MS
Martijn Starmans
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1
Background: Histopathological growth patterns (HGP) are a biomarker for predicting survival and systemic treatment effectiveness in colorectal liver metastasis (CRLM). Currently, HGP assessment in CRLM requires the resection specimen. Predicting the HGP from preoperative medical imaging could allow more personalised care and better outcomes. Methods: 252 patients underwent CRLM resection between 2004 and 2018 without receiving any systemic treatment. Patients were characterised as having either pure desmoplastic growth (dHGP) or any other type of growth pattern combination (non-dHGP) (21% dHGP; 79% non-dHGP). These categories were chosen because pure desmoplastic growth is predictive of better overall survival. regions of interest were automatically extracted using a UNet based segmentation model. These ROIs were passed to a radiomics model and a deep learning model to classify between dHGP/non-dHGP and predict the fraction of dHGP. Results: The best-performing classification method was the radiomic approach achieving an AUC of 0.67 (95% CI: 0.58-0.78), whereas the best-performance deep learning model achieved an average AUC value of 0.59 (95% CI: 0.53-0.65). Additionally, regression predicting the fraction of dHGP failed, with the predicted values showing no significant correlation with the actual value. Conclusions: Radiomics can be used to assess HGP, however further improvements in predictive performance are needed before these methods can be applied.
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Background: Histopathological growth patterns (HGP) are a biomarker for predicting survival and systemic treatment effectiveness in colorectal liver metastasis (CRLM). Currently, HGP assessment in CRLM requires the resection specimen. Predicting the HGP from preoperative medical imaging could allow more personalised care and better outcomes. Methods: 252 patients underwent CRLM resection between 2004 and 2018 without receiving any systemic treatment. Patients were characterised as having either pure desmoplastic growth (dHGP) or any other type of growth pattern combination (non-dHGP) (21% dHGP; 79% non-dHGP). These categories were chosen because pure desmoplastic growth is predictive of better overall survival. regions of interest were automatically extracted using a UNet based segmentation model. These ROIs were passed to a radiomics model and a deep learning model to classify between dHGP/non-dHGP and predict the fraction of dHGP. Results: The best-performing classification method was the radiomic approach achieving an AUC of 0.67 (95% CI: 0.58-0.78), whereas the best-performance deep learning model achieved an average AUC value of 0.59 (95% CI: 0.53-0.65). Additionally, regression predicting the fraction of dHGP failed, with the predicted values showing no significant correlation with the actual value. Conclusions: Radiomics can be used to assess HGP, however further improvements in predictive performance are needed before these methods can be applied.
Master thesis
(2021)
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C.S.O. van Gruijthuijsen, W. Pan, Martijn Starmans, Karin van Garderen, Stefan Klein, M. Wisse
Semantic Segmentation of medical images are used to improve diagnosis and treatment. In recent years, the application of machine learning methods are increasingly used. However, the design of these models is difficult and time-consuming. In this thesis, we investigated the automation of this process using an Automated Machine Learning (AutoML) algorithm called Bayesian Optimization with HyperBand (BOHB). We found several hyperparameters significantly influencing the performance and confirmed that BOHB is applicable to these kind of problems. However, we did not find a significant difference between BOHB and Random Search on a small search, stressing the need of matching the search space to the search strategy. Finally, our approach was only slightly worse than the current state-of-the-art, which shows that AutoML has potential in this field.
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Semantic Segmentation of medical images are used to improve diagnosis and treatment. In recent years, the application of machine learning methods are increasingly used. However, the design of these models is difficult and time-consuming. In this thesis, we investigated the automation of this process using an Automated Machine Learning (AutoML) algorithm called Bayesian Optimization with HyperBand (BOHB). We found several hyperparameters significantly influencing the performance and confirmed that BOHB is applicable to these kind of problems. However, we did not find a significant difference between BOHB and Random Search on a small search, stressing the need of matching the search space to the search strategy. Finally, our approach was only slightly worse than the current state-of-the-art, which shows that AutoML has potential in this field.