Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma
Background Current prognostic and predictive biomarkers for lung adenocarcinoma (LUAD) predominantly rely on unimodal approaches, limiting their characterization ability. There is an urgent need for a comprehensive and accurate biomarker to guide individualized adjuvant therapy decisions.Methods In...
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BMJ Publishing Group
2025-03-01
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| Series: | Journal for ImmunoTherapy of Cancer |
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| author | Xin Chen Wei Zhao Jun Liu Junjie Hua Yumeng Wang Huan Lin Mingwei Chen Zaiyi Liu Shiwei Luo Yanting Liang LiXu Yan Deqing Hong Xipeng Pan |
| author_facet | Xin Chen Wei Zhao Jun Liu Junjie Hua Yumeng Wang Huan Lin Mingwei Chen Zaiyi Liu Shiwei Luo Yanting Liang LiXu Yan Deqing Hong Xipeng Pan |
| author_sort | Xin Chen |
| collection | DOAJ |
| description | Background Current prognostic and predictive biomarkers for lung adenocarcinoma (LUAD) predominantly rely on unimodal approaches, limiting their characterization ability. There is an urgent need for a comprehensive and accurate biomarker to guide individualized adjuvant therapy decisions.Methods In this retrospective study, data from patients with resectable LUAD (stage I–III) were collected from two hospitals and a publicly available dataset, forming a training dataset (n=223), a validation dataset (n=95), a testing dataset (n=449), and the non-small cell lung cancer (NSCLC) Radiogenomics dataset (n=59). Tumor and peritumor scores were constructed from preoperative CT radiomics features (shape/intensity/texture). An immune score was derived from the density of tumor-infiltrating lymphocytes (TILs) within the cancer epithelium and stroma on hematoxylin and eosin-stained whole-slide images. A clinical score was constructed based on clinicopathological risk factors. A Cox regression model was employed to integrate these scores, thereby constructing a multimodal nomogram to predict disease-free survival (DFS). The adjuvant chemotherapy benefit rate was subsequently calculated based on this nomogram.Results The multimodal nomogram outperformed each of the unimodal scores in predicting DFS, with a C-index of 0.769 (vs 0.634–0.731) in the training dataset, 0.730 (vs 0.548–0.713) in the validation dataset, and 0.751 (vs 0.660–0.692) in the testing dataset. It was independently associated with DFS after adjusting for other clinicopathological risk factors (training dataset: HR=3.02, p<0.001; validation dataset: HR=2.33, p<0.001; testing dataset: HR=2.03, p=0.001). The adjuvant chemotherapy benefit rate effectively distinguished between patients benefiting from adjuvant chemotherapy and those from observation alone (interaction p<0.001). Furthermore, the high-/low-risk groups defined by the multimodal nomogram provided refined stratification of candidates for adjuvant chemotherapy identified by current guidelines (p<0.001). Gene set enrichment analyses using the NSCLC Radiogenomics dataset revealed associations between tumor/peritumor scores and pathways involved in epithelial-mesenchymal transition, angiogenesis, IL6-JAK-STAT3 signaling, and reactive oxidative species.Conclusion The multimodal nomogram, which incorporates tumor and peritumor morphology with anti-tumor immune response, provides superior prognostic accuracy compared with unimodal scores. Its defined adjuvant chemotherapy benefit rates can inform individualized adjuvant therapy decisions. |
| format | Article |
| id | doaj-art-3b35926d2f3d4bd394dc9e313612f640 |
| institution | DOAJ |
| issn | 2051-1426 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMJ Publishing Group |
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| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-3b35926d2f3d4bd394dc9e313612f6402025-08-20T02:46:50ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262025-03-0113310.1136/jitc-2024-010723Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinomaXin Chen0Wei Zhao1Jun Liu2Junjie Hua3Yumeng Wang4Huan Lin5Mingwei Chen6Zaiyi Liu7Shiwei Luo8Yanting Liang9LiXu Yan10Deqing Hong11Xipeng Pan127 Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China5 Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China5 Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China2 Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China3 School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi, China1 Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China3 School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi, China1 Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China5 Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China1 Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China4 Department of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China6 Institute of Computational Science and Technology, Guangzhou University, Guangzhou, Guangdong, China3 School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi, ChinaBackground Current prognostic and predictive biomarkers for lung adenocarcinoma (LUAD) predominantly rely on unimodal approaches, limiting their characterization ability. There is an urgent need for a comprehensive and accurate biomarker to guide individualized adjuvant therapy decisions.Methods In this retrospective study, data from patients with resectable LUAD (stage I–III) were collected from two hospitals and a publicly available dataset, forming a training dataset (n=223), a validation dataset (n=95), a testing dataset (n=449), and the non-small cell lung cancer (NSCLC) Radiogenomics dataset (n=59). Tumor and peritumor scores were constructed from preoperative CT radiomics features (shape/intensity/texture). An immune score was derived from the density of tumor-infiltrating lymphocytes (TILs) within the cancer epithelium and stroma on hematoxylin and eosin-stained whole-slide images. A clinical score was constructed based on clinicopathological risk factors. A Cox regression model was employed to integrate these scores, thereby constructing a multimodal nomogram to predict disease-free survival (DFS). The adjuvant chemotherapy benefit rate was subsequently calculated based on this nomogram.Results The multimodal nomogram outperformed each of the unimodal scores in predicting DFS, with a C-index of 0.769 (vs 0.634–0.731) in the training dataset, 0.730 (vs 0.548–0.713) in the validation dataset, and 0.751 (vs 0.660–0.692) in the testing dataset. It was independently associated with DFS after adjusting for other clinicopathological risk factors (training dataset: HR=3.02, p<0.001; validation dataset: HR=2.33, p<0.001; testing dataset: HR=2.03, p=0.001). The adjuvant chemotherapy benefit rate effectively distinguished between patients benefiting from adjuvant chemotherapy and those from observation alone (interaction p<0.001). Furthermore, the high-/low-risk groups defined by the multimodal nomogram provided refined stratification of candidates for adjuvant chemotherapy identified by current guidelines (p<0.001). Gene set enrichment analyses using the NSCLC Radiogenomics dataset revealed associations between tumor/peritumor scores and pathways involved in epithelial-mesenchymal transition, angiogenesis, IL6-JAK-STAT3 signaling, and reactive oxidative species.Conclusion The multimodal nomogram, which incorporates tumor and peritumor morphology with anti-tumor immune response, provides superior prognostic accuracy compared with unimodal scores. Its defined adjuvant chemotherapy benefit rates can inform individualized adjuvant therapy decisions.https://jitc.bmj.com/content/13/3/e010723.full |
| spellingShingle | Xin Chen Wei Zhao Jun Liu Junjie Hua Yumeng Wang Huan Lin Mingwei Chen Zaiyi Liu Shiwei Luo Yanting Liang LiXu Yan Deqing Hong Xipeng Pan Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma Journal for ImmunoTherapy of Cancer |
| title | Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma |
| title_full | Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma |
| title_fullStr | Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma |
| title_full_unstemmed | Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma |
| title_short | Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma |
| title_sort | prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma |
| url | https://jitc.bmj.com/content/13/3/e010723.full |
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