Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy

Non-small Cell Lung Cancer Clinical Trial

Official title:

A Artificial Intelligence Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06285058
• Other study ID #: LUNAI
• Status: Not yet recruiting
• Start date: March 2024
• Est. completion date: March 2026

Study information

• Verified date: February 2024
• Source: Wuhan Union Hospital, China
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 1000
• Est. completion date: March 2026
• Est. primary completion date: December 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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.

Related Conditions & MeSH terms

• Lung Neoplasms
• Non-small Cell Lung Cancer
• Pathological Complete Response

Intervention

Diagnostic Test:
• No interventions
The high-throughput extraction of large amounts of quantitative image features from medical images

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Wuhan Union Hospital, China

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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