Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, China.
Oral Oncol. 2020 May;104:104625. doi: 10.1016/j.oraloncology.2020.104625. Epub 2020 Mar 6.
To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases.
A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models.
Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37).
Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.
通过对头颈癌病例中放射组学方法与整合剂量组学的预测性能检验的比较研究，探讨剂量组学是否可以通过对局部区域复发（LR）的预测使调强放疗（IMRT）治疗的患者受益。
从癌症影像档案库中获得了来自四个机构的 237 例头颈部癌症患者队列，用于训练和验证仅基于放射组学的预后模型和整合剂量组学的预后模型。对于放射组学，最初从包括 CT 和 PET 在内的图像中提取放射组学特征，并根据其一致性指数（CI）值进行选择，然后通过主成分分析进行压缩。最后，构建了多变量 Cox 比例风险回归模型，通过输入这些压缩特征作为 LR 预测模型，进行了类别不平衡调整。对于剂量组学整合模型的建立，初始特征相似，但增加了来自放射治疗计划的三维剂量分布。采用 CI 和 Kaplan-Meier 曲线与对数秩检验来评估和比较这些模型。
从独立验证数据集观察到，剂量组学整合模型的 CI（0.66）与放射组学模型的 CI（0.59）有显著差异（Wilcoxon 检验，p=5.9×10）。整合模型成功地将患者分为高风险和低风险组（对数秩检验，p=2.5×10），而放射组学模型则无法进行这样的分类（对数秩检验，p=0.37）。
剂量组学可用于预测 IMRT 治疗患者的 LR，对于相关研究不应忽视。
