ESTRO 2024 - Abstract Book

S5003

Physics - Radiomics, functional and biological imaging and outcome prediction

ESTRO 2024

[2] Vandecaveye V, Dirix P, De Keyzer F, Op de Beeck K, Vander Poorten V, Roebben I, et al. Predictive value of diffusion weighted magnetic resonance imaging during chemoradiotherapy for head and neck squamous cell carcinoma. Eur Radiol. 2010;20:1703-14. [3] Boeke S, Winter RM, Leibfarth S, Krueger MA, Bowden G, Cotton J, et al. Machine learning identifies multi parametric functional PET/MR imaging cluster to predict radiation resistance in preclinical head and neck cancer models. Eur J Nucl Med Mol Imaging. 2023;50:3084-96. [4] Dirix P, Vandecaveye V, De Keyzer F, Stroobants S, Hermans R, Nuyts S. Dose painting in radiotherapy for head and neck squamous cell carcinoma: value of repeated functional imaging with (18)F-FDG PET, (18)F-fluoromisonidazole PET, diffusion-weighted MRI, and dynamic contrast-enhanced MRI. J Nucl Med. 2009;50:1020-7.

[5] Habrich J, Boeke S, Nachbar M, Nikolaou K, Schick F, Gani C, et al. Repeatability of diffusion-weighted magnetic resonance imaging in head and neck cancer at a 1.5 T MR-Linac. Radiother Oncol. 2022;174:141-8.

[6] McDonald BA, Salzillo T, Mulder S, Ahmed S, Dresner A, Preston K, et al. Prospective Evaluation of In Vivo and Phantom Repeatability and Reproducibility of Diffusion-Weighted MRI Sequences on 1.5T MRI-Linear Accelerator (MR Linac) and MR Simulator Devices for Head and Neck Cancers. Radiotherapy and Oncology. 2023:109717.

929

Digital Poster

Feasibility of using delta radiomics to predict pCR in LARC patients treated at MR-Linac

Bin Tang, Junxiang Wu, Fan Wu, Jie Li, Xinghong Yao, Yuan Ke, Lucia Clara Orlandini

Sichuan Cancer Hospital & Institute, Radiation Oncology, Chengdu, China

Purpose/Objective:

Radiomics enables the extraction of hidden data from medical images that cannot be detected through a visual examination and, through the development of classification models using machine learning or deep learning techniques seeks to provide diagnosis and treatment prognoses. The use of radiomics to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARCs) using magnetic resonance imaging (MRI) images can support the radiation oncologist's decision-making process to identify patients who may or may not benefit from total mesorectum excision (TME) surgery[1]. While radiomics is based on clinical images acquired at a single time point, delta radiomics studies the temporal variation of radiomic features extracted from a set of images acquired at different times during the course of treatment[2, 3]. This study aims to assess the feasibility of using delta radiomics in a short course of radiotherapy (SCRT) with MR-Linac, as a predictive tool to determine the pCR of LARC patients after neoadjuvant chemoradiotherapy (nCRT).

Material/Methods: