Coronary plaque risk classification in images acquired with photon-counting-detector (PCD) CT was performed using a radiomics-based machine learning (ML) model. With IRB approval, 17 coronary CTA patients were scanned on a PCD-CT (NAEOTOM Alpha, Siemens Healthineers) with median CTDIvol of 4.56 mGy. Four types of images: 120-kV PCD-CT image, virtual monoenergetic images (VMIs) at 50-keV and 100-keV, and iodine maps were reconstructed using an iterative reconstruction algorithm, a vascular kernel (Bv40) and 0.6-mm/0.4-mm slice thickness/increment. Atherosclerotic plaques were segmented using semi-automatic software (Research Frontier, Siemens). Segmentation confirmation and risk stratification (low- vs high-risk) were performed by a board-certified cardiac radiologist. A total of 1674 radiomic features were extracted from each image using PyRadiomics (v2.2.0b1). For each feature, a t-test was performed between low- and high-risk plaques (p<0.05 considered significant). Feature reduction was performed with a clustering algorithm and 6 non-redundant features were input into a linear support vector machine (SVM). A leave-one-out cross-validation strategy was adopted and the area under the ROC curve (AUC) was computed. Twelve low-risk and 5 high-risk plaques were identified by the radiologist. A total of 80, 66, 183, and 48 out of 1674 features in 120-kV, 50-keV, 100-keV, and iodine map images were statistically significant. The SVM classified 16/17 plaques correctly in the 120-kV PCD-CT and 50-keV VMI images. The AUC was 0.967, 0.967, 0.917, and 0.833 in 120-kV, 50-keV, 100-keV, and iodine map images, respectively. A ML model using coronary PCD-CTA images at 120-kV and 50-keV best automatically differentiated low- and high-risk coronary plaques.
Detection of low contrast liver metastases varies between radiologists. Training may improve performance for lower-performing readers and reduce inter-radiologist variability. We recruited 31 radiologists (15 trainees, eight non-abdominal staff, and eight abdominal staff) to participate in four separate reading sessions: pre-test, search training, classification training, and post-test. In the pre-test, each radiologist interpreted 40 liver CT exams containing 91 metastases, circumscribed suspected hepatic metastases while under eye tracker observation, and rated confidence. In search training, radiologists interpreted a separate set of 30 liver CT exams while receiving eye tracker feedback and after coaching to increase use of coronal reformations, interpretation time, and use of liver windows. In classification training, radiologists interpreted up to 100 liver CT image patches, most with benign or malignant lesions, and compared their annotations to ground truth. Post-test was identical to pre-test. Between pre- and post-test, sensitivity increased by 2.8% (p = 0.01) but AUC did not change significantly. Missed metastases were classified as search errors (<2 seconds gaze time) or classification errors (>2 seconds gaze time) using the eye tracker. Out of 2775 possible detections, search errors decreased (10.8% to 8.1%; p < 0.01) but classification errors were unchanged (5.7% vs 5.7%). When stratified by difficulty, easier metastases showed larger reductions in search errors: for metastases with average sensitivity of 0-50%, 50-90%, and 90-100%, reductions in search errors were 16%, 35%, and 58%, respectively. The training program studied here may be able to improve radiologist performance by reducing errors but not classification errors.
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