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| Name | Class |
|---|---|
| Ningbo No.2 Hospital | OTHER |
| Zunyi Medical College | OTHER |
| The First Affiliated Hospital of Nanchang University | OTHER |
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The purpose of this study is to evaluate the performance of a CT/PET/ WSI-based deep learning signature for predicting complete pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CT/PET/WSI-based Deep Learning Signature | Diagnostic Test | CT/PET/WSI-based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer |
| Measure | Description | Time Frame |
|---|---|---|
| Area under the receiver operating characteristic curve | The area under the receiver operating characteristic curve (ROC) of the deep learning model in predicting complete pathological response (CPR). CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy. | 2023.5.1-2023.10.31 |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity | The sensitivity of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy. |
| Measure | Description | Time Frame |
|---|---|---|
| Specificity | The specificity of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy. |
Inclusion Criteria:
Exclusion Criteria:
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Resected Stage I-III NSCLC following neoadjuvant chemoimmunotherapy
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Affiliated Hospital of Zunyi Medical University | Recruiting | Zunyi | Guizhou | China |
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| 2023.5.1-2023.10.31 |
| 2023.5.1-2023.10.31 |
| Positive predictive value | The positive predictive value of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy. | 2023.5.1-2023.10.31 |
| Negative predictive value | The negative predictive value of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy. | 2023.5.1-2023.10.31 |
| Accuracy | The accuracy of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy. | 2023.5.1-2023.10.31 |
| The First Affiliated Hospital of Nanchang University | Recruiting | Nanchang | Jiangxi | China |
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| Ningbo HwaMei Hospital | Recruiting | Ningbo | Zhejiang | China |
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| ID | Term |
|---|---|
| D002289 | Carcinoma, Non-Small-Cell Lung |
| D000095384 | Pathologic Complete Response |
| 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 |
| D018450 | Disease Progression |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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