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Completed

NCT Number: NCT05787522

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

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

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Key information

Conditions

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

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

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

Tianjin, Tianjin Municipality, 300060, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • ≥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.

Exclusion criteria

  • 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.

Treatment and study plan

Primary outcomes

  1. volumetric DICE similarity coefficient, vDSC

    Time frame: Within 6 months after enrollment

    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.

  2. Contouring time (min)

    Time frame: Within 6 months after enrollment

    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.

Secondary outcomes

  1. 95th percentile Hausdorff Distance, HD95

    Time frame: Within 6 months after enrollment

    HD95(A, B) = max (h95(A, B), h95(B, A)), where h95(A, B) is the 95th percentile of the shortest distances from all points on surface A to surface B, and vice-versa for h95(B, A). A represents the ground truth and B represents the manual, AI or AI-assisted delineation

  2. Surface DICE similarity coefficient, sDSC

    Time frame: Within 6 months after enrollment

    sDSC = (|S(A) ∩ S(B)τ| + |S(B) ∩ S(A)τ|) / (|S(A)| + |S(B)|), where S(A) and S(B) are the sets of points on the surfaces of A and B, S(B)τ represents the points on surface B that are within the tolerance τ of surface A, and S(A)τ represents the points on surface A that are within the tolerance τ of surface B. A represents the ground truth and B represents the manual, AI or AI-assisted delineation

  3. Rate of time efficiency improvement

    Time frame: Within 6 months after enrollment

    Rate of efficiency time improvement= (manual contouring duration - AI-assisted contouring duration)/ manual contouring duration*100%

  4. Volumetric revision index, VRI

    Time frame: Within 6 months after enrollment

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

  5. Recall, Rec

    Time frame: Within 6 months after enrollment

    Rec = | A∩B| / A, where A refers to the volume of the ground truth, and B refers to the volume of the manual, AI, or AI-assisted contour.

  6. Precision, Pre

    Time frame: Within 6 months after enrollment

    Pre= |A∩B| / B, where A refers to the volume of the ground truth, and B refers to the volume of manual, AI, or AI-assisted contour.

  7. Relative volume difference, RVD

    Time frame: Within 6 months after enrollment

    RVD = |A-B| /A, where A refers to the volume of the ground truth, and B refers to the volume of the manual, AI, or AI-assisted contour.

  8. Investigators satisfaction score for AI contouring

    Time frame: Within 6 months after enrollment

    Evaluated on a 1-5 Likert scale: 1 - strongly dissatisfied, 2 - dissatisfied, 3 - neutral, 4 - satisfied, 5 - strongly satisfied.

Other outcomes

  1. Number of adverse events, AEs

    Time frame: Within 1 day after CT scanning

    Participant Adverse events during CT scanning

  2. Number of device defects during AI-assisted contouring

    Time frame: Within 6 months after enrollment

    Number of failures in generating, transferring, or saving auto-segmentation results

Sponsors and collaborators

Lead sponsor

Tianjin Medical University Cancer Institute and Hospital

Other

Collaborators

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

Registry information

Official study title

Prospective, Multicenter, Randomized Evaluation of the Performance and Clinical Applicability of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk

Important dates

Study start
2022
Primary completion
2023
Study completion
2024
First posted
Mar 28, 2023
Registry last updated
Feb 12, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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