{"title":"中枢神经系统单发纤维性肿瘤中胶原蛋白含量的预后意义。","authors":"Xiaoling Li, Hua Zhang, Chengcong Hu, Liwen Hu, Huibin Guo, Hongbao Chen, Guoping Li, Qian Huang, Shuie Jiang, Sheng Zhang, Zhen Xing, Xingfu Wang","doi":"10.3389/fonc.2024.1450813","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>We aimed to explore the prognostic significance of collagen content in solitary fibrous tumors (SFTs) of the central nervous system (CNS) and preliminarily investigate its relationship with magnetic resonance imaging (MRI) features of SFTs.</p><p><strong>Methods: </strong>Collagen content was identified using Masson's trichrome staining, and quantitatively assessed. Radiomic methods were applied to extract quantitative MRI features of SFTs, which were then analyzed in relation to collagen content.</p><p><strong>Results: </strong>The collagen content in CNS SFTs was categorized into high- and low-content groups, with a cutoff value of 6%. Survival analysis indicated a positive correlation between collagen content and overall survival (OS). In multivariate Cox regression analysis, incorporating factors such as mitosis, necrosis, Ki67, and collagen content and other indicators, collagen content emerged as an independent prognostic factor. Collagen content demonstrated a negative correlation with tumor histological phenotype, Ki67, WHO grade, mitosis, necrosis, and brain invasion. Additionally, the signal intensity of SFTs on T2-weighted imaging (T2WI) decreased with increasing collagen content. Radiomics analysis identified 1,702 features from each patient's region of interest, with 12 features showing significant differences between the high and low collagen content groups. Among the quantitative parameters and radiomic models, the combined T1- and T2WI models exhibited the highest diagnostic performance.</p><p><strong>Conclusion: </strong>These findings suggest that collagen content is an independent prognostic risk factor for OS. Furthermore, combined radiomic models based on T1-and T2WI sequences may offer a more comprehensive, objective, and accurate assessment of collagen content in CNS SFTs.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"14 ","pages":"1450813"},"PeriodicalIF":3.5000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11588704/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prognostic significance of collagen content in solitary fibrous tumors of the central nervous system.\",\"authors\":\"Xiaoling Li, Hua Zhang, Chengcong Hu, Liwen Hu, Huibin Guo, Hongbao Chen, Guoping Li, Qian Huang, Shuie Jiang, Sheng Zhang, Zhen Xing, Xingfu Wang\",\"doi\":\"10.3389/fonc.2024.1450813\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>We aimed to explore the prognostic significance of collagen content in solitary fibrous tumors (SFTs) of the central nervous system (CNS) and preliminarily investigate its relationship with magnetic resonance imaging (MRI) features of SFTs.</p><p><strong>Methods: </strong>Collagen content was identified using Masson's trichrome staining, and quantitatively assessed. Radiomic methods were applied to extract quantitative MRI features of SFTs, which were then analyzed in relation to collagen content.</p><p><strong>Results: </strong>The collagen content in CNS SFTs was categorized into high- and low-content groups, with a cutoff value of 6%. Survival analysis indicated a positive correlation between collagen content and overall survival (OS). In multivariate Cox regression analysis, incorporating factors such as mitosis, necrosis, Ki67, and collagen content and other indicators, collagen content emerged as an independent prognostic factor. Collagen content demonstrated a negative correlation with tumor histological phenotype, Ki67, WHO grade, mitosis, necrosis, and brain invasion. Additionally, the signal intensity of SFTs on T2-weighted imaging (T2WI) decreased with increasing collagen content. Radiomics analysis identified 1,702 features from each patient's region of interest, with 12 features showing significant differences between the high and low collagen content groups. Among the quantitative parameters and radiomic models, the combined T1- and T2WI models exhibited the highest diagnostic performance.</p><p><strong>Conclusion: </strong>These findings suggest that collagen content is an independent prognostic risk factor for OS. Furthermore, combined radiomic models based on T1-and T2WI sequences may offer a more comprehensive, objective, and accurate assessment of collagen content in CNS SFTs.</p>\",\"PeriodicalId\":12482,\"journal\":{\"name\":\"Frontiers in Oncology\",\"volume\":\"14 \",\"pages\":\"1450813\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11588704/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fonc.2024.1450813\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fonc.2024.1450813","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Prognostic significance of collagen content in solitary fibrous tumors of the central nervous system.
Purpose: We aimed to explore the prognostic significance of collagen content in solitary fibrous tumors (SFTs) of the central nervous system (CNS) and preliminarily investigate its relationship with magnetic resonance imaging (MRI) features of SFTs.
Methods: Collagen content was identified using Masson's trichrome staining, and quantitatively assessed. Radiomic methods were applied to extract quantitative MRI features of SFTs, which were then analyzed in relation to collagen content.
Results: The collagen content in CNS SFTs was categorized into high- and low-content groups, with a cutoff value of 6%. Survival analysis indicated a positive correlation between collagen content and overall survival (OS). In multivariate Cox regression analysis, incorporating factors such as mitosis, necrosis, Ki67, and collagen content and other indicators, collagen content emerged as an independent prognostic factor. Collagen content demonstrated a negative correlation with tumor histological phenotype, Ki67, WHO grade, mitosis, necrosis, and brain invasion. Additionally, the signal intensity of SFTs on T2-weighted imaging (T2WI) decreased with increasing collagen content. Radiomics analysis identified 1,702 features from each patient's region of interest, with 12 features showing significant differences between the high and low collagen content groups. Among the quantitative parameters and radiomic models, the combined T1- and T2WI models exhibited the highest diagnostic performance.
Conclusion: These findings suggest that collagen content is an independent prognostic risk factor for OS. Furthermore, combined radiomic models based on T1-and T2WI sequences may offer a more comprehensive, objective, and accurate assessment of collagen content in CNS SFTs.
期刊介绍:
Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.