Robert Jeraj
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Probabilistic clinical target definition with nearest neighbor correlation Open
Objective. The delineation of the clinical target volume (CTV) in radiotherapy is fundamentally uncertain due to the invisibility of microscopic disease on medical images. The ICRU 83 report acknowledges this by proposing a probabilistic i…
View article: Deep learning-based PSMA PET segmentation repeatability: A post-hoc analysis of a single-center, prospective, test–retest trial
Deep learning-based PSMA PET segmentation repeatability: A post-hoc analysis of a single-center, prospective, test–retest trial Open
AI-based PSMA-positive tumour volume calculations have repeatability limits that are consistent with the use of the Response Evaluation Criteria in PSMA PET/CT (RECIP 1.0) criteria for higher volume disease patients when the same tracer is…
InfoOOD: information bottleneck optimization for post hoc medical image out-of-distribution detection Open
Objective. Deep learning models are prone to failure when inferring upon out-of-distribution (OOD) data, i.e. data whose features fundamentally differ from those in the training set. Existing OOD measures often lack sensitivity to the subt…
View article: Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression
Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression Open
Background Identification of accelerated aging and its biomarkers can lead to more timely therapeutic interventions and decision-making. Therefore, we sought to predict aging-related slow gait, a known predictor of accelerated aging, and i…
Uncertainty quantification for deep learning-based metastatic lesion segmentation on whole body PET/CT Open
Objective. Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be en…
Dual roles of calcification features in the Mirai mammographic breast cancer risk prediction model: early micro-calcification detection and identification of high-risk calcifications Open
sponsorship: The authors acknowledge the Flemish-Slovenian research grants FWO G0A7121N. (FWO G0A7121N)
Sensitivity of a deep-learning-based breast cancer risk prediction model Open
Objective. When it comes to the implementation of deep-learning based breast cancer risk (BCR) prediction models in clinical settings, it is important to be aware that these models could be sensitive to various factors, especially those ar…
Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy Open
. A 3D-Unet for CTV segmentation trained on a large single institute cohort consisting of planning CTs and manual segmentations was built and externally validated, reaching high performance.
Early-time-point <sup>18</sup> F-FDG-PET/CT and other prognostic biomarkers of survival in metastatic melanoma patients receiving immunotherapy Open
Background A considerable proportion of metastatic melanoma (mM) patients do not respond to immune checkpoint inhibitors (ICIs). There is a great need to develop noninvasive biomarkers to detect patients, who do not respond to ICIs early d…
Full-Body Tumor Response Heterogeneity of Metastatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radiopharmaceutical Therapy Open
Patients with metastatic neuroendocrine tumors (NETs) can present with hundreds of lesions, and each lesion might have a unique response pattern to peptide receptor radiopharmaceutical therapy (PRRT). This response heterogeneity has been o…
Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction Open
Objective. State-of-the-art breast cancer risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included. This study investigated whether excluding PM during training/fine-tuning improves the mod…
Longitudinal interpretability of deep learning based breast cancer risk prediction Open
Objective. Deep-learning-based models have achieved state-of-the-art breast cancer risk (BCR) prediction performance. However, these models are highly complex, and the underlying mechanisms of BCR prediction are not fully understood. Key q…
Virtual Mentoring for Medical Physicists: Results of a Global Online Survey Open
Purpose: Medical physics professional development is limited in parts of the globe and can be aided by virtual mentoring. A global online perception survey was conducted to elucidate the characteristics of the preferred virtual mentoring p…
Role of quantitative imaging biomarkers in an early FDG-PET/CT for detection of immune-related adverse events in melanoma patients: a prospective study Open
Background To evaluate the role of the novel quantitative imaging biomarker (QIB) SUV X% of 18 F-FDG uptake extracted from early 18 F-FDG-PET/CT scan at 4 weeks for the detection of immune-related adverse events (rAE) in a cohort of patien…
View article: DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy
DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy Open
Objective . Predicting potential deformations of patients can improve radiotherapy treatment planning. Here, we introduce new deep-learning models that predict likely anatomical changes during radiotherapy for head and neck cancer patients…
Uncertainty quantification via localized gradients for deep learning-based medical image assessments Open
Objective. Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tas…
Uncertainty estimation and evaluation of deformation image registration based convolutional neural networks Open
Objective. Fast and accurate deformable image registration (DIR), including DIR uncertainty estimation, is essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its…
An automated methodology for whole-body, multimodality tracking of individual cancer lesions Open
Objective . Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion…
Prospective inter- and intra-tracer repeatability analysis of radiomics features in [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET scans in metastatic prostate cancer Open
Objective: This study aimed to quantify both the intra- and intertracer repeatability of lesion-level radiomics features in [68Ga]Ga-prostate-specific membrane antigen (PSMA)-11 and [18F]F-PSMA-1007 positron emission tomography (PET) scans…
Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability Open
Objective. Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes of individual lesions is critical to properly inform clinical decisions. The goal of this work was to asse…
Uncertainty estimation for deep learning-based pectoral muscle segmentation via Monte Carlo dropout Open
Objective . Deep Learning models are often susceptible to failures after deployment. Knowing when your model is producing inadequate predictions is crucial. In this work, we investigate the utility of Monte Carlo (MC) dropout and the effic…
Challenges and opportunities for implementing hypofractionated radiotherapy in Africa: lessons from the HypoAfrica clinical trial Open
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