Predictive Models for Early Detection of Lung Cancer Based on Clinical and Radiological Data

EasyChair Preprint 13568

Abstract

Lung cancer is a leading cause of cancer-related deaths worldwide, emphasizing the critical need for early detection to improve patient outcomes. Predictive models based on clinical and radiological data have emerged as promising tools for identifying individuals at high risk of developing lung cancer. This abstract provides an overview of the application of predictive models in early detection, highlighting the integration of clinical and radiological information.

The process begins with data collection, encompassing clinical factors such as patient demographics, medical history, symptoms, and laboratory test results, as well as radiological data from chest X-rays, computed tomography (CT) scans, and positron emission tomography (PET) scans. Preprocessing steps involve data cleaning, handling missing values, and extracting relevant features.

Keyphrases: Challenges, Data Quality, Lung Cancer, clinical practice, data availability, early detection, limitations

@booklet{EasyChair:13568,
  author    = {Edwin Frank},
  title     = {Predictive Models for Early Detection of Lung Cancer Based on Clinical and Radiological Data},
  howpublished = {EasyChair Preprint 13568},
  year      = {EasyChair, 2024}}