Anjali K. Pahuja, Kundan S Chufal, Irfan Ahmad, Ram Bajpai, Rajpal Singh, Rahul Lal Chowdhary, M. Sharma
Dec 1, 2019
The objective of this research paper was to improve the prediction of treatment outcomes in patients with unresectable stage III nonsmall cell lung cancer (NSCLC) by utilizing cluster analysis (CA), a machine learning tool capable of identifying complex interactions among variables. The study analyzed treatment outcomes of 92 patients who underwent chemoradiation for inoperable locally advanced NSCLC between 2012 and 2018. Exploratory factor analysis was performed to extract factors with eigenvalue > 1, and cluster analysis was used to identify homogeneous groups based on these factors. K-mean cluster analysis further classified each patient into their respective clusters.
With a median follow-up of 18 months, the median overall survival (OS) was 14 months. Three prognostic clusters were obtained using cluster analysis. Cluster 2, consisting of 36 patients, had a median OS of 36 months, while Cluster 1, with 34 patients, had a median OS of 20 months (p = 0.004).
In conclusion, a cluster with a relatively good prognosis was identified within the stage III NSCLC group using cluster analysis. The approach aimed to create a model that could provide more specific prognostic information beyond what is provided by tumor node metastasis-based models.
