Automatic Contouring Analysis of Lung Cancer for Radiotherapy Radiation Planning from Ct-Simulator Image

Abstract

Accurate determination of tumor and radiotherapy target volume is crucial to prevent local and regional failure in lung cancer. Radiotherapy planning considers critical normal tissue structures or Organs at Risk (OAR). While manual contouring is the gold standard in radiotherapy planning, it is still susceptible to intra- and inter-observer variation. A digital image processing system capable of automatically and semi-automatically determining tumor targets and OARs through computer programming can assist in the contouring process. The study aims to create a digital contouring computer program to aid in planning radiotherapy irradiation for lung cancer cases, guided by CT-Simulator image guidance. The research involved contouring CT-Simulator images of lung cancer cases using automatic segmentation, semi-automatic segmentation, and a combination of automatic, semi-automatic, and manual segmentation. Contour accuracy was assessed by a radiation oncologist. Automatic segmentation showed high accuracy for lungs (>95%), moderate for tumor targets (72.22%) and the heart (73.33%), but low accuracy for the esophagus (34.44%) and spinal cord (36.67%). Semi-automatic segmentation achieved high accuracy for the esophagus (96.67%) and spinal cord (95.56%), and moderate accuracy for tumor targets (73.33%), heart (66.67%), right lung (66.25%), and left lung (72.94%). The combination of automatic, semi-automatic, and manual segmentation resulted in high accuracy for tumor targeting and OARs (>95%). Automatic or semi-automatic segmentation using location and gray level classification methods for lung cancer cases did not produce optimal results. However, a contouring program combining automatic, semi-automatic, and manual segmentation proved more effective, balancing human interaction and automation in the lung cancer contouring process. This digital contouring program offers valuable support for radiation oncologists, potentially leading to improved treatment outcomes for lung cancer patients