Contrast-enhanced Abdominal CT Research Articles

Diffuse Intense 18F-PSMA-1007 Uptake of the Breasts in a Woman With TFE3-Rearranged Renal Cell Carcinoma.

A 23-year-old woman presenting with gross hematuria was found to have a left renal mass suspicious for renal cell carcinoma on abdominal contrast-enhanced CT. An 18F-PSMA-1007 PET/CT scan was performed for evaluating the renal mass. 18F-PSMA-1007 PET/CT showed focal activity of the renal mass, which was a transcription factor E3-rearranged renal cell carcinoma proved after nephrectomy. Surprisingly, diffuse heterogeneous intense activity of the bilateral breasts and moderate activity of the right accessory breast was observed. There was no morphological abnormality of the bilateral breasts and right accessory breast on CT images, indicating physiological PSMA uptake.

Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial.

To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT. This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n = 74) or 80-kVp protocol (360 mgL/kg, n = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0.5 mm thickness. Size-specific dose estimate (SSDE) and iodine dose were compared between protocols. Image noise, CT attenuation, and contrast-to-noise ratio (CNR) were quantified. Noise power spectrum (NPS) and edge rise slope (ERS) were used to evaluate noise texture and edge sharpness, respectively. The subjective image quality was rated on a 4-point scale. SSDE and iodine doses of 80-kVp were 40.4% (8.1 ± 0.9 vs. 13.6 ± 2.7 mGy) and 36.3% (21.2 ± 3.9 vs. 33.3 ± 4.3 gL) lower, respectively, than those of 120-kVp (both, p < 0.001). CT attenuation of vessels and solid organs was higher in 80-kVp than in 120-kVp images (all, p < 0.001). Image noise of 80-kVp-HIR and 80-kVp-DLR was higher and lower, respectively than that of 120-kVp-HIR (both p < 0.001). The highest CNR and subjective scores were attained in 80-kVp-DLR (all, p < 0.001). There were no significant differences in average NPS frequency and ERS between 120-kVp-HIR and 80-kVp-DLR (p ≥ 0.38). Compared with the 120-kVp-HIR protocol, the combined use of 80-kVp and DLR techniques yielded superior subjective and objective image quality with reduced radiation and ICM doses at thin-section abdominal CT. Scanning at low-tube voltage (80-kVp) combined with the deep-learning reconstruction algorithm may enhance diagnostic efficiency and patient safety by improving image quality and reducing radiation and contrast doses of thin-slice abdominal CT. Reducing radiation and iodine doses is desirable; however, contrast and noise degradation can be detrimental. The 80-kVp scan with the deep-learning reconstruction technique provided better images with lower radiation and contrast doses. This technique may be efficient for improving diagnostic confidence and patient safety in thin-slice abdominal CT.

Evaluating aortomesenteric parameters in a tertiary center of Nepal for superior mesenteric artery syndrome diagnosis and risk factors: cross-sectional study.

This study examines aortomesenteric angle (AMA) and distance (AMD), which are critical in superior mesenteric artery (SMA) syndrome. Addressing the scarcity of SMA cases, the research explores potential links with lower BMI and aims to establish normative data for diagnostic and predictive purposes, using contrast-enhanced computed tomography (CT) scans across various BMI and sex categories. A retrospective quantitative cross-sectional study was conducted on 189 patients undergoing abdominal contrast-enhanced CT scans between December 2019 and December 2020. Ethical clearance was obtained, and participants provided informed consent. Exclusion criteria targeted specific medical histories. Patient demographics, BMI categories, and imaging data were recorded. Helical 128-slice CT scans were employed, with sagittal-oblique multiplanar reconstructions for parameter assessments. Statistical analysis utilized SPSS 26.0, including Pearson correlation coefficients and mean calculations. The study reveals a mean AMA of 54.07°±8.53° and a mean distance of 16.25±3.44 mm. Elevated BMI is found to positively correlate with AMA and distance, indicating that higher BMI values may augment these parameters, with an additional positive correlation observed between AMA and distance. No significant correlations are found with patient age or sex. The study concludes that decreased BMI may pose a potential risk for SMA syndrome, as evidenced by the observed correlations with aortomesenteric parameters. Understanding these normal values in the Nepalese population is critical for accurate diagnoses and predictions using CT scans. The research highlights the impact of demographic factors on these parameters and emphasizes their significance in clinical assessments related to SMA syndrome.

Amoebic liver abscess in an adult Nigerian: A case report

Background: Amoebic liver abscess (ALA) is a parasitic infection caused by Entamoeba histolytica. ALA is a significant health concern, particularly in regions with poor sanitation and hygiene practices. Radiological imaging plays a crucial role in the diagnosis and management of ALA. Methods: We present a case report of a 40-year-old male patient with ALA. The patient presented to a radio diagnostic centre in Umuahia with right upper quadrant abdominal pain, fever,diarrhea and weight loss. Radiological evaluation was performed using abdominal ultrasound and contrast-enhanced CT scan. The imaging findings were correlated with clinical presentation and laboratory investigations. Results: The radiological findings revealed a focal hypoechoic lesion in the right hepatic lobe with ill-defined margins and internal echoes suggestive of liquefaction. The contrast-enhanced CT scan confirmed the presence of a well-defined, hypodense lesion with peripheral enhancement and central hypoattenuation. No evidence of biliary ductal dilatation or vascular invasion was observed. Based on the findings, a diagnosis of ALA was made, and the patient was initiated on intravenous metronidazole therapy. Follow-up imaging demonstrated a decrease in abscess size and resolution of internal echoes, indicating a positive response to treatment. Conclusion: Radiological imaging, including ultrasound and CT scan, plays a pivotal role in the diagnosis and management of ALA. The characteristic imaging findings, such as hypoechoic lesions with internal echoes and peripheral enhancement with central hypoattenuation, aid in differentiating ALA from other hepatic lesions. Early diagnosis and prompt initiation of appropriate therapy are crucial for favorable patient outcomes. Radiological follow-up allows monitoring of treatment response and guides further management decisions.

Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.

Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

조영증강 복부 CT 검사에서 조영제 사용량 감소에 관한 연구

In our hospital, we implement a reduction in contrast media dose (calculated as body weight × 1.2 mL) for clinical examinations conducted with dual-source dual-energy CT during contrast-enhanced abdominal CT examinations. Therefore, by applying the CE-Boost technique, the degree of contrast enhancement of blood vessels and tissues was compared with standard contrast media dose (body weight × 1.6 mL) in 120 kV single-source computed tomography images. In contrast-enhanced abdominal CT using the standard contrast media dose, the CE-Boost technique was applied to investigate the feasibility of reducing the contrast media dose. From January 2023, a retrospective study was conducted on 62 subjects. The study included an experimental group (A) that received a reduced contrast media dose, an experimental group + CE-Boost (B), and a control group (C) that received the standard contrast media dose. For quantitative evaluation, in the image where the Main Portal Vein (MPV) is the widest, CT number (HU), Signal-to-Noise Ratio (SNR), and Contrast-to-Noise Ratio (CNR) were calculated. Qualitative evaluation was independently assessed by five experienced radiotechnologists, who evaluated 10 samples as the assessment items for contrast enhancement and overall image quality. In the HU values, the (B) group with CE-Boost technique showed the highest value (P &lt; .05), while the (A)-(C) group without CE-Boost technique showed no statistically significant difference. In the SNR, the group (B) that received the CE-Boost technique exhibited the highest value,(P&lt;.05) but there was no significant difference compared to the group (C) that did not receive the CE-Boost technique in the evaluation of liver parenchyma.(P&gt;.05) In CNR, the (B) group utilizing the CE-Boost technique showed the highest value (P&lt;.05), but there was no statistically significant difference between (B) and (C). In the qualitative evaluation, the group (B) to which CE-Boost was applied was rated the highest in two evaluation items. If an AI-based CE-Boost technique is used for contrast-enhanced abdominal CT, it is believed that safe medical services can be provided to patients by implementing a 25% reduction in the standard contrast media dose while still maintaining image quality, even in a single-source 120kV CT.

Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography.

In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.

Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening radiomics in bowel wall thickening.

To distinguish malignant and benign bowel wall thickening (BWT) by using computed tomography (CT) texture features based on machine learning (ML) models and to compare its success with the clinical model and combined model. One hundred twenty-two patients with BWT identified on contrast-enhanced abdominal CT and underwent colonoscopy were included in this retrospective study. Texture features were extracted from CT images using LifeX software. Feature selection and reduction were performed using the Least Absolute Shrinkage and Selection Operator (LASSO). Six radiomic features were selected with LASSO. In the clinical model, six features (age, gender, thickness, fat stranding, symmetry, and lymph node) were included. Six radiomic and six clinical features were used in the combined model. Classification was done using two machine learning algorithms: Support Vector Machine (SVM) and Logistic Regression (LR). The data sets were divided into 80% training set and 20% test set. Then, training took place with all three datasets. The model's success was tested with the test set consisting of features not used during training. In the training set, the combined model had the best performance with the area under the curve (AUC) value of 0.99 for SVM and 0.95 for LR. In the radiomic-derived model, the AUC value is 0.87 in SVM and 0.79 in LR. In the clinical model, SVM made this distinction with 0.95 AUC and LR with 0.92 AUC value. In the test set, the classifier with the highest success distinguishing malignant wall thickening is SVM in the radiomic-derived model with an AUC value of 0.90. In other models, the AUC value is in the range of 0.75-0.86, and the accuracy values are in the range of 0.72-0.84. In conclusion, radiomic-based machine learning has shown high success in distinguishing malignant and benign BWT and may improve diagnostic accuracy compared to clinical features only. The results of our study may help ensure early diagnosis and treatment of colorectal cancers by facilitating the recognition of malignant BWT.

Pancreatic cyst prevalence and detection with photon counting CT compared with conventional energy integrating detector CT

PurposeTo calculate the prevalence of pancreatic cysts on photon counting CT (PCCT) and compare with that of 128-slice conventional energy-integrating detector CT (EIDCT). MethodA retrospective single institution database search identified all contrast-enhanced abdominal CT examinations performed at an outpatient facility that has both a PCCT and EIDCT between 4/11/2022 and 7/26/2022. The presence and size of pancreatic cysts were recorded. In patients with PCCT reported pancreatic cysts, prior CT imaging (EIDCT) was reviewed for reported pancreatic cysts. Fisher’s exact test was used to compare the pancreatic cyst detection rate for PCCT and EIDCT. Wilcoxon rank sum test was used to compare cyst size and patient age. A p <.05 indicated statistical significance. Results2494 patients were included. Our pancreatic cyst detection rate was 4.9 % (49/1009) with PCCT and 3.0 % (44/1485) for EIDCT (p =.017). For CT angiograms, pancreatic cysts were detected in 6.6 % (21/319) with PCCT and 0.0 % (0/141) with EIDCT (p <.001). Pancreatic cyst detection rate was not statistically different for portal venous, enterography, renal mass, pancreas, 3-phase liver, or venogram protocols (all p >.05). Mean[SD] pancreatic cyst size was 13.7[9.7]mm for PCCT and 15.3[14.7] for EIDCT (p =.95). 55.1 % (27/49) of PCCT and 61.4 % (27/44) of EIDCT that described pancreatic cysts had prior contrast-enhanced EIDCTs. Of these, 40.7 % (11/27) of PCCT and 14.8 % (4/27) of EIDCT described pancreatic cysts were not previously reported (p =.027). ConclusionsPhoton-counting CT afforded greater pancreatic cyst detection than conventional energy-integrating detector CT, particularly with CT angiograms.

Anatomical attention can help to segment the dilated pancreatic duct in abdominal CT

PurposePancreatic duct dilation is associated with an increased risk of pancreatic cancer, the most lethal malignancy with the lowest 5-year relative survival rate. Automatic segmentation of the dilated pancreatic duct from contrast-enhanced CT scans would facilitate early diagnosis. However, pancreatic duct segmentation poses challenges due to its small anatomical structure and poor contrast in abdominal CT. In this work, we investigate an anatomical attention strategy to address this issue.MethodsOur proposed anatomical attention strategy consists of two steps: pancreas localization and pancreatic duct segmentation. The coarse pancreatic mask segmentation is used to guide the fully convolutional networks (FCNs) to concentrate on the pancreas’ anatomy and disregard unnecessary features. We further apply a multi-scale aggregation scheme to leverage the information from different scales. Moreover, we integrate the tubular structure enhancement as an additional input channel of FCN.ResultsWe performed extensive experiments on 30 cases of contrast-enhanced abdominal CT volumes. To evaluate the pancreatic duct segmentation performance, we employed four measurements, including the Dice similarity coefficient (DSC), sensitivity, normalized surface distance, and 95 percentile Hausdorff distance. The average DSC achieves 55.7%, surpassing other pancreatic duct segmentation methods on single-phase CT scans only.ConclusionsWe proposed an anatomical attention-based strategy for the dilated pancreatic duct segmentation. Our proposed strategy significantly outperforms earlier approaches. The attention mechanism helps to focus on the pancreas region, while the enhancement of the tubular structure enables FCNs to capture the vessel-like structure. The proposed technique might be applied to other tube-like structure segmentation tasks within targeted anatomies.

Diagnostic accuracy of abdominal contrast-enhanced multi-slice spiral CT after oral diluted iodide in a time segment for gastrointestinal fistula in patients with severe acute pancreatitis.

To evaluate the diagnostic accuracy of abdominal contrast-enhanced multi-slice spiral CT after oral diluted iodide in a time segment (post-ODI ACE-MSCT) for gastrointestinal fistula (GIF) in severe acute pancreatitis (SAP). Patients with SAP who underwent both post-ODI ACE-MSCT and endoscopy/surgery from 2017 to 2023 were continuously retrospectively involved. Their demographic information and clinical features were recorded prospectively in an in-hospital database. Using endoscopy/surgery results as the reference standard, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of post-ODI ACE-MSCT for diagnosing GIF in SAP were calculated by a four-cell table. The consistency of the two diagnostic methods was evaluated by the Kappa test and McNemar's test. Using endoscopy/surgery as the reference standard, a total of 86 cases were divided into the GIF group (N = 52) and the non-GIF group (N = 34). Among the 52 cases of GIF, 88.5% (46/52) cases had a positive result and 11.5% (5/52) cases had a negative result of post-ODI ACE-MSCT for GIF. Among the 34 cases of non-GIF, 2.9% (1/34) case had a positive result and 97.1% (33/34) cases had a negative result of post-ODI ACE-MSCT for GIF. Post-ODI ACE-MSCT had a sensitivity of 88.5% (95% CI 75.9%-95.2%), a specificity of 97.1% (95% CI 82.9%-99.8%), a positive predictive value of 97.9% (95% CI 87.3%-99.9%), a negative predictive value of 84.6% (95% CI 68.8%-93.6%), and an accuracy of 91.9% (83.4%-96.4%). The kappa value was 0.834, and P < 0.001 by McNemar's test. There were no significant differences in diagnostic test characteristics between the two modalities. Post-ODI ACE-MSCT can diagnose GIF in SAP in a simple, noninvasive, and accurate way, and can provide earlier imaging evidence for clinical diagnosis and treatment.

Computed tomography of a canine omental torsion secondary to a chronic abdominal gossypiboma and concurrent incidental broad ligament torsion.

A 5-year-old female spayed Dogue de Bordeaux was referred for concerns of an abdominal mass and peritoneal effusion. Abdominal radiographs identified a mid-ventral abdominal soft tissue opaque mass containing a radiopaque marker consistent with a gossypiboma. Contrast-enhanced abdominal CT identified two whirl signs associated with the abdominal gossypiboma. Exploratory laparotomy confirmed an omental torsion with encapsulated gossypiboma and concurrent incidental torsion of the remnant of the right broad ligament. Based on a literature review, omental torsions are an unreported complication of gossypibomas in canids.

Sigmoid Lipoma as an Exceptional Cause of Intussusception and Bowel Obstruction in Adults: A Case Report and Review of Literature

This case report describes a 60-year-old man who presented with acute abdominal pain with marked abdominal distention and red rectal bleeding. A contrast-enhanced abdominal CT scan was performed, which revealed a recto-sigmoid intussusception on lipoma, causing mechanical intestinal obstruction. The patient underwent a partial reduction of the intussusception with partial sigmoid resection and end colostomy. And histologic evaluation confirmed the diagnosis of colonic lipoma. Intussusception of the colon is uncommon in adults and is often associated with malignancy, but other nonmalignant causes such as lipoma may also present with obstructive symptoms, abdominal pain, and rectal bleeding. However, it should be a differential diagnosis of such situations.

A deep learning approach to remove contrast from contrast-enhanced CT for proton dose calculation.

Non-Contrast Enhanced CT (NCECT) is normally required for proton dose calculation while Contrast Enhanced CT (CECT) is often scanned for tumor and organ delineation. Possible tissue motion between these two CTs raises dosimetry uncertainties, especially for moving tumors in the thorax and abdomen. Here we report a deep-learning approach to generate NCECT directly from CECT. This method could be useful to avoid the NCECT scan, reduce CT simulation time and imaging dose, and decrease the uncertainties caused by tissue motion between otherwise two different CT scans. A deep network was developed to convert CECT to NCECT. The network receives a 3D image from CECT images as input and generates a corresponding contrast-removed NCECT image patch. Abdominal CECT and NCECT image pairs of 20 patients were deformably registered and 8000 image patch pairs extracted from the registered image pairs were utilized to train and test the model. CTs of clinical proton patients and their treatment plans were employed to evaluate the dosimetric impact of using the generated NCECT for proton dose calculation. Our approach achieved a Cosine Similarity score of 0.988 and an MSE value of 0.002. A quantitative comparison of clinical proton dose plans computed on the CECT and the generated NCECT for five proton patients revealed significant dose differences at the distal of beam paths. V100% of PTV and GTV changed by 3.5% and 5.5%, respectively. The mean HU difference for all five patients between the generated and the scanned NCECTs was ∼4.72, whereas the difference between CECT and the scanned NCECT was ∼64.52, indicating a ∼93% reduction in mean HU difference. A deep learning approach was developed to generate NCECTs from CECTs. This approach could be useful for the proton dose calculation to reduce uncertainties caused by tissue motion between CECT and NCECT.

An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction.

Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically. The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V). We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated. High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant. The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

Diagnostic yield of contrast-enhanced abdominal staging CT in patients with initially diagnosed breast cancer

PurposeTo estimate the diagnostic yield (DY) of abdominal staging CT for detecting breast cancer liver metastasis (BCLM) in patients with initially diagnosed breast cancer and to determine the indications for abdominal staging CT. MethodsPatients with newly diagnosed breast cancer who underwent abdominal CT as an initial staging work-up between January 2019 and December 2020 were retrospectively analyzed. DY was calculated and analyzed according to patient age, type of treatments, histologic type, histologic grade, lymphovascular invasion, Ki-67 status, hormone receptor status, subtype, and the American Joint Committee on Cancer anatomical staging. ResultsA total of 2056 patients (mean age, 51 ± 11 years) were included. The DY of abdominal staging CT for detecting BCLM was 1.1 % (22 of 2056). DY was significantly higher in stage III than in stage I or II cancers (3.9 % [18 of 467] vs. 0 % [0 of 412] or 0.4 % [4 of 1158], respectively, p < .001), and in human epidermal growth factor receptor-2 (HER2)-enriched cancers than in luminal or triple negative cancers (2.9 % [16 of 560] vs. 0.4 % [4 of 1090] or 0.5 % [2 of 406], respectively, p < .001). ConclusionsThe DY of abdominal staging CT for detecting BCLM was low among all patients with initially diagnosed breast cancer. However, although abdominal staging CT for detecting BCLM is probably unnecessary in all patients, it can be clinically useful in patients with stage III or human epidermal growth factor receptor-2-enriched breast cancers.

Reduced versus standard dose contrast volume for contrast-enhanced abdominal CT in overweight and obese patients using photon counting detector technology vs. second-generation dual-source energy integrating detector CT

PurposeTo compare image quality of contrast-enhanced abdominal-CT using 1st-generation Dual Source Photon-Counting Detector CT (DS-PCD-CT) versus 2nd-generation Dual-Source Energy Integrating-Detector CT (DS-EID-CT) in patients with BMI ≥ 25, applying two different contrast agent volumes, vendor proposed protocols and different virtual monoenergetic images (VMI). Method68 overweight (BMI ≥ 25 kgm2) patients (median age: 65 years; median BMI 33.3 kgm2) who underwent clinically indicated, portal-venous contrast-enhanced abdominal-CT on a commercially available 1st-generation DS-PCD-CT were prospectively included if they already have had a pre-exam on 2nd-generation DS-EID-CT using a standardized exam protocol. Obesity were defined by BMI-calculation (overweight: 25–29.9, obesity grade I: 30–34.9; obesity grade II: 35–39.9; obesity grade III: > 40) and by the absolute weight value. Body weight adapted contrast volume (targeted volume of 1.2 mL/kg for the 1st study and 0.8 mL/kg for the 2nd study) was applied in both groups. Dual Energy mode was used for both the DS-PCD-CT and the DS-EID-CT. Polychromatic images and VMI (40 keV and 70 keV) were reconstructed for both the DS-EID-CT and the DS-PCD-CT data (termed T3D). Two radiologists assessed subjective image quality using a 5-point Likert-scale. Each reader drew ROIs within parenchymatous organs and vascular structures to analyze image noise, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). ResultsMedian time interval between scans was 12 months (Min: 6 months; Max: 36 months). BMI classification included overweight (n = 10, 14.7 %), obesity grade I (n = 38, 55.9 %), grade II (n = 13, 19.1 %) and grade III (n = 7, 10.3 %). The SNR achieved with DS-PCD-CT at QIR level 3was 12.61 vs. 11.47 (QIR 2) vs. 10.53 (DS-EID-CT), irrespective of parenchymatous organs. For vessels, the SNR were 16.73 vs. 14.20 (QIR 2) vs. 12.07 (DS-EID-CT). Moreover, the obtained median noise at QIR level 3 was as low as that of the DS-EID-CT (8.65 vs. 8.65). Both radiologists rated the image quality higher for DS-PCD-CT data sets (p < 0.05). The highest CNR was achieved at 40 keV for both scanners. T3D demonstrated significantly higher SNR and lower noise level compared to 40 keV and 70 keV. Median CTDIvol and DLP values for DS-PCD-CT and DS-EID-CT were 10.90 mGy (IQR: 9.31 – 12.50 mGy) vs. 16.55 mGy (IQR: 15.45 – 18.17 mGy) and 589.50 mGy * cm (IQR: 498.50 – 708.25 mGy * cm) vs. 848.75 mGy * cm (IQR: 753.43 – 969.58 mGy * cm) (p < 0.001). ConclusionImage quality can be maintained while significantly reducing the contrast volume and the radiation dose (27% and 34% lower DLP and 31% lower CDTIvol) for abdominal contrast-enhanced CT using a 1st-generation DS-PCD-CT. Moreover, polychromatic reconstruction T3D on a DS-PCD-CT enables sufficient diagnostic image quality for oncological imaging.

A direct comparison of the diagnostic efficacy of alternative scaffoldbased radiopharmaceuticals [&lt;sup&gt;99m&lt;/sup&gt;Tc]Tc-ADAPT6 and [&lt;sup&gt;99m&lt;/sup&gt;Tc]Tc-(HE)&lt;sub&gt;3&lt;/sub&gt;-G3 in patients with HER2-positive breast cancer

Aim. To perform a direct comparison of the diagnostic efficacy of [99mTc]Tc-ADAPT6 and [99mTc]Tc-(HE) -G3 in HER2-positive breast cancer patients before the systemic treatment.Materials and methods. The study included 11 patients with HER2-positive breast cancer (T1–4N0–2M0–1) before the initiation of systemic treatment. All patients underwent a radionuclide examination with [99mTc]TcADAPT6 and [99mTc]Tc-(HE) -G3 with the interval of 3–4 days. Single-photon emission computed tomography (SPECT) /computed tomography (CT) was performed 2 and 4 hours after [99mTc]Tc-ADAPT6 and [99mTc]Tc(HE)3-G3 administration, respectively.Results. The analysis of [99mTc]Tc-ADAPT6 and [99mTc]Tc-(HE) -G3 distribution showed their high uptake in the kidneys and liver. Breast tumors were visualized in all cases. The average tumor uptake of [99mTc]Tc-ADAPT6 was 4.7 ± 2.1, which was significantly higher than in the [99mTc]Tc-(HE)3-G3 injection (3.5 ± 1.7) (p &lt; 0.005, paired t-test). The tumor-to-background ratio (15.2 ± 7.4 and 19.6 ± 12.4, respectively) had no statistical differences in both cases (p &gt; 0.05, paired t-test). Liver metastases were visualized in patients 1 and 5 and corresponded to the projection of metastases according to contrast-enhanced abdominal CT. The accumulation of [99mTc]Tc-ADAPT6 and [99mTc]Tc-(HE)3-G3 in the projection of metastases in both cases was significantly higher compared to the primary tumor (1.3 and 1.7 times higher in patient 1; 2.2 and 3.5 times higher in patient 5, respectively).Conclusion. Both [99mTc]Tc-ADAPT6 and [99mTc]Tc-(HE)3-G3 demonstrated the diagnostic efficacy in visualizing a primary HER2-positive tumor in breast cancer patients. However, [99mTc]Tc-ADEPT6 had higher accumulation values, which makes it a more promising diagnostic agent.

Vascular and Urinary Tract Anatomic Variants Relevant to Para-Aortic Lymphadenectomy in Women with Gynecological Cancers.

Para-aortic lymphadenectomy is an essential part of gynecologic oncologic surgical treatment. The surgeon should be aware of the complex usual anatomy and its common variants. Between January 2021 and May 2023, 58 women underwent para-aortic lymphadenectomy for gynecologic malignancies. Vascular and urinary tract anatomic variants were retrospectively reviewed from the prospective institutional database and results were compared with preoperative contrast-enhanced abdominal CT. Of these 58 women, 47 women had no vascular or urinary tract variants. One woman had a double inferior vena cava, two patients were found to have a retro-aortic left renal vein, four had accessory renal arteries, two had a double left ureter, one had a ptotic kidney in the iliac fossa, and one patient had bilateral kidney malrotation. Anatomic variants in the preoperative CT were described by a radiologist in only two patients, and additional vascular and urinary tract variants were found incidentally at the time of surgery. Acknowledgment of vascular and urinary tract variants is helpful for the surgeon to establish an appropriate surgical plan and to avoid iatrogenic surgical trauma.

CT-based deep learning model: a novel approach to the preoperative staging in patients with peritoneal metastasis.

Peritoneal metastasis (PM) is a frequent manifestation of advanced abdominal malignancies. Accurately assessing the extent of PM before surgery is essential for patients to receive optimal treatment. Therefore, we propose to construct a deep learning (DL) model based on enhanced computed tomography (CT) images to stage PM preoperatively in patients. All 168 patients with PM underwent contrast-enhanced abdominal CT before either open surgery or laparoscopic exploration, and peritoneal cancer index (PCI) was used to evaluate patients during the surgical procedure. DL features were extracted from portal venous-phase abdominal CT scans and subjected to feature selection using the Spearman correlation coefficient and LASSO. The performance of models for preoperative staging was assessed in the validation cohort and compared against models based on clinical and radiomics (Rad) signature. The DenseNet121-SVM model demonstrated strong patient discrimination in both the training and validation cohorts, achieving AUC was 0.996 in training and 0.951 validation cohort, which were both higher than those of the Clinic model and Rad model. Decision curve analysis (DCA) showed that patients could potentially benefit more from treatment using the DL-SVM model, and calibration curves demonstrated good agreement with actual outcomes. The DL model based on portal venous-phase abdominal CT accurately predicts the extent of PM in patients before surgery, which can help maximize the benefits of treatment and optimize the patient's treatment plan.