Influence of splenomegaly on aortic and liver parenchymal CT numbers during contrast-enhance CT in patients with cirrhosis

INTRODUCTION

To compare CT (computed tomography) values for enhancement of the abdominal aorta and liver parenchyma during dynamic contrast enhancement (CE) CT in cirrhotic patients with and without splenomegaly (SM).

METHODS

We considered 258 patients (83 males and 46 females for the splenomegaly group, and 83 males and 46 females for the control group) for this retrospective study. We measured CT values in the abdominal aorta and hepatic parenchyma during the hepatic arterial (HAP) and portal venous (PVP) phases. The aortic CE at HAP and the hepatic parenchymal CE at PVP were compared between the two groups. For success rate of scans, we also calculated the optimal CE rates (>280 HU in the abdominal aorta and >50 HU in the hepatic parenchyma) for each group.

RESULTS

In the SM group, the CE for abdominal aorta was decreased during the aortic phase for a dynamic CE-CT (p < 0.05). When evaluating the success rates, they were found to be 65.1 % and 58.9 % in the SM group and 81.4 % and 72.3 % in the non-SM group (p < 0.05).

CONCLUSION

The success rate of scans and CE for the abdominal aorta during the aortic phase exhibited a significant decrease during dynamic CE-CT scans on patients with SM. Patients with SM may have reduced diagnostic ability with typical contrast injection protocols.

IMPLICATIONS FOR PRACTICE

It may be necessary to change the injection rates and contrast medium volume during CE-CT depending on the presence or absence of SM.

RADIATION DOSE REDUCTION AT LOW TUBE VOLTAGE WITH CORONARY ARTERY BYPASS GRAFT COMPUTED TOMOGRAPHY ANGIOGRAPHY BASED ON THE CONTRAST NOISE RATIO INDEX

To compare the radiation dose and diagnostic ability of the 100-kVp protocol, based on the contrast noise ratio (CNR) index, during coronary artery bypass graft (CABG) vessels with those of the 120-kVp protocol. For the 120-kVp scans (150 patients), the targeted image level was set at 25 Hounsfield units (HU) (CNR120 = iodine contrast/25 HU). For the 100-kVp scans (150 patients), the targeted noise level was set at 30 HU to obtain the same CNR as in the 120-kVp scans (i.e. using 1.2-fold higher iodine contrast, CNR100 = 1.2 × iodine contrast/(1.2 × 25 HU) = CNR120). We compared the CNRs, radiation doses, detection of CABG vessels and visualisation scores of the scans acquired at 120 and 100 kVp, respectively. At the same CNR, the 100-kVp protocol may help reduce the radiation dose by ⁓30% compared with the 120-kVp protocol, without degradation of diagnostic ability during CABG.

Can Machine Learning Identify the Intravenous Contrast Dose and Injection Rate Needed for Optimal Enhancement on Dynamic Liver Computed Tomography?

OBJECTIVES

This study aimed to investigate whether machine learning (ML) is useful for predicting the contrast material (CM) dose required to obtain a clinically optimal contrast enhancement in hepatic dynamic computed tomography (CT).

METHODS

We trained and evaluated ensemble ML regressors to predict the CM doses needed for optimal enhancement in hepatic dynamic CT using 236 patients for a training data set and 94 patients for a test data set. After the ML training, we randomly divided using the ML-based (n = 100) and the body weight (BW)-based protocols (n = 100) by the prospective trial. The BW protocol was performed using routine protocol (600 mg/kg of iodine) by the prospective trial. The CT numbers of the abdominal aorta and hepatic parenchyma, CM dose, and injection rate were compared between each protocol using the paired t test. Equivalence tests were performed with equivalent margins of 100 and 20 Hounsfield units for the aorta and liver, respectively.

RESULTS

The CM dose and injection rate for the ML and BW protocols were 112.3 mL and 3.7 mL/s, and 118.0 mL and 3.9 mL/s (P < 0.05). There were no significant differences in the CT numbers of the abdominal aorta and hepatic parenchyma between the 2 protocols (P = 0.20 and 0.45). The 95% confidence interval for the difference in the CT number of the abdominal aorta and hepatic parenchyma between 2 protocols was within the range of predetermined equivalence margins.

CONCLUSIONS

Machine learning is useful for predicting the CM dose and injection rate required to obtain the optimal clinical contrast enhancement for hepatic dynamic CT without reducing the CT number of the abdominal aorta and hepatic parenchyma.

Prediction of Aortic Contrast Enhancement on Dynamic Hepatic Computed Tomography-Performance Comparison of Machine Learning Methods and Simulation Software

OBJECTIVES

The aim of this study was to compare prediction ability between ensemble machine learning (ML) methods and simulation software for aortic contrast enhancement on dynamic hepatic computed tomography.

METHODS

We divided 339 human hepatic dynamic computed tomography scans into 2 groups. One group consisted of 279 scans used to create cross-validation data sets, the other group of 60 scans were used as test data sets. To evaluate the effect of the patient characteristics on enhancement, we calculated changes in the contrast medium dose per enhancement of the abdominal aorta in the hepatic arterial phase. The parameters for ML were the patient sex, age, height, body weight, body mass index, and cardiac output. We trained 9 ML regressors by applying 5-fold cross-validation, integrated the predictions of all ML regressors for ensemble learning and the simulations, and used the training and test data to compare their Pearson correlation coefficients.

RESULTS

Comparison of different ML methods showed that the Pearson correlation coefficient for the real and predicted contrast medium dose per enhancement of the abdominal aorta was highest with ensemble ML (r = 0.786). It was higher than that obtained with the simulation software (r = 0.350). With ensemble ML, the Bland-Altman limit of agreement [mean difference, 5.26 Hounsfield units (HU); 95% limit of agreement, -112.88 to 123.40 HU] was narrower than that obtained with the simulation software (mean difference, 11.70 HU; 95% limit of agreement, -164.71 to 188.11 HU).

CONCLUSION

The performance for predicting contrast enhancement of the abdominal aorta in the hepatic arterial phase was higher with ensemble ML than with the simulation software.

Enhancement rate of venous phase to portal venous phase computed tomography and its correlation with ultrasound elastography determination of liver fibrosis

INTRODUCTION

This study aimed to compare the correlation between the computed tomography (CT) enhancement rate of the venous to portal venous phase (VP-ER) and the extracellular volume (ECV) fraction with shear-wave ultrasound elastography (USE) findings in patients with liver fibrosis.

METHODS

We included 450 patients with clinically suspected liver cirrhosis who underwent triphasic dynamic CT studies and USE. We compared the USE results with the unenhanced CT phase, with enhancement in the hepatic artery phase (HAP), portal venous phase (PVP), and venous phase (VP), and with the ECV fraction and the VP-ER. We also compared the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the ECV fraction and VP-ER with that of the values obtained with USE.

RESULTS

The VP-ER was the most highly correlated with the liver stiffness value determined with USE (Pearson's correlation coefficient: r = 0.37), followed by enhancement in the PVP (r = -0.25), CT number on unenhanced CT scans (r = -0.22), the ECV fraction (r = 0.19), enhancement in the VP (r = 0.059), and enhancement in the HAP (r = -0.023) (all p < 0.01). The VP-ER showed a significantly higher AUC than the ECV fraction (0.75 vs 0.62) when the liver stiffness was >15 kPa in USE studies (p = 0.04).

CONCLUSION

Compared to the ECV fraction, the VP-ER is more useful for predicting all degrees of liver fibrosis on routine triphasic dynamic CT images.

IMPLICATIONS FOR PRACTICE

Although improvement is needed, the VP-ER has a higher diagnostic ability for liver fibrosis than the ECV fraction in clinical practice.

Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies

INTRODUCTION

We compared the diagnostic performance of morphological methods such as the major axis, the minor axis, the volume and sphericity and of machine learning with texture analysis in the identification of lymph node metastasis in patients with thyroid cancer who had undergone contrast-enhanced CT studies.

METHODS

We sampled 772 lymph nodes with histology defined tissue types (84 metastatic and 688 benign lymph nodes) that were visualised on CT images of 117 patients. A support vector machine (SVM), free programming software (Python), and the scikit-learn machine learning library were used to discriminate metastatic-from benign lymph nodes. We assessed 96 texture and 4 morphological features (major axis, minor axis, volume, sphericity) that were reported useful for the differentiation between metastatic and benign lymph nodes on CT images. The area under the curve (AUC) obtained by receiver operating characteristic analysis of univariate logistic regression and SVM classifiers were calculated for the training and testing datasets.

RESULTS

The AUC for all classifiers in training and testing datasets was 0.96 and 0.86, at the SVM for machine learning. When we applied conventional methods to the training and testing datasets, the AUCs were 0.63 and 0.48 for the major axis, 0.70 and 0.44 for the minor axis, 0.66 and 0.43 for the volume, and 0.69 and 0.54 for sphericity, respectively. The SVM using texture features yielded significantly higher AUCs than univariate logistic regression models using morphological features (p = 0.001).

CONCLUSION

For the identification of metastatic lymph nodes from thyroid cancer on contrast-enhanced CT images, machine learning combined with texture analysis was superior to conventional diagnostic methods with the morphological parameters.

IMPLICATIONS FOR PRACTICE

Our findings suggest that in patients with thyroid cancer and suspected lymph node metastasis who undergo contrast-enhanced CT studies, machine learning using texture analysis is high diagnostic value for the identification of metastatic lymph nodes.

The combined application of the contrast-to-noise index and 80 kVp for cardiac CTA scanning before atrial fibrillation ablation reduces radiation dose exposure

INTRODUCTION

To compare the radiation dose, diagnostic accuracy, and the resultant ablation procedures using 80 and 120-kVp cardiac computed tomography angiography (CCTA) protocols with the same contrast-to-noise ratio in patients scheduled for atrial fibrillation (AF) ablation.

METHODS

This retrospective study was performed following institutional review board approval. We divided 140 consecutive patients who had undergone CCTA using a 64-MDCT scanner into two equal groups. Standard deviation (SD) of the CT number was set at 25 Hounsfield units (HU) for the 120-kVp protocol. To facilitate a reduction in radiation dose it was set at 40 HU for the 80 kVp protocol. We compared the two protocols with respect to the radiation dose, the diagnostic accuracy for detecting left atrial appendage (LAA) thrombi, matching for surface registration, and the resultant ablation procedures.

RESULTS

At 120 kVp, the dose length product (DLP) was 2.2 times that at 80 kVp (1269.0 vs 559.0 mGy cm, p < 0.01). The diagnostic accuracy for thrombus detection was 100% using both protocols. There was no difference between the two protocols with respect to matching for surface registration. The protocols did not differ with respect to the subsequent time required for the ablation procedures and the ablation fluoroscopy time, and the radiation dose (p = 0.54, 0.33, and 0.32, respectively).

CONCLUSION

For the same CNR, the DLP at 80 kVp (559.0 mGy cm) was 56% of that delivered at 120 kVp (1269.0 mGy cm). There was no reduction in diagnostic accuracy.

IMPLICATIONS FOR PRACTICE

Maintaining CNR allows for a reduction in the radiation dose without reducing the image quality.

Radiation Dose Reduction at Low Tube Voltage CCTA Based on the CNR Index

RATIONALE AND OBJECTIVES

We compared the radiation dose and diagnostic accuracy on 120- and 100-kVp coronary computed tomography angiography (CCTA) scans whose contrast-to-noise ratio (CNR) was the same.

MATERIALS AND METHODS

We studied 1311 coronary artery segments from 100 patients. For 120-kVp scans, the targeted image level was set at 25 Hounsfield units (HU). For 100-kVp scans, the targeted noise level was set at 30 HU to obtain the same CNR as at 120 kVp. We compared the CNR and the radiation dose on scans acquired at 120 and 100 kVp. Invasive coronary angiography (ICA) images were evaluated by an interventional coronary angiography specialist, and CCTA images were evaluated by a radiologist. Coronary artery disease was defined as a luminal narrowing ≧50% for ICA and CCTA. With ICA considered the gold standard, the diagnostic accuracy (sensitivity, specificity, positive predictive value, and negative predictive value) was analyzed on both 120- and 100-kVp CCTA images. We also compared the diagnostic accuracy for area under the receiver operating characteristic curve of the ICA and CCTA performed at 120 and 100 kVp. Two blinded observers visually evaluated the septal branch.

RESULTS

The mean dose-length product was 48% lower at 100 kVp than at 120 kVp (P < .01). Under the 120-kVp CCTA protocol, the area under the curve, 95% confidence interval, sensitivity, specificity, positive predictive value, and negative predictive value were 0.94%, 0.91%-0.96%, 94.0%, 93.0%, 82.3%, and 98.1%, respectively; at 100 kVp these values were 0.94%, 0.92%-0.97%, 96.1%, 92.0%, 85.2%, and 98.0%, respectively. Area under the receiver operating characteristic curve analysis revealed no significant difference in diagnostic accuracy between the two protocols (P = .87).

CONCLUSIONS

At the same CNR, the 100-kVp CCTA protocol may help to reduce the radiation dose by approximately 50% compared to the 120-kVp protocol without degradation of diagnostic accuracy.