School of Physics, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, PR China.
Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, PR China.
J Magn Reson Imaging. 2021 Sep;54(3):854-865. doi: 10.1002/jmri.27633. Epub 2021 Apr 8.
Accurately predicting the risk of death, recurrence, and metastasis of patients with nasopharyngeal carcinoma (NPC) is potentially important for personalized diagnosis and treatment. Survival outcomes of patients vary greatly in distinct stages of NPC. Prognostic models of stratified patients may aid in prognostication.
To explore the prognostic performance of MRI-based radiomics signatures in stratified patients with NPC.
Retrospective.
Seven hundred and seventy-eight patients with NPC (T1-2 stage: 298, T3-4 stage: 480; training cohort: 525, validation cohort: 253).
FIELD STRENGTH/SEQUENCE: Fast-spin echo (FSE) axial T1-weighted images, FSE axial T2-weighted images, contrast-enhanced FSE axial T1-weighted images at 1.5 T or 3.0 T.
Radiomics signatures, clinical nomograms, and radiomics nomograms combining the radiomic score (Radscore) and clinical factors for predicting progression-free survival (PFS) were constructed on T1-2 stage patient cohort (A), T3-4 stage patient cohort (B), and the entire dataset (C).
Least absolute shrinkage and selection operator (LASSO) method was applied for radiomics modeling. Harrell's concordance indices (C-index) were employed to evaluate the predictive power of each model.
Among 4,410 MRI-extracted features, we selected 16, 16, and 14 radiomics features most relevant to PFS for Models A, B, and C, respectively. Only 0, 1, and 4 features were found overlapped between models A/B, A/C, and B/C, respectively. Radiomics signatures constructed on T1-2 stage and T3-4 stage patients yielded C-indices of 0.820 (95% confidence interval [CI]: 0.763-0.877) and 0.726 (0.687-0.765), respectively, which were larger than those on the entire validation cohort (0.675 [0.637-0.713]). Radiomics nomograms combining Radscore and clinical factors achieved significantly better performance than clinical nomograms (P < 0.05 for all).
The selected radiomics features and prognostic performance of radiomics signatures differed per the type of NPC patients incorporated into the models. Radiomics models based on pre-stratified tumor stages had better prognostic performance than those on unstratified dataset.
4 Technical Efficacy Stage: 5.
准确预测鼻咽癌（NPC）患者的死亡、复发和转移风险对于个性化诊断和治疗具有重要意义。不同 NPC 分期患者的生存结果差异很大。分层患者的预后模型可能有助于预后判断。
探讨基于 MRI 的放射组学特征在 NPC 分层患者中的预后性能。
回顾性。
778 例 NPC 患者（T1-2 期：298 例，T3-4 期：480 例；训练队列：525 例，验证队列：253 例）。
场强/序列：快速自旋回波（FSE）轴位 T1 加权像、FSE 轴位 T2 加权像、1.5T 或 3.0T 增强 FSE 轴位 T1 加权像。
在 T1-2 期患者队列（A）、T3-4 期患者队列（B）和整个数据集（C）上构建放射组学特征、临床列线图和结合放射组学评分（Radscore）和临床因素的放射组学列线图，以预测无进展生存期（PFS）。
最小绝对收缩和选择算子（LASSO）方法用于放射组学建模。哈雷尔一致性指数（C 指数）用于评估每个模型的预测能力。
在 4410 个 MRI 提取特征中，我们分别为模型 A、B 和 C 选择了与 PFS 最相关的 16、16 和 14 个放射组学特征。模型 A/B、A/C 和 B/C 之间仅分别发现 0、1 和 4 个特征重叠。在 T1-2 期和 T3-4 期患者中构建的放射组学特征的 C 指数分别为 0.820（95%置信区间[CI]：0.763-0.877）和 0.726（0.687-0.765），均大于整个验证队列（0.675 [0.637-0.713]）。结合 Radscore 和临床因素的放射组学列线图的性能明显优于临床列线图（所有 P<0.05）。
纳入模型的 NPC 患者类型不同，所选放射组学特征和放射组学特征的预后性能也不同。基于肿瘤分期预先分层的放射组学模型比基于非分层数据集的模型具有更好的预后性能。
4 技术功效阶段：5。
