AI informed Treatment Algorithms Based on Tumor Molecular Profiling Data for Lung Cancer

TMA-AI: AI informed Treatment Algorithms Based on Tumor Molecular Profiling Data for Lung Cancer

Introduction

In the past decade, the number of successful targeted therapies with attendant mutational biomarkers has steadily increased. While oncologists have access to guidelines and up-to-date literature on advancements in oncology, navigating them can be challenging and time consuming.

We have recently reported [1] the successful development and implementation of an interactive online decision-tool to educate and inform providers on the importance of biomarker testing and clinical consequences of these results. Our current version of TMA platform (TMA.georgetown.edu) is a collection of clinical algorithms specific to NSCLC that offers step-by-step, question-and-answer based algorithmic approach to navigate the complexities of diagnosis/treatment based on the type of mutation and assist the clinician in making decisions on patient treatment. The goal of this pilot phase of our TMA platform was to increase the appropriate and timely use of NGS among health system providers, so that patients receive the best treatment or therapy in an expeditious fashion.

Incorporation of real-world evidence into the TMA platform with the help of AI based natural language processing algorithms [2] is currently being implemented as the next phase of this platform (TMA-AI)

Incorporating real world evidence from sources such as Clingen (https://clinicalgenome.org) , CIVIC and COSMIC databases (with future addition of oncoKB (https://www.oncokb.org), and other evidence obtained using natural language processing (NLP) tools for extracting genomic information from the published scientific articles, can significantly improve the knowledge offered in TMA platform.

References:

1. Targetable molecular algorithm and training platform development for the treatment of non-small cell lung cancer . Krithika Bhuvaneshwar, MS, Chul Kim, MD, Kaushal Parikh, MBBS, Joshua E Reuss, MD, Camelia Bencheqroun, MS, Anvitha G Agraharam, MBBS, MS, Ayesha Munir, MD, Adil Alaoui, MS, Yuriy Gusev, PhD, Irina G Veytsman, MD . JAMIA Open, Volume 7, Issue 4, December 2024, ooae124, https://doi.org/10.1093/jamiaopen/ooae124

2. Samir Gupta et al - NLP publication goes here