Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network

Abstract

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 of lung squamous carcinoma patients who received immunotherapy.

Methods: In our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of the total) and the test cohort (20% of the 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), and then the secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS).

Results: A total of 289 patients were included in this study. The DCR model had area under the receiver operating characteristic curve (AUC) value of 0.9526 (95%CI, 0.9088-0.9879) in internal validation and 0.9491 (95%CI, 0.8704-1.0000) in external validation. The ORR model had AUC of 0.8030 (95%CI, 0.7437-0.8545) in internal validation and 0.7040 (95%CI, 0.5457-0.8379) in external validation. The PFS model had AUC of 0.8531 (95%CI, 0.8024-0.8975) in internal validation and 0.7602 (95%CI, 0.6236-0.8733) in external validation. The OS model had AUC of 0.8006 (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 the DCR and ORR models. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.

Keywords: deep learning; immunotherapy; lung squamous carcinoma; neural network; predictive model.