Radiomics and machine learning–based methods offer exciting opportunities for improving
diagnostic performance and efficiency in musculoskeletal radiology for various tasks …

Soft‐tissue sarcomas (STS) represent a group of rare and heterogeneous tumors associated
with several challenges, including incorrect or late diagnosis, the lack of clinical expertise …

Background Treatment decisions for multimodal therapy in soft tissue sarcoma (STS)
patients greatly depend on the differentiation between low-grade and high-grade tumors …

Purpose In high-grade soft-tissue sarcomas (STS) the standard of care encompasses
multimodal therapy regimens. While there is a growing body of evidence for prognostic …

Background Retroperitoneal sarcomas are tumours with a poor prognosis. Upfront
characterisation of the tumour is difficult, and under-grading is common. Radiomics has the …

Simple Summary In soft-tissue sarcoma (STS) patients, the decision for the optimal treatment
modality largely depends on STS size, location, and a pathological measure that assesses …

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 …

Objectives To evaluate the performance of a deep learning radiomic nomogram (DLRN)
model at predicting tumor relapse in patients with soft tissue sarcomas (STS) who underwent …

Simple Summary Soft-tissue sarcomas constitute a rare cancer type, with approximately
40% of patients experiencing disease recurrence. There is a need for a better identification …

Normal tissue complication probability (NTCP) models that were formulated in the
Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the …