Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy
March 11, 2024 updated by: Wuhan Union Hospital, China
A Artificial Intelligence Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy
This study presents the development and validation of an artificial intelligence (AI) prediction system that utilizes pre-neoadjuvant immunotherapy plain scans and enhanced multimodal CT scans to extract deep learning features.
The aim is to predict the occurrence of pathological complete response in non-small cell lung cancer patients undergoing neoadjuvant immunochemotherapyy.
Study Overview
Status
Not yet recruiting
Conditions
Intervention / Treatment
Detailed Description
This study retrospectively obtained non-contrast enhanced and contrast enhanced CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy.
at multiple centers between August 2019 and February 2023.
Deep learning features were extracted from both non-contract enhanced and contract enhanced CT scans to construct the predictive models (LUNAI-nCT model and LUNAI-eCT model), respectively.
After feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed.
The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
SHapley Additive exPlanations (SHAP) analysis was used to quantify the impact of CT imaging features on model prediction.
To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping (Grad-CAM) to generate saliency heatmaps.
Study Type
Observational
Enrollment (Estimated)
1000
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
No
Sampling Method
Non-Probability Sample
Study Population
patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment
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
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
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)
|
The high-throughput extraction of large amounts of quantitative image features from medical images
|
|
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
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).
|
Baseline treatment
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Estimated)
March 1, 2024
Primary Completion (Estimated)
December 1, 2025
Study Completion (Estimated)
March 1, 2026
Study Registration Dates
First Submitted
February 22, 2024
First Submitted That Met QC Criteria
February 22, 2024
First Posted (Actual)
February 29, 2024
Study Record Updates
Last Update Posted (Actual)
March 13, 2024
Last Update Submitted That Met QC Criteria
March 11, 2024
Last Verified
February 1, 2024
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- LUNAI
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
UNDECIDED
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
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