DataSheet_1_Integrated CT Radiomics Features Could Enhance the Efficacy of 18F-FET PET for Non-Invasive Isocitrate Dehydrogenase Genotype Prediction in Adult Untreated Gliomas: A Retrospective Cohort Study.docx

PurposeWe aimed to investigate the predictive models based on O-[2-(18F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography (18F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas.
MethodsFifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment 18F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUVSD), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models.
ResultsThe AUC of the simple predictive model consists of semi-quantitative parameter SUVSD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three 18F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined 18F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model (p = 0.048) and simple predictive model (p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973).
ConclusionThe predictive model combining 18F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment.

研究目的：本研究旨在探索基于O-[2-(18F)氟乙基]-L-酪氨酸（O-[2-(18F)fluoroethyl]-L-tyrosine）正电子发射断层显像/计算机断层扫描（positron emission tomography/computed tomography，PET/CT）放射组学特征的预测模型，用于成人胶质瘤的异柠檬酸脱氢酶（isocitrate dehydrogenase，IDH）基因型鉴定。
研究方法：本研究回顾性纳入58例经病理证实的连续性成人胶质瘤患者，所有患者均于治疗前完成O-[2-(18F)氟乙基]-L-酪氨酸PET/CT（18F-FET PET/CT）检查。针对PET、CT两个成像模态，各提取105个放射组学特征用于后续分析。基于机器学习算法，采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）回归分析，构建了3个独立的预测IDH突变状态的放射组学模型：PET放射组学模型（PET-Rad Model）、CT放射组学模型（CT-Rad Model）以及PET/CT联合放射组学模型（PET/CT-Rad Model）。采用全子集回归与交叉验证对各放射组学预测模型进行筛选与校准。此外，本研究还提取了半定量参数，包括肿瘤与背景最大比值（maximum tumor to background ratio，TBRmax）、峰值比值（peak tumor to background ratio，TBRpeak）、平均比值（mean tumor to background ratio，TBRmean）、胶质瘤病灶标准化摄取值标准差（standard deviation of glioma lesion standardized uptake value，SUVSD）、代谢肿瘤体积（metabolic tumor volume，MTV）以及病灶总示踪剂摄取量（total lesion tracer uptake，TLU），并结合脑中线受累状态这一临床特征，筛选上述参数以构建简易预测模型。采用受试者工作特征曲线下面积（area under the receiver operating characteristic curve，AUC）对各预测模型的性能进行评估。
研究结果：仅包含半定量参数SUVSD与二分类脑中线受累状态的简易预测模型，其AUC为0.786（95%置信区间：0.659~0.883）。基于3个18F-FET PET放射组学参数构建的PET-Rad模型，其AUC为0.812（95%置信区间：0.688~0.902）。基于3个配准后CT放射组学参数构建的CT-Rad模型，其AUC为0.883（95%置信区间：0.771~0.952）。而结合3个CT放射组学特征与1个PET放射组学特征构建的PET/CT-Rad模型，其AUC可达0.912（95%置信区间：0.808~0.970）。DeLong检验结果显示，PET/CT-Rad模型的性能优于PET-Rad模型（p=0.048）与简易预测模型（p=0.034）。进一步将PET/CT-Rad模型与二分类肿瘤位置这一临床特征相结合，可将AUC小幅提升至0.917（95%置信区间：0.814~0.973）。
研究结论：结合18F-FET PET与整合CT放射组学特征的预测模型，可显著提升未经治疗胶质瘤的无创IDH基因型预测效能并实现良好的性能平衡，这对于指导个性化治疗的临床决策具有重要价值。