Application of Machine Learning for Tumor Growth Inhibition – Overall Survival Modeling Platform

Abstract

Machine learning (ML) was used to leverage tumor growth inhibition (TGI) metrics to characterize the relationship with overall survival (OS) as a novel approach and to compare with traditional TGI‐OS modeling methods. Historical dataset from a phase III non‐small cell lung cancer study (OAK, atezolizumab vs. docetaxel, N = 668) was used. ML methods support the validity of TGI metrics in predicting OS. With lasso, the best model with TGI metrics outperforms the best model without TGI metrics. Boosting was the best linear ML method for this dataset with reduced estimation bias and lowest Brier score, suggesting better prediction accuracy. Random forest did not outperform linear ML methods despite hyperparameter optimization. Kernel machine was marginally the best nonlinear ML method for this dataset and uncovered nonlinear and interaction effects. Nonlinear ML may improve prediction by capturing nonlinear effects and covariate interactions, but its predictive performance and value need further evaluation with larger datasets.

Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility

“…3 If one now intends to reproduce such a predictive model, it is immensely important to clarify the model's ML process and how predictions are obtained. This is exactly what we have approached when reproducing the results of the previously published original work of Chan et al 2 In particular, we addressed three fundamental questions:…”

“…3 If one now intends to reproduce such a predictive model, it is immensely important to clarify the model’s ML process and how predictions are obtained. This is exactly what we have approached when reproducing the results of the previously published original work of Chan et al 2 In particular, we addressed three fundamental questions: (1) what is needed for reproducible ML analyses, (2) how can these methods be described in a transparent and interpretable way, and (3) what else can be predicted from the original work?…”

“…ML methods promise a higher predictive value, which may result from the flexible analysis of nonlinear correlations or higher‐order interactions. 2 At the same time, there is a desire on the clinical side that the path to good prediction should be comprehensible and transparent. 3 If one now intends to reproduce such a predictive model, it is immensely important to clarify the model’s ML process and how predictions are obtained.…”

Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition–overall survival modeling framework

“…Furthermore, the association between tumor growth rates and OS could be explored using novel modeling approaches, such as machine learning. 40 , 49 …”

“…However, whether the TGI-OS platform will apply to other anticancer treatments with different mechanisms of action remains to be determined and external validation studies with other datasets are warranted. Furthermore, the association between tumor growth rates and OS could be explored using novel modeling approaches, such as machine learning [40,49].…”