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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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) |
| |
| 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) |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No interventions | Diagnostic Test | The high-throughput extraction of large amounts of quantitative image features from medical images |
|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41127561 | Derived | 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. |
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| ID | Term |
|---|---|
| D002289 | Carcinoma, Non-Small-Cell Lung |
| ID | Term |
|---|---|
| D002283 | Carcinoma, Bronchogenic |
| D001984 | Bronchial Neoplasms |
| D008175 | Lung Neoplasms |
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
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