PurposeThe aim of this study was to evaluate coronary computed tomography angiography (CCTA)-based in vitro and in vivo coronary artery calcium scoring (CACS) using a novel virtual noniodine reconstruction (PureCalcium) on a clinical first-generation photon-counting detector–computed tomography system compared with virtual noncontrast (VNC) reconstructions and true noncontrast (TNC) acquisitions.Materials and MethodsAlthough CACS and CCTA are well-established techniques for the assessment of coronary artery disease, they are complementary acquisitions, translating into increased scan time and patient radiation dose. Hence, accurate CACS derived from a single CCTA acquisition would be highly desirable. In this study, CACS based on PureCalcium, VNC, and TNC, reconstructions was evaluated in a CACS phantom and in 67 patients (70 [59/80] years, 58.2% male) undergoing CCTA on a first-generation photon counting detector–computed tomography system. Coronary artery calcium scores were quantified for the 3 reconstructions and compared using Wilcoxon test. Agreement was evaluated by Pearson and Spearman correlation and Bland-Altman analysis. Classification of coronary artery calcium score categories (0, 1–10, 11–100, 101–400, and >400) was compared using Cohen κ.ResultsPhantom studies demonstrated strong agreement between CACSPureCalcium and CACSTNC (60.7 ± 90.6 vs 67.3 ± 88.3, P = 0.01, r = 0.98, intraclass correlation [ICC] = 0.98; mean bias, 6.6; limits of agreement [LoA], −39.8/26.6), whereas CACSVNC showed a significant underestimation (42.4 ± 75.3 vs 67.3 ± 88.3, P < 0.001, r = 0.94, ICC = 0.89; mean bias, 24.9; LoA, −87.1/37.2). In vivo comparison confirmed a high correlation but revealed an underestimation of CACSPureCalcium (169.3 [0.7/969.4] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.98; mean bias, −113.5; LoA, −470.2/243.2). In comparison, CACSVNC showed a similarly high correlation, but a substantially larger underestimation (24.3 [0/272.3] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.54; mean bias, −551.6; LoA, −2037.5/934.4). CACSPureCalcium showed superior agreement of CACS classification (κ = 0.88) than CACSVNC (κ = 0.60).ConclusionsThe accuracy of CACS quantification and classification based on PureCalcium reconstructions of CCTA outperforms CACS derived from VNC reconstructions.
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.
In dual-energy CT datasets, the conspicuity of liver metastases can be enhanced by virtual monoenergetic imaging (VMI) reconstructions at low keV levels. Our study investigated whether this effect can be reproduced in photon-counting detector CT (PCD-CT) datasets. We analyzed 100 patients with liver metastases who had undergone contrast-enhanced CT of the abdomen on a PCD-CT (n = 50) or energy-integrating detector CT (EID-CT, single-energy mode, n = 50). PCD-VMI-reconstructions were performed at various keV levels. Identical regions of interest were positioned in metastases, normal liver, and other defined locations assessing image noise, tumor-to-liver ratio (TLR), and contrast-to-noise ratio (CNR). Patients were compared inter-individually. Subgroup analyses were performed according to BMI. On the PCD-CT, noise and CNR peaked at the low end of the keV spectrum. In comparison with the EID-CT, PCD-VMI-reconstructions exhibited lower image noise (at 70 keV) but higher CNR (for ≤70 keV), despite similar CTDIs. Comparing high- and low-BMI patients, CTDI-upregulation was more modest for the PCD-CT but still resulted in similar noise levels and preserved CNR, unlike the EID-CT. In conclusion, PCD-CT VMIs in oncologic patients demonstrated reduced image noise–compared to a standard EID-CT–and improved conspicuity of hypovascularized liver metastases at low keV values. Patients with higher BMIs especially benefited from constant image noise and preservation of lesion conspicuity, despite a more moderate upregulation of CTDI.
A 3-D molecularly imprinted polymer (MIP) film comprising a unit for recognition of 2,4,6-trinitrophenol (TNP) embedded with a fluorophore for signal transduction and quantification is newly fabricated and shown to be selective and sensitive to the target TNP analyte in solution. The limit of detection of this chemosensor reached a level of subnanogram per liter of TNP concentration. Moreover, this MIP film was fabricated by just one-step electropolymerization from a prepolymerization solution; therefore, the procedure is readily extendable for selective determination of other nitroaromatic explosives.
PurposeMachine learning based on radiomics features has seen huge success in a variety of clinical applications. However, the need for standardization and reproducibility has been increasingly recognized as a necessary step for future clinical translation. We developed a novel, intuitive open-source framework to facilitate all data analysis steps of a radiomics workflow in an easy and reproducible manner and evaluated it by reproducing classification results in eight available open-source datasets from different clinical entities.MethodsThe framework performs image preprocessing, feature extraction, feature selection, modeling, and model evaluation, and can automatically choose the optimal parameters for a given task. All analysis steps can be reproduced with a web application, which offers an interactive user interface and does not require programming skills. We evaluated our method in seven different clinical applications using eight public datasets: six datasets from the recently published WORC database, and two prostate MRI datasets—Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-UCLA) and PROSTATEx.ResultsIn the analyzed datasets, AutoRadiomics successfully created and optimized models using radiomics features. For WORC datasets, we achieved AUCs ranging from 0.56 for lung melanoma metastases detection to 0.93 for liposarcoma detection and thereby managed to replicate the previously reported results. No significant overfitting between training and test sets was observed. For the prostate cancer detection task, results were better in the PROSTATEx dataset (AUC = 0.73 for prostate and 0.72 for lesion mask) than in the Prostate-UCLA dataset (AUC 0.61 for prostate and 0.65 for lesion mask), with external validation results varying from AUC = 0.51 to AUC = 0.77.ConclusionAutoRadiomics is a robust tool for radiomic studies, which can be used as a comprehensive solution, one of the analysis steps, or an exploratory tool. Its wide applicability was confirmed by the results obtained in the diverse analyzed datasets. The framework, as well as code for this analysis, are publicly available under https://github.com/pwoznicki/AutoRadiomics.
(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48–74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657–0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617–0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.
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