Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer
ObjectiveInvestigating the effect of M2 macrophage infiltration on overall survival and to use histopathological imaging features (HIF) to predict M2 macrophage infiltration in patients with serous ovarian cancer (SOC) is important for improving prognostic accuracy, identifying new therapeutic targe...
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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.2025.1505509/full |
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| author | Ling Zhao Jiajia Tan Qiuyuan Su Yan Kuang Yan Kuang |
| author_facet | Ling Zhao Jiajia Tan Qiuyuan Su Yan Kuang Yan Kuang |
| author_sort | Ling Zhao |
| collection | DOAJ |
| description | ObjectiveInvestigating the effect of M2 macrophage infiltration on overall survival and to use histopathological imaging features (HIF) to predict M2 macrophage infiltration in patients with serous ovarian cancer (SOC) is important for improving prognostic accuracy, identifying new therapeutic targets, and advancing personalized treatment approaches.MethodsWe downloaded data from 86 patients with SOC from The Cancer Genome Atlas (TCGA) and divided these patients into a training set and a validation set with a ratio of 8:2. In addition, tissue microarrays from 106 patients with SOC patients were included as an external validation set. HIF were recognized by deep multiple instance learning (MIL) to predict M2 macrophage infiltration via theResNet18 network in the training set. The final model was evaluated using the internal and external validation set.ResultsUsing data acquired from the TCGA database, we applied univariate Cox analysis and determined that higher levels of M2 macrophage infiltration were associated with a poor prognosis (hazard ratio [HR]=6.8; 95% CI [confidence interval]: 1.6–28, P=0.0083). External validation revealed that M2 macrophage infiltration was an independent risk factor for the prognosis of patients with SOC (HR=3.986; 95% CI: 2.436–6.522; P<0.001). Next, we constructed four MIL strategies (Mean probability, Top-10 Mean, Top-100 Mean, and Maximum probability) to identify histopathological images that could predict M2 macrophage infiltration. The Mean Probability Method was the most suitable and was used to generate a HIF model with an AUC, recall rate, precision and F1 score of 0.7500, 0.6932, 0.600, 0.600, and 0.600, respectively.ConclusionsCollectively, our findings indicated that M2 macrophage infiltration may increase prognostic prediction for SOC patients. Machine deep learning of pathological immunohistochemical images exhibited good potential for the direct prediction of M2 macrophage infiltration. |
| format | Article |
| id | doaj-art-3124cda2a92147d3a5b7f889bc3e01e6 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-3124cda2a92147d3a5b7f889bc3e01e62025-08-20T03:01:26ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-03-011610.3389/fimmu.2025.15055091505509Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancerLing Zhao0Jiajia Tan1Qiuyuan Su2Yan Kuang3Yan Kuang4Department of Gynecology, First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Gynecology, First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Gynecology, First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Gynecology, First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Gynecology, Guangzhou First People’s Hospital, Guangzhou, ChinaObjectiveInvestigating the effect of M2 macrophage infiltration on overall survival and to use histopathological imaging features (HIF) to predict M2 macrophage infiltration in patients with serous ovarian cancer (SOC) is important for improving prognostic accuracy, identifying new therapeutic targets, and advancing personalized treatment approaches.MethodsWe downloaded data from 86 patients with SOC from The Cancer Genome Atlas (TCGA) and divided these patients into a training set and a validation set with a ratio of 8:2. In addition, tissue microarrays from 106 patients with SOC patients were included as an external validation set. HIF were recognized by deep multiple instance learning (MIL) to predict M2 macrophage infiltration via theResNet18 network in the training set. The final model was evaluated using the internal and external validation set.ResultsUsing data acquired from the TCGA database, we applied univariate Cox analysis and determined that higher levels of M2 macrophage infiltration were associated with a poor prognosis (hazard ratio [HR]=6.8; 95% CI [confidence interval]: 1.6–28, P=0.0083). External validation revealed that M2 macrophage infiltration was an independent risk factor for the prognosis of patients with SOC (HR=3.986; 95% CI: 2.436–6.522; P<0.001). Next, we constructed four MIL strategies (Mean probability, Top-10 Mean, Top-100 Mean, and Maximum probability) to identify histopathological images that could predict M2 macrophage infiltration. The Mean Probability Method was the most suitable and was used to generate a HIF model with an AUC, recall rate, precision and F1 score of 0.7500, 0.6932, 0.600, 0.600, and 0.600, respectively.ConclusionsCollectively, our findings indicated that M2 macrophage infiltration may increase prognostic prediction for SOC patients. Machine deep learning of pathological immunohistochemical images exhibited good potential for the direct prediction of M2 macrophage infiltration.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1505509/fullserous ovarian cancerhistopathological image featuresResNet18M2 macrophage infiltrationdeep learning artificial intelligence |
| spellingShingle | Ling Zhao Jiajia Tan Qiuyuan Su Yan Kuang Yan Kuang Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer Frontiers in Immunology serous ovarian cancer histopathological image features ResNet18 M2 macrophage infiltration deep learning artificial intelligence |
| title | Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer |
| title_full | Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer |
| title_fullStr | Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer |
| title_full_unstemmed | Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer |
| title_short | Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer |
| title_sort | integration of histopathological images and immunological analysis to predict m2 macrophage infiltration and prognosis in patients with serous ovarian cancer |
| topic | serous ovarian cancer histopathological image features ResNet18 M2 macrophage infiltration deep learning artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1505509/full |
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