Clinical trial · Observational

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

NCT06285058CI-TRIAL-00075017unknownClinicalTrials.gov clinicaltrialsPublishedExplore in graph →

Summary

Brief summary (as posted)

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

Conditions (4)

Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.

Condition (as posted)	Mapped entity	Match	Confidence
Deep Learning Model	—	UNRESOLVED	—
Neoadjuvant Chemoimmunotherapy	—	UNRESOLVED	—
Non-small Cell Lung Cancer	Lung Non-Small Cell Carcinoma	ALIAS	0.90
Pathological Complete Response	—	UNRESOLVED	—

Interventions

Interventions (1)

Intervention	Type	Mapped drug	Match
No interventions	Diagnostic Test	—	UNRESOLVED

Design

Arms and outcomes

Arms (2)

label
Training dataset
description
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)
interventionNames
1. Diagnostic Test: No interventions
label
test dataset
description
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)

Primary outcomes (1)

measure
the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of predicting model
timeFrame
Baseline treatment
description
several metrics were calculated, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Eligibility

Eligibility (as posted)

Sex: All
Minimum age: 18 Years
Maximum age: 80 Years

Show eligibility criteria text

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.

References

Publications (1)

DERIVEDYe 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