Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network - Li, Siqi; Li, Wei; Ma, Tianyu; Fu, Siyun; Gao, Xiang; Qin, Na; Wu, Yuhua; Zhang, Xinyong; Wang, Jinghui; Pan, Yuanming; Liu, Zhidong (Frontiers in Immunology, 2022)
Background: At present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy for lung squamous carcinoma patients who received immunotherapy. Methods: In our retrospective study we enrolled lung squamous carcinoma patients who received immunotherapy in Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of total) and the test cohort (20% of total). The training cohort was used to build neural network models to assess the efficacy and outcome of immunotherapy in lung squamous carcinoma based on clinical information. The main outcome was the disease control rate (DCR), then secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Results: 289 patients were included in this study. DCR model owned 0.9526 (95%CI, 0.9088-0.9879) AUC in internal validation and 0.9491 (95%CI, 0.8704-1.0000) in external validation. ORR model performed with 0.8030 AUC (95%CI, 0.7437-0.8545) in internal validation and 0.7040 (95%CI, 0.5457-0.8379) in external validation. PFS model had 0.8531 (95%CI, 0.8024-0.8975) AUC in internal validation and 0.7602 (95%CI, 0.6236-0.8733) in external validation. OS model had a 0.8006 AUC (95%CI, 0.7995-0.8017) in internal validation and 0.7382 (95%CI, 0.7366- 0.7398) in external validation. Conclusions: The neural network models show benefits in the efficacy evaluation of immunotherapy to lung squamous carcinoma patients, especially DCR and ORR. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.