Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas
Abstract Purpose We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs). Methods 279 features were extracted from each ROI including 9 histogram features, 220 G...
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2025-01-01
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| Online Access: | https://doi.org/10.1186/s12880-024-01483-2 |
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| author | Chen Chen Lifang Hao Bin Bai Guijun Zhang |
| author_facet | Chen Chen Lifang Hao Bin Bai Guijun Zhang |
| author_sort | Chen Chen |
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| description | Abstract Purpose We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs). Methods 279 features were extracted from each ROI including 9 histogram features, 220 Gy-level co-occurrence matrix features, 20 Gy-level run-length matrix features, 5 auto-regressive model features, 20 wavelets transform features and 5 absolute gradient statistics features. The datasets were randomly divided into two groups, the training set (~ 70%) and the test set (~ 30%). Combinations of data preprocessing methods, including normalization (Min-Max, Z-score, Mean), dimensionality reduction (Pearson Correlation Coefficients (PCC)), feature selector (max-Number, cluster) and ten-fold cross-validation were analyzed for their prediction performance. Kaplan–Meier curve, Cox proportional hazards regression model were used and concordance index (C-index), integrated Brier score (IBS) were selected. Model performance was assessed using the C-index. Results WHO grade, age, gender, histogram (Mean, Perc.90%, Perc.99%), Gray-level co-occurrence matrix (S(3, -3)DifVarnc, S(5, 5)Correlat, S(1, 0)SumEntrp, S(2, -2)InvDfMom), Teta1, WavEnLL_s-2 and GrVariance were identified as the significant recurrence factors. The pipeline using Mean_PCC_Cluster_10 of T1C yielded the highest efficiency with an IBS of 0.170, 0.188, 0.208 and C-index of 0.709, 0.705, 0.602 in the train, test and validation sets, respectively. The pipeline using MinMax_PCC_Cluster_19 of T2WI yielded the highest efficiency with an IBS of 0.189, 0.175, 0.185 and C-index of 0.783, 0.66, 0.649 in the train, test and validation sets. The pipeline using MinMax_PCC_Cluster_13 of T2WI + T1C yielded the highest efficiency with an IBS of 0.152, 0.164, 0.191 and C-index of 0.701, 0.656, 0.593 in the train, test and validation sets, respectively. Conclusion Knowledge discovery from MRI radiomic features can slightly help predict recurrence risk in HGMs. T2WI or T1C yielded better efficiency than T2WI + T1C. The parameters with the best power were Mean, Perc.99%, WavEnLL_s-2, Teta1 and GrVariance. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-e8b65e4f0510478ca4e517eb59a6ab7f2025-01-12T12:44:43ZengBMCBMC Medical Imaging1471-23422025-01-0125111010.1186/s12880-024-01483-2Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomasChen Chen0Lifang Hao1Bin Bai2Guijun Zhang3Department of Radiology, Henan Provincial People’s Hospital and Zhengzhou University People’s HospitalDepartment of Radiology, Liao Cheng The Third People’s HospitalDepartment of Neurosurgery, Tianjin Fifth Central HospitalDepartment of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityAbstract Purpose We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs). Methods 279 features were extracted from each ROI including 9 histogram features, 220 Gy-level co-occurrence matrix features, 20 Gy-level run-length matrix features, 5 auto-regressive model features, 20 wavelets transform features and 5 absolute gradient statistics features. The datasets were randomly divided into two groups, the training set (~ 70%) and the test set (~ 30%). Combinations of data preprocessing methods, including normalization (Min-Max, Z-score, Mean), dimensionality reduction (Pearson Correlation Coefficients (PCC)), feature selector (max-Number, cluster) and ten-fold cross-validation were analyzed for their prediction performance. Kaplan–Meier curve, Cox proportional hazards regression model were used and concordance index (C-index), integrated Brier score (IBS) were selected. Model performance was assessed using the C-index. Results WHO grade, age, gender, histogram (Mean, Perc.90%, Perc.99%), Gray-level co-occurrence matrix (S(3, -3)DifVarnc, S(5, 5)Correlat, S(1, 0)SumEntrp, S(2, -2)InvDfMom), Teta1, WavEnLL_s-2 and GrVariance were identified as the significant recurrence factors. The pipeline using Mean_PCC_Cluster_10 of T1C yielded the highest efficiency with an IBS of 0.170, 0.188, 0.208 and C-index of 0.709, 0.705, 0.602 in the train, test and validation sets, respectively. The pipeline using MinMax_PCC_Cluster_19 of T2WI yielded the highest efficiency with an IBS of 0.189, 0.175, 0.185 and C-index of 0.783, 0.66, 0.649 in the train, test and validation sets. The pipeline using MinMax_PCC_Cluster_13 of T2WI + T1C yielded the highest efficiency with an IBS of 0.152, 0.164, 0.191 and C-index of 0.701, 0.656, 0.593 in the train, test and validation sets, respectively. Conclusion Knowledge discovery from MRI radiomic features can slightly help predict recurrence risk in HGMs. T2WI or T1C yielded better efficiency than T2WI + T1C. The parameters with the best power were Mean, Perc.99%, WavEnLL_s-2, Teta1 and GrVariance.https://doi.org/10.1186/s12880-024-01483-2RecurrenceRadiomicsKnowledge discoveryNeoplasms |
| spellingShingle | Chen Chen Lifang Hao Bin Bai Guijun Zhang Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas BMC Medical Imaging Recurrence Radiomics Knowledge discovery Neoplasms |
| title | Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas |
| title_full | Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas |
| title_fullStr | Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas |
| title_full_unstemmed | Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas |
| title_short | Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas |
| title_sort | knowledge discovery from database mri radiomic features to assess recurrence risk in high grade meningiomas |
| topic | Recurrence Radiomics Knowledge discovery Neoplasms |
| url | https://doi.org/10.1186/s12880-024-01483-2 |
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