Role of Peripheral Blood Markers for Detecting Response and Predicting Prognosis in Patients with Non-small-cell Lung Cancer Undergoing Neoadjuvant Therapy and Surgery

Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC

Objective

To compare the performance of three machine learning algorithms with the tumor, node, and metastasis (TNM) staging system in survival prediction and validate the individual adjuvant treatment recommendations plan based on the optimal model.

Methods

In this study, we trained three machine learning madel and validated 3 machine learning survival models-deep learning neural network, random forest and cox proportional hazard model- using the data of patients with stage-al3 NSCLC patients who received resection surgery from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database from 2012 to 2017,the performance of survival predication from all machine learning models were assessed using a concordance index (c-index) and the averaged c-index is utilized for cross-validation. The optimal model was externally validated in an independent cohort from Shaanxi Provincial People’s Hospital. Then we compare the performance of the optimal model and TNM staging system. Finally, we developed a Cloud-based recommendation system for adjuvant therapy to visualize survival curve of each treatment plan and deployed on the internet.

Results

A total of 4617 patients were included in this study. The deep learning network performed more stably and accurately in predicting stage-iii NSCLC resected patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.834 vs. 0.678 vs. 0.640) and better than TNM staging system (C-index=0.820 vs. 0.650) in the external validation. The individual patient who follow the reference from recommendation system had superior survival compared to those who did not. The predicted 5-year-survival curve for each adjuvant treatment plan could be accessed in the recommender system via the browser.

Conclusion

Deep learning model has several advantages over linear model and random forest model in prognostic predication and treatment recommendations. This novel analytical approach may provide accurate predication on individual survival and treatment recommendations for resected Stage-iii NSCLC patients.

The Effect of Gefitinib on Treatment Necessity and Prognosis of NSCLC Patients with Early EGFR Mutations

Objective

To investigate the need for and prognostic impact of gefitinib on the treatment of patients with early-stage epidermal growth factor receptor (EGFR) mutated non-small-cell lung cancer (NSCLC).

Methods

Clinical data of patients with stage IB-IIA non-squamous non-small-cell lung cancer admitted to our thoracic surgery department from January 2020 to January 2022 were collected, and a total of 94 cases were included, divided into 44 cases in the control group (EGFR mutation-negative) and 50 cases in the experimental group (EGFR mutation-positive (including those on medication (19 cases) and those not on medication (31 cases)) according to the outcome of EGFR mutation. To evaluate the necessity and prognostic effect of gefitinib in the treatment of NSCLC patients with early EGFR mutations.

Results

The lung cancer recurrence rate in the experimental group (66.00%) was higher than that in the control group (40.91%), and the difference was statistically significant (χ ² = 5.937, P=0.015); in the subgroup analysis of the experimental group samples, the pharmacological intervention of gefitinib had a significant effect on lung cancer recurrence (χ ² = 7.797, P=0.005), and the proportion of lung cancer recurrence in patients not taking the drug (80.65%) was significantly higher than in the drug-taking group (42.11%); the median survival time was 53.6 months using EGFR mutation type as the study factor, with a statistically significant difference in change in 5-year survival rate for EGFR mutation type (χ ² = 6.095, P=0.047) and the lowest 5-year survival rate for subjects with EGFR mutation type Exon 20 T790M.

Conclusion

Patients with early gene drive positive lung adenocarcinoma are significantly more likely to recur and metastasise and have shorter survival times in the absence of pharmacological intervention.