An individualized trigger delay improves contrast opacification in CT angiography of the aorta, yielding a higher and more constant attenuation as well as an improved subjective image quality. BackgroundOptimal timing of the CT scan relative to the contrast media bolus remains a challenging task given the shorter scan durations of modern CT scanners, as well as interpatient variability.PurposeTo compare contrast opacification in CT angiography of the aorta between a cohort with fixed trigger delay and a cohort with patient-specific individualized trigger delay for contrast media timing with bolus tracking.Materials and MethodsIn this prospective study (January–August 2018), CT angiography of the thoracoabdominal aorta with bolus tracking was performed in two different study cohorts: one with a fixed trigger delay of 4 seconds (fixed cohort) and one with a patient-specific trigger delay (individualized cohort). All CT and contrast media protocol parameters were kept identical among cohorts. Objective image quality was evaluated by one reader; two readers assessed subjective image quality. Student t test was used to test for differences in mean attenuation; the Wilcoxon-Mann-Whitney test was used...

Objectives: To investigate, in patients with metastatic prostate cancer, whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone using ⁶⁸ Ga-PSMA PET imaging as reference standard. Methods: In this IRB-approved retrospective study, 67 patients (mean age 71 ± 7 years; range: 55–84 years) showing a total of 205 ⁶⁸ Ga-PSMA-positive prostate cancer bone metastases in the thoraco-lumbar spine and pelvic bone being invisible in CT were included. Metastases and 86 ⁶⁸ Ga-PSMA-negative bone volumes in the same body region were segmented and further post-processed. Intra- and inter-reader reproducibility was assessed, with ICCs < 0.90 being considered non-reproducible. To account for imbalances in the dataset, data augmentation was performed to achieve improved class balance and to avoid model overfitting. The dataset was split into training, test, and validation set. After a multi-step dimension reduction process and feature selection process, the 11 most important and independent features were selected for statistical analyses. Results: A gradient-boosted tree was trained on the selected 11 radiomic features in order to classify patients’ bones into bone metastasis and normal bone using the training dataset. This trained model achieved a classification accuracy of 0.85 (95% confidence interval [CI]: 0.76–0.92, p < .001) with 78% sensitivity and 93% specificity. The tuned model was applied on the original, non-augmented dataset resulting in a classification accuracy of 0.90 (95% CI: 0.82–0.98) with 91% sensitivity and 88% specificity. Conclusion: Our proof-of-concept study indicates that radiomics may accurately differentiate unaffected bone from metastatic bone, being invisible by the human eye on CT. Key Points: • This proof-of-concept study showed that radiomics applied on CT images may accurately differentiate between bone metastases and metastatic-free bone in patients with prostate cancer. • Future promising applications include automatic bone segmentation, followed by a radiomics classifier, allowing for a screening-like approach in the detection of bone metastases.

The coronary artery calcium score independently predicted 30-day and 1-year all-cause mortality in participants undergoing transcatheter aortic valve replacement. Background Current risk models show limited performances for predicting all-cause mortality after transcatheter aortic valve replacement (TAVR). Purpose To determine the prognostic value of coronar...

To investigate whether [¹⁸F]-FDG PET/CT-derived radiomics may correlate with driver gene mutations in non-small cell lung cancer (NSCLC) patients. In this IRB-approved retrospective study, 203 patients with surgically treated NSCLC who underwent subsequent genomic analysis of the primary tumour at our institution between December 2004 and January 2014 were identified. Of those, 128 patients (mean age 62.4 ± 10.8 years; range: 35-84) received preoperative [¹⁸F]-FDG PET/CT as part of their initial staging and thus were included in the study. PET and CT image segmentation and feature extraction were performed semi-automatically with an open-source software platform (LIFEx, Version 6.30, lifexsoft.org). Molecular profiles using different next-generation sequencing (NGS) panels were collected from a web-based resource (cBioPortal.ca for Cancer genomics). Two statistical models were then built to evaluate the predictive ability of [¹⁸F]-FDG PET/CT-derived radiomics features for driver gene mutations in NSCLC. More than half (68/128, 53%) of all tumour samples harboured three or more gene mutations. Overall, 55% of tumour samples demonstrated a mutation in TP53, 26% of samples had alterations in KRAS and 17% in EGFR. Extensive statistical analysis resulted in moderate to good predictive ability. The highest Youden Index for TP53 was achieved using combined PET/CT features (0.70), for KRAS using PET features only (0.57) and for EGFR using CT features only (0.60). Our study demonstrated a moderate to good correlation between radiomics features and driver gene mutations in NSCLC, indicating increased predictive ability of genomic profiles using combined [¹⁸F]-FDG PET/CT-derived radiomics features.

We investigated, whether ¹⁸[18F]-FDG PET/CT-derived radiomics combined with sarcopenia measurements improves survival prognostication among patients with advanced, metastatic gastroesophageal cancer. In our study, 128 consecutive patients with advanced, metastatic esophageal and gastroesophageal cancer (n = 128; 26 females; 102 males; mean age 63.5 ± 11.7 years; age range: 29–91 years) undergoing ¹⁸[18F]-FDG PET/CT for staging between November 2008 and December 2019 were included. Segmentation of the primary tumor and radiomics analysis derived from PET and CT images was performed semi-automatically with a commonly used open-source software platform (LIFEX, Version 6.30, lifexsoft.org). Patients’ nutritional status was determined by measuring the skeletal muscle index (SMI) at the level of L3 on the CT component. Univariable and multivariable analyses were performed to establish a survival prediction model including radiomics, clinical data, and SMI score. Univariable Cox proportional hazards model revealed ECOG (<0.001) and bone metastasis (p = 0.028) to be significant clinical parameters for overall survival (OS) and progression free survival (PFS). Age (p = 0.017) was an additional prognostic factor for OS. Multivariable analysis showed improved prognostication for overall and progression free survival when adding sarcopenic status, PET and CT radiomics to the model with clinical parameters only. PET and CT radiomics derived from hybrid ¹⁸[18F]-FDG PET/CT combined with sarcopenia measurements and clinical parameters may improve survival prediction among patients with advanced, metastatic gastroesophageal cancer.