Venous Phase Research Articles

MRI analysis of relative tumor enhancement in liver metastases and correlation with immunohistochemical features

ObjectiveInvestigate the association between the relative tumor enhancement (RTE) of gadoxetic acid across various MRI phases and immunohistochemical (IHC) features in patients with liver metastases (LM) from colorectal cancer (CRC), breast cancer (BC), and pancreatic cancer (PC).MethodsA retrospective analysis was conducted on 68 patients with LM who underwent 1.5-T MRI scans. Non-contrast and contrast-enhanced T1-weighted (T1-w) gradient echo (GRE) sequences were acquired before LM biopsy. RTE values among LM groups were compared by cancer type using analysis of variance. The relationships between RTE and IHC features tumor stroma ratio, cell count, Ki67 proliferation index, and CD45 expression were evaluated using Spearman’s rank correlation coefficients.ResultsSignificant differences in RTE were observed across different MRI phases among patients with BCLM, CRCLM, and PCLM: arterial phase (0.75 ± 0.42, 0.37 ± 0.36, and 0.44 ± 0.19), portal venous phase (1.09 ± 0.41, 0.59 ± 0.44, and 0.53 ± 0.24), and venous phase (1.11 ± 0.45, 0.65 ± 0.61, and 0.50 ± 0.20). In CRCLM, RTE inversely correlated with mean Ki67 (r = −0.50, p = 0.01) in the hepatobiliary phase. Negative correlations between RTE and CD45 expression were found in PCLM and CRCLM in the portal venous phase (r = −0.69, p = 0.01 and r = −0.41, p = 0.04) and the venous phase (r = −0.65, p = 0.01 and r = −0.44, p = 0.02).ConclusionSignificant variations in RTE were identified among different types of LM, with correlations between RTE values and IHC markers such as CD45 and Ki67 suggesting that RTE may serve as a non-invasive biomarker for predicting IHC features in LM.Critical relevance statementRTE values serve as a predictive biomarker for IHC features in liver metastasis, potentially enhancing non-invasive patient assessment, disease monitoring, and treatment planning.Key PointsFew studies link gadoxetic acid-enhanced MRI with immunohistochemistry in LM.RTE varies by liver metastasis type and correlates with CD45 and Ki67.RTE reflects IHC features in LM, aiding non-invasive assessment.Graphical

Assessment of vascular invasion of pancreatic ductal adenocarcinoma based on CE-boost black blood CT technique

ObjectivesTo explore the diagnostic efficacy of advanced intelligent clear-IQ engine (AiCE) and adaptive iterative dose reduction 3D (AIDR 3D), combination with and without the black blood CT technique (BBCT), for detecting vascular invasion in patients diagnosed with nonmetastatic pancreatic ductal adenocarcinoma (PDAC).MethodsA total of 35 consecutive patients diagnosed with PDAC, proceeding with contrast-enhanced abdominal CT scans, were enrolled in this study. The arterial and portal venous phase images were reconstructed using AiCE and AIDR 3D. The corresponding BBCT images were established as AiCE–BBCT and AIDR 3D–BBCT, respectively. Two observers scored the image quality independently. Cohen’s kappa (k) value or intraclass correlation coefficient (ICC) was used to analyze consistency. The diagnostic performance of four algorithms in detecting vascular invasion in PDAC patients was assessed using the area under the curve (AUC).ResultsThe AiCE and AiCE–BBCT groups demonstrated superior image noise and diagnostic acceptability compared with AIDR 3D and AIDR 3D–BBCT groups (all p < 0.001), and the k value was 0.861–0.967 for both reviewers. In terms of diagnostic capability for vascular invasion in PDAC, the AiCE–BBCT group exhibited higher specificity (95.0%) and sensitivity (93.3%) compared to the AIDR 3D and AIDR 3D–BBCT groups, with an AUC of 0.942 (95% CI: 0.849–1.000, p < 0.05). Furthermore, all vascular evaluations conducted using AiCE–BBCT demonstrated better consistency (ICC: 0.847–0.935).ConclusionThe BBCT technique in conjunction with AiCE could lead to notable enhancements in both the image quality of PDAC images and the diagnostic performance for tumor vascular invasion.Critical relevance statementBetter diagnostic accuracy of vascular invasion of PDAC based on BBCT in combination with an AiCE is a critical factor in determining treatment strategies and patient outcomes.Key PointsIdentifying vascular invasion of PDAC is important for prognostication.Combined images provide improved image quality and higher diagnostic accuracy.Combined images can excellently display the vascular wall and invasion.Graphical

Clinical impact of magnetic resonance angiography in patients undergoing free fibular flap transfer

AbstractObjectiveThis retrospective study was carried out to assess the impact of dynamic time‐resolved magnetic resonance angiography (DTR‐MRA) in detecting lower extremity vascular abnormalities and its impact on deciding the harvest site in patients undergoing free fibular transfer.MethodsConsecutive patients who underwent DTR‐MRA imaging of lower extremities prior to free fibular flap transfer (FFFT) during the period of 2008–2020 were retrospectively included in the study. Retrospective review of medical records was performed to include the patients who had provisional side of harvest decided prior to DTR‐MRA based on clinical findings. The criteria for change in management included changing the harvest site, either to the contralateral leg or a different site based on DTR‐MRA.ResultsImaging findings in 103 patients who underwent DTR‐MRA prior to FFFT were included in the study. Overall incidence of vascular abnormalities of clinical significance for FFFT was 30/103 (29%), and site of harvest was changed in 30/103 (29%) patients after DTR‐MRA study. Addition of delayed venous phase imaging detected additional venous pathologies of clinical significance in 7/103 (6.8%) patients.ConclusionsDTR‐MRA as a preoperative study before FFFT has an excellent accuracy in detecting vascular abnormalities of clinical significance. Addition of delayed phase images can also detect clinically significant venous abnormalities.

Radiomic features of primary retroperitoneal sarcomas: a prognostic study

BackgroundRisk-stratification of patients with retroperitoneal sarcomas (RPS) relies on validated nomograms, such as Sarculator. This retrospective study investigated whether radiomic features extracted from computed tomography (CT) imaging could i) enhance the performance of Sarculator and ii) identify G3 dedifferentiated liposarcoma (DDLPS) or leiomyosarcoma (LMS), which are currently consider in a randomized clinical trial testing neoadjuvant chemotherapy. MethodsPatients with primary localized RPS treated with curative-intent surgery (2011-2015) and available pre-operative CT imaging were included. Regions of interest (ROIs) were manually annotated on both unenhanced and portal venous phase acquisitions. Top performing radiomic features were selected with outcome-specific random forest models, through generation of replicative experiments (contexts) where patients were split into training and testing sets. Endpoints were overall and disease-free survival (OS, DFS).Prognostic models for DFS and OS included the top five selected radiomic features and the Sarculator nomogram score.Models accuracy was assessed with Harrell’s Concordance (C-)index. ResultsThe study included 112 patients, with a median follow-up of 77 months (IQR 65-92 months).Sarculator alone achieved a C-index of 0.622 and 0.686 for DFS and OS, respectively. Radiomic features only marginally enhanced the prediction accuracy of Sarculator for OS (C-index=0.726, C-index gain: 0.04) or DFS (C-index=0.639, C-index gain: 0.017). Finally, radiomic features identified patients with G3 DDLPS or LMS with an accuracy of 0.806. ConclusionRadiomic features marginally improved the performance of Sarculator in RPS.However, they accurately identified G3 DDLPS or LMS at diagnosis, potentially improving patients selection for neoadjuvant treatments.

A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort study.

To develop and validate deep learning (DL) models using preoperative contrast-enhanced CT images for tumor auto-segmentation and microsatellite instability(MSI) prediction incolorectal cancer (CRC). Patients with CRC who underwent surgery or biopsy between January 2018 and April 2023 were retrospectively enrolled. Mismatch repair protein expression was determined via immunohistochemistry or fluorescence multiplex polymerase chain reaction-capillary electrophoresis. Manually delineated tumor contours using arterial and venous phase CT images by three abdominal radiologists are served as ground truth. Tumor auto-segmentation used nnU-Net. MSI prediction employed ViT or convolutional neural networks models, trained and validated with arterial and venous phase images (image model) or combined clinical-pathological factors (combined model). The segmentation model was evaluated using patch coverage ratio, Dice coefficient, recall, precision, and F1-score. The predictive models' efficacy was assessed using areas under the curves and decision curve analysis. Overall, 2180 patients (median age: 61years ± 17 [SD]; 1285 males) were divided into training (n = 1159), validation (n = 289), and independent external test (n = 732) groups. High-level MSI status was present in 435 patients (20%). In the external test set, the segmentation model performed well in the arterial phase, with patch coverage ratio, Dice coefficient, recall, precision, and F1-score values of 0.87, 0.71, 0.72, 0.74, and 0.71, respectively. For MSI prediction, the combined models outperformed the clinical model (AUC = 0.83 and 0.82 vs 0.67, p < 0.001) and two image models (AUC = 0.75 and 0.77, p < 0.001). Decision curve analysis confirmed the higher net benefit of the combined model compared to the other models across probability thresholds ranging from 0.1 to 0.45. DL enhances tumor segmentation efficiency and, when integrated with contrast-enhanced CT and clinicopathological factors, exhibits good diagnostic performance in predicting MSI in CRC.

Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA)

ObjectivesTotal tumor volume (TTV) is associated with overall and recurrence-free survival in patients with colorectal cancer liver metastases (CRLM). However, the labor-intensive nature of such manual assessments has hampered the clinical adoption of TTV as an imaging biomarker. This study aimed to develop and externally evaluate a CRLM auto-segmentation model on CT scans, to facilitate the clinical adoption of TTV.MethodsWe developed an auto-segmentation model to segment CRLM using 783 contrast-enhanced portal venous phase CTs (CT-PVP) of 373 patients. We used a self-learning setup whereby we first trained a teacher model on 99 manually segmented CT-PVPs from three radiologists. The teacher model was then used to segment CRLM in the remaining 663 CT-PVPs for training the student model. We used the DICE score and the intraclass correlation coefficient (ICC) to compare the student model’s segmentations and the TTV obtained from these segmentations to those obtained from the merged segmentations. We evaluated the student model in an external test set of 50 CT-PVPs from 35 patients from the Oslo University Hospital and an internal test set of 21 CT-PVPs from 10 patients from the Amsterdam University Medical Centers.ResultsThe model reached a mean DICE score of 0.85 (IQR: 0.05) and 0.83 (IQR: 0.10) on the internal and external test sets, respectively. The ICC between the segmented volumes from the student model and from the merged segmentations was 0.97 on both test sets.ConclusionThe developed colorectal cancer liver metastases auto-segmentation model achieved a high DICE score and near-perfect agreement for assessing TTV.Critical relevance statementAI model segments colorectal liver metastases on CT with high performance on two test sets. Accurate segmentation of colorectal liver metastases could facilitate the clinical adoption of total tumor volume as an imaging biomarker for prognosis and treatment response monitoring.Key PointsDeveloped colorectal liver metastases segmentation model to facilitate total tumor volume assessment.Model achieved high performance on internal and external test sets.Model can improve prognostic stratification and treatment planning for colorectal liver metastases.Graphical

Predicting higher-risk growth patterns in invasive lung adenocarcinoma with multiphase multidetector computed tomography and 18F-fluorodeoxyglucose PET radiomics.

To develop a predictive model for identifying the higher-risk growth pattern of invasive lung adenocarcinoma using multiphase multidetector computed tomography (MDCT) and 18F-fluorodeoxyglucose (FDG) PET radiomics. A total of 203 patients with confirmed invasive lung adenocarcinoma between January 2018 and December 2021 were enrolled and randomly divided into training (n = 143) and testing sets (n = 60). Patients were classified into two groups according to the predominant growth pattern (lower-risk group: lepidic/acinar; higher-risk group: papillary/solid/micropapillary). Preoperative multiphase MDCT and 18F-FDG PET images were evaluated. The Artificial Intelligence Kit software was used to extract radiomic features. Five predictive models [arterial phase, venous phase, and plain scan (AVP), PET, AVP-PET, clinical, and radiomic-clinical (Rad-Clin) combined model] were developed. The models' performance was assessed using receiver-operating characteristic (ROC) curves and compared using the DeLong test. Among the radiomics models (AVP, PET, and AVP-PET), the AVP-PET model [area under ROC curve (AUC) = 0.888] outperformed the PET model (AUC = 0.814; P = 0.015) in predicting the higher-risk growth patterns. The combined Rad-Clin model (AUC = 0.923), which integrates AVP-PET radiomics and five independent clinical predictors (gender, spiculation, long-axis diameter, maximum standardized uptake value, and average standardized uptake value), exhibited superior performance in predicting the higher-risk growth pattern compared with radiomic models (P = 0.043, vs. AVP-PET; P = 0.016, vs. AVP; P = 0.002, vs. PET) or the clinical model alone (constructing based on five clinical predictors; AUC = 0.793; P < 0.001). The combined Rad-Clin model can predict the higher-risk growth patterns of invasive adenocarcinoma (IAC). This approach could help determine individual therapeutic strategies for IAC patients by distinguishing predominant growth patterns with high risk.

Assessment of high-risk gastroesophageal varices in cirrhotic patients using quantitative parameters from dual-source dual-energy CT.

To investigate the clinical value of dual-source dual-energy CT (dsDECT) quantitative parameters in evaluating hemodynamics and predicting high-risk gastroesophageal varices in cirrhotic patients. 98 consecutive patients were collected in this prospectively study and all patients underwent an abdominal triple-phase contrasted-enhanced examination with dsDECT. Iodine concentration (IC) and normalized iodine concentration (NIC) of the liver parenchyma, spleen parenchyma and aorta at different phases were recorded, and arterial iodine fraction (AIF), iodine washout rate (IWR), and extracellular volume (ECV) were calculated. Using upper gastrointestinal endoscopy as the reference standard, patients who met the inclusion and exclusion criteria were divided into groups with varices need treatment (VNT) and non-VNT. The clinical characteristics, traditional CT features and quantitative dsDECT parameters were compared between the VNT group and the non-VNT group using univariate analysis. The binary logistics analysis was used to build a model for diagnosing VNT. The receiver operating characteristic (ROC) curve was used for analysis and the DeLong test was used to compare different ROC curves. Finally, 57 patients were included in this study. Univariate analysis showed statistically significant differences in NIC of the liver at the portal venous phase (NIC-LPVP), IWR of the liver (IWR-L) and spleen volume between the VNT group and the non-VNT group (p < 0.05). The mixed-CT model was built by binary logistics analysis. The ROC curves of NIC-LPVP, IWR-L, spleen volume and the mixed-CT model were statistically significant (p < 0.05) for predicting VNT in cirrhotic patients, among which the area under the ROC curve of the mixed-CT model was the highest. Dual-source dual-energy CT has added clinical value in evaluating hepatic hemodynamics and diagnosing VNT in patients with liver cirrhosis.

Comparison of multiple machine learning models for predicting prognosis of pancreatic ductal adenocarcinoma based on contrast-enhanced CT radiomics and clinical features.

The aim of this study was to evaluate the prognostic potential of combining clinical features and radiomics with multiple machine learning (ML) algorithms in pancreatic ductal adenocarcinoma (PDAC). A total of 116 patients with PDAC who met the eligibility criteria were randomly assigned to a training or validation cohort. Seven ML algorithms, including Supervised Principal Components, stepwise Cox, Random Survival Forest, CoxBoost, Least absolute shrinkage and selection operation (Lasso), Ridge, and Elastic network, were integrated into 43 algorithm combinations. Forty-three radiomics models were constructed separately using radiomics features extracted from arterial phase (AP), venous phase (VP), and combined arterial and venous phase (AP+VP) images. The concordance index (C-index) of each model was calculated. The model with the highest mean C-index was identified as the best model for calculating the radiomics score (Radscore). Univariate and multivariate Cox analyses were used to identify independent prognostic indicators and create a clinical model for prognosis prediction. The multivariable Cox regression was used to combine Radscore with clinical features to create a combined model. The efficacy of the model was evaluated using the C-index, calibration curves, and decision curve analysis (DCA). The model based on the Lasso+StepCox[both] algorithm constructed using AP+VP radiomics features showed the best predictive ability among the 114 radiomics models. The C-indices of the model in the training and validation cohorts were 0.742 and 0.722, respectively. Based on the results of the univariate and multivariate Cox regression analyses, sex, Tumor-Node-Metastasis (TNM) stage, and systemic inflammation response index were included to build the clinical model. The combined model, incorporating three clinical factors and AP+VP-Radscore, achieved the highest C-indices of 0.764 and 0.746 in the training and validation cohorts, respectively. In terms of preoperative prognosis prediction for PDAC, the calibration curve and DCA showed that the combined model had a good consistency and greatest net benefit. A combined model of clinical features and AP+VP-Radscore screened using multiple ML algorithms has an excellent ability to predict the prognosis of PDAC and may provide a noninvasive and effective method for clinical decision-making.

Validating a Practical Correction for Intravenous Contrast on Computed Tomography–Based Muscle Density

Objective Computed tomography (CT) measured muscle density is prognostic of health outcomes. However, the use of intravenous contrast obscures prognoses by artificially increasing CT muscle density. We previously established a correction to equalize contrast and noncontrast muscle density measurements. While this correction was validated internally, the objective of this study was to obtain external validation using different patient cohorts, muscle regions, and CT series. Methods CT images from 109 patients with kidney tumors who received abdominal CT scans with a multiphase intravenous contrast protocol were analyzed. Paraspinal muscle density measurements taken during noncontrast, venous phase, and delayed phase contrast scans were collected. An a priori correction of −7.5 Hounsfield units (HU) was applied to muscle measurements. Equivalence testing was utilized to determine statistical similarity. Results In the sample of 109 patients (mean age: 63 years [SD: 14.3]; 41.3% female), densities in smaller regions of interest within the paraspinal muscles and the entire paraspinal muscle density (PS) in venous and delayed phase contrast scans were higher than in noncontrast. Equivalence testing showed that average corrected contrast and noncontrast muscle densities were within 3 HU for both muscle measures for the total patient sample, and for a majority of male and female subsamples. The correction is suitable for regions of interests of venous contrast (90% CI: −1.90, −0.69 HU) and delayed contrast scans (90% CI: 0.075, 1.29 HU) and within the PS measures of venous contrast (90% CI: −2.04, −0.94 HU) and delayed contrast scans (90% CI: −0.11, 0.89 HU) Conclusions The previously established correction for contrast of −7.5 HU was applied in a new patient population, axial muscle region, muscle measurement size, and expanded on previously studied contrast phases. The correction produced contrast-corrected muscle densities that were statistically equivalent to noncontrast muscle densities. The simplicity of the correction gives clinicians a tool that seamlessly integrates into practice or research to improve harmonization of data between contrast and noncontrast scans.

Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic Mass-Forming Cholangiocarcinoma

Objectives The aim of the study is to investigate the ability of preoperative CT (Computed Tomography)-based radiomics signature to predict microvascular invasion (MVI) of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Materials and Methods Preoperative clinical data, basic CT features, and radiomics features of 121 IMCC patients (44 with MVI and 77 without MVI) were retrospectively reviewed. The loading and display of CT images, delineation of the volume of interest, and feature extraction were performed using 3D Slicer. Radiomics features were selected by the LASSO logistic regression model. Multivariate logistic regression analysis was used to establish the radiomics model, radiologic model, and combined model in the training set (n = 85) to predict the MVI of IMCC, and then verified in the validation set (n = 36). Results Among the 3948 radiomics features extracted from multiphase dynamic enhanced CT imaging, 16 most stable features were selected. The AUC of the radiomics model for predicting MVI in the training set and validation set were 0.935 and 0.749, respectively. The AUC of the radiologic model for predicting MVI in the training set and validation set were 0.827 and 0.796, respectively. When radiomics and radiologic models are combined, the predictive performance of the combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) is optimal, with an AUC of 0.958 in the training set and 0.829 in the test set for predicting MVI. Conclusions CT radiomics signature is a powerful predictor for predicting MVI. The preoperative combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) performed well in predicting the MVI.

Improved Diagnostic Performance Using Dual-Energy CT-Derived Slope Parameter Images in Crohn's Disease.

The objective of the study is to explore the image quality and diagnosis performance of the dual-energy CT-derived slope parameter images (SPI) generated by the algorithm based on the slope function in the diagnosis of Crohn's disease (CD). Seventy-six CD patients and 53 disease-free control group subjects who underwent dual-energy CT enterography were retrospectively collected. Portal venous phase 120kVp-like and virtual monoenergetic images at 40-100keV (VMI40-100) were reconstructed. SPIs corresponding to the spectral curve between 40 and 100keV (SPI40-100) were generated using Python. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of normal and abnormal intestinal walls were calculated. Image quality, noise, and contrast were independently scored by two radiologists using a 5-point scale. Four radiologists conducted CD diagnosis with three reading models (120kVp-like, 120kVp-like with optimal VMI, and 120kVp-like with SPI40-100). The diagnostic performances of the three reading models for diagnosing CD were evaluated using receiver operating characteristic (ROC) curves. The CNR in SPI40-100 was higher than in the other images (P < 0.05). The subjective evaluation showed that there was no statistical difference between the contrast of SPI40-100 and VMI40 (P > 0.05), but that of the two images was higher than the other images (P < 0.05). The scoring on the overall image quality of VMI50 was superior to that of other images (P < 0.05). The combined model of 120kVp-like with SPI40-100 had the strongest confidence (cases with high confidence: 36, 58, 49, 47 for radiologists 1, 2, 3, 4) and the highest efficiency in diagnosing CD (areas under the ROC curve: 0.973, 0.977, 0.982, 0.991 for radiologists 1, 2, 3, 4). SPI40-100 generated by the algorithm based on the slope function exhibited good image quality. The combined model of 120kVp-like with SPI40-100 could improve radiologists' diagnostic efficiency and confidence in diagnosing CD.

HPB SO72 - Predicting Major Intra-operative Blood Loss in Patients undergoing Liver Resection: A Machine Learning Radiomics Analysis Using Pre-Operative Liver CTs

Abstract Background Liver resection offers the best opportunity for curing patients with colorectal metastases and is an important treatment option for patients with hepatocellular carcinoma. However, it can be associated with significant morbidity and mortality, with one of the primary reasons being due to intra-operative blood loss which serves as an important predictor of post-operative outcomes. Predicting blood loss pre-operatively would be a powerful tool for clinicians in identifying higher risk patients. In this study, we examine whether radiomic features of the liver can predict major blood loss (&amp;gt;500 mls) alongside clinical and morphometric features in patients undergoing resection for colorectal metastases. Method Patient data was extracted from a prospectively maintained database of individuals undergoing resection for colorectal liver metastases (CRLMs). Radiomic features were extracted from pre-operative portal venous phase CTs. The background liver was randomly segmented in a two-dimensional plane away from cancer areas. Highly correlated features were dropped, and further feature selection techniques applied. Nested cross validation was performed to train and evaluate the machine learning models with the selected radiomic, clinical, and morphometric features. Results Radiomic features (159) were extracted from 106 patients, and 10 radiomic features comprised of first order and texture features, 3 morphometric, and 4 clinical features were selected for the machine learning models. The models demonstrated a robust ability to discriminate between major and minor blood loss. The best performing machine learning model demonstrated an AUC of 0.76 (95% CI 0.7-0.82). Other performance metrics for this model included a mean accuracy of 0.78 and an F1 Score of 0.87 on the validation data. Conclusion Pre-operative CT radiomic, clinical, and morphometric features have shown a modest ability to predict major intra-operative blood loss in patients undergoing hepatic resection for colorectal liver metastases. Further work is needed to validate and refine these models to improve performance. Additionally, we plan to incorporate data from other centres to validate our findings in further experiments to create a robust tool to guide surgical practice in this patient cohort.

HPB O06 - A computer tomography-based radiomics signature predicts overall survival following curative resection of colorectal liver metastasis: A multicentre, retrospective study

Abstract Background Colorectal cancer liver metastasis (CRLM) has a heterogeneous outcome and improved prognostic biomarkers are needed to aid patient-tailored management. Here, we assess the value of Computer Tomography (CT)-guided radiomics in the prognostication of post-resection CRLM using data from two large tertiary referral centres in the UK. Method Patient data was extracted from prospectively maintained databases of patients undergoing CRLM resection at two centres. CRLM and regions of interest from background normal liver were segmented. Radiomics features were extracted from portal venous phase CT images using open-source software. Feature selection techniques were applied. Machine learning models incorporating radiomic or clinical features, and their combination, were developed and assessed for mortality prediction at fixed time points. Cox proportional hazard regression was utilised for risk score generation to classify patients as high or low-risk for overall survival estimation. The models were evaluated on unseen testing data. Results Radiomics features (269) were generated from 959 metastases in 399 patients across the two sites. Models with combined clinical and radiomic features performed similarly to those with radiomic features alone, and often outperformed models with clinical features alone demonstrating good discrimination and risk prediction at 2 and 3 years. The computed radiomic scores allowed us to generate Kaplan–Meier curves which showed CRLM patients in the high-risk group had a lower overall survival vs the low-risk group(p&amp;lt;0.05). This risk-score independently predicted mortality in our multivariate Cox proportional-hazards model. Conclusion We demonstrate a robust combination of CT-based radiomics features can predict CRLM outcomes across two different hospital sites. Our radiomic risk-scores predicts overall survival and can prognosticate patients into high and low risk groups. These findings support the prognostic utility of CT-based radiomics in patients undergoing resection of CRLM and provide rationale for further investigation in a prospective, multi-centre setting.

Abstract Sa1104: Head and Pulmonary Artery Chest CT Detect Most but Not All Causes of Sudden Death and Resuscitation Injury Compared to Head to Pelvis CT Angiography in Survivors of Out of Hospital Circulatory Arrest

Introduction: Head-to-pelvis “sudden death computed tomography” angiography (SDCT) has a high diagnostic yield to identify causes and complications of out-of-hospital cardiac arrest (OHCA). The differences in yields of non-contrast head CT (H-CT) and pulmonary embolism chest CT (CT PE) angiography alone or in combination is not clear. Methods: Our prospective observational cohort enrolled patients that survived OHCA resuscitation and had a SDCT (H-CT, ECG-gated chest CT, and non-ECG gated venous phase abdomen/pelvis CT) within 6 hours of arrival (December 2015 – February 2018). For this sub-study, we assessed the same scans for diagnoses obtainable from routine H-CT and CT PE axial coverage alone, excluding any added (SDCT) findings from ECG-gating and abdomen/pelvis assessment. The primary outcome was the difference in diagnostic yield of time-critical and non-time-critical diagnoses by CT PE with or without H-CT compared to SDCT. Results: SDCT identified 86 time-critical diagnoses in 104 enrolled patients, including 39 pneumonias (45% of diagnoses), 13 myocardial infarctions [MI] (15%), 8 pulmonary embolisms (9%), 8 pneumothoraces (9%), 8 abdominal catastrophes (9%), 3 intracranial hemorrhages (3%), and 1 vascular access hemorrhage (1%) [ Table ]. CT PE alone identified 61 of the 86 time-critical diagnoses (71%), missing 25 diagnoses (29%) compared to SDCT. Missing diagnoses included MI (13/13), abdominal catastrophe (8/8), and active vascular access hemorrhage (1/1). H-CT diagnosed all 3 intracranial hemorrhages. Of the 208 non-time-critical diagnoses identified by SDCT, CT PE identified 183 non-time critical diagnoses (88%), missing 25 diagnoses (12%). H-CT did not identify any non-time-critical diagnoses. Conclusions: CT PE imaging with H-CT identifies a majority—but not all—time-critical and non-time-critical diagnoses in OHCA patients, mainly failing to capture coronary and intra-abdominal pathologies. SDCT, which includes ECG-gated chest CT and abdomen/pelvis CT, improves total diagnostic yield, but its incremental utility requires further study.

Radiomics analysis of dual-energy CT-derived iodine maps for differentiating malignant from benign thyroid nodules.

Many thyroid nodules are detected incidentally with the widespread use of sensitive imaging techniques; however, only a fraction of these nodules are malignant, resulting in unnecessary medical expenditures and anxiety. The major challenge is to differentiate benign thyroid nodules from malignant ones. The application of dual-energy computed tomography (DECT) and radiomics provides a new diagnostic approach. Studies applying radiomics from primary tumours on iodine maps to differentiate malignant from benign thyroid nodules are still lacking. To determine the ability of an iodine map-based radiomic nomogram in the venous phase for differentiating malignant thyroid nodules from benign nodules. A total of 141 patients with thyroid nodules who underwent DECT were enrolled and randomly assigned to the training and test cohorts between January 2018 and January 2019. The radiomic score (Rad-score) was derived from nine quantitative features of the iodine maps. Stepwise logistic regression analysis was used to develop radiomic, clinical and combined models. Age, normalized iodine concentration (NIC), and cyst changes were used to construct the clinical model. Receiver operating characteristic (ROC) curve analysis, sensitivity and specificity were performed to analyse the ability of the models to predict malignant thyroid nodules. Calibration analysis was used to test the fitness of the models. Decision curve analysis (DCA) and nomogram construction were also performed. According to the clinical model, age (0.989 [0.984, 0.995]; p<0.001), NIC (0.778 [0.640, 0.995]; p=0.01), and cyst changes (0.617 [0.507, 0.751]; p<0.001) were independently associated with malignant thyroid nodules. According to the combined model, age (0.994 [0.989, 0.999]; p=0.01), NIC (0.797 [0.674, 0.941]; p=0.008), cyst changes (0.786 [0.653, 0.947]; p=0.01), and the rad-score (1.106 [1.070, 1.143]; p<0.001) were independently associated with malignant thyroid nodules. The combined model achieved satisfactory discrimination in predicting malignant thyroid nodules and had greater predictive value in the training (AUC [areas under the curve], 0.96vs. 0.87; p=0.01) and test (AUC, 0.90vs. 0.79; p=0.04) cohorts than did the clinical model. The radiomics nomogram based on iodine maps is useful to distinguish malignant thyroid nodules from benign thyroid nodules.

Unveiling Tim-3 immune checkpoint expression in hepatocellular carcinoma through abdominal contrast-enhanced CT habitat radiomics.

Immune checkpoint inhibitors (ICIs) are important systemic therapeutic agents for hepatocellular carcinoma (HCC), among which T-cell immunoglobulin and mucin-domain containing protein 3 (Tim-3) is considered an emerging target for ICI therapy. This study aims to evaluate the prognostic value of Tim-3 expression and develop a predictive model for Tim-3 infiltration in HCC. We collected data from 424 HCC patients in The Cancer Genome Atlas (TCGA) and data from 102 pathologically confirmed HCC patients from our center for prognostic analysis. Multivariate Cox regression analyses were performed on both datasets to determine the prognostic significance of Tim-3 expression. In radiomics analysis, we used the K-means algorithm to cluster regions of interest in arterial phase enhancement and venous phase enhancement images from patients at our center. Radiomic features were extracted from three subregions as well as the entire tumor using pyradiomics. Five machine learning methods were employed to construct Habitat models based on habitat features and Rad models based on traditional radiomic features. The predictive performance of the models was compared using ROC curves, DCA curves, and calibration curves. Multivariate Cox analyses from both our center and the TCGA database indicated that high Tim-3 expression is an independent risk factor for poor prognosis in HCC patients. Higher levels of Tim-3 expression were significantly associated with worse prognosis. Among the ten models evaluated, the Habitat model constructed using the LightGBM algorithm showed the best performance in predicting Tim-3 expression status (training set vs. test set AUC 0.866 vs. 0.824). This study confirmed the importance of Tim-3 as a prognostic marker in HCC. The habitat radiomics model we developed effectively predicted intratumoral Tim-3 infiltration, providing valuable insights for the evaluation of ICI therapy in HCC patients.

CT-based radiomics model for predicting perineural invasion status in gastric cancer.

Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients. This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models. A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779-0.887) and 0.806 (95% CI: 0.628-0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility. We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients.

Enhancing preoperative diagnosis of pancreatic ductal adenocarcinoma and mass-forming chronic pancreatitis: a study on normalized conventional MR imaging parameters.

To assess the utility of signal intensity ratio (SIR) in distinguishing between mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), thereby reducing unnecessary pancreatectomies or delayed diagnosis brought by misdiagnosis. This retrospective study included 170 participants (34 with MFCP and 136 with PDAC) who underwent radical pancreatic surgery and were diagnosed via specimen pathology. The study group was carefully selected with a 1:4 ratio matching for sex, age, and operation time between two entities. T1 SIR, T2 SIR, arterial phase (AP) SIR, portal venous phase (VP) SIR, delay phase (DP) SIR, DWI0-50 SIR, and DWI500-1000 SIR, were calculated by dividing the signal intensity of lesions by that of the paraspinal muscle, serving as a reference organ. Intraclass Correlation Coefficient (ICC) was estimated to evaluate the intraobserver and interobserver reliability. Wilcoxon tests were employed for univariate analysis, and receiver operating characteristic (ROC) curves were generated to determine optimal cutoff points and AUC values for selected predictors. A tenfold cross-validation method was applied to validate the robustness of the results. The ICC demonstrated excellent correlation for both intraobserver and interobserver(ICCs > 0.8). T1 SIR, AP SIR, VP SIR, and DP SIR were significantly lower in the PDAC group compared to the MFCP group, and exhibited good independent predictive properties with the sensitivities of 61.8, 61.8, 70.6, and 73.5%, specificities of 66.2, 68.4, 59.6, and 55.9%, and AUCs of 0.620, 0.659, 0.670, and 0.668, respectively, hovering around 0.7. The tenfold cross-validation confirmed the reliability and robustness of our findings, with consistent AUC, sensitivity, specificity, and 95% confidence intervals over 1000 iterations. T1 SIR, AP SIR, VP SIR, and DP SIR show promise as potential imaging biomarkers for distinguishing between MFCP and PDAC.

Development of a user-friendly calculator for a pediatric split-bolus polytrauma computed tomography protocol

We present here a user-friendly calculator for the setting of a pediatric split-bolus polytrauma computed tomography (CT) protocol with a mixed arterial and venous phase, aiming to both reduce radiation dose and improve workflow while assuring optimal image quality. All the different parameters are calculated based on patient’s weight with rapid computation of the injected contrast media and saline volumes, injection’s flow rate, injection’s timing, and optimal acquisition time. The designed calculator is built in a widely available Google Sheets file, accessible by a quick response (QR) code. Although polytrauma imaging represents the main goal of the technique, it can be used in a wide variety of contexts, including oncological, infectious, and vascular pathologies.Graphical