Objectives: Accurate outcome prediction is important for making informed clinical decisions in cancer treatment. In this study, we assessed the feasibility of using changes in radiomic features over time (Delta radiomics: absolute and relative) following chemotherapy, to predict relapse/progression and time to progression (TTP) of primary mediastinal large B-cell lymphoma (PMBCL) patients. Material and Methods: Given the lack of standard staging PET scans until 2011, only 31 out of 103 PMBCL patients in our retrospective study had both pre-treatment and end-of-treatment (EoT) scans. Consequently, our radiomics analysis focused on these 31 patients who underwent [18F]FDG PET-CT scans before and after R-CHOP chemotherapy. Expert manual lesion segmentation was conducted on their scans for delta radiomics analysis, along with an additional 19 EoT scans, totaling 50 segmented scans for single time point analysis. Radiomics features (on PET and CT), along with maximum and mean standardized uptake values (SUVmax and SUVmean), total metabolic tumor volume (TMTV), tumor dissemination (Dmax), total lesion glycolysis (TLG), and the area under the curve of cumulative standardized uptake value-volume histogram (AUC-CSH) were calculated. We additionally applied longitudinal analysis using radial mean intensity (RIM) changes. For prediction of relapse/progression, we utilized the individual coefficient approximation for risk estimation (ICARE) and machine learning (ML) techniques (K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)) including sequential feature selection (SFS) following correlation analysis for feature selection. For TTP, ICARE and CoxNet approaches were utilized. In all models, we used nested cross-validation (CV) (with 10 outer folds and 5 repetitions, along with 5 inner folds and 20 repetitions) after balancing the dataset using Synthetic Minority Oversampling TEchnique (SMOTE). Results: To predict relapse/progression using Delta radiomics between the baseline (staging) and EoT scans, the best performances in terms of accuracy and F1 score (F1 score is the harmonic mean of precision and recall, where precision is the ratio of true positives to the sum of true positives and false positives, and recall is the ratio of true positives to the sum of true positives and false negatives) were achieved with ICARE (accuracy = 0.81 ± 0.15, F1 = 0.77 ± 0.18), RF (accuracy = 0.89 ± 0.04, F1 = 0.87 ± 0.04), and LDA (accuracy = 0.89 ± 0.03, F1 = 0.89 ± 0.03), that are higher compared to the predictive power achieved by using only EoT radiomics features. For the second category of our analysis, TTP prediction, the best performer was CoxNet (LASSO feature selection) with c-index = 0.67 ± 0.06 when using baseline + Delta features (inclusion of both baseline and Delta features). The TTP results via Delta radiomics were comparable to the use of radiomics features extracted from EoT scans for TTP analysis (c-index = 0.68 ± 0.09) using CoxNet (with SFS). The performance of Deauville Score (DS) for TTP was c-index = 0.66 ± 0.09 for n = 50 and 0.67 ± 03 for n = 31 cases when using EoT scans with no significant differences compared to the radiomics signature from either EoT scans or baseline + Delta features (p-value> 0.05). Conclusion: This work demonstrates the potential of Delta radiomics and the importance of using EoT scans to predict progression and TTP from PMBCL [18F]FDG PET-CT scans.
目的：在癌症治疗中，准确预测结果对于制定明智的临床决策至关重要。本研究旨在评估利用化疗后影像组学特征随时间的变化（Delta影像组学：绝对变化和相对变化）来预测原发性纵隔大B细胞淋巴瘤（PMBCL）患者复发/进展及进展时间（TTP）的可行性。材料与方法：由于2011年前缺乏标准的分期PET扫描，我们回顾性研究中的103例PMBCL患者中仅有31例同时拥有治疗前和治疗结束（EoT）的扫描数据。因此，我们的影像组学分析聚焦于这31例在R-CHOP化疗前后均接受[18F]FDG PET-CT扫描的患者。对其扫描图像进行了专家手动病灶分割以进行Delta影像组学分析，并额外纳入了19例EoT扫描，总计50例分割扫描用于单时间点分析。计算了影像组学特征（基于PET和CT）、最大和平均标准化摄取值（SUVmax和SUVmean）、总代谢肿瘤体积（TMTV）、肿瘤播散程度（Dmax）、总病灶糖酵解（TLG）以及累积标准化摄取值-体积直方图曲线下面积（AUC-CSH）。我们还应用了基于径向平均强度（RIM）变化的纵向分析。对于复发/进展的预测，我们采用了基于个体系数近似法的风险估计（ICARE）和机器学习（ML）技术（K最近邻（KNN）、线性判别分析（LDA）和随机森林（RF）），包括在相关性分析后进行顺序特征选择（SFS）以筛选特征。对于TTP预测，则采用了ICARE和CoxNet方法。在所有模型中，我们在使用合成少数类过采样技术（SMOTE）平衡数据集后，均采用了嵌套交叉验证（CV）（外部10折、5次重复，内部5折、20次重复）。结果：在利用基线（分期）与EoT扫描之间的Delta影像组学特征预测复发/进展方面，ICARE（准确率 = 0.81 ± 0.15，F1分数 = 0.77 ± 0.18）、RF（准确率 = 0.89 ± 0.04，F1分数 = 0.87 ± 0.04）和LDA（准确率 = 0.89 ± 0.03，F1分数 = 0.89 ± 0.03）取得了最佳性能（F1分数是精确率和召回率的调和平均数，其中精确率是真阳性与真阳性和假阳性之和的比值，召回率是真阳性与真阳性和假阴性之和的比值），其预测能力高于仅使用EoT影像组学特征。在我们分析的第二部分，即TTP预测中，当使用基线+Delta特征（同时包含基线和Delta特征）时，表现最佳的是CoxNet（LASSO特征选择），其c指数 = 0.67 ± 0.06。通过Delta影像组学得到的TTP预测结果，与使用CoxNet（结合SFS）从EoT扫描中提取影像组学特征进行TTP分析的结果（c指数 = 0.68 ± 0.09）相当。对于TTP预测，Deauville评分（DS）在使用EoT扫描时，在n=50例中的c指数为0.66 ± 0.09，在n=31例中为0.67 ± 0.03，与来自EoT扫描或基线+Delta特征的影像组学特征谱相比均无显著差异（p值 > 0.05）。结论：本研究证明了Delta影像组学的潜力，以及利用EoT扫描从PMBCL患者的[18F]FDG PET-CT扫描中预测疾病进展和TTP的重要性。
肿瘤(癌症)患者之家