Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer
Study Details
Study Description
Brief Summary
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
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Study Design
Outcome Measures
Primary Outcome Measures
- Area under the receiver operating characteristic curve [2023.5.1-2023.10.31]
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.
Secondary Outcome Measures
- Sensitivity [2023.5.1-2023.10.31]
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.
Other Outcome Measures
- Specificity [2023.5.1-2023.10.31]
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.
- Positive predictive value [2023.5.1-2023.10.31]
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.
- Negative predictive value [2023.5.1-2023.10.31]
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.
- Accuracy [2023.5.1-2023.10.31]
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.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Age ranging from 20-75 years;
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Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC;
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Obtained written informed consent.
Exclusion Criteria:
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Missing image data;
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Pathological N3 disease.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Affiliated Hospital of Zunyi Medical University | Zunyi | Guizhou | China | |
| 2 | The First Affiliated Hospital of Nanchang University | Nanchang | Jiangxi | China | |
| 3 | Ningbo HwaMei Hospital | Ningbo | Zhejiang | China |
Sponsors and Collaborators
- Shanghai Pulmonary Hospital, Shanghai, China
- Ningbo HwaMei Hospital, Zhejiang, China
- Zunyi Medical College
- The First Affiliated Hospital of Nanchang University, Jiangxi, China
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- DLCPR