Complete Pathological Response, Neoadjuvant Chemoimmunotherapy, Non-small Cell Lung Cancer
Conditions
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
Interventions
DIAGNOSTIC_TESTCT/PET/WSI-based Deep Learning Signature
CT/PET/WSI-based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer
Sponsors
Ningbo No.2 Hospital
Zunyi Medical College
The First Affiliated Hospital of Nanchang University
Shanghai Pulmonary Hospital, Shanghai, China
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to 75 Years
Inclusion criteria
1. Age ranging from 20-75 years; 2. Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC; 3. Obtained written informed consent.
Exclusion criteria
1. Missing image data; 2. Pathological N3 disease.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Outcome results
None listed