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.
摘要：
目的：从肿瘤分区域反应的FDG-PET影像组学中了解到的重要规则可以为精确的治疗适应提供临床决策支持。我们将基于规则的机器学习(ML)模型(RuleFit)与启发式算法(格雷沃尔夫优化器，GWO）用于局部晚期非小细胞肺癌患者的中期放化疗FDG-PET反应预测。
方法:使用K均值聚类来识别肿瘤亚区。GWO+RuleFit包括三个主要部分:(i)基于从FDG-PET图像中的肿瘤区域或子区域提取的常规特征或放射学特征构建随机森林,生成初始规则；（ii）GWO用于迭代规则选择；（iii）将所选规则拟合到线性模型，以对目标变量进行预测。考虑了两个目标变量：用于分类的二元响应度量（ΔSUVmean下降20％）和用于回归的连续响应度量（ΔSUVmean）。GWO+RuleFit以常见的ML算法和RuleFit为基准，通过分类中的受试者工作特征曲线下面积（AUC）和回归中的均方根误差（RMSE）来评估留一法交叉验证的性能。
主要结果:GWO+RuleFit从23名患者的放射学特征数据集中选择了15条规则。对于治疗反应分类，GWO+RuleFit在肿瘤区域和特征集上比RuleFit获得了更好的交叉验证性能（AUC：0.58-0.86vs.0.52-0.78，p=0.170-0.925）。与所有条件下的所有其他算法相比，GWORulefit在数值上也具有最佳或次优的性能。对于治疗反应回归预测，GWO+RuleFit（RMSE：0.162-0.192）对于低维模型（p=0.097-0.614）在数值上表现更好，对于高维模型，除了一个肿瘤区域（RMSE：0.189-0.219，p<0.004）。
意义：GWO+RuleFit选择的规则是可解释的，突出显示调节治疗反应的不同放射学表型。GWO+Rulefit实现了简约模型，同时保持了治疗反应预测的效用，这可以帮助临床决定患者的风险分层，治疗选择，和生物驱动的适应。
临床试验:NCT02773238。
