Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands; Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
Radiother Oncol. 2020 May;146:58-65. doi: 10.1016/j.radonc.2020.02.005. Epub 2020 Feb 27.
To develop and validate a pre-treatment radiomics-based prediction model to identify pathological lymph nodes (pLNs) at risk of failures after definitive radiotherapy in head and neck squamous cell carcinoma patients.
Training and validation cohorts consisted of 165 patients with 558 pLNs and 112 patients with 467 pLNs, respectively. All patients were primarily treated with definitive radiotherapy, with or without systemic treatment. The endpoint was the cumulative incidence of nodal failure. For each pLN, 82 pre-treatment CT radiomic features and 7 clinical features were included in the Cox proportional-hazard analysis.
There were 68 and 23 nodal failures in the training and validation cohorts, respectively. Multivariable analysis revealed three clinical features (T-stage, gender and WHO Performance-status) and two radiomic features (Least-axis-length representing nodal size and gray level co-occurrence matrix based - Correlation representing nodal heterogeneity) as independent prognostic factors. The model showed good discrimination with a c-index of 0.80 (0.69-0.91) in the validation cohort, significantly better than models based on clinical features (p < 0.001) or radiomics (p = 0.003) alone. High- and low-risk groups were defined by using thresholds of estimated nodal failure risks at 2-year of 60% and 10%, resulting in positive and negative predictive values of 94.4% and 98.7%, respectively.
A pre-treatment prediction model was developed and validated, integrating the quantitative radiomic features of individual lymph nodes with generally used clinical features. Using this prediction model, lymph nodes with a high failure risk can be identified prior to treatment, which might be used to select patients for intensified treatment strategies targeted on individual lymph nodes.
为了开发和验证一种基于治疗前影像组学的预测模型，以识别头颈部鳞状细胞癌患者接受根治性放疗后发生病理性淋巴结（pLN）失败的风险。
训练和验证队列分别包含 165 例患者的 558 个 pLN 和 112 例患者的 467 个 pLN。所有患者均接受根治性放疗联合或不联合全身治疗。终点是淋巴结失败的累积发生率。对于每个 pLN，纳入了 82 个治疗前 CT 影像组学特征和 7 个临床特征，进行 Cox 比例风险分析。
训练和验证队列中分别有 68 个和 23 个淋巴结发生失败。多变量分析显示，3 个临床特征（T 分期、性别和世界卫生组织体力状况）和 2 个影像组学特征（代表淋巴结大小的最小轴长和代表淋巴结异质性的灰度共生矩阵相关）是独立的预后因素。该模型在验证队列中的区分度较好，c 指数为 0.80（0.69-0.91），显著优于仅基于临床特征（p<0.001）或影像组学（p=0.003）的模型。通过使用 2 年时估计的淋巴结失败风险为 60%和 10%的阈值，定义了高风险和低风险组，从而产生了 94.4%和 98.7%的阳性和阴性预测值。
开发并验证了一种预测模型，将个体淋巴结的定量影像组学特征与常用的临床特征相结合。使用该预测模型，可以在治疗前识别出具有高失败风险的淋巴结，从而可以选择针对个体淋巴结的强化治疗策略。
