Development of Clinical Prediction Score for Chemotherapy Response in Advanced Non-Small Cell Lung Cancer Patients

Abstract

The outcomes of advanced non-small cell lung cancer (NSCLC) patients have been significantly improved with novel therapies, such as tyrosine kinase inhibitors and immune checkpoint inhibitors. However, in resource-limited countries, platinum-doublet chemotherapy is mainly used as a first-line treatment. We investigate clinical parameters to predict the response after chemotherapy, which may be useful for patient selection. A clinical prediction score (CPS) was developed, based on data from a retrospective cohort study of unresectable stage IIIB or IV NSCLC patients who were treated with platinum-doublet chemotherapy in the first-line setting with at least two cycles and an evaluated response by RECIST 1.1 at Surin Hospital Cancer Center, Thailand, between July 2014 and December 2018. The clinical parameters in the prediction model were derived by risk regression analysis. There were 117 responders (CR or PR) and 90 non-responders (SD or PD). The clinical prediction score was developed by six clinical parameters including gender, age, smoking status, ECOG, pre-treatment albumin, and histologic subtype. The AuROC of the model was 0.71 (95% CI 0.63–0.78). The internal validation was performed using a bootstrap technique and showed a consistent AuROC of 0.66 (95% CI 0.59–0.72). The prediction score ranged from 0–13, with a score of 0–8 meaning a low probability (PPV = 50%) and a score of 8.5–13 meaning a high probability (PPV = 83.7%) for chemotherapy response. Advanced NSCLC patients who cannot access novel therapies and have a CPS of 8.5–13 have a high probability for chemotherapy response in the first-line setting. This CPS could be used for risk communication and making decisions with patients, especially in regard to chemotherapy.

Keywords

• clinical prediction rule
• chemotherapy
• limited resources
• advanced lung cancer
• decision making