School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
Department of Radiology, Medical Center Boulevard, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.
Eur Radiol. 2019 May;29(5):2196-2206. doi: 10.1007/s00330-018-5770-y. Epub 2018 Dec 6.
The aim of this study was to develop a radiomics nomogram by combining the optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical predictors to assess the overall survival of patients with non-small cell lung cancer (NSCLC).
One training cohort of 239 and two validation datasets of 80 and 52 NSCLC patients were enrolled in this study. Nine hundred seventy-five radiomics features were extracted from each patient's 2D and 3D CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate a radiomics signature. Cox hazard survival analysis and Kaplan-Meier were performed in both cohorts. The radiomics nomogram was developed by integrating the optimized radiomics signature and clinical predictors, its calibration and discrimination were evaluated.
The radiomics signatures were significantly associated with NSCLC patients' survival time. The signature derived from the combined 2D and 3D features showed a better prognostic performance than those from 2D or 3D alone. Our radiomics nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of patients' survival compared with clinical predictors alone in the validation cohort. The calibration curve showed predicted survival time was very close to the actual one.
The radiomics signature from the combined 2D and 3D features further improved the predicted accuracy of survival prognosis for the patients with NSCLC. Combination of the optimal radiomics signature and clinical predictors performed better for individualied survival prognosis estimation in patients with NSCLC. These findings might affect trearment strategies and enable a step forward for precise medicine.
• We found both 2D and 3D radiomics signature have favorable prognosis, but 3D signature had a better performance. • The radiomics signature generated from the combined 2D and 3D features had a better predictive performance than those from 2D or 3D features. • Integrating the optimal radiomics signature with clinical predictors significantly improved the predictive power in patients' survival compared with clinical TNM staging alone.
本研究旨在结合从 2D 和/或 3D CT 图像中提取的优化放射组学特征和临床预测因子，开发一个放射组学列线图，以评估非小细胞肺癌（NSCLC）患者的总生存率。
本研究纳入了一个训练队列的 239 例患者和两个验证队列的 80 例和 52 例 NSCLC 患者。从每位患者的 2D 和 3D CT 图像中提取了 975 个放射组学特征。采用最小绝对值收缩和选择算子（LASSO）回归选择特征并生成放射组学特征。在两个队列中均进行 Cox 风险生存分析和 Kaplan-Meier 分析。通过整合优化的放射组学特征和临床预测因子，开发放射组学列线图，并对其校准和区分能力进行评估。
放射组学特征与 NSCLC 患者的生存时间显著相关。来自 2D 和 3D 联合特征的特征衍生的放射组学特征比来自 2D 或 3D 单独特征的特征具有更好的预后性能。我们的放射组学列线图将最佳放射组学特征与临床预测因子相结合，在验证队列中，与仅基于临床预测因子相比，显著提高了患者生存预测的准确性。校准曲线表明预测的生存时间与实际生存时间非常接近。
来自 2D 和 3D 联合特征的放射组学特征进一步提高了 NSCLC 患者生存预后的预测准确性。最佳放射组学特征与临床预测因子的结合在 NSCLC 患者个体化生存预后估计方面表现更好。这些发现可能会影响治疗策略，并为精准医学迈出一步。
· 我们发现 2D 和 3D 放射组学特征都具有良好的预后，但 3D 特征的性能更好。
· 来自 2D 和 3D 联合特征的放射组学特征生成的预测性能优于来自 2D 或 3D 特征的预测性能。
· 与仅基于临床 TNM 分期相比，整合最佳放射组学特征与临床预测因子显著提高了患者生存预测的能力。
