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.

Diagnostic performance of photon-counting detector CT for differentiation between adrenal adenomas and metastases.

OBJECTIVES: Aim of this study was to assess the value of virtual non-contrast (VNC) reconstructions in differentiating between adrenal adenomas and metastases on a photon-counting detector CT (PCD-CT). MATERIAL AND METHODS: Patients with adrenal masses and contrast-enhanced CT scans in portal venous phase were included. Image reconstructions were performed, including conventional VNC (VNCConv) and PureCalcium VNC (VNCPC), as well as virtual monochromatic images (VMI, 40-90 keV) and iodine maps. We analyzed images using semi-automatic segmentation of adrenal lesions and extracted quantitative data. Logistic regression models, non-parametric tests, Bland-Altman plots, and a random forest classifier were used for statistical analyses. RESULTS: The final study cohort consisted of 90 patients (36 female, mean age 67.8 years [range 39-87]) with adrenal lesions (45 adenomas, 45 metastases). Compared to metastases, adrenal adenomas showed significantly lower CT-values in VNCConv and VNCPC (p = 0.007). Mean difference between VNC and true non-contrast (TNC) was 17.67 for VNCConv and 14.85 for VNCPC. Random forest classifier and logistic regression models both identified VNCConv and VNCPC as the best discriminators. When using 26 HU as the threshold in VNCConv reconstructions, adenomas could be discriminated from metastases with a sensitivity of 86.7% and a specificity of 75.6%. CONCLUSION: VNC algorithms overestimate CT values compared to TNC in the assessment of adrenal lesions. However, they allow a reliable discrimination between adrenal adenomas and metastases and could be used in clinical routine in near future with an increased threshold (e.g., 26 HU). Further (multi-center) studies with larger patient cohorts and standardized protocols are required. CLINICAL RELEVANCE STATEMENT: VNC reconstructions overestimate CT values compared to TNC. Using a different threshold (e.g., 26 HU compared to the established 10 HU), VNC has a high diagnostic accuracy for the discrimination between adrenal adenomas and metastases. KEY POINTS: • Virtual non-contrast reconstructions may be promising tools to differentiate adrenal lesions and might save further diagnostic tests. • The conventional and a new calcium-preserving virtual non-contrast algorithm tend to systematically overestimate CT-values compared to true non-contrast images. • Therefore, increasing the established threshold for true non-contrast images (e.g., 10HU) may help to differentiate between adrenal adenomas and metastases on contrast-enhanced CT.

Optimal conspicuity of pancreatic ductal adenocarcinoma in virtual monochromatic imaging reconstructions on a photon-counting detector CT: comparison to conventional MDCT.

PURPOSE: To analyze the conspicuity of pancreatic ductal adenocarcinoma (PDAC) in virtual monoenergetic images (VMI) on a novel photon-counting detector CT (PCD-CT) in comparison to energy-integrating CT (EID-CT). METHODS: Inclusion criteria comprised initial diagnosis of PDAC (reference standard: histopathological analysis) and standardized contrast-enhanced CT imaging either on an EID-CT or a PCD-CT. Patients were excluded due to different histopathological diagnosis or missing tumor delineation on CT. On the PCD-CT, 40-190 keV VMI reconstructions were generated. Image noise, tumor-to-pancreas ratio (TPR) and contrast-to-noise ratio (CNR) were analyzed by ROI-based measurements in arterial and portal venous contrast phase. Two board-certified radiologist evaluated image quality and tumor delineation at both, EID-CT and PCD-CT (40 and 70 keV). RESULTS: Thirty-eight patients (mean age 70.4 years ± 10.3 [range 45-91], 27 males; PCD-CT: n=19, EID-CT: n=19) were retrospectively included. On the PCD-CT, tumor conspicuity (reflected by low TPR and high CNR) was significantly improved at low-energy VMI series (≤ 70 keV compared to > 70 keV), both in arterial and in portal venous contrast phase (P < 0.001), reaching the maximum at 40 keV. Comparison between PCD-CT and EID-CT showed significantly higher CNR on the PCD-CT in portal venous contrast phase at < 70 keV (P < 0.016). On the PCD-CT, tumor conspicuity was improved in portal venous contrast phase compared to arterial contrast phase especially at the lower end of the VMI spectrum (≤ 70 keV). Qualitative analysis revealed that tumor delineation is improved in 40 keV reconstructions compared to 70 keV reconstructions on a PCD-CT. CONCLUSION: PCD-CT VMI reconstructions (≤ 70 keV) showed significantly improved conspicuity of PDAC in quantitative and qualitative analysis in both, arterial and portal venous contrast phase, compared to EID-CT, which may be important for early detection of tumor tissue in clinical routine. Tumor delineation was superior in portal venous contrast phase compared to arterial contrast phase.

Medical Imaging Decision And Support (MIDAS): Study protocol for a multi-centre cluster randomized trial evaluating the ESR iGuide.

OBJECTIVES: Medical imaging plays an essential role in healthcare. As a diagnostic test, imaging is prone to substantial overuse and potential overdiagnosis, with dire consequences to patient outcomes and health care costs. Clinical decision support systems (CDSSs) were developed to guide referring physicians in making appropriate imaging decisions. This study will evaluate the effect of implementing a CDSS (ESR iGuide) with versus without active decision support in a physician order entry on the appropriate use of imaging tests and ordering behaviour. METHODS: A protocol for a multi-center cluster-randomized trial with departments acting as clusters, combined with a before-after-revert design. Four university hospitals with eight participating departments each for a total of thirty-two clusters will be included in the study. All departments start in control condition with structured data entry of the clinical indication and tracking of the imaging exams requested. Initially, the CDSS is implemented and all physicians remain blinded to appropriateness scores based on the ESR imaging referral guidelines. After randomization, half of the clusters switch to the active intervention of decision support. Physicians in the active condition are made aware of the categorization of their requests as appropriate, under certain conditions appropriate, or inappropriate, and appropriate exams are suggested. Physicians may change their requests in response to feedback. In the revert condition, active decision support is removed to study the educational effect. RESULTS/CONCLUSIONS: The main outcome is the proportion of inappropriate diagnostic imaging exams requested per cluster. Secondary outcomes are the absolute number of imaging exams, radiation from diagnostic imaging, and medical costs. TRIAL REGISTRATION NUMBER: Approval from the Medical Ethics Review Committee was obtained under protocol numbers 20-069 (Augsburg), B 238/21 (Kiel), 20-318 (Lübeck) and 2020-15,125 (Mainz). The trial is registered in the ClinicalTrials.gov register under registration number NCT05490290.

Anemia detection and quantification in contrast-enhanced CT scans on a novel photon-counting detector CT: A prospective proof-of-concept study.

PURPOSE: To correlate CT values of the blood pool on VNC series with serum hemoglobin values for the detection of anemia in oncologic patients undergoing contrast-enhanced PCD-CT scans. METHODS: This prospective study (NCT04989192) included consecutive oncologic patients undergoing contrast-enhanced CT on a novel PCD-CT system between 08/2021 and 01/2022. The interval between complete blood count (CBC) and CT scan acquisition had to be no more than seven days. CT-values of the blood pool were measured on 70 keV VMI series (CT-values(BP)70keV) and on VNC series (CT-values(BP)VNC) at five anatomic positions (left atrium, left ventricle, main pulmonary artery, ascending and descending aorta) and averaged per patient. Pearson correlation analyses and ROC analyses were performed to identify relations between CT-values(BP)VNC, CBC parameters, and degrees of anemia as defined by the WHO (no anemia, mild, moderate, severe anemia). RESULTS: A total of 329 patients (age 68 ± 12 years; 200 men) were included. CT-values(BP)VNC showed a strong linear correlation to serum hemoglobin (r2 = 0.80, p <.001) and hematocrit (r2 = 0.76, p <.001) and were significantly different between anemia subgroups in both women and men (ΔHU: 3.5-11.4; all p <.01). ROC analyses yielded high diagnostic performance for the identification of patients without anemia, patients without and with mild anemia, and patients with severe anemia using gender-specific cutoffs for CT-value(BP)VNC (all AUC's > 0.90). CONCLUSIONS: The spectral information inherent in PCD-CT acquisitions allows the detection and quantification of anemia in contrast-enhanced CT acquisitions of oncologic patients with high diagnostic accuracy.

Artifact Reduction From Dental Material in Photon-Counting Detector Computed Tomography Data Sets Based on High-keV Monoenergetic Imaging and Iterative Metal Artifact Reduction Reconstructions-Can We Combine the Best of Two Worlds?

OBJECTIVE: The aim of this study was to compare the effectiveness of common strategies for artifact reduction of dental material in photon-counting detector computed tomography data sets. MATERIALS AND METHODS: Patients with dental material who underwent clinically indicated CT of the neck were enrolled. Image series were reconstructed using a standard and sharp kernel, with and without iterative metal artifact reduction (IMAR) (Qr40, Qr40 IMAR , Qr60, Qr60 IMAR ) at different virtual monoenergetic imaging (VMI) levels (40-190 keV). On representative slice positions with and without dental artifacts, mean and standard deviation of CT values were measured in all series at identical locations. The mean absolute error of CT values ( ) and the artifact index (AIX) were calculated and analyzed focusing on 3 main comparisons: ( a ) different VMI levels versus 70 keV, ( b ) standard versus sharp kernel, and ( c ) nonuse or use of IMAR reconstruction. The Wilcoxon test was used to assess differences for nonparametric data. RESULTS: The final cohort comprised 50 patients. Artifact measures decreased for VMI levels >70 keV, yet only significantly so for reconstructions using IMAR (maximum reduction, 25%). The higher image noise of the sharp versus standard kernel is reflected in higher AIX values and is more pronounced in IMAR series (maximum increase, 38%). The most profound artifact reduction was observed for IMAR reconstructions (maximum reduction : 84%; AIX: 90%). CONCLUSIONS: Metal artifacts caused by large amounts of dental material can be substantially reduced by IMAR, regardless of kernel choice or VMI settings. Increasing the keV level of VMI series, on the other hand, only slightly reduces dental artifacts; this effect, however, is additive to the benefit conferred by IMAR reconstructions.

Anemia Detection by Hemoglobin Quantification on Contrast-enhanced Photon-counting CT Data Sets.

See also the editorial by Dodd and MacDermott in this issue.

Virtual Non-Contrast Reconstructions of Photon-Counting Detector CT Angiography Datasets as Substitutes for True Non-Contrast Acquisitions in Patients after EVAR-Performance of a Novel Calcium-Preserving Reconstruction Algorithm.

The purpose of this study was to evaluate virtual-non contrast reconstructions of Photon-Counting Detector (PCD) CT-angiography datasets using a novel calcium-preserving algorithm (VNCPC) vs. the standard algorithm (VNCConv) for their potential to replace unenhanced acquisitions (TNC) in patients after endovascular aneurysm repair (EVAR). 20 EVAR patients who had undergone CTA (unenhanced and arterial phase) on a novel PCD-CT were included. VNCConv- and VNCPC-series were derived from CTA-datasets and intraluminal signal and noise compared. Three readers evaluated image quality, contrast removal, and removal of calcifications/stent parts and assessed all VNC-series for their suitability to replace TNC-series. Image noise was higher in VNC- than in TNC-series (18.6 ± 5.3 HU, 16.7 ± 7.1 HU, and 14.9 ± 7.1 HU for VNCConv-, VNCPC-, and TNC-series, p = 0.006). Subjective image quality was substantially higher in VNCPC- than VNCConv-series (4.2 ± 0.9 vs. 2.5 ± 0.6; p < 0.001). Aortic contrast removal was complete in all VNC-series. Unlike in VNCConv-reconstructions, only minuscule parts of stents or calcifications were erroneously subtracted in VNCPC-reconstructions. Readers considered 95% of VNCPC-series fully or mostly suited to replace TNC-series; for VNCConv-reconstructions, however, only 75% were considered mostly (and none fully) suited for TNC-replacement. VNCPC-reconstructions of PCD-CT-angiography datasets have excellent image quality with complete contrast removal and only minimal erroneous subtractions of stent parts/calcifications. They could replace TNC-series in almost all cases.

Low-dose CT of the abdomen: Initial experience on a novel photon-counting detector CT and comparison with energy-integrating detector CT.

PURPOSE: To analyze the quantitative and qualitative image quality of low-dose CT scans of the abdomen on a novel photon-counting detector CT (PCD-CT) in comparison with a traditional energy-integrating detector CT (EID-CT). METHODS: Consecutive patients with clinically indicated low-dose CT were scanned on a PCD-CT and compared with a BMI-matched EID-CT-cohort scanned during the same timeframe. Radiation dose, image noise, and signal-to-noise ratio (SNR) were measured for each patient. Furthermore, image quality and conspicuity of abdominal structures (adrenal glands, mesenteric vessels, ureters, and renal pelvis) were assessed on 5-point Likert-scales (1 = very poor quality/not detectable; 5 = excellent quality/differentiability). RESULTS: Twenty patients (mean age 46.2 [range: 19-77]; 13 men) were included. Image noise was significantly lower (24.9 ± 3.3 vs. 31.4 ± 5.6 SD HU, p < 0.001) and SNR significantly higher (2.1 ± 0.3 vs. 1.5 ± 0.4; p < 0.001) on the PCD-CT. Subjective image quality was substantially higher (4.0 ± 0.3 vs. 3.1 ± 0.6; p < 0.001) and conspicuity better for the renal pelvis, ureters, and mesenteric vessels on the PCD-CT. There was no significant difference in the conspicuity of the adrenal glands. With increasing BMI (1st-4th BMI quartile), noise increased, and SNR decreased more strongly on the EID-CT than on the PCD-CT (ΔNoise: 39% vs. 2%, ΔSNR: -33% vs. -7% for EID-CT vs. PCD-CT, respectively) while radiation dose increased comparably (70 vs. 59%). CONCLUSIONS: Low-dose CT scans of the abdomen performed on a novel PCD-CT exhibit reduced noise, higher SNR, increased subjective image quality, and superior conspicuity of abdominal fine structures compared to scans in comparable patients on an EID-CT.