Nomogram based on CT imaging and clinical data to predict the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancer
BackgroundPD-1 inhibitors, in combination with chemotherapy, have become the first-line treatment option for patients with advanced metastatic gastric cancer. However, some patients still do not benefit from this treatment, highlighting an urgent need for simple and reliable markers to predict the e...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1504387/full |
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| author | Yinchao Ma Yinchao Ma Zhipeng Wang Zhipeng Wang Chenyang Qiu Mengjun Xiao Shuzhen Wu Kun Han Hui Xu Haiyan Wang |
| author_facet | Yinchao Ma Yinchao Ma Zhipeng Wang Zhipeng Wang Chenyang Qiu Mengjun Xiao Shuzhen Wu Kun Han Hui Xu Haiyan Wang |
| author_sort | Yinchao Ma |
| collection | DOAJ |
| description | BackgroundPD-1 inhibitors, in combination with chemotherapy, have become the first-line treatment option for patients with advanced metastatic gastric cancer. However, some patients still do not benefit from this treatment, highlighting an urgent need for simple and reliable markers to predict the efficacy of immunotherapy.MethodsImmunotherapy efficacy was evaluated using RECIST 1.1 and categorized into complete remission (CR), partial remission (PR), stable disease (SD), and disease progression (PD). Patients with CR, PR, and SD were classified as non-PD responders, while PD patients were categorized as PD responders. Clinical characteristics and CT imaging features of gastric cancer patients from two centers, before receiving PD-1 inhibitor combination chemotherapy, were retrospectively analyzed. A univariate logistic regression analysis was performed for each variable, and separate models for clinical and imaging characteristics, as well as a nomogram, were developed. Area under the curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) were used to evaluate all models.ResultsData from 272 patients (non-PD responders = 206, PD responders = 66) from Center 1 were collected for this study. Data from 76 patients (non-PD responders = 54, PD responders = 22) from Center 2 were used as an external validation cohort to verify the robustness of the models. We developed a clinical model, an imaging features model, and a nomogram. The nomogram, combining clinical and imaging features, demonstrated superior performance with an AUC of 0.904 (95% CI: 0.862–0.947) in the training set and an AUC of 0.801 (95% CI: 0.683-0.918) in the validation set, with sensitivity, specificity, and accuracy of 0.889, 0.682, and 0.829, respectively. Calibration curves further confirmed the agreement between actual results and predictions.ConclusionsA nomogram combining clinical features and CT imaging features before treatment was developed, which can effectively and simply predict the efficacy response of advanced gastric cancer patients treated with PD-1 inhibitors combined with chemotherapy. This tool can aid in optimizing treatment strategies in clinical practice. |
| format | Article |
| id | doaj-art-a4ed5a76d5b34756b93a820e7fdca0f8 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-a4ed5a76d5b34756b93a820e7fdca0f82025-08-20T02:50:16ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-03-011610.3389/fimmu.2025.15043871504387Nomogram based on CT imaging and clinical data to predict the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancerYinchao Ma0Yinchao Ma1Zhipeng Wang2Zhipeng Wang3Chenyang Qiu4Mengjun Xiao5Shuzhen Wu6Kun Han7Hui Xu8Haiyan Wang9Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaSchool of Radiology, Shandong First Medical University, Taian, Shandong, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaSchool of Radiology, Shandong First Medical University, Taian, Shandong, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaDepartment of General Education, Shandong First Medical University, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaBackgroundPD-1 inhibitors, in combination with chemotherapy, have become the first-line treatment option for patients with advanced metastatic gastric cancer. However, some patients still do not benefit from this treatment, highlighting an urgent need for simple and reliable markers to predict the efficacy of immunotherapy.MethodsImmunotherapy efficacy was evaluated using RECIST 1.1 and categorized into complete remission (CR), partial remission (PR), stable disease (SD), and disease progression (PD). Patients with CR, PR, and SD were classified as non-PD responders, while PD patients were categorized as PD responders. Clinical characteristics and CT imaging features of gastric cancer patients from two centers, before receiving PD-1 inhibitor combination chemotherapy, were retrospectively analyzed. A univariate logistic regression analysis was performed for each variable, and separate models for clinical and imaging characteristics, as well as a nomogram, were developed. Area under the curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) were used to evaluate all models.ResultsData from 272 patients (non-PD responders = 206, PD responders = 66) from Center 1 were collected for this study. Data from 76 patients (non-PD responders = 54, PD responders = 22) from Center 2 were used as an external validation cohort to verify the robustness of the models. We developed a clinical model, an imaging features model, and a nomogram. The nomogram, combining clinical and imaging features, demonstrated superior performance with an AUC of 0.904 (95% CI: 0.862–0.947) in the training set and an AUC of 0.801 (95% CI: 0.683-0.918) in the validation set, with sensitivity, specificity, and accuracy of 0.889, 0.682, and 0.829, respectively. Calibration curves further confirmed the agreement between actual results and predictions.ConclusionsA nomogram combining clinical features and CT imaging features before treatment was developed, which can effectively and simply predict the efficacy response of advanced gastric cancer patients treated with PD-1 inhibitors combined with chemotherapy. This tool can aid in optimizing treatment strategies in clinical practice.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1504387/fullnomogramgastric cancercomputed tomographyprogrammed cell death 1 inhibitorschemo-immunotherapy |
| spellingShingle | Yinchao Ma Yinchao Ma Zhipeng Wang Zhipeng Wang Chenyang Qiu Mengjun Xiao Shuzhen Wu Kun Han Hui Xu Haiyan Wang Nomogram based on CT imaging and clinical data to predict the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancer Frontiers in Immunology nomogram gastric cancer computed tomography programmed cell death 1 inhibitors chemo-immunotherapy |
| title | Nomogram based on CT imaging and clinical data to predict the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancer |
| title_full | Nomogram based on CT imaging and clinical data to predict the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancer |
| title_fullStr | Nomogram based on CT imaging and clinical data to predict the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancer |
| title_full_unstemmed | Nomogram based on CT imaging and clinical data to predict the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancer |
| title_short | Nomogram based on CT imaging and clinical data to predict the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancer |
| title_sort | nomogram based on ct imaging and clinical data to predict the efficacy of pd 1 inhibitors combined with chemotherapy in advanced gastric cancer |
| topic | nomogram gastric cancer computed tomography programmed cell death 1 inhibitors chemo-immunotherapy |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1504387/full |
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