Stomach NeoplasmsNCT06548464
Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients
An observational study of Stomach Neoplasms and Gastrectomy, run by Chang-Ming Huang, Prof. at 1 site in China.
Trial summary
This multicenter, retrospective cohort study aimed to develop and validate an explainable prediction model for prognosis after gastrectomy in patients with gastric cancer. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Stomach Neoplasms, Gastrectomy, Machine Learning
- How many people
- 18,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2024to 6 Aug 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: This study includes adult patients of all genders who were diagnosed with primary gastric or gastroesophageal junction adenocarcinoma and underwent radical gastrectomy at participating institutions in China. The study population represents a diverse group of gastric cancer patients, varying in age, tumor stage, and treatment approaches, including those who received neoadjuvant therapy. This cohort aims to provide a comprehensive representation of gastric cancer patients treated with curative intent, allowing for the development and validation of prognostic models applicable to a wide range of clinical scenarios.
Who may be able to join
- Patients diagnosed with primary gastric or gastroesophageal junction cancer
- Underwent radical gastrectomy
- Complete clinical and pathological data available
Who cannot join
- Presence of distant metastases before surgery
- Non-adenocarcinoma histology
- Incomplete follow-up data
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Development and Validation of Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients: a Multicenter Retrospective Study
This multicenter, retrospective cohort study aimed to develop and validate an explainable prediction model for prognosis after gastrectomy in patients with gastric cancer.
Read the detailed description
This multicenter, retrospective cohort study aimed to develop and validate an explainable prediction model for prognosis after gastrectomy in patients with gastric cancer. The study included patients who underwent radical gastrectomy for primary gastric or gastroesophageal junction cancer across multiple institutions in China.
The primary objective was to create a machine learning-based model to predict postoperative outcomes following gastrectomy, using readily available clinical and pathological parameters. The main outcome of interest was early recurrence within 2 years after surgery, which significantly impacts overall prognosis.
The study employed various machine learning algorithms to develop prediction models, which were then compared and validated. Model performance was assessed through measures such as area under the receiver operating characteristic curve (AUC), calibration, and Brier score. The SHapley Additive exPlanations (SHAP) method was used to interpret the model and rank feature importance.
This research aims to provide clinicians with a tool for identifying patients at higher risk of poor postoperative outcomes who may benefit from more intensive post-operative monitoring and early intervention strategies, potentially improving prognosis for gastric cancer patients.
- Conditions
- Stomach Neoplasms, Gastrectomy, Machine Learning
- Keywords
- Machine Learning, Prognosis, Stomach Neoplasms, Gastrectomy
- Medical subject headings
- Stomach Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Survival
Assessment of overall survival outcomes in gastric cancer patients after gastrectomy.
Measured: Up to 5 years after surgery
Secondary outcomes (5)
Early Recurrence
Incidence of cancer recurrence within 2 years after gastrectomy.
Measured: Within 2 years after surgery
Late Recurrence
Incidence of cancer recurrence occurring more than 2 years after gastrectomy.
Measured: From 2 years up to 5 years after surgery
Postoperative Complications
Incidence and severity of complications following gastrectomy.
Measured: Within 30 days after surgery
Neoadjuvant Treatment Efficacy
Assessment of tumor response to neoadjuvant therapy before gastrectomy.
Measured: From initiation of neoadjuvant therapy to surgery (typically 2-3 months)
5-Year Survival Rate
Percentage of patients alive 5 years after gastrectomy.
Measured: 5 years after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Aug 2024
- Study start1 Jun 2024
- Primary completion1 Aug 2024
- Study completion6 Aug 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Stomach NeoplasmsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 18,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chang-Ming Huang, Prof.
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Chang-Ming Huang, Prof., Fujian Medical University
- Investigators
- Chang-Ming Huang, MD, Principal investigator, Fujian Medical University Union Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06548464
- Sponsor's protocol ID
- 2024KY154
- Status last verified
- Aug 2024
- First submitted
- 6 Aug 2024
- Last updated
- 12 Aug 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients: a Multicenter Retrospective Study
- ClinicalTrials.gov ID
- NCT06548464
- Acronym / sponsor ID
- 2024KY154
- Conditions
- Stomach Neoplasms, Gastrectomy, Machine Learning
- Keywords
- Machine Learning, Prognosis, Stomach Neoplasms, Gastrectomy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 18,000 participants (actual)
- Lead sponsor
- Chang-Ming Huang, Prof.University, hospital or other
- Study start
- 1 Jun 2024 (actual)
- Primary completion
- 1 Aug 2024 (actual)
- Study completion
- 6 Aug 2024 (actual)
- First posted
- 12 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This multicenter, retrospective cohort study aimed to develop and validate an explainable prediction model for prognosis after gastrectomy in patients with gastric cancer.
Detailed description
This multicenter, retrospective cohort study aimed to develop and validate an explainable prediction model for prognosis after gastrectomy in patients with gastric cancer. The study included patients who underwent radical gastrectomy for primary gastric or gastroesophageal junction cancer across multiple institutions in China.
The primary objective was to create a machine learning-based model to predict postoperative outcomes following gastrectomy, using readily available clinical and pathological parameters. The main outcome of interest was early recurrence within 2 years after surgery, which significantly impacts overall prognosis.
The study employed various machine learning algorithms to develop prediction models, which were then compared and validated. Model performance was assessed through measures such as area under the receiver operating characteristic curve (AUC), calibration, and Brier score. The SHapley Additive exPlanations (SHAP) method was used to interpret the model and rank feature importance.
This research aims to provide clinicians with a tool for identifying patients at higher risk of poor postoperative outcomes who may benefit from more intensive post-operative monitoring and early intervention strategies, potentially improving prognosis for gastric cancer patients.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- This study includes adult patients of all genders who were diagnosed with primary gastric or gastroesophageal junction adenocarcinoma and underwent radical gastrectomy at participating institutions in China. The study population represents a diverse group of gastric cancer patients, varying in age, tumor stage, and treatment approaches, including those who received neoadjuvant therapy. This cohort aims to provide a comprehensive representation of gastric cancer patients treated with curative intent, allowing for the development and validation of prognostic models applicable to a wide range of clinical scenarios.
Key inclusion criteria (3 listed)
- Patients diagnosed with primary gastric or gastroesophageal junction cancer
- Underwent radical gastrectomy
- Complete clinical and pathological data available
Key exclusion criteria (3 listed)
- Presence of distant metastases before surgery
- Non-adenocarcinoma histology
- Incomplete follow-up data
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Survival
Time frame: Up to 5 years after surgery
Assessment of overall survival outcomes in gastric cancer patients after gastrectomy.
5 secondary outcomes
Early Recurrence
Time frame: Within 2 years after surgery
Late Recurrence
Time frame: From 2 years up to 5 years after surgery
Postoperative Complications
Time frame: Within 30 days after surgery
Neoadjuvant Treatment Efficacy
Time frame: From initiation of neoadjuvant therapy to surgery (typically 2-3 months)
5-Year Survival Rate
Time frame: 5 years after surgery
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Aug 2024.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Chang-Ming Huang, MD principal investigator
Fujian Medical University Union Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Stomach Neoplasms and Gastrectomy, run by Chang-Ming Huang, Prof. at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 12 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Survival (time frame: Up to 5 years after surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Aug 2024.