Author
Marco Das
Other affiliations: Maastricht University Medical Centre, RWTH Aachen University
Bio: Marco Das is an academic researcher from Maastricht University. The author has contributed to research in topics: Coronary artery disease & Angiography. The author has an hindex of 29, co-authored 102 publications receiving 2230 citations. Previous affiliations of Marco Das include Maastricht University Medical Centre & RWTH Aachen University.
Papers published on a yearly basis
Papers
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TL;DR: Most radiomic features were affected by slice thickness, but this effect could be reduced by resampling the CT-images before feature extraction, and optimization of gray-level discretization to potentially improve prognostic value can be performed without compromising feature stability.
Abstract: Background: Radiomic analyses of CT images provide prognostic information that can potentially be used for personalized treatment. However, heterogeneity of acquisition- and reconstruction protocol...
171 citations
TL;DR: Clinical radiotherapy related applications for improved dose calculation accuracy of brachytherapy and proton therapy, metal artifact reduction techniques and normal tissue characterization are summarized together with future perspectives on the use of DECT for radiotherapy purposes.
135 citations
TL;DR: Electrogram reconstructions attained a moderate accuracy compared with epicardial recordings, but variability in electrogram reconstruction can be sizable, and clinical interpretations of ECGI should not be made on the basis of single electrograms only.
93 citations
TL;DR: In patients with suspected stable CAD, a tiered cardiac CT protocol with dynamic perfusion imaging offers a fast and efficient alternative to functional testing.
Abstract: Objectives This study sought to assess the effectiveness, efficiency, and safety of a tiered, comprehensive cardiac computed tomography (CT) protocol in comparison with functional testing. Background Although CT angiography accurately rules out coronary artery disease (CAD), incorporation of CT myocardial perfusion imaging as part of a tiered diagnostic approach could improve the clinical value and efficiency of cardiac CT in the diagnostic work-up of patients with angina pectoris. Methods Between July 2013 and November 2015, 268 patients (mean age 58 years; 49% female) with stable angina (mean pre-test probability 54%) were prospectively randomized between cardiac CT and standard guideline-directed functional testing (95% exercise electrocardiography). The tiered cardiac CT protocol included a calcium scan, followed by CT angiography if calcium was detected. Patients with ≥50% stenosis on CT angiography underwent CT myocardial perfusion imaging. Results By 6 months, the primary endpoint, the rate of invasive coronary angiograms without a European Society of Cardiology class I indication for revascularization, was lower in the CT group than in the functional testing group (2 of 130 [1.5%] vs. 10 of 138 [7.2%]; p = 0.035), whereas the proportion of invasive angiograms with a revascularization indication was higher (88% vs. 50%; p = 0.017). The median duration until the final diagnosis was 0 (0 of 0) days in the CT group and 0 (0 of 17) in the functional testing group (p Conclusions In patients with suspected stable CAD, a tiered cardiac CT protocol with dynamic perfusion imaging offers a fast and efficient alternative to functional testing. (Comprehensive Cardiac CT Versus Exercise Testing in Suspected Coronary Artery Disease 2 [CRESCENT2]; NCT02291484)
87 citations
TL;DR: Prospectively ECG-triggered, dual-step pulsing cardiac DSCT accurately quantifies left and right ventricular function and myocardial mass in comparison with cMRI with substantially lower radiation exposure than reported for traditional retrospective ECg-gating.
82 citations
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TL;DR: A correction has been published: European Heart Journal, ehaa895, https://doi.org/10.1093/eurheartj/ehaa-895.
Abstract: A correction has been published: European Heart Journal, ehaa895, https://doi.org/10.1093/eurheartj/ehaa895
2,361 citations
German Cancer Research Center1, Helmholtz-Zentrum Dresden-Rossendorf2, McGill University3, Moffitt Cancer Center4, Harvard University5, Brigham and Women's Hospital6, Kettering University7, Johns Hopkins University8, University of Pennsylvania9, University Medical Center Groningen10, University of Zurich11, King's College London12, University of Lausanne13, Netherlands Cancer Institute14, Stanford University15, University of Michigan16, Maastricht University Medical Centre17, University of Tübingen18, University of Bergen19, University of California, San Francisco20, University of Geneva21, University of British Columbia22, Cardiff University23, Leiden University Medical Center24
TL;DR: A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software and could be excellently reproduced.
Abstract: Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.
1,563 citations
TL;DR: The "2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure" as discussed by the authors provides patient-centric recommendations for clinicians to prevent, diagnose, and manage patients with heart failure.
955 citations
TL;DR: Deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process.
Abstract: Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017.
870 citations