A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis
This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T<sub>1<...
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2021
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oai:doaj.org-article:d97cb2f6355240bcbc4ce6c8ed0aa3312021-11-25T17:04:00ZA Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis10.3390/cancers132257932072-6694https://doaj.org/article/d97cb2f6355240bcbc4ce6c8ed0aa3312021-11-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/22/5793https://doaj.org/toc/2072-6694This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T<sub>1</sub>WI, T<sub>2</sub>WI, T<sub>2</sub>_FLAIR, and CE_T<sub>1</sub>WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOI<sub>ET</sub>) had dominant predictive performances over features from other VOI combinations. Fusion of VOI<sub>ET</sub> features from the T<sub>1</sub>WI and T<sub>2</sub>_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (<i>p</i> = 0.03, 0.01, 0.02, and 0.01, and <i>p</i> = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (<i>p</i> = 0.03, 0.02, 0.03, and 0.02, and <i>p</i> = 0.03, 0.02, 0.44, and 0.03, respectively).Jialiang WuFangrong LiangRuili WeiShengsheng LaiXiaofei LvShiwei LuoZhe WuHuixian ChenWanli ZhangXiangling ZengXianghua YeYong WuXinhua WeiXinqing JiangXin ZhenRuimeng YangMDPI AGarticleglioblastoma multiformesolitary brain metastasisMRIradiomicsfusionNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5793, p 5793 (2021) |
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| collection |
DOAJ |
| language |
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| topic |
glioblastoma multiforme solitary brain metastasis MRI radiomics fusion Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
| spellingShingle |
glioblastoma multiforme solitary brain metastasis MRI radiomics fusion Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Jialiang Wu Fangrong Liang Ruili Wei Shengsheng Lai Xiaofei Lv Shiwei Luo Zhe Wu Huixian Chen Wanli Zhang Xiangling Zeng Xianghua Ye Yong Wu Xinhua Wei Xinqing Jiang Xin Zhen Ruimeng Yang A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis |
| description |
This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T<sub>1</sub>WI, T<sub>2</sub>WI, T<sub>2</sub>_FLAIR, and CE_T<sub>1</sub>WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOI<sub>ET</sub>) had dominant predictive performances over features from other VOI combinations. Fusion of VOI<sub>ET</sub> features from the T<sub>1</sub>WI and T<sub>2</sub>_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (<i>p</i> = 0.03, 0.01, 0.02, and 0.01, and <i>p</i> = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (<i>p</i> = 0.03, 0.02, 0.03, and 0.02, and <i>p</i> = 0.03, 0.02, 0.44, and 0.03, respectively). |
| format |
article |
| author |
Jialiang Wu Fangrong Liang Ruili Wei Shengsheng Lai Xiaofei Lv Shiwei Luo Zhe Wu Huixian Chen Wanli Zhang Xiangling Zeng Xianghua Ye Yong Wu Xinhua Wei Xinqing Jiang Xin Zhen Ruimeng Yang |
| author_facet |
Jialiang Wu Fangrong Liang Ruili Wei Shengsheng Lai Xiaofei Lv Shiwei Luo Zhe Wu Huixian Chen Wanli Zhang Xiangling Zeng Xianghua Ye Yong Wu Xinhua Wei Xinqing Jiang Xin Zhen Ruimeng Yang |
| author_sort |
Jialiang Wu |
| title |
A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis |
| title_short |
A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis |
| title_full |
A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis |
| title_fullStr |
A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis |
| title_full_unstemmed |
A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis |
| title_sort |
multiparametric mr-based radiofusionomics model with robust capabilities of differentiating glioblastoma multiforme from solitary brain metastasis |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/d97cb2f6355240bcbc4ce6c8ed0aa331 |
| work_keys_str_mv |
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| _version_ |
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