Multiphasic CT Scans Research Articles

Impact of contrast enhancement phase on CT-based radiomics analysis for predicting post-surgical recurrence in renal cell carcinoma.

To investigate the effect of CT enhancement phase on radiomics features for predicting post-surgical recurrence of clear cell renal cell carcinoma (ccRCC). This retrospective study included 144 patients who underwent radical or partial nephrectomy for ccRCC. Preoperative multiphase abdominal CT scans (non-contrast, corticomedullary, and nephrographic phases) were obtained for each patient. Automated segmentation of renal masses was performed using the nnU-Net framework. Radiomics signatures (RS) were developed for each phase using ensembles of machine learning-based models (Random Survival Forests [RSF], Survival Support Vector Machines [S-SVM], and Extreme Gradient Boosting [XGBoost]) with and without feature selection. Feature selection was performed using Affinity Propagation Clustering. The primary endpoint was disease-free survival, assessed by concordance index (C-index). The study included 144 patients. Radical and partial nephrectomies were performed in 81% and 19% of patients, respectively, with 81% of tumors classified as high grade. Disease recurrence occurred in 74 patients (51%). A total of 1,316 radiomics features were extracted per phase per patient. Without feature selection, C-index values for RSF, S-SVM, XGBoost, and Penalized Cox models ranged from 0.43 to 0.61 across phases. With Affinity Propagation feature selection, C-index values improved to 0.51-0.74, with the corticomedullary phase achieving the highest performance (C-index up to 0.74). The results of our study indicate that radiomics analysis of corticomedullary phase contrast-enhanced CT images may provide valuable predictive insight into recurrence risk for non-metastatic ccRCC following surgical resection. However, the lack of external validation is a limitation, and further studies are needed to confirm these findings in independent cohorts.

Predicting Renal Cell Carcinoma Subtypes and Fuhrman Grading Using Multiphasic CT-Based Texture Analysis and Machine Learning Techniques

Abstract Objectives The aim of this study is to evaluate computed tomography texture analysis (CTTA) on multiphase CT scans for distinguishing clear cell renal cell carcinoma (ccRCC) from non-ccRCC and predicting Fuhrman's grade in ccRCC using open-source Python libraries. Methods Conducted retrospectively, the study included 144 patients with RCCs (108 ccRCCs and 36 non-ccRCCs) who underwent preoperative multiphasic CT. Ninety ccRCCs were categorized into 71 low-grade and 19 high-grade ccRCCs. Tumor was marked on the largest axial tumor slice using “LabelMe” across different CT phases. First- and second-order texture features were computed using Python's scipy, numpy, and opencv libraries. Multivariable logistic regression analysis and machine learning (ML) models were used to evaluate CTTA parameters from different CT phases for RCC classification. The best ML model for distinguishing ccRCC and non-ccRCC was externally validated using data from the 2019 Kidney and Kidney Tumor Segmentation Challenge. Results Entropy in the corticomedullary (CM) phase was the best individual parameter for distinguishing ccRCC from non-ccRCC with (F1 score: 0.83). The support vector machine (SVM) based ML model, incorporating CM phase features, performed the best, with an F1 score of 0.87. External validation for the same model yielded an accuracy of 0.82 and an F1 score of 0.81. ML models and individual texture parameters showed less accuracy for classifying low- versus high-grade ccRCCs, with a maximum F1 score of 0.76 for the CM phase SVM model. Other CT phases yielded inferior results for both classification tasks. Conclusion CTTA employing open-source Python tools is a viable tool for differentiating ccRCCs from non-ccRCCs and predicting ccRCC grade.

Predictive Value of Multi-Phase CT Radiomics in Delineating Early and Advanced T Staging of Colon Cancer

This study leveraged the power of multi-phase CT radiomics, which extracts detailed features from multi-phase images, to explore the distinction between early (T1-T2) and advanced (T3-T4) colon cancer stages in each of multiple phases, providing valuable insights to healthcare professionals and enhancing their decision-making capabilities. A total of 191 patients with surgically confirmed primary colon cancer were retrospectively included, and multi-phase CT scans (non-enhanced contrast phase, arterial phase, portal venous phase, and delayed phase) were conducted within one week before surgery. Three-dimensional segmentation of colonic tumors was performed on the images of the four phases, and radiomics features of each colonic tumor were automatically extracted. Minimum redundancy maximum relevance (mRMR) was applied to features selection in each of the four phases. The least absolute shrinkage and selection operator logistic regression was conducted to determine the association between radiomics features and early (T1-T2) and advanced (T3-T4) stages of colon cancer. Additionally, diagnostic performance comparison was carried out in four phases. The mean (±SD) age of the 191 individuals was 61.87±13.137 years, with females comprising 43.5% of the cohort. The selected features for the non-enhanced, arterial, portal venous, and delayed phases numbered 7, 11, 6, and 10, respectively. In the test set, the AUC values for the on-enhanced, arterial, portal venous, and delayed phases were 0.86 (0.76-0.96), 0.84 (0.73-0.94), 0.82 (0.71-0.93), and 0.86 (0.75-0.97), with corresponding accuracies of 0.84, 0.80, 0.75, and 0.88, sensitivities of 0.73, 0.64, 0.86, and 0.68, and specificities of 0.91, 0.91, 0.68, and 1.00, respectively. DeLong's test revealed no statistically significant differences in the AUC values between the four phases within the test sets (P = 0.3233~ 0.9912). Multi-phase CT radiomics demonstrated substantial value in differentiating between early (T1-T2) and advanced (T3-T4) stages of patients with colon cancer.

A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans

BackgroundLiver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals.MethodsWe designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model’s robustness.ResultsThe H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9.ConclusionThe proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics.

The association of anatomical renal mass complexity with surgical approach, Hb drop, and the rate of blood transfusion.

The third most prevalent malignant neoplasm involving the urinary tract is renal cell carcinoma (RCC), encompassing nearly 3.5% of the entire cancers afflicting the body. The aim of this research was to explore how the R.E.N.A.L. nephrometry score relates to the decisions made regarding surgery in individuals with localized RCC. This prospective study, assessed patients with localized parenchymal renal masses (stages I and II) tentatively diagnosed as RCC. Utilizing preoperative multiphasic renal CT scans and MRI, the R.E.N.A.L. score categorized masses for nephrometry values. Inclusion criteria involved collecting patient data, and data collection utilizing a structured format focusing on the nephrometry grading system. The study included 64 patients aged (mean ± SD) 49.78 ± 12.35 yrs. Undergoing renal mass surgery, there were 17 (26.5%) low, 28 (43.8%) moderate and 19 (29.7%) high-complexity lesions. All patients with a low Nephrometry score (n = 17) underwent partial nephrectomy, and all cases with a high score (n = 19) underwent radical nephrectomy. For those with a moderate Nephrometry score (n = 28), 13 (46.4%) underwent partial nephrectomy, while the remaining 15 (53.6%) cases underwent radical nephrectomy. Morbidity was low, and no mortality occurred at 180 days. Patients who had lesions fully above or below polar lines were less likely to need blood transfusions. A trend towards higher Fuhrman grades in patients receiving transfusions suggests a potential link between tumor aggressiveness and bleeding risk. Our findings provide insight on the utilization of the R.E.N.A.L. nephrometry score in forecasting perioperative, post-surgical, and oncological results. Such data might help optimize surgical methods and pre-operative patient counseling.

Intra-Individual Reproducibility of Automated Abdominal Organ Segmentation-Performance of TotalSegmentator Compared to Human Readers and an Independent nnU-Net Model.

The purpose of this study is to assess segmentation reproducibility of artificial intelligence-based algorithm, TotalSegmentator, across 34 anatomical structures using multiphasic abdominal CT scans comparing unenhanced, arterial, and portal venous phases in the same patients. A total of 1252 multiphasic abdominal CT scans acquired at our institution between January 1, 2012, and December 31, 2022, were retrospectively included. TotalSegmentator was used to derive volumetric measurements of 34 abdominal organs and structures from the total of 3756 CT series. Reproducibility was evaluated across three contrast phases per CT and compared to two human readers and an independent nnU-Net trained on the BTCV dataset. Relative deviation in segmented volumes and absolute volume deviations (AVD) were reported. Volume deviation within 5% was considered reproducible. Thus, non-inferiority testing was conducted using a 5% margin. Twenty-nine out of 34 structures had volume deviations within 5% and were considered reproducible. Volume deviations for the adrenal glands, gallbladder, spleen, and duodenum were above 5%. Highest reproducibility was observed for bones (- 0.58% [95% CI: - 0.58, - 0.57]) and muscles (- 0.33% [- 0.35, - 0.32]). Among abdominal organs, volume deviation was 1.67% (1.60, 1.74). TotalSegmentator outperformed the reproducibility of the nnU-Net trained on the BTCV dataset with an AVD of 6.50% (6.41, 6.59) vs. 10.03% (9.86, 10.20; p < 0.0001), most notably in cases with pathologic findings. Similarly, TotalSegmentator's AVD between different contrast phases was superior compared to the interreader AVD for the same contrast phase (p = 0.036). TotalSegmentator demonstrated high intra-individual reproducibility for most abdominal structures in multiphasic abdominal CT scans. Although reproducibility was lower in pathologic cases, it outperforms both human readers and a nnU-Net trained on the BTCV dataset.

A combination model of CT-based radiomics and clinical biomarkers for staging liver fibrosis in the patients with chronic liver disease

A combined model was developed using contrast-enhanced CT-based radiomics features and clinical characteristics to predict liver fibrosis stages in patients with chronic liver disease (CLD). We retrospectively analyzed multiphase CT scans and biopsy-confirmed liver fibrosis. 160 CLD patients were randomly divided into 7:3 training/validation ratio. Clinical laboratory indicators associated with liver fibrosis were identified using Spearman's correlation and multivariate logistic regression correlation. Radiomic features were extracted after segmenting the entire liver from multiphase CT images. Feature dimensionality reduction was performed using RF-RFE, LASSO, and mRMR methods. Six radiomics-based models were developed in the training cohort of 112 patients. Internal validation was conducted on 48 randomly assigned patients. Receptor Operating Characteristic (ROC) curves and confusion matrices were constructed to evaluate model performance. The radiomics model exhibited robust performance, with AUC values of 0.810 to 1.000 for significant fibrosis, advanced fibrosis, and cirrhosis. The integrated clinical-radiomics model had superior diagnostic efficacy in the validation cohort, with AUC values of 0.836 to 0.997. Moreover, these models outperformed established biomarkers such as the aspartate aminotransferase to platelet ratio index (APRI) and the fibrosis 4 score (FIB-4), as well as the gamma glutamyl transpeptidase to platelet ratio (GPR), in predicting the fibrotic stages. The clinical-radiomics model holds considerable promise as a non-invasive diagnostic tool for the assessment and staging of liver fibrosis in the patients with CLD, potentially leading to better patient management and outcomes.

Validity of Prothrombin-induced Vitamin K antagonist versus Alpha-Fetoprotein (Tumor Markers) in Diagnosis of Hepatocellular Carcinoma, Using Computed Tomography scan as Gold Standard

Biomarkers like alpha-fetoprotein and prothrombin-induced Vitamin K deficiency/ antagonist are used in the early diagnosis and staging of hepatocellular carcinoma and are useful for better outcomes in the treatment and overall survival of the patient. Objective: To compare the diagnostic accuracy of alpha-fetoprotein and prothrombin-induced Vitamin K antagonist in the diagnosis of hepatocellular carcinoma using a computed tomography scan as a gold standard. Methods: A cross-sectional study was conducted in the Liver Transplant Unit of Shaikh Zayed Postgraduate Medical Complex, Lahore from July 2023 to January 2024. A total of 94 patients older than 12 years old with cirrhosis and CT scan suggestive of hepatocellular carcinoma were selected. Blood was collected to test for AFP and PIVKA-II. The samples were sent to the labs after labeling them properly and the results were collected and entered in the data sheet. Patients were advised to have a multiphase contrast-enhanced CT scan. Patients were followed up in the clinic after 7 days. Results: The diagnostic accuracy of AFP was 78% with a 74.7% sensitivity, 100% specificity, 100% positive predictive value, and 41.67% negative predictive value. The diagnostic accuracy of PIKVA-II was found to be 87.76% with 89% sensitivity, 80% specificity, 96.1% PPV, and 57.14% NPV. Conclusions: On comparing the tumor markers AFP with PIVKA-II against the gold standard multiphase CT scan it was found that PIVKA-II has better diagnostic accuracy than AFP.

Evaluating Treatment Response in GEJ Adenocarcinoma: The Role of Pretherapeutic and Posttherapeutic Iodine Mapping.

Neoadjuvant therapy regimens have significantly improved the prognosis of GEJ (gastroesophageal junction) cancer; however, there are a significant percentage of patients who benefit from earlier resection or adapted therapy regimens, and the true response rate can only be determined histopathologically. Methods that allow preoperative assessment of response are lacking. The purpose of this retrospective study is to assess the potential of pretherapeutic and posttherapeutic spectral CT iodine density (IoD) in predicting histopathological response to neoadjuvant chemotherapy in patients diagnosed with adenocarcinoma of the GEJ. In this retrospective cohort study, a total of 62 patients with GEJ carcinoma were studied. Patients received a multiphasic CT scan at diagnosis and preoperatively. Iodine-density maps were generated based on spectral CT data. All tumors were histopathologically analyzed, and the tumor regression grade (TRG) according to Becker et al ( Cancer . 2003;98:1521-1530) was determined. Two experienced radiologists blindly placed 5 defined ROIs in the tumor region of highest density, and the maximum value was used for further analysis. Iodine density was normalized to the aortic iodine uptake. In addition, tumor response was assessed according to standard RECIST measurement. After assessing interrater reliability, the correlation of IoD values with treatment response and with histopathologic TRG was evaluated. The normalized ΔIoD (IoD at diagnosis - IoD after neoadjuvant treatment) and the normalized IoD after neoadjuvant treatment correlated significantly with the TRG. For the detection of responders and nonresponders, the receiver operating characteristic (ROC) curve for normalized ΔIoD yielded the highest area under the curve of 0.95 and achieved a sensitivity and specificity of 92.3% and 92.1%, respectively. Iodine density after neoadjuvant treatment achieved an area under the curve of 0.88 and a sensitivity and specificity of 86.8% and 84.6%, respectively (cutoff, 0.266). Iodine density at diagnosis and RECIST did not provide information to distinguish responders from nonresponders. Using the cutoff value for IoD after neoadjuvant treatment, a reliable classification of responders and nonresponders was achieved for both readers in a test set of 11 patients. Intraclass correlation coefficient revealed excellent interrater reliability (intraclass correlation coefficient, >0.9). Lastly, using the cutoff value for normalized ΔIoD as a definition for treatment response, a significantly longer survival of responders was shown. Changes in IoD after neoadjuvant treatment of GEJ cancer may be a potential surrogate for therapy response.

Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists

ObjectivesTo develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesions. Materials and methodsThis retrospective two-center study included 1146 adrenal lesions from 1059 patients who underwent multiphase CT scanning between January 2008 and March 2021. The study encompassed 564 surgically confirmed adenomas, along with 135 benign lesions and 447 metastases confirmed by observation. DL models based on multiphase CT images were developed, validated and tested. The diagnostic performance of the classification models, imaging phases and radiologists with or without DL were compared using accuracy (ACC) and receiver operating characteristic (ROC) curves. Integrated discrimination improvement (IDI) analysis and the DeLong test were used to compare the area under the curve (AUC) among models. Decision curve analysis (DCA) was used to assess the clinical usefulness of the predictive models. ResultsThe DL signature based on LASSO (DLSL) had a higher AUC than that of the other classification models (IDI > 0, P < 0.05). Furthermore, the precontrast phase (PCP)-based DLSL performed best in the independent external validation (AUC = 0.881, ACC = 82.9 %) and clinical test cohorts (AUC = 0.790, ACC = 70.4 %), outperforming DLSL based on the other single-phase or three-phase images (IDI > 0, P < 0.05). DCA demonstrated that PCP-based DLSL provided a higher net benefit (0.01–0.95). The diagnostic performance led to statistically significant improvements when radiologists incorporated the DL model, with the AUC improving by 0.056–0.159 and the ACC improving by 0.069–0.178 (P < 0.05). ConclusionThe DL model based on PCP CT images performed acceptably in differentiating adrenal metastases from benign lesions, and it may assist radiologists in accurate tumor staging for patients with a history of extra-adrenal malignancy.

Raised Alpha-Fetoprotein Level and its Association with Hepatocellular Carcinoma

Objective: To evaluate raised alpha feto-protein levels for socio-demographic and clinicopathological features in hepatocellular carcinoma. Study Design: Cross-sectional study. Place and Duration of Study: Department of Medical Oncology, Jinnah Postgraduate Medical Center, Karachi Pakistan, from Jan 2019 to Jan 2020. Methodology: Two hundred and thirty-six patients of age more than 20 years, of either gender diagnosed with hepatocellular carcinoma were included. Detailed demographic data, information regarding addiction and medical history were collected. According to The National Comprehensive Cancer Network, Hepatocellular carcinoma was diagnosed using a multiphasic liver protocol CT scan with intravenous contrast. The alpha-fetoprotein levels were checked after confirmation of hepatocellular carcinoma on a multiphasic CT scan. The cut-off value for elevated alpha feto-protein was ≥20 ng/mL. Results: The median alpha protein levels were reported as 411 ng/mL. About 183(78%) patients had elevated alpha protein levels. In univariate analysis, age, gender, diabetes mellitus, hepatitis B, hepatitis C, portal vein thrombosis, number of lesions, cirrhotic liver, features of portal hypertension, anti-viral treatment status, cigarette smoking, and segment of the liver showed a statistically significant relationship with elevated alpha protein levels (p&lt;0.05). On the multivariate model, age, hepatitis C, number of lesions, portal vein thrombosis, and Child Pugh score showed statistically significant association with elevated alpha protein (p&lt;0.05). Conclusions: The elevated alpha protein levels level was found to be higher among hepatocellular carcinoma and associated with age, hepatitis C, number of lesions, portal vein thrombosis and Child Pugh score.

Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning

Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images. MULLET enables effectively embedding the important ROIs of CECT images and exploring multi-phase contexts by introducing a transformer-based attention mechanism. Evaluated on 1,229 CECT scans from 1,197 patients, MULLET demonstrated significant performance gains in terms of Dice, Recall, and F2 score, which are 5.80%, 6.57%, and 5.87% higher than state of the arts, respectively. MULLET has been successfully deployed in real-world settings. The deployed AI web server provides a powerful system to boost clinical workflows of liver lesion diagnosis and could be straightforwardly extended to general CECT analyses.

Value of CT and MR Imaging in Assessment of Loco-regional Ablated Hepatocellular Carcinoma using LI-RADS Treatment Response Algorithm (version 2018)

Aim: The aim of this work is to evaluate the role of multiphasic CT and dynamic contrast enhanced MRI using LI-RADS treatment response algorithm (version 2018) and the added values of diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps in the assessment of loco-Regional ablated Hepatocellular Carcinoma (HCC). Methods: This study included 45 patients with 50 loco-Regional ablated HCC lesions. All patients underwent multiphasic CT scan, diffusion weighted and dynamic contrast enhanced MRI. The reference standards for assessment were based on serum alpha-fetoprotein level and dynamic contrast enhanced MRI. Results: Multi-phasic CT had moderate sensitivity (87.5%) and high specificity (100%) for assessment of HCC LI-RADS treatment response algorithm. Dynamic contrast enhanced MRI had high sensitivity (100%) and specificity (100%) for the assessment of HCC LI-RADS treatment response algorithm. The sensitivity and specificity of DWI to diagnose HCC malignant residual or recurrence were 93.7% and 100%, respectively, with very good performance. The mean ADC values of the malignant residual or recurrence were significantly lower than that of benign post-ablation tissue changes areas and the best ADC cutoff value for differentiation between viable and nonviable lesions was 1.1x10-3 mm²/s. Conclusion: Performance of dynamic contrast enhanced MRI is better than that of multiphasic CT in the assessment of LIRADS treatment response algorithm. DWI and ADC maps could be used as ancillary methods for differentiation between viable and non-viable loco-regional ablated HCC lesions and should be included in LR-TR treatment response algorithm.

A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75).

Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/- 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and "intensity mean value" was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.

Spontaneously Ruptured Hepatocellular Carcinoma: Computed Tomography-Based Assessment

Spontaneously ruptured hepatocellular carcinoma (SRHCC) is an uncommon and life-threatening complication in patients with hepatocellular carcinoma (HCC). It is usually associated with chronic liver disease and has a poor prognosis with a high mortality rate during the acute phase. SRHCC can cause a severe and urgent condition of acute abdomen disease and requires a correct diagnosis to achieve adequate treatment. Clinical presentation is related to the presence of hemoperitoneum, and abdominal pain is the most common symptom (66-100% of cases). Although the treatment approach is not unique, trans-arterial (chemo)embolization (TAE/TACE) followed by staged hepatectomy has shown better results in long-term survival. A multi-phase contrast-enhanced CT (CECT) scan is a pivotal technique in the diagnosis of SRHCC due to its diagnostic accuracy and optimal temporal resolution. The correct interpretation of the main CT findings in SRHCC, such as active contrast extravasation and the sentinel clot sign, is fundamental for a prompt and correct diagnosis. Furthermore, CT also plays a role as a post-operative control procedure, especially in patients treated with TAE/TACE. Therefore, a multi-phase CECT scan should be the diagnostic tool of choice in SRHCC since it suggests an immediate need for treatment with a consequent improvement in prognosis.

Prospective Multicentric Assessment of 68Ga-DOTANOC PET/CT in Grade 1-2 GEP-NET.

The aim of this multicentric study was to prospectively compare 68Ga-DOTANOC PET/CT versus somatostatin receptor scintigraphy (SRS) with SPECT/CT, combined with multiphasic CT scan and MRI in patients with grade 1 or 2 gastroenteropancreatic neuroendocrine tumors (GEP-NET). Patients with histologically proven grade 1 or 2 GEP-NET with suspicion of recurrence or progression, or with typical aspects of GEP-NET on morphological imaging, were explored with conventional imaging (CI): SRS with SPECT/CT, multiphasic CT scan and/or liver MRI followed by 68Ga-DOTANOC PET/CT. The gold standard was based on histology and imaging follow-up. The data of 105 patients (45 woman and 60 men; median age) were analyzed. 68Ga-DOTANOC PET/CT sensitivity was significantly higher than CI sensitivity in per-patient (98.9% vs. 88.6%, p = 0.016) and per-region (97.6% vs. 75.6%, p &lt; 0.001) analyses, in the detection of the primary (97.9% vs. 78.7%; p = 0.016), peritoneal carcinomatosis (95% vs. 30%, p &lt; 0.001), and bone metastases (100% vs. 33.3%, p = 0.041). 68Ga-DOTANOC PET/CT had an impact on the therapeutic management of 41.9% (44/105) patients compared to decisions based on CI explorations. Our data confirm the superiority of 68Ga-DOTANOC PET/CT over CI in the detection of peritoneal carcinomatosis and bone metastasis, as well as its strong therapeutic impact on the management of patients with grade 1-2 GEP-NETs.

Gastric duplication cyst with peristalsis.

A 24-year-old male presented with an asymptomatic right perirenal cyst that was detected on ultrasound performed during a physical screening. Abdominal computed tomography showed a hypodense cystic mass between the liver and right kidney. Peristalsis of the cystic mass was observed via multi-phase CT scan, including plain scan, arterial phase, venous phase and delayed phase. The mass was completely resected by laparoscopy.

Preoperative evaluation of malignant pancreatobiliary obstruction: A novel technique for multiphase fusion 3D CT images

The purpose of this article is to describe a novel technique of multiphase fusion three-dimensional (3D) images in patients with malignant pancreatobiliary obstruction. Multiphase fusion 3D images of CT arteriography, portovenography and hepatic venography combined with negative-contrast CT cholangiopancreatography can be done with enhanced multiphase CT scan using intravenous contrast agent at once. This technique may be feasible for one-stop evaluation of malignant pancreatobiliary obstruction.

Radiomics-Based Prediction of Future Portal Vein Tumor Infiltration in Patients with HCC-A Proof-of-Concept Study.

Portal vein infiltration (PVI) is a typical complication of HCC. Once diagnosed, it leads to classification as BCLC C with an enormous impact on patient management, as systemic therapies are henceforth recommended. Our aim was to investigate whether radiomics analysis using imaging at initial diagnosis can predict the occurrence of PVI in the course of disease. Between 2008 and 2018, we retrospectively identified 44 patients with HCC and an in-house, multiphase CT scan at initial diagnosis who presented without CT-detectable PVI but developed it in the course of disease. Accounting for size and number of lesions, growth type, arterial enhancement pattern, Child-Pugh stage, AFP levels, and subsequent therapy, we matched 44 patients with HCC who did not develop PVI to those developing PVI in the course of disease (follow-up ended December 2021). After segmentation of the tumor at initial diagnosis and texture analysis, we used LASSO regression to find radiomics features suitable for PVI detection in this matched set. Using an 80:20 split between training and holdout validation dataset, 17 radiomics features remained in the fitted model. Applying the model to the holdout validation dataset, sensitivity to detect occurrence of PVI was 0.78 and specificity was 0.78. Radiomics feature extraction had the ability to detect aggressive HCC morphology likely to result in future PVI. An additional radiomics evaluation at initial diagnosis might be a useful tool to identify patients with HCC at risk for PVI during follow-up benefiting from a closer surveillance.

A Comparison of the Celiacomesenteric Trunk in the Caribbean with Global Prevalence Calculated by a Systematic Review.

Background Typically, the celiac trunk and superior mesenteric artery branch off separately from the anterior aspect of the abdominal aorta. The celiacomesenteric trunk (CMT) is a rare variant in which those arteries share a common origin. We sought to compare the prevalence of CMT in the Caribbean with the global prevalence as calculated by a systematic review. Methods In this study, we evaluated all consecutive patients who had multiphase contrast-enhanced CT scans at two major referral centres in the Caribbean from August 30, 2017, to September 1, 2019. In patients with a CMT, we recorded demographic and anatomic details. We then conducted a systematic literature search and retrieved raw data to calculate the global prevalence (number of individuals with a CMT divided by the sum total of study samples). We compared CMT prevalence in our sample with the global prevalence using Pearson's chi-square and Fisher's exact tests. Statistical significance was considered to be present when the P value was <0.05. Results From 832 CTs, 665 scans met the inclusion criteria. There were 16 (2.41%) CMTs: 3 (0.45%) classic CMTs, 12 (1.8%) hepato-mesenteric trunks, and 1 (0.15%) hepato-spleno-mesenteric trunk. Forty-two studies reported on CMTs in a total of 74,320 persons. The global CMT prevalence was comparable (3.88%; P = 0.054), but the incidence of hepato-mesenteric variants was significantly lower in our sample (1.8% vs. 3.24%; P = 0.0352). Conclusion There was no difference in the prevalence of a classic CMT in the Caribbean compared to the global prevalence. However, the hepato-mesenteric trunk (incomplete CMT variant) was significantly less prevalent in the Caribbean. Advances in Knowledge: Healthcare professionals performing hepatobiliary interventions must be aware of these differences in order to minimize morbidity during their interventions.