An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach

Zhang, Lan; Liu, Xiao; Xu, Xin; Liu, Weifan; Jia, Yuxi; Chen, Weiqiang; Fu, Xiaona; Li, Qiang; X, Sun; Zhang, Yangjing; Shu, Shenglei; Zhang, Xinli; Xiang, Rui; Chen, Hongyi; Sun, Peng; Geng, Daoying; Yu, Zekuan; Liu, Jie; Wang, Jing

doi:10.1016/j.ejrad.2022.110639

Cited by 11 publications

(1 citation statement)

References 49 publications

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“…We constructed a ML-based radiomics model by the radiomics approach we proposed in [32]. Subsequently, the MRI images of all cases in the dataset were independently assessed by three physicians specializing in diagnostic neuro-oncology radiology.…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Aided Diagnosis Model Based on Deep Learning for Glioblastoma, Solitary Brain Metastases, and Primary Central Nervous System Lymphoma with Multi-Modal MRI

Liu,

Liu

2024

Biology

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(1) Background: Diagnosis of glioblastoma (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) plays a decisive role in the development of personalized treatment plans. Constructing a deep learning classification network to diagnose GBM, SBM, and PCNSL with multi-modal MRI is important and necessary. (2) Subjects: GBM, SBM, and PCNSL were confirmed by histopathology with the multi-modal MRI examination (study from 1225 subjects, average age 53 years, 671 males), 3.0 T T2 fluid-attenuated inversion recovery (T2-Flair), and Contrast-enhanced T1-weighted imaging (CE-T1WI). (3) Methods: This paper introduces MFFC-Net, a classification model based on the fusion of multi-modal MRIs, for the classification of GBM, SBM, and PCNSL. The network architecture consists of parallel encoders using DenseBlocks to extract features from different modalities of MRI images. Subsequently, an L1−norm feature fusion module is applied to enhance the interrelationships among tumor tissues. Then, a spatial-channel self-attention weighting operation is performed after the feature fusion. Finally, the classification results are obtained using the full convolutional layer (FC) and Soft-max. (4) Results: The ACC of MFFC-Net based on feature fusion was 0.920, better than the radiomics model (ACC of 0.829). There was no significant difference in the ACC compared to the expert radiologist (0.920 vs. 0.924, p = 0.774). (5) Conclusions: Our MFFC-Net model could distinguish GBM, SBM, and PCNSL preoperatively based on multi-modal MRI, with a higher performance than the radiomics model and was comparable to radiologists.

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Section: Evaluation Indicatorsmentioning

confidence: 99%