Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy
A Artificial Intelligence Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients' with non-small cell lung cancer, diagnosed through biopsy pathology and clinically classified as stage IB to III;
- Patients who receive at least two cycles of neoadjuvant immunotherapy combined with chemotherapy induction therapy;
- According to the IASLC guidelines, postoperative pathological evaluation was performed on the treatment response of the tumor primary lesion and lymph nodes.
Exclusion Criteria:
- Missing or inadequate quality of CT;
- Time interval between CT and start of treatment is greater than 1 month;
- Incomplete clinicopathologic data.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Training dataset
patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment at hospital 1 (Tongji Medical College Affiliated Union Hospital)
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The high-throughput extraction of large amounts of quantitative image features from medical images
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test dataset
patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment at hospital (Zhengzhou University First Affiliated Hospital, Yichang Central Hospital, Anyang Cancer Hospital)
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of predicting model
Time Frame: Baseline treatment
|
several metrics were calculated, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
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Baseline treatment
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Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- LUNAI
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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