Clinical trial NCT06285058: Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy
Official title: A Artificial Intelligence Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Deep Learning Model
Pathological Complete Response
Non-small Cell Lung Cancer
Neoadjuvant Chemoimmunotherapy
Lead sponsor: Wuhan Union Hospital, China
Sponsor class: OTHER
First posted: 2024-02-29
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment

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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of predicting model
timeFrame: Baseline treatment
Secondary outcomes:
Other outcomes:
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