Yolchuyeva Sevinj, Giacomazzi Elena, Tonneau Marion, Ebrahimpour Leyla, Lamaze Fabien C, Orain Michele, Coulombe François, Malo Julie, Belkaid Wiam, Routy Bertrand, Joubert Philippe, Manem Venkata S K
Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada.
Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada.
Cancers (Basel). 2023 Jul 28;15(15):3829. doi: 10.3390/cancers15153829.
Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy.
This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort.
We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively.
We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.
免疫检查点抑制剂（ICIs）是癌症治疗领域的一项重大突破，可提高部分非小细胞肺癌（NSCLC）患者的长期生存率。然而，免疫治疗的预后和预测生物标志物仍未满足临床需求。在本研究中，我们旨在利用影像数据和临床变量，为接受免疫治疗的晚期NSCLC患者建立生存风险模型。
这项回顾性研究共纳入了来自两个机构的385例接受ICIs治疗的患者。从治疗前CT扫描中提取的放射组学特征用于构建预测模型。目标是预测总生存期（OS），并建立一个区分短期和长期生存组的分类器。我们采用XGBoost学习方法构建放射组学模型以及整合临床-放射组学预测模型。在多中心队列中进行特征选择和模型构建，并进行验证。
我们建立了与OS相关的简约模型以及一个区分短期和长期生存组的分类器。放射组学模型在发现队列和验证队列中预测OS的一致性指数（C-index）分别为0.61和0.57。而放射组学模型在发现队列和验证队列中对短期和长期组的曲线下面积（AUC）值分别为0.65和0.58。放射组学-临床联合模型预测OS的准确率在发现队列和验证队列中分别为0.63和0.62，区分生存组的准确率分别为0.77和0.62。
我们在接受ICIs治疗的NSCLC患者中开发并验证了新型放射组学模型以及整合放射组学-临床生存模型。该模型具有重要的转化意义，可用于识别不太可能从免疫治疗中获益的患者亚组。所开发的影像生物标志物可能有助于早期预测低生存组患者，不过这些放射组学模型仍需进一步验证。
