In addition, this paper also discusses the limitations and prospects of using spectral CT in GI cancer diagnosis and treatment.

Survival prediction of hepatocellular carcinoma by measuring the extracellular volume fraction with single-phase contrast-enhanced dual-energy CT imaging

Purpose

This study aimed to investigate the value of quantified extracellular volume fraction (fECV) derived from dual-energy CT (DECT) for predicting the survival outcomes of patients with hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE).

Materials and methods

A total of 63 patients with HCC who underwent DECT before treatment were retrospectively included. Virtual monochromatic images (VMI) (70 keV) and iodine density images (IDI) during the equilibrium phase (EP) were generated. The tumor VMI-fECV and IDI-fECV were measured and calculated on the whole tumor (Whole) and maximum enhancement of the tumor (Maximum), respectively. Univariate and multivariate Cox models were used to evaluate the effects of clinical and imaging predictors on overall survival (OS) and progression-free survival (PFS).

Results

The correlation between tumor VMI-fECV and IDI-fECV was strong (both p< 0.001). The Bland-Altman plot between VMI-fECV and IDI-fECV showed a bias of 5.16% for the Whole and 6.89% for the Maximum modalities, respectively. Increasing tumor VMI-fECV and IDI-fECV were positively related to the effects on OS and PFS (both p< 0.05). The tumor IDI-fECV-Maximum was the only congruent independent predictor in patients with HCC after TACE in the multivariate analysis on OS (p = 0.000) and PFS (p = 0.028). Patients with higher IDI-fECV-Maximum values had better survival rates above the optimal cutoff values, which were 35.42% for OS and 29.37% for PFS.

Conclusion

The quantified fECV determined by the equilibrium-phase contrast-enhanced DECT can potentially predict the survival outcomes of patients with HCC following TACE treatment.

Extracellular volume fraction determined by dual-layer spectral detector CT: Possible role in predicting the efficacy of preoperative neoadjuvant chemotherapy in pancreatic ductal adenocarcinoma

PURPOSE

To clarify the relationship between extracellular volume (ECV) measured by dual-energy CT (DECT) and efficacy of preoperative neoadjuvant chemotherapy (NAC) in patients with pancreatic ductal adenocarcinoma (PDAC), as compared with single-energy CT (SECT).

METHODS

We enrolled 67 patients with PDAC who underwent dynamic contrast-enhanced CT with a dual-energy CT system prior to NAC. Attenuation values were measured on unenhanced and the equilibrium-phase 120-kVp equivalent CT images for PDAC and the aorta. ΔHU-tumor, ΔHU-tumor/ΔHU-aorta, and SECT-ECV were calculated. Iodine densities of the tumor and aorta were measured in the equilibrium phase, and DECT-ECV of the tumor was calculated. Response to NAC was evaluated and the correlation between imaging parameters and response to NAC was statistically assessed.

RESULTS

Tumor DECT-ECVs were significantly lower in the response group (n = 7) than in the non-response group (n = 60), with most significant difference (p = 0.0104). DECT-ECV showed highest diagnostic value with an Az value of 0.798. When using the optimal cut off value of DECT-ECV (<26.0 %), sensitivity, specificity, accuracy, positive predictive value, and negative value for predicting response group were 71.4 %, 85.0 %, 83.6 %, 35.7 % and 96.2 %, respectively.

CONCLUSION

PDAC with lower DECT-ECV can potentially show better response to NAC. DECT-ECV might be a useful biomarker for predicting response to NAC in patients with PDAC.

Preliminary MRI Study of Extracellular Volume Fraction for Identification of Lymphovascular Space Invasion of Cervical Cancer

BACKGROUND

Lymphovascular space invasion (LVSI) is a risk factor for poor prognosis of cervical cancer. Preoperative identification of LVSI is very difficult.

PURPOSE

To evaluate the potential of extracellular volume (ECV) fraction based on T1 mapping in preoperative identification of LVSI in cervical cancer compared with dynamic contrast-enhanced MRI (DCE-MRI).

STUDY TYPE

Retrospective.

SUBJECTS

A total of 79 patients (median age 54 years) with cervical cancer were classified into LVSI group (n = 29) and without LVSI group (n = 50) according to postoperative pathology.

FIELD STRENGTH/SEQUENCE

A 3-T, noncontrast and contrast-enhanced T1 mapping performed with volume interpolated breath hold examination (VIBE) sequence, DCE-MRI applied with 3D T1-weighted VIBE sequence.

ASSESSMENT

Regions of interest along the medial edge of the lesion were drawn on slices depicting the maximum cross-section of the tumor. The noncontrast and contrast-enhanced T1 value of the tumor and arteries in the same slice were measured, and ECV was calculated from T1 values. The parametric maps (K^trans , k_ep , and v_e ) derived from DCE-MRI standard Toft's model were evaluated.

STATISTICAL TESTS

ECV, K^trans , k_ep , and v_e between groups with and without LVSI were compared using Student's t-test. The receiver operating characteristic (ROC) curve and DeLong test were used to evaluate and compare the diagnostic performance of ECV, K^trans , k_ep , and v_e for differentiating LVSI. P < 0.05 was considered statistically significant.

RESULTS

The ECV and K^trans of the LVSI group were significantly higher than that of non-LVSI group (52.86% vs. 36.77%, 0.239 vs. 0.176, respectively), and no significant differences in K_ep or v_e values were observed (P = 0.071 and P = 0.168, respectively). The ECV fraction showed significantly higher area under ROC curve than K^trans for differentiating LVSI (0.874 vs. 0.655, respectively).

DATA CONCLUSION

ECV measurements based on T1 mapping might improve the discrimination between patients with and without LVSI in cervical cancer, showing better performance for this purpose than DCE-MRI.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

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 (ECV_pre) and/or ECV after NCRT (ECV_post) of rectal tumors was assessed, and ECV_Δ was calculated as ECV_post - ECV_pre. 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 ECV_post (17.05 ± 2.36% vs. 29.94 ± 1.20%; p < 0.001) and ECV_Δ (- 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 ECV_post with ECV_Δ achieved an AUC of 0.92 (95% confidence interval (CI) = 0.81-0.98), which was higher than that of ECV_post (AUC, 0.91 (95% CI = 0.80-0.97); p = 0.60) or ECV_Δ (AUC, 0.90 (95% CI = 0.79-0.97); p = 0.61).

CONCLUSIONS

ECV_post and ECV_Δ determined by using equilibrium contrast-enhanced CT were useful in distinguishing between pCR and non-pCR patients with LARC who received NCRT.

KEY POINTS

• ECV_post and ECV_Δ (ECV_post - ECV_pre) differed significantly between the non-pCR and pCR groups. • ECV_pre cannot be used to predict the efficacy of neoadjuvant chemoradiotherapy. • ECV_post combined with ECV_Δ had the best performance with an AUC of 0.92 for predicting pCR after NCRT in LARC.

Extracellular Volume Fraction Derived From Dual-Layer Spectral Detector Computed Tomography for Diagnosing Cervical Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer: A Preliminary Study

Objectives

The current study evaluates the performance of dual-energy computed tomography (DECT) derived extracellular volume (ECV) fraction based on dual-layer spectral detector CT for diagnosing cervical lymph nodes (LNs) metastasis from papillary thyroid cancer (PTC) and compares it with the value of ECV derived from conventional single-energy CT (SECT).

Methods

One hundred and fifty-seven cervical LNs (81 non-metastatic and 76 metastatic) were recruited. Among them, 59 cervical LNs (27 non-metastatic and 32 metastatic) were affected by cervical root artifact on the contrast-enhanced CT images in the arterial phase. Both the SECT-derived ECV fraction (ECV_S) and the DECT-derived ECV fraction (ECV_D) were calculated. A Pearson correlation coefficient and a Bland–Altman analysis were performed to evaluate the correlations between ECV_D and ECV_S. Receiver operator characteristic curves analysis and the Delong method were performed to assess and compare the diagnostic performance.

Results

ECV_D correlated significantly with ECV_S (r = 0.925; p <0.001) with a small bias (−0.6). Metastatic LNs showed significantly higher ECV_D (42.41% vs 22.53%, p <0.001) and ECV_S (39.18% vs 25.45%, p <0.001) than non-metastatic LNs. By setting an ECV_D of 36.45% as the cut-off value, optimal diagnostic performance could be achieved (AUC = 0.813), which was comparable with that of ECV_S (cut-off value = 34.99%; AUC = 0.793) (p = 0.265). For LNs affected by cervical root artifact, ECV_D also showed favorable efficiency (AUC = 0.756), which was also comparable with that of ECV_S (AUC = 0.716) (p = 0.244).

Conclusions

ECV_D showed a significant correlation with ECV_S. Compared with ECV_S, ECV_D showed comparable performance in diagnosing metastatic cervical LNs in PTC patients, even though the LNs were affected by cervical root artifacts on arterial phase CT.

Estimation of pancreatic fibrosis and prediction of postoperative pancreatic fistula using extracellular volume fraction in multiphasic contrast-enhanced CT

OBJECTIVE

To investigate the diagnostic performance of the extracellular volume (ECV) fraction in multiphasic contrast-enhanced computed tomography (CE-CT) for estimating histologic pancreatic fibrosis and predicting postoperative pancreatic fistula (POPF).

METHODS

Eighty-five patients (49 men; mean age, 69 years) who underwent multiphasic CE-CT followed by pancreaticoduodenectomy with pancreaticojejunal anastomosis between January 2012 and December 2018 were retrospectively included. The ECV fraction was calculated from absolute enhancements of the pancreas and aorta between the precontrast and equilibrium-phase images, followed by comparisons among histologic pancreatic fibrosis grades (F0‒F3). The diagnostic performance of the ECV fraction in advanced fibrosis (F2‒F3) was evaluated using receiver operating characteristic curve analysis. Multivariate logistic regression analysis was used to evaluate the associations of the risk of POPF development with patient characteristics, histologic findings, and CT imaging parameters.

RESULTS

The mean ECV fraction of the pancreas was 34.4% ± 9.5, with an excellent intrareader agreement of 0.811 and a moderate positive correlation with pancreatic fibrosis (r = 0.476; p < 0.001). The mean ECV fraction in advanced fibrosis was significantly higher than that in no/mild fibrosis (44.4% ± 10.8 vs. 31.7% ± 6.7; p < 0.001), and the area under the receiver operating characteristic curve for the diagnosis of advanced fibrosis was 0.837. Twenty-two patients (25.9%) developed clinically relevant POPF. Multivariate logistic regression analysis demonstrated that the ECV fraction was a significant predictor of POPF.

CONCLUSIONS

The ECV fraction can offer quantitative information for assessing pancreatic fibrosis and POPF after pancreaticojejunal anastomosis.

KEY POINTS

• There was a moderate positive correlation of the extracellular volume (ECV) fraction of the pancreas in contrast-enhanced CT with the histologic grade of pancreatic fibrosis (r = 0.476; p < 0.001). • The ECV fraction was higher in advanced fibrosis (F2‒F3) than in no/mild fibrosis (F0‒F1) (p < 0.001), with an AUC of 0.837 for detecting advanced fibrosis. • The ECV fraction was an independent risk factor for predicting subclinical (odds ratio, 0.81) and clinical (odds ratio, 0.80) postoperative pancreatic fistula.

Prognostic significance of extracellular volume fraction with equilibrium contrast-enhanced computed tomography for pancreatic neuroendocrine neoplasms

BACKGROUND

/Objectives: Identifying reliable pretreatment imaging biomarkers for pancreatic neuroendocrine neoplasm (PanNEN) is a key imperative. Extracellular volume (ECV) fraction quantified with equilibrium contrast-enhanced CT can be easily integrated into routine examinations. This study aimed to determine whether ECV fraction with equilibrium contrast-enhanced computed tomography (CECT) could predict long-term outcomes in patients with PanNEN.

METHODS

This study was a retrospective observational study of 80 patients pathologically diagnosed with PanNEN at a single institution. ECV fraction of the primary lesion was calculated using region-of-interest measurement within PanNEN and the aorta on unenhanced and equilibrium CECT. The impact of clinical factors and tumor ECV fraction on progression-free survival (PFS) and overall survival (OS) was assessed with univariate and multivariate analyses using Cox proportional hazards models. The correlation between WHO classification and tumor ECV fraction was evaluated using Kendall rank correlation coefficients.

RESULTS

PFS and OS rates were estimated as 93.4% and 94.6%, 78.7% and 86.2%, 78.7% and 77.0%, and 78.7% and 66.6% at 1, 3, 5, and 10 years, respectively. Multivariate analysis revealed that Union for International Cancer Control (UICC) stage (hazard ratio [HR] = 3.95, P = 0.003), WHO classification (HR = 12.27, P = 0.003), and tumor ECV fraction (HR = 11.93, P = 0.039) were independent predictors of PFS. Patient age (HR = 1.11, P < 0.001), UICC stage (HR = 3.14, P = 0.001), and tumor ECV fraction (HR = 5.27, P = 0.024) were independent significant variables for predicting OS. Tumor ECV fraction had a weak inverse relationship with WHO classification (P = 0.045, τ = -0.178).

CONCLUSIONS

ECV fraction determined by equilibrium CECT and UICC stage may predict survival in patients with PanNEN.

The extracellular volume fraction of the pancreas measured by dual-energy computed tomography: The association with impaired glucose tolerance

PURPOSE

To investigate the clinical value of measuring the ECV fraction of the pancreas by DECT in association with an impaired glucose tolerance (IGT) estimated by the hemoglobin A1C (HbA1C) value in patients with or without cirrhosis.

MATERIALS AND METHODS

This retrospective study included patients who underwent contrast-enhanced dynamic CT with dual-energy mode between March 2018 and February 2019. The ECV fraction of the pancreas was calculated from iodine map images created from equilibrium-phase contrast-enhanced DECT images. The cross-sectional areas of the pancreas were also measured.

RESULTS

In total, 51 patients were analyzed (median age, 69 years old; 22 women). The ECV fraction of the pancreas showed a significant negative correlation with the HbA1c value in the cirrhotic group (ρ=-0.346, p = 0.048), while there was no significant correlation in the non-cirrhotic group (ρ=-0.086, p = 0.734). In the elevated HbA1C group, the ECV fraction of the pancreas in the cirrhotic patients (median, 0.247; interquartile range [IQR], 0.098) was significantly lower than that in the non-cirrhotic patients (0.332, IQR 0.113) (p = 0.024). In the elevated HbA1C group, the cross-sectional area of the pancreas was significantly larger in the cirrhotic patients than that in the non-cirrhotic patients (median [IQR]; 2945 [904] vs. 1885 [909] mm², p = 0.019).

CONCLUSION

A reduction in the ECV fraction of the pancreas measured by DECT as well as the enlargement of the pancreatic parenchyma was observed in cirrhotic patients with IGT. These findings suggest that the measurement of the pancreatic ECV fraction by DECT may help clarify the pathophysiology of IGT in patients with cirrhosis.

Hepatic fibrosis grading with extracellular volume fraction from iodine mapping in spectral liver CT

PURPOSE

To determine whether hepatic extracellular volume fraction (ECV) obtained from iodine density map (ECV-iodine) can be used to estimate hepatic fibrosis grade and to compare performance with ECV measured using Hounsfield units (ECV-HU).

METHODS

From December 2016 to March 2019, patients who underwent liver resection or biopsy within four weeks after spectral liver CT were included. ECV-iodine and ECV-HU were calculated using the equilibrium phase. Within each of these, comparison of ECVs was made for different fibrosis grades (F0 - 1 vs. F2 - 3 vs. F4) and also for patients with compensated and decompensated cirrhosis. The diagnostic performance of ECVs in detecting clinically significant fibrosis (≥ F2) and cirrhosis (F4) was assessed using ROC analysis.

RESULTS

A total of 144 patients (men = 98, mean age 58.1 ± 11.5 years) were included. The ECV-iodine value was significantly higher in cirrhosis (33.6 ± 6.8 %) than those with F0 - 1 (25.0 ± 3.7 %) or F2 - 3 (28.3 ± 3.4 %, P < 0.001 for all). It was significantly higher in decompensated cirrhosis than those with compensated cirrhosis (36.5 ± 7.2 % vs. 30.7 ± 5.0 %, respectively; P < 0.001). The AUC of ECV-iodine was 0.82 for detecting F2 or above (cut-off value, > 26.9 %) and 0.81 for detecting cirrhosis (cut-off value, > 29 %). ECV-iodine had a significantly higher AUC than ECV-HU for detecting F2 or above (AUC: 0.69, P <  0.001) and cirrhosis (AUC: 0.74, P =  0.04).

CONCLUSIONS

ECV-iodine from spectral CT was able to detect clinically significant hepatic fibrosis and cirrhosis.

Multiparametric MRI for prediction of treatment response to neoadjuvant FOLFIRINOX therapy in borderline resectable or locally advanced pancreatic cancer

OBJECTIVES

To identify multiparametric MRI biomarkers to predict the tumor response to neoadjuvant FOLFIRINOX therapy in patients with borderline resectable (BR) or locally advanced (LA) pancreatic ductal adenocarcinoma (PDAC).

METHODS

From May 2016 to March 2018, adult patients with BR or LA PDAC were prospectively enrolled in this study. They received eight cycles of FOLFIRINOX therapy and underwent multiparametric MRI twice (at baseline and after the second cycle). MRI evaluations included dynamic contrast-enhanced MRI, intravoxel incoherent motion diffusion-weighted imaging, and assessment of T2* relaxivity (R2*) and the change in T1 relaxivity (ΔR1, equilibrium phase R1 minus non-enhanced R1) of the tumors. Factors to predict the responders determined by the best overall response during FOLFIRINOX therapy and those to predict progression-free survival (PFS) and overall survival (OS) were evaluated using multivariable logistic regression and the Cox proportional hazard model.

RESULTS

Forty-one patients (mean age, 60.3 years ± 9.3; 24 men) were included. Among the clinical and MRI factors, the baseline ΔR1 (adjusted odds ratio, 31.07; p = 0.008) was the only independent predictor for tumor response. The baseline ΔR1 was also an independent predictor for PFS (adjusted hazard ratio, 0.40; p = 0.033) along with R0 resection. The use of a cutoff ΔR1 value of ≥ 1.31 s^-1 enabled prognostic stratification (median PFS, 16.0 months vs.10.0 months; p = 0.029; median OS, 34.9 months vs. 16.6 months; p = 0 .023, respectively).

CONCLUSIONS

The baseline tumor ΔR1 value may be useful to predict tumor response and survival in patients with BR or LA PDAC receiving FOLFIRINOX neoadjuvant therapy.

KEY POINTS

• Baseline ΔR1 was an independent predictor for tumor response (adjusted odds ratio, 31.07; p = 0.008) and progression-free survival (adjusted hazard ratio, 0.40; p = 0.033) in patients with borderline resectable or locally advanced pancreatic ductal adenocarcinoma receiving neoadjuvant FOLFIRINOX therapy. • The criterion of baseline ΔR1 value ≥ 1.31 s^-1 allowed for the prediction of favorable tumor response and survival outcome after neoadjuvant FOLFIRINOX therapy.