Clinical trial NCT06002425: Treatment Recommendations for Gastrointestinal Cancers Via Large Language Models
Official title: Application of Large Language Models in the Recommendation of Treatment Plans for Gastrointestinal Cancers
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Gastrointestinal Neoplasm Malignant
Keywords: Artificial Intelligence
Large Language Model
Gastrointestinal Cancers
Treatment Recommendation
Lead sponsor: Chinese Academy of Sciences
Sponsor class: OTHER_GOV
Collaborators: name: ZhuHai Hospital
name: Fifth Affiliated Hospital, Sun Yat-Sen University
name: Jiangmen Central Hospital
name: Peking University Cancer Hospital (Inner Mongolia Campus)
name: San Raffaele University Hospital, Italy
name: University Hospital Magdeburg, Germany
name: City of Hope Medical Center
First posted: 2023-08-21
Enrollment: 400
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: TREATMENT

Brief summary: This study will evaluate the utility of ChatGPT in recommending treatment plans for patients with gastrointestinal cancers, using both retrospective and prospective data.

Detailed description: The medical records of over 1,200 patients with gastrointestinal cancers will be collected retrospectively from participating hospitals. This data will be split into an exploratory dataset (n=200) and a validation dataset (n\>=1,000). Within the exploratory dataset, various prompt methods will be used to determine the treatment plans suggested by ChatGPT. Additionally, several clinicians of varied seniority levels will provide their treatment recommendations. For the validation dataset, ChatGPT's suggestions for treatment plans will undergo both qualitative and quantitative assessments by a multidisciplinary consultation (MDT) team. The recommendations from ChatGPT will then be compared with those from the clinicians.

Furthermore, this study will incorporate a prospective dataset comprising 400 participants with gastrointestinal cancers. The participants will be randomly allocated to either a control group (n=200) or a ChatGPT-Assisted group (n=200). In the control group, treatment plan recommendations will solely be provided by the clinicians and will guide subsequent treatments. In the ChatGPT-Assisted group, initial treatment plan recommendations will be independently proposed by both ChatGPT and the clinicians. Based on ChatGPT's suggestions, clinicians might selectively adjust their initial plans. Participants will then receive treatments as per these refined plans. Within the ChatGPT-Assisted group, the treatment plans of the initial 100 participants will be evaluated to determine the percentage of patients whose treatment plans are influenced by ChatGPT. Subsequently, the proportion of participants in the entire ChatGPT-Assisted group with treatment plans modified by ChatGPT will be calculated. The study will further monitor the 3-year progression-free survival (PFS) and the 5-year overall survival (OS) rates, contrasting the outcomes between the control and ChatGPT-assisted groups.

Eligibility criteria: Inclusion Criteria:

* Age ≥18 years, both male and female.
* Pathologically confirmed diagnosis of gastrointestinal cancer (gastric cancer or colorectal Cancer).
* Detailed medical records available prior to treatment (including chief complaint, history of present illness, radiological examinations, pathological examinations, laboratory tests, etc.).
* Participants will receive complete treatment in the participating hospitals.

Exclusion Criteria:

* Participants with cancers other than gastrointestinal cancers.
* Participants who receive treatment in multiple hospitals.

Primary outcomes:
measure: Influence Rate of ChatGPT on Treatment Plans
timeFrame: Within 24 hours after the treatment plan is determined from the onset of study participation.
Secondary outcomes:
measure: 3-year Progression-Free Survival (PFS) Rate
timeFrame: 3 years
Other outcomes:
measure: 5-year Overall Survival (OS) Rate
timeFrame: 5 years
Locations:
city: Duarte
country: United States
facility: City of Hope
state: California
city: Jiangmen
country: China
facility: Jiangmen Central Hospital
state: Guangdong
city: Zhuhai
country: China
facility: The Fifth Affiliated Hospital of Sun Yat-sen University
state: Guangdong
city: Zhuhai
country: China
facility: Zhuhai People's Hospital
state: Guangdong
city: Hohhot
country: China
facility: Peking University Cancer Hospital (Inner Mongolia Campus)
state: Inner Mongolia
city: Magdeburg
country: Germany
facility: University Hospital Magdeburg
state: Saxony-Anhalt
city: Milan
country: Italy
facility: San Raffaele University Hospital, Italy
Study officials:
affiliation: Institute of Automation, Chinese Academy of Sciences
name: Di Dong, PhD
role: PRINCIPAL_INVESTIGATOR
References: