Clinical Validation of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk

NCT05787522 | Completed

• 1 other identifier: PVMEDCT202201
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this clinical trial is to evaluate performance and clinical applicability of AI-assisted radiotherapy contouring software (iCurveE) for thoracic organs at risk. The main question it aims to answer is: • Does AI-assisted contouring (AI contouring with manual modification) offer greater accuracy and time efficiency compared to manual contouring? After screening, the qualified participants' thoracic CT images will be anonymized and segmented using three methods: manual, AI (AI-only), and AI-assisted contouring. The researchers will compare the results generated by the three different contouring methods with the ground truth established by expert consensus, in order to evaluate both accuracy and time-related parameters

Conditions

Lung Cancer; Breast Cancer; Esophageal Cancer

Keywords

Artificial Intelligence; Radiotherapy; contouring; thoracic organs at risk

Outcome Measures

Primary Outcomes (2)

• volumetric DICE similarity coefficient, vDSC

  vDSC= 2×（A∩B）/(A+B), where A refers to the volume of the ground truth, and B refers to the volume of the manual, AI, or AI-assisted contour.

  Within 6 months after enrollment

• Contouring time (min)

  Manual contouring time is recorded from the time the CT is loaded on the contouring platform to the completion of contouring. AI-assisted contouring time is defined as the sum of the auto-segmentation model runtime, the transfer to the contouring platform, and the subsequent manual modification.

  Within 6 months after enrollment

Secondary Outcomes (8)

• 95th percentile Hausdorff Distance, HD95

  Within 6 months after enrollment

• Surface DICE similarity coefficient, sDSC

  Within 6 months after enrollment

• Rate of time efficiency improvement

  Within 6 months after enrollment

• Volumetric revision index, VRI

  Within 6 months after enrollment

• Recall, Rec

  Within 6 months after enrollment

Other Outcomes (2)

• Number of adverse events, AEs

  Within 1 day after CT scanning

• Number of device defects during AI-assisted contouring

  Within 6 months after enrollment

Study Arms (3)

Independent manual contouring

Manual contouring refers to physicians using the brush tool on the contouring platform to segment thoracic organs at risk manually, without the use of auto-segmentation tools.

AI contouring

AI contouring refers to the auto-segmentation results generated by the Res-SE Net model, with the model integrated into the auto-segmentation software (iCurveE).

AI-assisted contouring

After generating the AI contouring results, investigators will import them into the contouring platform and perform manual modifications, producing the AI-assisted contouring.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

This trial will enroll 500 patients with lung, esophageal, or breast cancer, who are scheduled to receive thoracic radiotherapy across five clinical cancer institutes.

You may qualify if:

• ≥18 years old, no gender limit.
• Patients diagnosed with breast cancer, lung cancer, or esophageal cancer, who are scheduled for chest CT scanning followed by thoracic radiotherapy.
• CT slice thickness ≤5mm.
• Patients understand the goal of the trial, are willing to attend the trial and sign the informed consent.

You may not qualify if:

• Congenital malformations or abnormal anatomical structures resulting from non-tumor factors in the scan area.
• Artifact, prosthesis or implantation causing images undistinguishable.
• CT images not conforming to DICOM standards.
• Investigators consider not suitable.

Sponsors & Collaborators

• Shanxi Province Cancer Hospital: collaborator
• Fifth Affiliated Hospital, Sun Yat-Sen University: collaborator
• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: collaborator
• Tianjin Medical University Cancer Institute and Hospital: lead
• Guangzhou Perception Vision Medical Technology Co. Ltd: collaborator
• People's Hospital of Guangxi Zhuang Autonomous Region: collaborator

Study Sites (1)

Tianjin Medical University Cancer Institute and Hospital, Tianjin Key Laboratory of Cancer Prevention and Therapy

Tianjin, Tianjin Municipality, 300060, China

Location

MeSH Terms

Conditions

Lung Neoplasms; Breast Neoplasms; Esophageal Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Gastrointestinal Neoplasms; Digestive System Neoplasms; Head and Neck Neoplasms; Digestive System Diseases; Esophageal Diseases; Gastrointestinal Diseases

Study Officials

• Zhiyong Yuan, Ph.D.

  Tianjin Medical University Cancer Institute and Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 2, 2023
• First Posted: March 28, 2023
• Study Start: September 30, 2022
• Primary Completion: July 27, 2023
• Study Completion: March 6, 2024
• Last Updated: February 12, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Beginning 1 year after publication with no end date.
• Access Criteria: Requests must include a detailed protocol, analysis plan, and data exchange with institutional approvals in place before data transfer of any information.

Locations

CN (1)