Winter René M, Boeke Simon, Leibfarth Sara, Habrich Jonas, Clasen Kerstin, Nikolaou Konstantin, Zips Daniel, Thorwarth Daniela
Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany; Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Radiation Oncology, University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
Radiother Oncol. 2025 Mar;204:110702. doi: 10.1016/j.radonc.2024.110702. Epub 2024 Dec 27.
To retrain a model based on a previously identified prognostic imaging biomarker using apparent diffusion coefficient (ADC) values from diffusion-weighted magnetic resonance imaging (DW-MRI) in a preclinical setting and validate the model using clinical DW-MRI data of patients with locally advanced head-and-neck cancer (HNC) acquired before radiochemotherapy.
A total of 31 HNC patients underwent T2-weighted and DW-MRI using 3 T MRI before radiochemotherapy (35 x 2 Gy). Gross tumor volumes (GTV) were delineated based on T2-weighted and b500 images. A preclinical model previously revealed that the size of high-risk subvolumes (HRS) defined by a band of ADC-values was correlated to radiation resistance. To validate this model, different bands of ADC-values were tested using two-sided thresholds on the low-ADC histogram flank to determine HRSs inside the GTV and correlated to treatment outcome after three years. The best model was used to fit a logistic regression model. Stratification potential regarding outcome was internally validated using bootstrap, receiver-operator-characteristic (ROC)-analysis, Kaplan-Meier- and Cox-method, and compared to GTV, ADC and clinical factors.
The best model was defined by 800<ADC<1100·10mm/s and correlated significantly to treatment outcome (p = 0.003). Optimal HRS cut-off value was found to be 5.8 cm according to ROC-analysis. This HRS demonstrated highly significant stratification potential (p < 0.001, bootstrap AUC ≥ 0.84) similar to GTV size (p < 0.001, AUC ≥ 0.79), in contrast to ADC (p = 0.361, AUC = 0.53).
A preclinical prognostic model defined by an ADC-based HRS was successfully retrained and validated in HNC patients treated with radiochemotherapy. After thorough external validation, such functional HRS based on a band of ADC values may in the future allow interventional response-adaptive MRI-guided radiotherapy in online and offline approaches.
在临床前环境中，基于先前确定的预后影像生物标志物，利用扩散加权磁共振成像（DW-MRI）的表观扩散系数（ADC）值对模型进行重新训练，并使用放化疗前获取的局部晚期头颈癌（HNC）患者的临床DW-MRI数据对该模型进行验证。
31例HNC患者在放化疗（35×2 Gy）前使用3T MRI进行T2加权和DW-MRI检查。基于T2加权和b500图像勾勒大体肿瘤体积（GTV）。先前的一个临床前模型显示，由ADC值带定义的高风险子体积（HRS）大小与放射抗性相关。为验证该模型，在低ADC直方图侧翼使用双侧阈值测试不同的ADC值带，以确定GTV内的HRS，并将其与三年后的治疗结果相关联。使用最佳模型拟合逻辑回归模型。使用自举法、受试者操作特征（ROC）分析、Kaplan-Meier法和Cox法对结果的分层潜力进行内部验证，并与GTV、ADC和临床因素进行比较。
最佳模型由800<ADC<1100·10⁻⁶mm²/s定义，与治疗结果显著相关（p = 0.003）。根据ROC分析，最佳HRS截断值为5.8 cm。与ADC（p = 0.361，AUC = 0.53）相比，该HRS显示出与GTV大小（p < 0.001，AUC≥0.79）相似的高度显著分层潜力（p < 0.001，自举AUC≥0.84）。
在接受放化疗的HNC患者中，成功地对基于ADC的HRS定义的临床前预后模型进行了重新训练和验证。经过全面的外部验证后，这种基于ADC值带的功能性HRS未来可能允许在在线和离线方法中进行介入性反应适应性MRI引导放疗。
