AI-Driven Web-Based Tool for Lung Cancer Classification and Treatment Recommendation
Principal Investigator
Name
Manita Tamang
Institution
Southampton Solent University
Position Title
Student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1403
Initial CDAS Request Approval
Mar 12, 2025
Title
AI-Driven Web-Based Tool for Lung Cancer Classification and Treatment Recommendation
Summary
Despite the advancement of Artificial Intelligence (AI) in healthcare, the survival rate in lung cancer patients remains low despite ongoing efforts. Due to the variety of risk factors, the existing models lack transparency and interpretability, limiting to incorporation of personalized factors such as genetic variations, patient history, and other individual characteristics. Integrating all these risk factors would allow for more precise classification, resulting in more effective treatment plans for each individual. While there has been significant progress in the research and development of AI-based lung classification tools, most of these remain in the trial phase or face regulatory and clinical validation challenges.
The main objective of this project is to develop a predictive model based on imaging datasets to classify lung cancer as either NSCLC or SCLC to narrow down the treatment options.
The main objective of this project is to develop a predictive model based on imaging datasets to classify lung cancer as either NSCLC or SCLC to narrow down the treatment options.
Aims
- Based on the model’s prediction, it would recommend appropriate personalized treatment plans for individual patients, enhancing clinical decision-making for better patient outcomes.
- explore the key challenges in developing a user-friendly web-based system for lung cancer treatment analysis.
Collaborators
Manita Tamang, Southampton Solent University