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GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.
- Source :
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Physics in medicine and biology [Phys Med Biol] 2024 Jul 23; Vol. 69 (15). Date of Electronic Publication: 2024 Jul 23. - Publication Year :
- 2024
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Abstract
- Objective. Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (gray wolf optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer. Approach. Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (ΔSUVmean ⩾ 20% decline) for classification and a continuous response measure (ΔSUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression. Main results. GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC: 0.58-0.86 vs. 0.52-0.78, p = 0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE: 0.162-0.192) performed better numerically for low-dimensional models ( p = 0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE: 0.189-0.219, p < 0.004). Significance . The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.<br /> (© 2024 Institute of Physics and Engineering in Medicine.)
- Subjects :
- Humans
Heuristics
Male
Middle Aged
Female
Treatment Outcome
Aged
Image Processing, Computer-Assisted methods
Carcinoma, Non-Small-Cell Lung diagnostic imaging
Carcinoma, Non-Small-Cell Lung radiotherapy
Carcinoma, Non-Small-Cell Lung therapy
Machine Learning
Lung Neoplasms diagnostic imaging
Lung Neoplasms radiotherapy
Lung Neoplasms therapy
Fluorodeoxyglucose F18
Positron-Emission Tomography
Chemoradiotherapy
Subjects
Details
- Language :
- English
- ISSN :
- 1361-6560
- Volume :
- 69
- Issue :
- 15
- Database :
- MEDLINE
- Journal :
- Physics in medicine and biology
- Publication Type :
- Academic Journal
- Accession number :
- 38981590
- Full Text :
- https://doi.org/10.1088/1361-6560/ad6118