Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Front Immunol. 2024 Jun 12;15:1414954. doi: 10.3389/fimmu.2024.1414954. eCollection 2024.
To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image.
This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).
In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686.
The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.
利用术前 CT 图像定量评估肿瘤内异质性，探讨预测接受新辅助免疫化疗（NAIC）的非小细胞肺癌（NSCLC）患者病理完全缓解（pCR）的能力。
本回顾性研究纳入了在 4 个不同中心接受 NAIC 的 178 例 NSCLC 患者。训练集包括中心 A 的 108 例患者，外部验证集由中心 B、中心 C 和中心 D 的 70 例患者组成。通过比较传统放射组学模型和放射组学特征，利用感兴趣区（ROI）内肿瘤的每个像素的放射组学特征，采用 K 均值无监督聚类方法确定肿瘤亚区的最佳划分，利用每个肿瘤亚区的特征建立肿瘤内部异质性生境模型。本研究采用逻辑回归（LR）算法构建机器学习预测模型，使用受试者工作特征曲线下面积（AUC）、准确率、特异度、敏感度、阳性预测值（PPV）和阴性预测值（NPV）等标准评估模型的诊断性能。
在训练队列中，传统放射组学模型的 AUC 为 0.778[95%置信区间（CI）：0.688-0.868]，而肿瘤内部异质性生境模型的 AUC 为 0.861（95%CI：0.789-0.932）。肿瘤内部异质性生境模型具有更高的 AUC 值，其准确率为 0.815，高于传统放射组学模型的 0.685。在外部验证队列中，两个模型的 AUC 值分别为 0.723（CI：0.591-0.855）和 0.781（95%CI：0.673-0.889），生境模型的 AUC 值仍然较高。在准确性评估方面，肿瘤异质性生境模型优于传统放射组学模型，得分为 0.743，而传统放射组学模型得分为 0.686。
利用 CT 对肿瘤内异质性进行定量分析，预测接受新辅助免疫化疗的 NSCLC 患者的 pCR，可能有助于为可切除 NSCLC 患者的临床决策提供信息，防止过度治疗，并实现个体化和精准的癌症管理。
