Clinical trial NCT05787522: Clinical Validation of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs At Risk
Official title: Prospective, Multicenter, Randomized Evaluation of the Performance and Clinical Applicability of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs At Risk
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Lung Cancer
Breast Cancer
Esophageal Cancer
Keywords: Artificial Intelligence
Radiotherapy
contouring
thoracic organs at risk
Lead sponsor: Tianjin Medical University Cancer Institute and Hospital
Sponsor class: OTHER
Collaborators: name: Guangzhou Perception Vision Medical Technology Co. Ltd
name: People's Hospital of Guangxi Zhuang Autonomous Region
name: Shanxi Province Cancer Hospital
name: Fifth Affiliated Hospital, Sun Yat-Sen University
name: Wuhan Union Hospital, China
First posted: 2023-03-28
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

Brief summary: The goal of this clinical trial is to evaluate performance and clinical applicability of AI-assisted radiotherapy contouring software 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


Eligibility criteria: Inclusion Criteria:

1. ≥18 years old, no gender limit.
2. Patients diagnosed with breast cancer, lung cancer, or esophageal cancer, who are scheduled for chest CT scanning followed by thoracic radiotherapy.
3. CT slice thickness ≤5mm.
4. Patients understand the goal of the trial, are willing to attend the trial and sign the informed consent.

Exclusion Criteria:

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

Primary outcomes:
measure: DICE similarity coefficient, DSC
timeFrame: Within 6 months after enrollment
measure: Contouring time (min)
timeFrame: Within 6 months after enrollment
Secondary outcomes:
measure: Volumetric revision index, VRI
timeFrame: Within 6 months after enrollment
measure: Rate of time efficiency improvement
timeFrame: Within 6 months after enrollment
measure: Rate of volume difference, DV
timeFrame: Within 6 months after enrollment
measure: Recall, Rec
timeFrame: Within 6 months after enrollment
measure: Precision, Pre
timeFrame: Within 6 months after enrollment
measure: Investigators satisfaction score for AI contouring
timeFrame: Within 6 months after enrollment
Other outcomes:
measure: Number of adverse events, AEs
timeFrame: Within 1 day after CT scanning
measure: Number of device defects during AI-assisted contouring
timeFrame: Within 6 months after enrollment
Locations:
city: Tianjin
country: China
facility: Tianjin Medical University Cancer Institute and Hospital, Tianjin Key Laboratory of Cancer Prevention and Therapy
state: Tianjin
Study officials:
affiliation: Tianjin Medical University Cancer Institute and Hospital
name: Zhiyong Yuan, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References: