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

NCT06285058 | Not Yet Recruiting

• 1 other identifier: LUNAI
• Study Type: observational
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

Brief 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.

Conditions

Deep Learning Model; Pathological Complete Response; Non-small Cell Lung Cancer; Neoadjuvant Chemoimmunotherapy

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (2)

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

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)

Interventions

No interventions DIAGNOSTIC_TEST

The high-throughput extraction of large amounts of quantitative image features from medical images

Training dataset

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: lead

Related Publications (1)

• Ye G, Wei Z, Han C, Wu G, Wong C, Liang Y, Chen X, Zhou W, Gao J, Liang C, Liao Y, Hendriks LEL, Wee L, De Ruysscher D, Dekker A, Zhou H, Qi Y, Liu Z, Shi Z. AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study. EClinicalMedicine. 2025 Oct 7;89:103551. doi: 10.1016/j.eclinm.2025.103551. eCollection 2025 Nov.

  PMID: 41127561 DERIVED

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung

Condition Hierarchy (Ancestors)

Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 22, 2024
• First Posted: February 29, 2024
• Study Start: March 1, 2024
• Primary Completion: December 1, 2025
• Study Completion: March 1, 2026
• Last Updated: March 13, 2024

Record last verified: 2024-02