Personalized Deep Learning Model for Clinical Target Volume on Daily Cone Beam Computed Tomography in Breast Cancer Patients.

ABSTRACT:

Purpose

Herein, we developed a deep learning algorithm to improve the segmentation of the clinical target volume (CTV) on daily cone beam computed tomography (CBCT) scans in breast cancer radiation therapy. By leveraging the Intentional Deep Overfit Learning (IDOL) framework, we aimed to enhance personalized image-guided radiation therapy based on patient-specific learning.

Methods and materials

We used 240 CBCT scans from 100 breast cancer patients and employed a 2-stage training approach. The first stage involved training a novel general deep learning model (Swin UNETR, UNET, and SegResNET) on 90 patients. The second stage used intentional overfitting on the remaining 10 patients for patient-specific CBCT outputs. Quantitative evaluation was conducted using the Dice Simila

SUBMITTER: Hwang J 

PROVIDER: S-EPMC11381721 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature