The aim of this study was to associate and predict B-rapidly accelerated fibrosarcoma valine 600 (BRAFV600) mutation status with both conventional and radiomics ¹⁸F-FDG PET/CT features, while exploring several methods of feature selection in melanoma radiomics. Methods: Seventy unresectable stage III–IV melanoma patients who underwent a baseline ¹⁸F-FDG PET/CT scan were identified. Patients were assigned to the BRAFV600 group or BRAF wild-type group according to mutational status. ¹⁸F-FDG uptake quantification was performed by semiautomatic lesion delineation. Four hundred eighty radiomics features and 4 conventional PET features (SUV_max, SUV_mean, SUV_peak, and total lesion glycolysis) were extracted per lesion. Six different methods of feature selection were implemented, and 10-fold cross-validated predictive models were built for each. Model performances were evaluated with areas under the curve (AUCs) for the receiver operating characteristic curves. Results: Thirty-five BRAFV600 mutated patients (100 lesions) and 35 BRAF wild-type patients (79 lesions) were analyzed. AUCs predicting the BRAFV600 mutation varied from 0.54 to 0.62 and were susceptible to feature selection method. The best AUCs were achieved by feature selection based on literature, a penalized binary logistic regression model, and random forest model. No significant difference was found between the BRAFV600 and BRAF wild-type group in conventional PET features or predictive value. Conclusion: BRAFV600 mutation status is not associated with, nor can it be predicted with, conventional PET features, whereas radiomics features were of low predictive value (AUC = 0.62). We showed feature selection methods to influence predictive model performance, describing and evaluating 6 unique methods. Detecting BRAFV600 status in melanoma based on ¹⁸F-FDG PET/CT alone does not yet provide clinically relevant knowledge.

这项研究的目的是关联和预测B迅速加速的纤维肉瘤缬氨酸600（BRAFV600）突变状态与常规和放射学¹⁸ F-FDG PET / CT的特征，同时探索黑素瘤放射学中特征选择的几种方法。方法：确定了接受基线¹⁸ F-FDG PET / CT扫描的70例不可切除的III–IV期黑色素瘤患者。根据突变状态将患者分为BRAFV600组或BRAF野生型组。¹⁸ F-FDG摄取定量通过半自动病变描绘进行。480个放射性元素特征和4个常规PET特征（SUV _max，SUV _mean，SUV _peak，和总病变糖酵解）提取每个病变。实现了六种不同的特征选择方法，并为每种模型建立了10倍交叉验证的预测模型。使用接收器工作特性曲线的曲线下面积（AUC）评估模型性能。结果：分析了35例BRAFV600突变患者（100个病灶）和35例BRAF野生型患者（79个病灶）。预测BRAFV600突变的AUC从0.54到0.62不等，并且易受特征选择方法的影响。通过基于文献的特征选择，惩罚性二元逻辑回归模型和随机森林模型，可以获得最佳的AUC。在传统的PET特征或预测价值上，BRAFV600和BRAF野生型组之间没有发现显着差异。结论： BRAFV600突变状态与常规PET功能无关，也无法预测，而放射性组特征的预测价值低（AUC = 0.62）。我们展示了影响预测模型性能的特征选择方法，描述和评估了6种独特的方法。仅基于¹⁸ F-FDG PET / CT检测黑色素瘤中的BRAFV600状态尚不能提供临床相关知识。