Semiautomatic Segmentation and Radiomics for Dual-Energy CT: A Pilot Study to Differentiate Benign and Malignant Hepatic Lesions

Single-Energy, Dual-Energy, and Photon-Counting Computed Tomography of the Liver: Current Development and Clinical Utility for the Assessment of Focal Liver Lesions

ABSTRACT

Advancements in computed tomography (CT) technology, particularly the emergence of dual-energy CT (DE-CT) and photon-counting detector CT (PCD-CT), can improve detection, characterization, and treatment monitoring of focal liver lesions. DE-CT, through its ability to differentiate tissues with similar densities and produce diverse datasets, has enhanced lesion visibility and diagnostic precision. PCD-CT further advances imaging with superior spatial resolution and material decomposition capabilities, offering potential for complex diagnostic scenarios. This review aimed to highlight the role of CT in hepatic imaging and its application to focal liver lesions.DE-CT improves lesion detectability using low-energy virtual monochromatic images, which enhance iodine contrast and reduce radiation and contrast agent doses. It also facilitates treatment response evaluation after locoregional therapies for hepatocellular carcinoma by quantifying biomarkers, such as the extracellular volume fraction. This review underscores the transformative impact of DE-CT and PCD-CT on liver imaging, emphasizing their complementary roles alongside magnetic resonance imaging. These innovations have paved the way for more precise diagnostics, improved treatment planning, and enhanced patient outcomes in the management of liver diseases.

Correlation of Radiomics with Treatment Response in Liver Metastases

RATIONALE AND OBJECTIVES

To assess differences in radiomics derived from semi-automatic segmentation of liver metastases for stable disease (SD), partial response (PR), and progressive disease (PD) based on RECIST1.1 and to assess if radiomics alone at baseline can predict response.

MATERIALS AND METHODS

Our IRB-approved study included 203 women (mean age 54 ± 11 years) with metastatic liver disease from breast cancer. All patients underwent contrast abdomen-pelvis CT in the portal venous phase at two points: baseline (pre-treatment) and follow-up (between 3 and 12 months following treatment). Patients were subcategorized into three subgroups based on RECIST 1.1 criteria (Response Evaluation Criteria in Solid Tumors version 1.1): 66 with SD, 69 with PR, and 68 with PD on follow-up CT. The deidentified baseline and follow-up CT images were exported to the radiomics prototype. The prototype enabled semi-automatic segmentation of the target liver lesions for the extraction of first and high order radiomics. Statistical analyses with logistic regression and random forest classifiers were performed to differentiate SD from PD and PR.

RESULTS

There was no significant difference between the radiomics on the baseline and follow-up CT images of patients with SD (area under the curve (AUC): 0.3). Random forest classifier differentiated patients with PR with an AUC of 0.845. The most relevant feature was the large dependence emphasis's high and low pass wavelet filter (derived gray level dependence matrix features). Random forest classifier differentiated PD with an AUC of 0.731, with the most relevant feature being the surface-to-volume ratio. There was no difference in radiomics among the three groups at baseline; therefore, a response could not be predicted.

CONCLUSION

Radiomics of liver metastases with semi-automatic segmentation demonstrate differences between SD from PR and PD.

SUMMARY STATEMENT

Semiautomatic segmentation and radiomics of metastatic liver disease demonstrate differences in SD from the PR and progressive metastatic on the baseline and follow-up CT. Despite substantial variations in the scanners, acquisition, and reconstruction parameters, radiomics had an AUC of 0.84-0.89 for differentiating stable hepatic metastases from decreasing and increasing metastatic disease.

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

Study of radiomics based on dual-energy CT for nuclear grading and T-staging in renal clear cell carcinoma

INTRODUCTION

Clear cell renal cell carcinoma (ccRCC) is the most lethal subtype of renal cell carcinoma with a high invasive potential. Radiomics has attracted much attention in predicting the preoperative T-staging and nuclear grade of ccRCC.

OBJECTIVE

The objective was to evaluate the efficacy of dual-energy computed tomography (DECT) radiomics in predicting ccRCC grade and T-stage while optimizing the models.

METHODS

200 ccRCC patients underwent preoperative DECT scanning and were randomized into training and validation cohorts. Radiomics models based on 70 KeV, 100 KeV, 150 KeV, iodine-based material decomposition images (IMDI), virtual noncontrasted images (VNC), mixed energy images (MEI) and MEI + IMDI were established for grading and T-staging. Receiver operating characteristic analysis and decision curve analysis (DCA) were performed. The area under the curve (AUC) values were compared using Delong test.

RESULTS

For grading, the AUC values of these models ranged from 0.64 to 0.97 during training and from 0.54 to 0.72 during validation. In the validation cohort, the performance of MEI + IMDI model was optimal, with an AUC of 0.72, sensitivity of 0.71, and specificity of 0.70. The AUC value for the 70 KeV model was higher than those for the 100 KeV, 150 KeV, and MEI models. For T-staging, these models achieved AUC values of 0.83 to 1.00 in training and 0.59 to 0.82 in validation. The validation cohort demonstrated AUCs of 0.82 and 0.70, sensitivities of 0.71 and 0.71, and specificities of 0.80 and 0.60 for the MEI + IMDI and IMDI models, respectively. In terms of grading and T-staging, the MEI + IMDI model had the highest AUC in validation, with IMDI coming in second. There were statistically significant differences between the MEI + IMDI model and the 70 KeV, 100 KeV, 150 KeV, MEI, and VNC models in terms of grading (P < .05) and staging (P ≤ .001). DCA showed that both MEI + IDMI and IDMI models outperformed other models in predicting grade and stage of ccRCC.

CONCLUSIONS

DECT radiomics models were helpful in grading and T-staging of ccRCC. The combined model of MEI + IMDI achieved favorable results.

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

OBJECTIVES

Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?

METHODS

A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.

RESULTS

One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.

CONCLUSION

Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.

KEY POINTS

• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

A novel Gateaux derivatives with efficient DCNN-Resunet method for segmenting multi-class brain tumor

In hospitals and pathology, observing the features and locations of brain tumors in Magnetic Resonance Images (MRI) is a crucial task for assisting medical professionals in both treatment and diagnosis. The multi-class information about the brain tumor is often obtained from the patient's MRI dataset. However, this information may vary in different shapes and sizes for various brain tumors, making it difficult to detect their locations in the brain. To resolve these issues, a novel customized Deep Convolution Neural Network (DCNN) based Residual-Unet (ResUnet) model with Transfer Learning (TL) is proposed for predicting the locations of the brain tumor in an MRI dataset. The DCNN model has been used to extract the features from input images and select the Region Of Interest (ROI) by using the TL technique for training it faster. Furthermore, the min-max normalizing approach is used to enhance the color intensity value for particular ROI boundary edges in the brain tumor images. Specifically, the boundary edges of the brain tumors have been detected by utilizing Gateaux Derivatives (GD) method to identify the multi-class brain tumors precisely. The proposed scheme has been validated on two datasets namely the brain tumor, and Figshare MRI datasets for detecting multi-class Brain Tumor Segmentation (BTS).The experimental results have been analyzed by evaluation metrics namely, accuracy (99.78, and 99.03), Jaccard Coefficient (93.04, and 94.95), Dice Factor Coefficient (DFC) (92.37, and 91.94), Mean Absolute Error (MAE) (0.0019, and 0.0013), and Mean Squared Error (MSE) (0.0085, and 0.0012) for proper validation. The proposed system outperforms the state-of-the-art segmentation models on the MRI brain tumor dataset.

Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability

OBJECTIVES

To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms.

METHODS

A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI_70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI_70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated.

RESULTS

92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI_70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI_70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI_70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI_70keV (r² 0.3870-0.6178, all p < 0.001).

CONCLUSIONS

The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.

Spectral CT: Current Liver Applications

Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.

Imaging biomarkers to stratify lymph node metastases in abdominal CT - Is radiomics superior to dual-energy material decomposition?

PURPOSE

To assess the potential of radiomic features in comparison to dual-energy CT (DECT) material decomposition to objectively stratify abdominal lymph node metastases.

MATERIALS AND METHODS

In this retrospective study, we included 81 patients (m, 57; median age, 65 (interquartile range, 58.7-73.3) years) with either lymph node metastases (n = 36) or benign lymph nodes (n = 45) who underwent contrast-enhanced abdominal DECT between 06/2015-07/2019. All malignant lymph nodes were classified as unequivocal according to RECIST criteria and confirmed by histopathology, PET-CT or follow-up imaging. Three investigators segmented lymph nodes to extract DECT and radiomics features. Intra-class correlation analysis was applied to stratify a robust feature subset with further feature reduction by Pearson correlation analysis and LASSO. Independent training and testing datasets were applied on four different machine learning models. We calculated the performance metrics and permutation-based feature importance values to increase interpretability of the models. DeLong test was used to compare the top performing models.

RESULTS

Distance matrices and t-SNE plots revealed clearer clusters using a combination of DECT and radiomic features compared to DECT features only. Feature reduction by LASSO excluded all DECT features of the combined feature cohort. The top performing radiomic features model (AUC = 1.000; F1 = 1.000; precision = 1.000; Random Forest) was significantly superior to the top performing DECT features model (AUC = 0.942; F1 = 0.762; precision = 0.800; Stochastic Gradient Boosting) (DeLong < 0.001).

CONCLUSION

Imaging biomarkers have the potential to stratify unequivocal lymph node metastases. Radiomics models were superior to DECT material decomposition and may serve as a support tool to facilitate stratification of abdominal lymph node metastases.

Quantitative dual-energy CT techniques in the abdomen

Advances in dual-energy CT (DECT) technology and spectral techniques are catalyzing the widespread implementation of this technology across multiple radiology subspecialties. The inclusion of energy- and material-specific datasets has ushered overall improvements in CT image contrast and noise as well as artifacts reduction, leading to considerable progress in radiologists' ability to detect and characterize pathologies in the abdomen. The scope of this article is to provide an overview of various quantitative clinical DECT applications in the abdomen and pelvis. Several of the reviewed applications have not reached mainstream clinical use and are considered investigational. Nonetheless awareness of such applications is critical to having a fully comprehensive knowledge base to DECT and fostering future clinical implementation.

Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest

Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of radiomics models for BM detection using lesion-center-based geometric ROIs. The planning-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various feature selection methods and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. Lesion-center-based segmentation approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation.

Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?

BACKGROUND

The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown.

PURPOSE

To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating enhancing from non-enhancing small renal lesions.

MATERIAL AND METHODS

A total of 349 patients with 519 small renal lesions (390 non-enhancing, 129 enhancing) who underwent contrast-enhanced nephrographic phase DECT examinations between June 2013 and January 2020 on multiple DECT platforms were retrospectively recruited. Cohort A included all lesions, while cohort B included Bosniak II-IV and solid enhancing renal lesions. Radiomic models were built with features selected by the least absolute shrinkage and selection operator regression (LASSO). ROC analyses were performed to compare the diagnostic accuracy among conventional and radiomic models for predicting enhancing renal lesions.

RESULTS

The individual iodine concentration (IC), normalized IC, mean attenuation on 75-keV images, radiomic model of iodine images, 75-keV images and a combined model integrating all the above-mentioned features all demonstrated high AUCs for predicting renal lesion enhancement in cohort A (AUCs = 0.934-0.979) as well as in the test dataset (AUCs = 0.892-0.962) of cohort B (P values with Bonferroni correction >0.003). The AUC (0.864) of mean attenuation on 75-keV images was significantly lower than those of other models (all P values ≤0.001) except the radiomic model of 75-keV images (P = 0.038) in the training dataset of cohort B.

CONCLUSION

No incremental value was found by adding radiomic and machine learning analyses to iodine images for differentiating enhancing from non-enhancing renal lesions.

Characterization of Benign and Malignant Pancreatic Lesions with DECT Quantitative Metrics and Radiomics

RATIONALE AND OBJECTIVES

To compare dual energy CT (DECT) quantitative metrics and radiomics for differentiating benign and malignant pancreatic lesions on contrast enhanced abdomen CT.

MATERIALS AND METHODS

Our study included 103 patients who underwent contrast-enhanced DECT for assessing focal pancreatic lesions at one of the two hospitals (Site A: age 68 ± 12 yrs; malignant = 41, benign = 18; Site B: age 46 ± 2 yrs; malignant = 23, benign = 21). All malignant lesions had histologic confirmation, and benign lesions were stable on follow up CT (>12 months) or had characteristic benign features on MRI. Arterial-phase, low- and high-kV DICOM images were processed with the DECT Tumor Analysis (DETA) to obtain DECT quantitative metrics such as HU, iodine and water content from a region of interest (ROI) over focal pancreatic lesions. Separately, we obtained DECT radiomics from the same ROI. Data were analyzed with multiple logistic regression and receiver operating characteristics to generate area under the curve (AUC) for best predictive variables.

RESULTS

DECT quantitative metrics and radiomics had AUCs of 0.98-0.99 at site A and 0.89-0.94 at site B data for classifying benign and malignant pancreatic lesions. There was no significant difference in the AUCs and accuracies of DECT quantitative metrics and radiomics from lesion rims and volumes among patients at both sites (p > 0.05). Supervised learning-based model with data from the two sites demonstrated best AUCs of 0.94 (DECT radiomics) and 0.90 (DECT quantitative metrics) for characterizing pancreatic lesions as benign or malignant.

CONCLUSION

Compared to complex DECT radiomics, quantitative DECT information provide a simpler but accurate method of differentiating benign and malignant pancreatic lesions.

Radiomics of dual-energy computed tomography for predicting progression-free survival in patients with early glottic cancer

Aim: This study aimed to predict progression-free survival (PFS) in patients with early glottic cancer using radiomic features on dual-energy computed tomography iodine maps. Methods: Radiomic features were extracted from arterial and venous phase iodine maps, and radiomic risk scores were determined by univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator regression with tenfold cross-validation. The Kaplan-Meier method was used to evaluate the association between radiomic risk scores and PFS. Results: Patients were stratified into low-risk and high-risk groups using radiomics, the PFS corresponding rates with statistical significance between the two groups. The high-risk group showed better survival, benefiting from laryngectomy. Conclusion: Radiomics could provide a promising biomarker for predicting the PFS of early glottic cancer patients.

Dual-Energy Computed Tomography Imaging in Early-Stage Hepatocellular Carcinoma: A Preliminary Study

Background

This study aims to evaluate the application of dual-energy computed tomography (DECT) for multiparameter quantitative measurement in early-stage hepatocellular carcinoma (HCC).

Methods

The study retrospectively enrolled 30 patients with early-stage HCC and 43 patients with early-stage HCC who received radiofrequency ablation (RFA) and underwent abdomen enhanced CT scans in GSI mode. The GSI viewer was used for image display and data analysis. The regions of interest (ROIs) were delineated in the arterial phase and the venous phase. The optimal single energy value, CT values on different energy levels (40 keV, 70 keV, 100 keV, and 140 keV), the optimal energy level, the slope of the spectral attenuation curve, the effective atomic number (Z_eff), iodine concentration (IC), water concentration (WC), normalized iodine concentration (NIC), and normalized water concentration (NWC) are measured and quantitatively analyzed.

Results

The CT values of early-stage HCC at different single energy levels in dual phases were significantly different, and the single energy values were negatively correlated with the CT values. In the arterial phase and the venous phase, the optimal energy values for the best contrast-to-noise ratio were (68.34 ± 3.20) keV and (70.14 ± 2.01) keV, respectively. The slope of the spectral attenuation curve showed a downward trend at 40 keV, 70 keV, 100 keV, and 140 keV, but there was no statistically significant difference (P > 0.05). Z_eff was positively correlated with IC and standardized IC, but has no significant correlation with WC and NWC in dual phases.

Conclusion

DECT imaging contains multiparameter information and has different application values for early-stage HCC, and it is necessary to select the parameters reasonably for personalized and comprehensive evaluation.

Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen

Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have achieved performance comparable to humans in less time for several organ segmentation tasks, and some lesion detection and classification tasks. This article introduces the current published articles of AI applied to chest and abdominal radiology, including organ segmentation, lesion detection, classification, and predicting prognosis.

Dual-Energy CT Vital Iodine Tumor Burden for Response Assessment in Patients With Metastatic GIST Undergoing TKI Therapy: Comparison to Standard CT and FDG PET/CT Criteria

Background: CT-based criteria for assessing gastroinstestinal stromal tumor (GIST) response to tyroskine kinase inhibitor (TKI) therapy are limited partly because tumor attenuation is influenced by treatment-related changes including hemorrhage and calcification. Iodine concentration may be less impacted by such changes. Objective: To determine whether DECT vital iodine tumor burden (TB) provides improved differentiation between responders and non-responders in patients with metastatic GIST undergoing TKI therapy compared to established CT and PET/CT criteria. Methods: An anthropomorphic phantom with spherical inserts mimicking GIST lesions of varying iodine concentrations and having non-enhancing central necrotic cores underwent DECT to determine a threshold iodine concentration. Forty patients (median age 57 years; 25 women, 15 men) treated with TKI for metaststic GIST were retrospectively evaluated. Patients underwent baseline and follow-up DECT and FDG PET/CT. Response assessment was performed using RECIST 1.1, modified Choi (mChoi), vascular tumor burden (VTB), DECT vital iodine TB, and European Organization for Research and Treatment of Cancer (EORTC PET) criteria. DECT vital iodine TB used the same percentage changes as RECIST 1.1 response categories. Progression-free survival (PFS) was compared between responders and non-responders for each response criteria using Cox proportional hazard ratios and Harrell's c-indices. Results: The phantom experiment identified a 0.5 mg/mL threshold to differentiate vital from non-vital tissue. Using DECT vital iodine TB, median PFS was significantly different between non-responders and responders (587 vs 167 days, respectively; p=.02). Hazard ratio for progression for DECT vital iodine TB non-responders versus responders was 6.9, versus 7.6 for EORTC PET, 3.3 for VTB, 2.3 for RECIST 1.1, and 2.1 for mChoi. C-index was 0.74 for EORTC PET, 0.73 for DECT vital iodine TB, 0.67 for VTB, 0.61 for RECIST 1.1, and 0.58 for mChoi. C-index was significantly greater for DECT vital iodine TB than RECIST 1.1 (p=.02) and mChoi (p=.002), but not different than VTB and EORTC PET (p>.05). Conclusion: DECT vital iodine TB criteria showed comparable performance as EORTC PET and outperformed RECIST 1.1 and mChoi for response assessment of metastatic GIST under TKI therapy. Clinical Impact: DECT vital iodine TB could help guide early management decisions in patients on TKI therapy.

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

Objective

To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC).

Methods

A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed.

Result

Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model.

Conclusion

The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.

Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer

Radiomics is a method to mine large numbers of quantitative imaging features and develop predictive models. It has shown exciting promise for improved cancer decision support from early detection to personalized precision treatment, and therefore offers a desirable new direction for pancreatic cancer where the mortality remains high despite the current care and intense research. For radiomics, interobserver segmentation variability and its effect on radiomic feature stability is a crucial consideration. While investigations have been reported for high-contrast cancer sites such as lung cancer, no studies to date have investigated it on CT-based radiomics for pancreatic cancer. With three radiation oncology observers and three radiology observers independently contouring on the contrast CT of 21 pancreatic cancer patients, we conducted the first interobserver segmentation variability study on CT-based radiomics for pancreatic cancer. Moreover, our novel investigation assessed whether there exists an interdisciplinary difference between the two disciplines. For each patient, a consensus tumor volume was generated using the simultaneous truth and performance level expectation algorithm, using the dice similarity coefficient (DSC) to assess each observer's delineation against the consensus volume. Radiation oncology observers showed a higher average DSC of 0.81 ± 0.06 than the radiology observers at 0.69 ± 0.16 (p = 0.002). On a panel of 1277 radiomic features, the intraclass correlation coefficients (ICC) was calculated for all observers and those of each discipline. Large variations of ICCs were observed for different radiomic features, but ICCs were generally higher for the radiation oncology group than for the radiology group. Applying a threshold of ICC > 0.75 for considering a feature as stable, 448 features (35%) were found stable for the radiation oncology group and 214 features (16%) were stable from the radiology group. Among them, 205 features were found stable for both groups. Our results provide information for interobserver segmentation variability and its effect on CT-based radiomics for pancreatic cancer. An interesting interdisciplinary variability found in this study also introduces new considerations for the deployment of radiomics models.

Prediction of Coronary Calcification and Stenosis: Role of Radiomics From Low-Dose CT

RATIONALE AND OBJECTIVES

We aimed to assess relationship between single-click, whole heart radiomics from low-dose computed tomography (LDCT) for lung cancer screening with coronary artery calcification and stenosis.

MATERIALS AND METHODS

The institutional review board-approved, retrospective study included all 106 patients (68 men, 38 women, mean age 64 ± 7 years) who underwent both LDCT for lung cancer screening and had calcium scoring and coronary computed tomography angiography in our institution. We recorded the clinical variables including patients' demographics, smoking history, family history, and lipid profiles. Coronary calcium scores and grading of coronary stenosis were recorded from the radiology information system. We calculated the multiethnic scores for atherosclerosis risk scores to obtain 10-year coronary heart disease (MESA 10-Y CHD) risk of cardiovascular disease for all patients. Deidentified LDCT exams were exported to a Radiomics prototype for automatic heart segmentation, and derivation of radiomics. Data were analyzed using multiple logistic regression and kernel Fisher discriminant analyses.

RESULTS

Whole heart radiomics were better than the clinical variables for differentiating subjects with different Agatston scores (≤400 and >400) (area under the curve [AUC] 0.92 vs 0.69). Prediction of coronary stenosis and MESA 10-Y CHD risk was better on whole heart radiomics (AUC:0.86-0.87) than with clinical variables (AUC:0.69-0.79). Addition of clinical variables or visual assessment of coronary calcification from LDCT to whole heart radiomics resulted in a modest change in the AUC.

CONCLUSION

Single-click, whole heart radiomics obtained from LDCT for lung cancer screening can differentiate patients with different Agatston and MESA risk scores for cardiovascular diseases.

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

Purpose

To assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi.

Methods

The local ethical committee-approved, retrospective study included 202 adult patients (mean age: 53 ± 17 years; male: 103; female: 99) who underwent clinically indicated, non-contrast abdomen-pelvis CT for suspected or known renal calculi. All CT examinations were reviewed to determine the presence (n = 123 patients) or absence (n = 79) of renal calculi. On CT images with renal calculi, each kidney stone was annotated and measured (maximum dimension, Hounsfield unit (HU), and combined and dominant stone volumes) using a HU threshold-based segmentation. We recorded the presence of hydronephrosis, number of renal calculi, and treatment strategies. Deidentified CT images were processed with the radiomics prototype (Radiomics, Frontier, Siemens Healthineers), which automatically segmented each kidney to obtain 1690 first-, shape-, and higher-order radiomics. Data were analyzed using multiple logistic regression analysis with areas under the curve (AUC) as output.

Results

Among 202 patients, only 28 patients (18%) needed procedural treatment (lithotripsy or ureteroscopic stone extraction). Gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) differentiated patients with and without procedural treatment (AUC 0.91, 95% CI 0.85–0.92). Higher-order radiomics (gray-level size zone matrix – GLSZM) differentiated kidneys with and without hydronephrosis (AUC: 0.99, p < 0.001) as well those with different stone volumes (AUC up to 0.89, 95% CI 0.89–0.92).

Conclusion

Automated segmentation and radiomics of entire kidneys can assess hydronephrosis presence, stone burden, and treatment strategies for renal calculi with AUCs > 0.85.

Use of radiomics to differentiate left atrial appendage thrombi and mixing artifacts on single-phase CT angiography

To assess if radiomics can differentiate left atrial appendage (LAA) contrast-mixing artifacts and thrombi on early-phase CT angiography without the need for late-phase images. Our study included 111 patients who underwent early- and late-phase, contrast-enhanced cardiac CT. Of these, 79 patients had LAA filling defects from thrombus (n = 46, mean age: 72  ±  12 years, M:F 26:20) or contrast-mixing artifact (n = 33, mean age: 71  ±  13 years, M:F 21:12) on early-contrast-enhanced phase. The remaining 32 patients (mean age: 66  ±  10 years, M:F 19:13) had homogeneous LAA opacification without filling defects. The entire LAA volume on early-phase CT images was manually segmented to obtain radiomic features (Frontier, Siemens). A radiologist assessed for the presence of LAA filling defects and recorded the size and mean CT attenuation (HU) of filling defects and normal LAA. The data were analyzed using multiple logistic regression with receiver operating characteristics area under the curve (AUC) as an output. The radiologist correctly identified all 32 patients without LAA filling defects, 42/46 LAA with thrombi, and 23/33 contrast mixing artifacts. Although HU of LAA thrombi and contrast mixing artifacts was significantly different, with the lowest AUC (0.66), it was inferior to both radiologist assessment and radiomics (p = 0.05). Combination of radiologist assessment and radiomics (AUC 0.92) was superior to HU (0.66), radiomics (0.85), and radiologist (0.80) alone (p < 0.008). Radiomics can differentiate between LAA filling defects from thrombi and contrast mixing artifacts on early-phase contrast-enhanced CT images without the need for late-phase CT.

Accuracy of spectral curves at different phantom sizes and iodine concentrations using dual-source dual-energy computed tomography

To validate the accuracy of spectral curves obtained by an image-data-based algorithm and clarify the error factors that reduce accuracy. Iodine rods of known composition and different concentrations were inserted into a cylinder or elliptic-cylinder phantom and scanned according to the dual-energy protocol. Spectral curves were obtained by (i) theoretical calculation, (ii) image-data-based 2-material decomposition, and (iii) using a dedicated workstation. Accuracy was verified by comparing the spectral curve obtained by theoretical calculations with those obtained by the image-data-based algorithms or the dedicated workstations. For a quantitative evaluation, the error and relative error (RE) were calculated. In the image-data-based calculation, the errors with respect to the theoretical CT number ranged from - 8.3 to 71.1 HU. For all 192 combinations, 80.7% of the errors were under ± 15 HU, and 97.9% of the REs were under 10%. In the dedicated workstation, the errors ranged from - 94.7 to 26.8 HU. For all combinations, 68.8% of the errors were under ± 15 HU, and 68.2% of the REs were under 10%. By appropriately setting the effective energy corresponding to the CT number of the basis materials, an accurate spectral curve can be obtained. The beam-hardening effect is canceled by the 2-material decomposition process even without beam-hardening correction. Accuracy is primarily reduced by scattered radiation rather than the beam-hardening effect.