Pengxin Shen , Yangyang Li , Wenji Xu , Zhiyi Zhang , Xiaochun Wang , Guoqiang Yang , Jiangfeng Du , Hui Zhang , Yan Tan
{"title":"定量敏感性作图和弥散峰度成像的直方图分析,用于预测2021年WHO成人型弥漫性胶质瘤的分级。","authors":"Pengxin Shen , Yangyang Li , Wenji Xu , Zhiyi Zhang , Xiaochun Wang , Guoqiang Yang , Jiangfeng Du , Hui Zhang , Yan Tan","doi":"10.1016/j.mri.2025.110528","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><div>To evaluate the role of quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI) histogram features in improving the 2021 World Health Organization Classification of Central Nervous System Tumors (WHO CNS 5) grading accuracy for adult-type diffuse gliomas when combined with conventional imaging sequences.</div></div><div><h3>Methods</h3><div>A total of 62 patients were retrospectively collected. Histogram features of QSM, DKI, CE-T1WI, T2FLAIR were extracted from tumor parenchyma. Independent <em>t</em>-tests and Mann-Whitney <em>U</em> tests were used to compare differences between grade 2/3 and grade 4 gliomas. The evaluation of the model included receiver operating characteristic (ROC) curves, 5-fold cross-validation, nomogram construction, and calibration curve analysis. Prognostic differences between two groups were assessed using Kaplan-Meier survival analysis.</div></div><div><h3>Results</h3><div>The functional imaging model (AUC = 0.892) was constructed using QSM mean absolute deviation (MAD) and relative mean kurtosis 90th percentile (rMK P90), while the conventional imaging model (AUC = 0.776) was built using CE-T1WI robust mean absolute deviation (RMAD) and T2FLAIR maximum. The imaging combined model, incorporating CE-T1WI RMAD, QSM MAD, and rMK P90, achieved an AUC of 0.936. Among the clinical factors, age showed a statistically significant difference between the two groups, with an AUC of 0.769. The integrated model combining the imaging model and age achieved the highest AUC of 0.949. The 5-fold internal cross validation showed that the average AUC was 0.944. Survival analysis revealed a significant difference between grade 2/3 and grade 4 gliomas.</div></div><div><h3>Conclusion</h3><div>Histogram features of QSM and DKI can complement conventional sequences, enhancing the diagnostic performance for adult-type diffuse gliomas grading.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110528"},"PeriodicalIF":2.0000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Histogram analysis of quantitative susceptibility mapping and diffusion kurtosis imaging for the grading prediction of 2021 WHO adult-type diffuse gliomas\",\"authors\":\"Pengxin Shen , Yangyang Li , Wenji Xu , Zhiyi Zhang , Xiaochun Wang , Guoqiang Yang , Jiangfeng Du , Hui Zhang , Yan Tan\",\"doi\":\"10.1016/j.mri.2025.110528\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><div>To evaluate the role of quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI) histogram features in improving the 2021 World Health Organization Classification of Central Nervous System Tumors (WHO CNS 5) grading accuracy for adult-type diffuse gliomas when combined with conventional imaging sequences.</div></div><div><h3>Methods</h3><div>A total of 62 patients were retrospectively collected. Histogram features of QSM, DKI, CE-T1WI, T2FLAIR were extracted from tumor parenchyma. Independent <em>t</em>-tests and Mann-Whitney <em>U</em> tests were used to compare differences between grade 2/3 and grade 4 gliomas. The evaluation of the model included receiver operating characteristic (ROC) curves, 5-fold cross-validation, nomogram construction, and calibration curve analysis. Prognostic differences between two groups were assessed using Kaplan-Meier survival analysis.</div></div><div><h3>Results</h3><div>The functional imaging model (AUC = 0.892) was constructed using QSM mean absolute deviation (MAD) and relative mean kurtosis 90th percentile (rMK P90), while the conventional imaging model (AUC = 0.776) was built using CE-T1WI robust mean absolute deviation (RMAD) and T2FLAIR maximum. The imaging combined model, incorporating CE-T1WI RMAD, QSM MAD, and rMK P90, achieved an AUC of 0.936. Among the clinical factors, age showed a statistically significant difference between the two groups, with an AUC of 0.769. The integrated model combining the imaging model and age achieved the highest AUC of 0.949. The 5-fold internal cross validation showed that the average AUC was 0.944. Survival analysis revealed a significant difference between grade 2/3 and grade 4 gliomas.</div></div><div><h3>Conclusion</h3><div>Histogram features of QSM and DKI can complement conventional sequences, enhancing the diagnostic performance for adult-type diffuse gliomas grading.</div></div>\",\"PeriodicalId\":18165,\"journal\":{\"name\":\"Magnetic resonance imaging\",\"volume\":\"124 \",\"pages\":\"Article 110528\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Magnetic resonance imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0730725X25002127\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic resonance imaging","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0730725X25002127","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Histogram analysis of quantitative susceptibility mapping and diffusion kurtosis imaging for the grading prediction of 2021 WHO adult-type diffuse gliomas
Objectives
To evaluate the role of quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI) histogram features in improving the 2021 World Health Organization Classification of Central Nervous System Tumors (WHO CNS 5) grading accuracy for adult-type diffuse gliomas when combined with conventional imaging sequences.
Methods
A total of 62 patients were retrospectively collected. Histogram features of QSM, DKI, CE-T1WI, T2FLAIR were extracted from tumor parenchyma. Independent t-tests and Mann-Whitney U tests were used to compare differences between grade 2/3 and grade 4 gliomas. The evaluation of the model included receiver operating characteristic (ROC) curves, 5-fold cross-validation, nomogram construction, and calibration curve analysis. Prognostic differences between two groups were assessed using Kaplan-Meier survival analysis.
Results
The functional imaging model (AUC = 0.892) was constructed using QSM mean absolute deviation (MAD) and relative mean kurtosis 90th percentile (rMK P90), while the conventional imaging model (AUC = 0.776) was built using CE-T1WI robust mean absolute deviation (RMAD) and T2FLAIR maximum. The imaging combined model, incorporating CE-T1WI RMAD, QSM MAD, and rMK P90, achieved an AUC of 0.936. Among the clinical factors, age showed a statistically significant difference between the two groups, with an AUC of 0.769. The integrated model combining the imaging model and age achieved the highest AUC of 0.949. The 5-fold internal cross validation showed that the average AUC was 0.944. Survival analysis revealed a significant difference between grade 2/3 and grade 4 gliomas.
Conclusion
Histogram features of QSM and DKI can complement conventional sequences, enhancing the diagnostic performance for adult-type diffuse gliomas grading.
期刊介绍:
Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.