All conference submissions

Radiomics enables quantitative characterization of tumors from PET and CT imaging. When anatomic-region-specific cancer data are limited, transfer and semi-supervised learning can improve prediction by leveraging data from differ...

To evaluate the generalizability of a deep learning (DL)-model, trained exclusively with external beam radiotherapy (EBRT) organs‑at‑risk (OARs) contours, for autocontouring in cervical cancer brachytherapy (CC-BT).

This study aimed to cross validate a conditional generative adversarial network (cGAN) model for noninvasive histological subtype classification of pleural mesothelioma on computed tomography (CT) scans.

To develop and validate a CT-based habitat radiomics approach for quantitative characterization of intratumoral heterogeneity and assessment of p40 protein expression derived from immunohistochemistry.

As thoracic reirradiation becomes more common in clinical practice, the changes of underlying lung comorbidities due to multiple treatments are relatively unknown. Parametric response mapping (PRM) has shown promise as a method o...

The patch-based network training is widely used for CT denoising due to computation memory and time constraints. However, conventional techniques are limited to learncontextual information and preserve fine structures. In this st...

To systematically evaluate CT number accuracy on virtual monoenergetic images (VMIs) generated by a clinical photon-counting CT (PCCT) system using projection-domain multi-material modeling with a water/iodine basis.

Dual‑source photon‑counting CT (DS‑PCCT) enables fast FLASH acquisition combined with spectral imaging—capabilities not available in single‑source PCCT (SS‑PCCT). This study evaluates whether DS‑PCCT maintains quantitative accura...

Studies show measurable changes in brain metastasis over 1-2 weeks, with delays between MRI and radiosurgery delivery reducing local control. We aim to minimize this gap by developing a multi-target radiosurgery workflow that eli...

The aim of the current study is the development of a novel custom skin flap (CSF) for HDR treatment of skin lesions, as well as, assess the usability and planning technicalities of our CSF in a clinical setup.

Stereotactic radiosurgery (SRS) requires submillimeter geometric accuracy and commonly relies on combined computed tomography (CT) and magnetic resonance imaging (MRI). However, MRI is susceptible to geometric distortions that in...

In MR-only radiotherapy planning (MROP), limited field-of-view (LFOV) acquisition and imaging artifacts can introduce truncated anatomy and density inaccuracies, reducing the reliability of dose calculation and preventing compreh...

Tumor heterogeneity within and across tumors contributes to variable therapeutic response by altering biophysical transport barriers in the tumor microenvironment (TME). Elevated interstitial fluid pressure (IFP) and reduced hydr...

To generalize decentralized mutual learning to multi-class 3D organ-at-risk (OAR) segmentation and evaluate whether gossip-based peer-to-peer training improves federated radiotherapy auto-segmentation across heterogeneous clinica...

Longitudinal brain MRI enables analysis of anatomical changes associated with aging and neurodegenerative disease, yet interpreting structural evolution over time remains challenging due to subtle changes such as inter-subject he...

To quantitatively benchmark dosimetric variation associated with tumor regression during head and neck (HN) radiotherapy and to evaluate the benefit of adaptive replanning as a basis for adapt-on-demand decision support.

Hypertensive heart disease in Africa progresses as a silent epidemic of myocardial fibrosis, undetectable by conventional echocardiography until irreversible failure. This study hypothesized that early fibrosis manifests as a per...

The aim of this work is to predict the arc level gamma passing rate (GPR) values for IMRT, VMAT, and FSRT plans at the prostate site using machine learning-based models using the SVM algorithm.

To propose a deep-learning approach for predicting high from low-energy 4D-CBCT.

Stereotactic body radiotherapy (SBRT) for spinal metastases requires accurate delineation of the spinal cord and thecal sac to maximize treatment efficacy while minimizing toxicity. Deep learning-based segmentation models improve...

Thyroid nodules, characterized by abnormal cell growth within the thyroid gland, pose a significant diagnostic challenge, particularly when distinguishing between benign and malignant nodules. Accurate differentiation is crucial...

Intracranial hemorrhage in premature infants is a frequent and clinically significant finding, with cranial ultrasound serving as the first-line imaging modality. Interpretation can be challenging due to subtle and variable imagi...

Online adaptive prostate SIB treatment on the Unity MR-Linac is challenged by the limited robustness of deformable image registration (DIR)-based contour propagation. This study investigates the use of a deep learning (DL) dose p...

To develop risk-stratification and prognosis-prediction models for limited-stage small cell lung cancer (LS-SCLC) patients, suggesting potential candidates that may benefit from the new treatment protocol using high-dose hyperfra...