2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDBIO-3.9
Paper Title KINET: A NON-INVASIVE METHOD FOR PREDICTING KI67 INDEX OF GLIOMA
Authors Xuhui Li, Yong Xu, Central South University, China; Feng Xiang, Qing Liu, Xiangya Hospital of Central South University, China; Weihong Huang, Mobile Health Ministry of Education-China Mobile Joint Laboratory, China; Bin Xie, Central South University, Hunan Xiangjiang Artificial Intelligence Academy, China
SessionBIO-3: Biomedical Signal Processing 3
LocationArea C
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Biomedical Signal Processing: Medical image analysis
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Abstract In this paper, a multimodal magnetic resonance imaging (MRI) and heterogeneous metadata (including age, gender) dataset containing 263 patients was established. Based on this dataset, a new multimodal deep neural network (KiNet) was proposed, aiming to effectively predict the Ki67 index in gliomas in a non-invasive way by fusing multimodal MRI features and metadata. We adopted a five-fold cross-validation approach to verify the performance of the network. KiNet achieved results with an AUC of 0.79 and a kappa coefficient of 0.47. The proposed approach’s outperformance indicated the feasibility of predicting the Ki67 index in gliomas in a non-invasive way.