CC BY 4.0 · Indian J Med Paediatr Oncol
DOI: 10.1055/s-0044-1780510
Original Article

Clinical Feasibility of Artificial Intelligence-Based Autosegmentation of the Left Anterior Descending Artery in Radiotherapy for Breast Cancer

Aditi Mohan
1   NMIMS University, MPSTME, Mumbai, Maharashtra, India
,
Kaustav Talapatra
2   Department of Radiation Oncology, Nanavati Max Super Speciality Hospital, Mumbai, Maharashtra, India
,
Manoj Sankhe
3   NMIMS University, MPSTME, Mumbai, Maharashtra, India
,
G. Kiron
2   Department of Radiation Oncology, Nanavati Max Super Speciality Hospital, Mumbai, Maharashtra, India
,
Priyal Maniar
1   NMIMS University, MPSTME, Mumbai, Maharashtra, India
,
Akshat Kolekar
1   NMIMS University, MPSTME, Mumbai, Maharashtra, India
,
Saakshi Kushe
1   NMIMS University, MPSTME, Mumbai, Maharashtra, India
,
Prashant Kharote
3   NMIMS University, MPSTME, Mumbai, Maharashtra, India
,
Anuradha Singh
2   Department of Radiation Oncology, Nanavati Max Super Speciality Hospital, Mumbai, Maharashtra, India
,
Keval Patel
1   NMIMS University, MPSTME, Mumbai, Maharashtra, India
,
Aayush Gupta
1   NMIMS University, MPSTME, Mumbai, Maharashtra, India
,
Devarsh Patel
1   NMIMS University, MPSTME, Mumbai, Maharashtra, India
,
Nikhil Bardeskar
4   Nanavati Max Institute of Cancer Care, Nanavati Max Super Speciality Hospital, Mumbai, Maharashtra, India
,
Deepak Patkar
5   Department of Medical Service and Head Imaging Nanavati Max Super Speciality Hospital, Mumbai, Maharashtra, India
› Institutsangaben
Funding None.

Abstract

Introduction Breast cancer is a prevalent global disease, and radiotherapy plays a crucial role in its treatment. However, radiotherapy may lead to cardiac complications, particularly in patients receiving left-sided radiotherapy who may experience increased risks due to toxicity in the left anterior descending (LAD) artery. The manual contouring of the LAD artery is time-consuming and subject to variability. This study aimed to provide an overview of artificial intelligence (AI) based LAD artery contouring, assess its feasibility, and identify its limitations.

Objectives The primary objectives were to evaluate the feasibility of AI-based LAD artery contouring, compare different approaches, and quantify properties impacting accuracy. The secondary objective was to recommend algorithms with greater accuracy.

Materials and Methods A (noncontrast) computed tomography dataset of nine patients with breast cancer was used to analyze the features and behavior of the LAD artery. The functioning of different AI models used for autosegmentation was studied, and the LAD artery imaging features were identified and quantified using the widely used AI models. Additionally, an algorithm to reliably compute interpatient variability in the LAD artery contours was proposed.

Results A lack of distinctive features, diminutive contour size (∼5 pixels on average), and inconsistent position of the LAD artery were observed. The interpatient variability in the LAD artery contours was five to seven times the average size of the contours. The dataset also had a high standard deviation of 28.9 and skewed data distribution.

Conclusions The results indicated that the variable path of the LAD artery and high interpatient variability were the primary reasons for the inability of AI algorithms to have a concordance. Further, the small contour size amplified model inaccuracy. For higher autosegmentation accuracy, an anatomical landmark–based approach is necessary to capture surrounding structures that affect the path of the LAD artery.

Patient Consent




Publikationsverlauf

Artikel online veröffentlicht:
27. Februar 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Thieme Medical and Scientific Publishers Pvt. Ltd.
A-12, 2nd Floor, Sector 2, Noida-201301 UP, India