Enhancing Lung Cancer Treatment Decisions through AI-driven Analysis of PLCO Patient Data
Principal Investigator
Name
Ricardo Gonzales Vera
Degrees
D.Phil.
Institution
University of Oxford
Position Title
D.Phil. Candidate
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1514
Initial CDAS Request Approval
Mar 27, 2024
Title
Enhancing Lung Cancer Treatment Decisions through AI-driven Analysis of PLCO Patient Data
Summary
Lung cancer remains the leading cause of cancer-related mortality in the United States, underscoring the critical need for optimized treatment strategies. This project proposes to leverage the vast dataset of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to inform and enhance lung cancer treatment through artificial intelligence (AI). By applying advanced machine learning techniques to analyze patterns and outcomes within the PLCO lung cancer dataset, our goal is to derive predictive models that can support clinical decision-making. Specifically, we aim to identify which treatment pathways yield the best outcomes for patients based on a multitude of variables, including demographic information, cancer stage, histology, and prior treatment responses. The project seeks to bridge the gap between the rich data resource of PLCO and the practical needs of clinicians by providing evidence-based, data-driven guidance for personalized lung cancer treatment plans.
Aims
1. Development of a Predictive AI Model: Construct and train an AI model using the PLCO lung cancer dataset. The model will analyze patient demographics, disease characteristics, treatment histories, and outcomes to identify patterns and predictors of treatment success.
2. Validation and Testing of the AI Model: Implement a rigorous testing phase using a subset of the PLCO data not utilized during the training phase. This step aims to assess the model's accuracy, sensitivity, and specificity in predicting treatment outcomes.
Collaborators
Arjun Ulag - Veritas AI