{"title":"基于人工智能的免疫评分与晚期非鳞状非小细胞肺癌患者化疗免疫疗法疗效的关系：一项多中心回顾性研究。","authors":"Jiaqing Liu, Dongchen Sun, Shuoyu Xu, Jiayi Shen, Wenjuan Ma, Huaqiang Zhou, Yuxiang Ma, Yaxiong Zhang, Wenfeng Fang, Yuanyuan Zhao, Shaodong Hong, Jianhua Zhan, Xue Hou, Hongyun Zhao, Yan Huang, Bingdou He, Yunpeng Yang, Li Zhang","doi":"10.3389/fimmu.2024.1485703","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomarkers for predicting the efficacy of chemoimmunotherapy would be useful to identify patients who would benefit from chemoimmunotherapy. The primary objective of our study was to develop an artificial intelligence-based immunoscore and to evaluate the value of patho-immunoscore in predicting clinical outcomes in patients with advanced non-squamous non-small cell lung cancer (NSCLC).</p><p><strong>Methods: </strong>We have developed an artificial intelligence-powered immunoscore analyzer based on 1,333 whole-slide images from TCGA-LUAD. The predictive efficacy of the model was further validated in the CPTAC-LUAD cohort and the biomarker cohort of the ORIENT-11 study, a randomized, double-blind, phase 3 study. Finally, the clinical significance of the patho-immunoscore was evaluated using the ORIENT-11 study cohort.</p><p><strong>Results: </strong>Our immunoscore analyzer achieved good accuracy in all the three cohort mentioned above (TCGA-LUAD, mean AUC: 0.783; ORIENT-11 cohort, AUC: 0.741; CPTAC-LUAD cohort, AUC: 0.769). In the 259 patients treated with chemoimmunotherapy, those with high patho-immunoscore (n = 146) showed significantly longer median progression-free survival than those with low patho-immunoscore (n = 113) (13.8 months vs 7.13 months, hazard ratio [HR]: 0.53, 95% confidence interval [CI]: 0.38 - 0.73; <i>p</i> < 0.001). In contrast, no significant difference was observed in patients who were treated with chemotherapy only (5.07 months vs 5.07 months, HR: 1.04, 95% CI: 0.71 - 1.54; <i>p</i> = 0.83). Similar trends were observed in overall survival.</p><p><strong>Conclusion: </strong>Our study indicates that AI-powered immunoscore applied on LUAD digital slides can serve as a biomarker for survival outcomes in patients with advanced non-squamous NSCLC who received chemoimmunotherapy. This methodology could be applied to other cancers and facilitate cancer immunotherapy.</p>","PeriodicalId":12622,"journal":{"name":"Frontiers in Immunology","volume":"15 ","pages":"1485703"},"PeriodicalIF":5.7000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576461/pdf/","citationCount":"0","resultStr":"{\"title\":\"Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced non-squamous non-small cell lung cancer: a multicentre retrospective study.\",\"authors\":\"Jiaqing Liu, Dongchen Sun, Shuoyu Xu, Jiayi Shen, Wenjuan Ma, Huaqiang Zhou, Yuxiang Ma, Yaxiong Zhang, Wenfeng Fang, Yuanyuan Zhao, Shaodong Hong, Jianhua Zhan, Xue Hou, Hongyun Zhao, Yan Huang, Bingdou He, Yunpeng Yang, Li Zhang\",\"doi\":\"10.3389/fimmu.2024.1485703\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomarkers for predicting the efficacy of chemoimmunotherapy would be useful to identify patients who would benefit from chemoimmunotherapy. The primary objective of our study was to develop an artificial intelligence-based immunoscore and to evaluate the value of patho-immunoscore in predicting clinical outcomes in patients with advanced non-squamous non-small cell lung cancer (NSCLC).</p><p><strong>Methods: </strong>We have developed an artificial intelligence-powered immunoscore analyzer based on 1,333 whole-slide images from TCGA-LUAD. The predictive efficacy of the model was further validated in the CPTAC-LUAD cohort and the biomarker cohort of the ORIENT-11 study, a randomized, double-blind, phase 3 study. Finally, the clinical significance of the patho-immunoscore was evaluated using the ORIENT-11 study cohort.</p><p><strong>Results: </strong>Our immunoscore analyzer achieved good accuracy in all the three cohort mentioned above (TCGA-LUAD, mean AUC: 0.783; ORIENT-11 cohort, AUC: 0.741; CPTAC-LUAD cohort, AUC: 0.769). In the 259 patients treated with chemoimmunotherapy, those with high patho-immunoscore (n = 146) showed significantly longer median progression-free survival than those with low patho-immunoscore (n = 113) (13.8 months vs 7.13 months, hazard ratio [HR]: 0.53, 95% confidence interval [CI]: 0.38 - 0.73; <i>p</i> < 0.001). In contrast, no significant difference was observed in patients who were treated with chemotherapy only (5.07 months vs 5.07 months, HR: 1.04, 95% CI: 0.71 - 1.54; <i>p</i> = 0.83). Similar trends were observed in overall survival.</p><p><strong>Conclusion: </strong>Our study indicates that AI-powered immunoscore applied on LUAD digital slides can serve as a biomarker for survival outcomes in patients with advanced non-squamous NSCLC who received chemoimmunotherapy. 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引用次数: 0
摘要
目的：目前，化学免疫疗法仅对部分晚期非鳞状非小细胞肺癌患者有效。预测化疗免疫疗法疗效的可靠生物标志物将有助于确定哪些患者可从化疗免疫疗法中获益。我们研究的主要目的是开发一种基于人工智能的免疫评分，并评估病理免疫评分在预测晚期非鳞状非小细胞肺癌（NSCLC）患者临床预后方面的价值：方法：我们基于TCGA-LUAD的1,333张全切片图像开发了一种人工智能驱动的免疫评分分析仪。该模型的预测效果在CPTAC-LUAD队列和ORIENT-11研究（一项随机、双盲、三期研究）的生物标志物队列中得到了进一步验证。最后，利用 ORIENT-11 研究队列评估了病理免疫评分的临床意义：结果：我们的免疫评分分析仪在上述三个队列中都达到了良好的准确性（TCGA-LUAD，平均AUC：0.783；ORIENT-11 队列，AUC：0.741；CPTAC-LUAD队列，AUC：0.769).在接受化疗免疫疗法的 259 例患者中，病理免疫评分高的患者（146 例）的中位无进展生存期明显长于病理免疫评分低的患者（113 例）（13.8 个月 vs 7.13 个月，危险比 [HR]：0.53，95% 置信区间 [CI]：0.38 - 0.73; p < 0.001).相比之下，仅接受化疗的患者没有观察到明显差异（5.07 个月 vs 5.07 个月，HR：1.04，95% 置信区间：0.71 - 1.54；P = 0.83）。在总生存期方面也观察到类似的趋势：我们的研究表明，在LUAD数字切片上应用人工智能驱动的免疫评分可作为接受化疗免疫治疗的晚期非鳞癌NSCLC患者生存结果的生物标志物。这种方法可应用于其他癌症，促进癌症免疫疗法的发展。
Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced non-squamous non-small cell lung cancer: a multicentre retrospective study.
Purpose: Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomarkers for predicting the efficacy of chemoimmunotherapy would be useful to identify patients who would benefit from chemoimmunotherapy. The primary objective of our study was to develop an artificial intelligence-based immunoscore and to evaluate the value of patho-immunoscore in predicting clinical outcomes in patients with advanced non-squamous non-small cell lung cancer (NSCLC).
Methods: We have developed an artificial intelligence-powered immunoscore analyzer based on 1,333 whole-slide images from TCGA-LUAD. The predictive efficacy of the model was further validated in the CPTAC-LUAD cohort and the biomarker cohort of the ORIENT-11 study, a randomized, double-blind, phase 3 study. Finally, the clinical significance of the patho-immunoscore was evaluated using the ORIENT-11 study cohort.
Results: Our immunoscore analyzer achieved good accuracy in all the three cohort mentioned above (TCGA-LUAD, mean AUC: 0.783; ORIENT-11 cohort, AUC: 0.741; CPTAC-LUAD cohort, AUC: 0.769). In the 259 patients treated with chemoimmunotherapy, those with high patho-immunoscore (n = 146) showed significantly longer median progression-free survival than those with low patho-immunoscore (n = 113) (13.8 months vs 7.13 months, hazard ratio [HR]: 0.53, 95% confidence interval [CI]: 0.38 - 0.73; p < 0.001). In contrast, no significant difference was observed in patients who were treated with chemotherapy only (5.07 months vs 5.07 months, HR: 1.04, 95% CI: 0.71 - 1.54; p = 0.83). Similar trends were observed in overall survival.
Conclusion: Our study indicates that AI-powered immunoscore applied on LUAD digital slides can serve as a biomarker for survival outcomes in patients with advanced non-squamous NSCLC who received chemoimmunotherapy. This methodology could be applied to other cancers and facilitate cancer immunotherapy.
期刊介绍：
Frontiers in Immunology is a leading journal in its field, publishing rigorously peer-reviewed research across basic, translational and clinical immunology. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
Frontiers in Immunology is the official Journal of the International Union of Immunological Societies (IUIS). Encompassing the entire field of Immunology, this journal welcomes papers that investigate basic mechanisms of immune system development and function, with a particular emphasis given to the description of the clinical and immunological phenotype of human immune disorders, and on the definition of their molecular basis.
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