Bruixola Gema, Dualde-Beltrán Delfina, Jimenez-Pastor Ana, Nogué Anna, Bellvís Fuensanta, Fuster-Matanzo Almudena, Alfaro-Cervelló Clara, Grimalt Nuria, Salhab-Ibáñez Nader, Escorihuela Vicente, Iglesias María Eugenia, Maroñas María, Alberich-Bayarri Ángel, Cervantes Andrés, Tarazona Noelia
Medical Oncology Department, Hospital Clinico Universitario de Valencia-INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain.
Radiology Department, Hospital Clinico Universitario de Valencia, University of Valencia, Valencia, Spain.
Eur Radiol. 2024 Dec 20. doi: 10.1007/s00330-024-11301-6.
Definitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk stratification.
This single-centre observational study collected clinical data and baseline CT scans from 171 LAHNSCC patients treated with chemoradiation. The dataset was divided into training (80%) and test (20%) sets, with a 5-fold cross-validation on the training set. Researchers extracted 108 radiomics features from each primary tumour and applied survival analysis and classification models to predict progression-free survival (PFS) and 5-year progression, respectively. Performance was evaluated using inverse probability of censoring weights and c-index for the PFS model and AUC, sensitivity, specificity, and accuracy for the 5-year progression model. Feature importance was measured by the SHapley Additive exPlanations (SHAP) method and patient stratification was assessed through Kaplan-Meier curves.
The final dataset included 171 LAHNSCC patients, with 53% experiencing disease progression at 5 years. The random survival forest model best predicted PFS, with an AUC of 0.64 and CI of 0.66 on the test set, highlighting 4 radiomics features and TNM8 as significant contributors. It successfully stratified patients into low and high-risk groups (log-rank p < 0.005). The extreme gradient boosting model most effectively predicted a 5-year progression, incorporating 12 radiomics features and four clinical variables, achieving an AUC of 0.74, sensitivity of 0.53, specificity of 0.81, and accuracy of 0.66 on the test set.
The combined clinical-radiomics model improved the standard TNM8 and clinical variables in predicting 5-year progression though further validation is necessary.
Question There is an unmet need for non-invasive biomarkers to guide treatment in locally advanced head and neck cancer. Findings Clinical data (TNM8 staging, primary tumour site, age, and smoking) plus radiomics improved 5-year progression prediction compared with the clinical comprehensive model or TNM staging alone. Clinical relevance SHAP simplifies complex machine learning radiomics models for clinicians by using easy-to-understand graphical representations, promoting explainability.
确定性放化疗是局部晚期头颈癌（LAHNSCC）的主要治疗方法。由于TNM8和人乳头瘤病毒（HPV）可能存在局限性，因此优化预后预测需要经过验证的生物标志物。放射组学可能会增强风险分层。
这项单中心观察性研究收集了171例接受放化疗的LAHNSCC患者的临床数据和基线CT扫描。数据集被分为训练集（80%）和测试集（20%），并在训练集上进行5折交叉验证。研究人员从每个原发性肿瘤中提取了108个放射组学特征，并应用生存分析和分类模型分别预测无进展生存期（PFS）和5年进展情况。使用删失权重的逆概率和PFS模型的c指数以及5年进展模型的AUC、敏感性、特异性和准确性来评估性能。通过SHapley加性解释（SHAP）方法测量特征重要性，并通过Kaplan-Meier曲线评估患者分层。
最终数据集包括171例LAHNSCC患者，其中53%在5年后出现疾病进展。随机生存森林模型对PFS的预测效果最佳，测试集上的AUC为0.64，CI为0.66，突出显示4个放射组学特征和TNM8是重要贡献因素。它成功地将患者分为低风险和高风险组（对数秩p<0.005）。极端梯度提升模型对5年进展的预测最有效，纳入了12个放射组学特征和4个临床变量，测试集上的AUC为0.74，敏感性为0.53，特异性为0.81，准确性为0.66。
临床-放射组学联合模型在预测5年进展方面改进了标准的TNM8和临床变量，不过仍需要进一步验证。
问题在局部晚期头颈癌的治疗中，对非侵入性生物标志物的需求尚未得到满足。研究结果与临床综合模型或单独的TNM分期相比，临床数据（TNM8分期、原发性肿瘤部位、年龄和吸烟情况）加上放射组学改善了5年进展预测。临床意义SHAP通过使用易于理解的图形表示简化了复杂的机器学习放射组学模型，提高了可解释性。
