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.
Chunyan DuanQiantuo LiuJiajie WangQianqian TongFangyun BaiJie HanShouyi WangDaniel S HippeJing ZengStephen R BowenPublished in: Physics in medicine and biology (2024)
Vital rules learned from 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 (∆SUVmean⩾20% decline) for classification and a continuous response measure (∆SUVmean) 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.
Keyphrases
- machine learning
- pet ct
- deep learning
- advanced non small cell lung cancer
- positron emission tomography
- pet imaging
- artificial intelligence
- computed tomography
- big data
- clinical trial
- epidermal growth factor receptor
- end stage renal disease
- clinical decision support
- magnetic resonance imaging
- convolutional neural network
- ejection fraction
- peritoneal dialysis
- rectal cancer
- randomized controlled trial
- chronic kidney disease
- squamous cell carcinoma
- electronic health record
- radiation therapy
- prognostic factors
- locally advanced
- patient reported outcomes
- replacement therapy
- image quality
- open label
- ionic liquid
- single cell
- neural network