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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Machine learning approach for Breast Cancer Radiotherapy degradation prediction using clinical data and photos

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Madureira Sanches Ribeiro, Guilherme ULiège
Promotor(s) : Geurts, Pierre ULiège ; Kleyntssens, Thomas
Date of defense : 26-Jun-2023/27-Jun-2023 • Permalink : http://hdl.handle.net/2268.2/17694
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Title : Machine learning approach for Breast Cancer Radiotherapy degradation prediction using clinical data and photos
Author : Madureira Sanches Ribeiro, Guilherme ULiège
Date of defense  : 26-Jun-2023/27-Jun-2023
Advisor(s) : Geurts, Pierre ULiège
Kleyntssens, Thomas 
Committee's member(s) : Marée, Raphaël ULiège
Phillips, Christophe ULiège
COUCKE, Philippe ULiège
Language : English
Discipline(s) : Engineering, computing & technology > Computer science
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] Breast cancer is the most common cancer worldwide but modern treatment has increased the survival rate at 5 years to 90% or more in high-income countries. Most breast cancers are treated with breast-conserving surgery that can be followed by radiotherapy. However, this exposure to radiation may come with short and long-term side-effects that can have an impact on a patient's quality of life. This work investigates the possibility to predict the degradation and the final aspect of the breast after radiotherapy by exploiting AI models trained on small datasets made up of clinical data or pictures taken from the front and the side of Caucasian patients. Unfortunately, the best models only achieve slightly better than random predictions, and with a lot of instability. It was found that the pathological stage, the biomarkers of the removed tumor, the number of sentinel nodes removed, the number of lymph nodes and the indication that a patient is diabetic and/or a smoker, are the attributes that have the biggest impact on predictions. The best model trained on images over-performed the best clinical data trained model. Fine-tuning convolutional neural networks instead of using them as features extractors yields better performance.


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Author

  • Madureira Sanches Ribeiro, Guilherme ULiège Université de Liège > Master ingé. civ. info., à fin.

Promotor(s)

Committee's member(s)

  • Marée, Raphaël ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes stochastiques
    ORBi View his publications on ORBi
  • Phillips, Christophe ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
    ORBi View his publications on ORBi
  • COUCKE, Philippe ULiège Centre Hospitalier Universitaire de Liège - CHU > Département de Physique Médicale > Service médical de radiothérapie
    ORBi View his publications on ORBi
  • Total number of views 17
  • Total number of downloads 1










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