# name
“MRI-based radiomics machine learning  model for tumour response prediction to  neoadjuvant chemoradiotherapy (nCRT) in  locally advanced rectal cancer (LARC): a  retrospective study

# metadata.dc.description[].value
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2024-2025. Tutor: Núria Gavara i Casas ; Director: Josep Munuera del Cerro

# metadata.dc.description.abstract[].value
Rectal cancer (RC) is one of the most commonly diagnosed malignant tumours worldwide. Its most 
aggressive form, Locally Advanced Rectal Cancer (LARC), is treated with neoadjuvant 
chemoradiotherapy (nCRT) to downstage the tumour and improve results of the total mesorectal excision 
(TME). Approximately 20% to 25% achieve complete response and may be eligible for a more 
conservative watch and wait strategy. Early prediction of response to nCRT using information from the 
staging MRI could help adjust neoadjuvant treatment andimprove response, potentially avoiding 
surgeries. 
The aim of this project is to develop a machine learning (ML) model capable of predicting response to 
nCRT using clinical data and radiomics characteristics extracted from the pre-treatment MRI. The
radiomics are extracted from manually delineated tumour masks (Core Tumour Radiomics) and from
border masks computed from the manual segmentations (Border Radiomics). Nine ML models have been 
optimized and tested across the seven feature set combinations. The models with best performance were 
Random Forest for the core tumour radiomics dataset (Accuracy = 0.77, AUC = 0.70, Sensitivity = 0.67, 
Specificity = 0.86) and Multilayer Perceptron for the dataset with all features (Accuracy = 0.77, AUC = 
0.70, Sensitivity = 0.83, Specificity = 0.71). However, the predictive capability of tumour borders could 
not be confirmed as these models yielded worse performance. Furthermore, via a feature importance 
analysis, it has been concluded that both shape and texture related radiomic features are predictors of 
treatment response although no specific marker has been identified.

# metadata.dc.rights[].value
cc-by-nc-nd (c)  Paula Sanahuja Rosich, 2025

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Enginyeria Biomèdica

# metadata.dc.title[].value
“MRI-based radiomics machine learning  model for tumour response prediction to  neoadjuvant chemoradiotherapy (nCRT) in  locally advanced rectal cancer (LARC): a  retrospective study