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

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: Non-small Cell Lung Cancer; Pathological Complete Response; Neoadjuvant Chemoimmunotherapy; Deep Learning Model
• Intervention / Treatment: Diagnostic test: No interventions

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

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:

1. Patients' with non-small cell lung cancer, diagnosed through biopsy pathology and clinically classified as stage IB to III;
2. Patients who receive at least two cycles of neoadjuvant immunotherapy combined with chemotherapy induction therapy;
3. According to the IASLC guidelines, postoperative pathological evaluation was performed on the treatment response of the tumor primary lesion and lymph nodes.

Exclusion Criteria:

1. Missing or inadequate quality of CT;
2. Time interval between CT and start of treatment is greater than 1 month;
3. Incomplete clinicopathologic data.

Study Plan

How is the study designed?

Number of groups / cohorts

2

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)	Diagnostic test: No interventions; 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	several metrics were calculated, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).	Baseline treatment

Collaborators and Investigators

• Sponsor: Wuhan Union Hospital, China

Study record dates

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: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• 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