This article proposes a summary of the current status of the research regarding the use of
radiomics and artificial intelligence to improve the radiological assessment of patients with …

Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of
consensus on how to assess and validate auto-contouring systems currently limits clinical …

Annotation ambiguity due to inherent data uncertainties such as blurred boundaries in
medical scans and different observer expertise and preferences has become a major …

Soft tissue sarcomas (STSs) represent a group of heterogeneous mesenchymal tumors of
which are generally classified as per the histopathology. Despite being rare in incidence …

Simple Summary The gap in understanding of radiation therapy (RT) procedures among
non-radiation oncologists poses a significant barrier to optimal cancer care. Aimed at …

In numerous medical scenarios, segmenting clinical targets is highly subjective, influenced
by the doctors' expertise and preferences, which results in significant multi-rater variability …

Delineating the clinical target volume (CTV) and organs at risk (OARs) is crucial in rectal
cancer radiotherapy. However, the accuracy of manual delineation (MD) is variable and the …

Purpose To develop a deep learning (DL) method exploiting active learning and source-free
domain adaptation for gross tumor volume (GTV) delineation in nasopharyngeal carcinoma …

Background Accurate gross tumor volume (GTV) delineation is a critical step in radiation
therapy treatment planning. However, it is reader dependent and thus susceptible to intra …

Lesions or organ boundaries visible through medical imaging data are often ambiguous,
thus resulting in significant variations in multi-reader delineations, ie, the source of aleatoric …