Martens Roland M, Koopman Thomas, Lavini Cristina, Brug Tim van de, Zwezerijnen Gerben J C, Marcus J Tim, Vergeer Marije R, Leemans C René, Bree Remco de, Graaf Pim de, Boellaard Ronald, Castelijns Jonas A
Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.
Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
Cancers (Basel). 2022 Jan 3;14(1):216. doi: 10.3390/cancers14010216.
Patients with locally-advanced head and neck squamous cell carcinoma (HNSCC) have variable responses to (chemo)radiotherapy. A reliable prediction of outcomes allows for enhancing treatment efficacy and follow-up monitoring.
Fifty-seven histopathologically-proven HNSCC patients with curative (chemo)radiotherapy were prospectively included. All patients had an MRI (DW,-IVIM, DCE-MRI) and F-FDG-PET/CT before and 10 days after start-treatment (intratreatment). Primary tumor functional imaging parameters were extracted. Univariate and multivariate analysis were performed to construct prognostic models and risk stratification for 2 year locoregional recurrence-free survival (LRFFS), distant metastasis-free survival (DMFS) and overall survival (OS). Model performance was measured by the cross-validated area under the receiver operating characteristic curve (AUC).
The best LRFFS model contained the pretreatment imaging parameters ADC_kurtosis, K and SUV_peak, and intratreatment imaging parameters change (Δ) Δ-ADC_skewness, Δ-f, Δ-SUV_peak and Δ-total lesion glycolysis (TLG) (AUC = 0.81). Clinical parameters did not enhance LRFFS prediction. The best DMFS model contained pretreatment ADC_kurtosis and SUV_peak (AUC = 0.88). The best OS model contained gender, HPV-status, N-stage, pretreatment ADC_skewness, D, f, metabolic-active tumor volume (MATV), SUV_mean and SUV_peak (AUC = 0.82). Risk stratification in high/medium/low risk was significantly prognostic for LRFFS ( = 0.002), DMFS ( < 0.001) and OS ( = 0.003).
Intratreatment functional imaging parameters capture early tumoral changes that only provide prognostic information regarding LRFFS. The best LRFFS model consisted of pretreatment, intratreatment and Δ functional imaging parameters; the DMFS model consisted of only pretreatment functional imaging parameters, and the OS model consisted ofHPV-status, gender and only pretreatment functional imaging parameters. Accurate clinically applicable risk stratification calculators can enable personalized treatment (adaptation) management, early on during treatment, improve counseling and enhance patient-specific post-therapy monitoring.
局部晚期头颈部鳞状细胞癌（HNSCC）患者对（放）化疗的反应各不相同。可靠的预后预测有助于提高治疗效果和随访监测。
前瞻性纳入57例经组织病理学证实接受根治性（放）化疗的HNSCC患者。所有患者在开始治疗前及治疗开始后10天（治疗期间）均接受了MRI（DW、IVIM、DCE-MRI）和F-FDG-PET/CT检查。提取原发肿瘤的功能成像参数。进行单因素和多因素分析，以构建2年局部区域无复发生存率（LRFFS）、无远处转移生存率（DMFS）和总生存率（OS）的预后模型和风险分层。通过交叉验证的受试者工作特征曲线下面积（AUC）来衡量模型性能。
最佳LRFFS模型包含治疗前成像参数ADC峰度、K和SUV峰值，以及治疗期间成像参数变化（Δ）Δ-ADC偏度、Δ-f、Δ-SUV峰值和Δ总病变糖酵解（TLG）（AUC = 0.81）。临床参数并未增强LRFFS预测。最佳DMFS模型包含治疗前ADC峰度和SUV峰值（AUC = 0.88）。最佳OS模型包含性别、HPV状态、N分期、治疗前ADC偏度、D、f、代谢活性肿瘤体积（MATV）、SUV均值和SUV峰值（AUC = 0.82）。高/中/低风险的风险分层对LRFFS（P = 0.002）、DMFS（P < 0.001）和OS（P = 0.003）具有显著的预后价值。
治疗期间功能成像参数可捕捉早期肿瘤变化，这些变化仅提供有关LRFFS的预后信息。最佳LRFFS模型由治疗前、治疗期间和Δ功能成像参数组成；DMFS模型仅由治疗前功能成像参数组成，OS模型由HPV状态、性别和仅治疗前功能成像参数组成。准确的临床适用风险分层计算器可在治疗早期实现个性化治疗（调整）管理，改善咨询并加强针对患者的治疗后监测。
