An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma

doi:10.12122/j.issn.1673-4254.2024.08.15

Abstract

Abstract:

Objective To evaluate the performance of magnetic resonance imaging (MRI) multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma (HGG) from low-grade glioma (LGG). Methods We retrospectively collected multi-sequence MR images from 305 glioma patients, including 189 HGG patients and 116 LGG patients. The region of interest (ROI) of T1-weighted images (T1WI), T2-weighted images (T2WI), T2 fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI) were delineated to extract the radiomics features. A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data. The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy, balanced accuracy, area under the ROC curve (AUC), specificity, and sensitivity. The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG. Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane. Convergence experiments were used to verify the feasibility of the model. Results For differentiation of HGG from LGG with a missing rate of 10%, the proposed model achieved accuracy, balanced accuracy, AUC, specificity, and sensitivity of 0.777, 0.768, 0.826, 0.754 and 0.780, respectively. The fused latent features showed excellent performance in the class separability experiment, and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30% and 50%. Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models, demonstrating its potential for efficient processing of non-holonomic multimodal data.

Key words: sequence deletion, feature imputation, representation learning, high-grade glioma, low-grade glioma

Chuixing WU, Weixiong ZHONG, Jincheng XIE, Ruimeng YANG, Yuankui WU, Yikai XU, Linjing WANG, Xin ZHEN. An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma[J]. Journal of Southern Medical University, 2024, 44(8): 1561-1570.

Figures/Tables 10

Tab.1 Demographic characteristics of the patients with HGG and LGG ［n (%)］

Characteristics	HGG(n=189)	LGG(n=116)	$χ 2$	P
Age (year)
≤50	108 (57.1%)	107(92.2%)	-7.021*	2.460
>50	81 (42.9%)	9(7.8%)	-7.021*	2.460
Gender
Female	73 (38.6%)	48(41.4%)	1.652^#	0.199
Male	116 (61.4%)	68(58.6%)	1.652^#	0.199

Tab.1 Demographic characteristics of the patients with HGG and LGG ［n (%)］

Characteristics	HGG(n=189)	LGG(n=116)	$χ 2$	P
Age (year)
≤50	108 (57.1%)	107(92.2%)	-7.021*	2.460
>50	81 (42.9%)	9(7.8%)	-7.021*	2.460
Gender
Female	73 (38.6%)	48(41.4%)	1.652^#	0.199
Male	116 (61.4%)	68(58.6%)	1.652^#	0.199

Fig.1 Framework of MRI multi-sequence feature imputation and fusion mutual aid model based on sequence deletion.

Tab.2 Extracted MRI radiomics features

Categories of radiomics features	Radiomics features
First Order Features (m=19)	Mean, Median, Maximum, Minimum, Robust Mean Absolute Deviation, Root Mean Squared, Mean Absolute Deviation, 10th percentile, 90th percentile, Uniformity, Standard Deviation, Skewness, Kurtosis, Variance, Energy, Total Energy, Entropy Minimum, Range, Interquartile Range
Shape Features (m=15)	Elongation, Flatness, Major Axis Length, Least Axis Length, Maximum 2D diameter (Column), Maximum 2Ddiameter (Row), Maximum 2D diameter (Slice), Maximum 3D diameter, Minor Axis Length, Sphericity, Surface Area, Mesh Volume, Surface Volume Ratio, Spherical Disproportion, Voxel Volume
Texture Features (m=75)	¹NGTDM (m=5), ²GLDM (m=14), ³GLSZM (m=16), ⁴GLRLM (m =16), ⁵GLCM (m =24)

Tab.3 Pseudocode of MRI multi-sequence feature imputation and fusion mutual aid model based on sequence deletion

Algorithm 1 Pseudocode of the Proposed Method

Training stage

Input: multimodal feature matrix $X = X 1; …; X i; …; X D, i ∈ 1, D$ ,feature dimension $m i$ and hyperparameters $k, λ i$ and $γ i$ .

Output: $V$ , $P$

Begin

Initialize $Q, V, P, H$ as random matrices;

For $t = 1$ to $T$ do

Update $Q$ by using $Q - α 1 ∇ Φ Q$

Update $V$ by using $V - α 2 ∇ Φ V$

Update $P$ column by column by using $p j = Z f j T f j f j T + γ 3 / p j 2 - 1$

Update $H$ by using $H - α 3 ∇ Φ H$

End

Testing stage

Input: new unseen multimodal feature $X = X 1; …; X i; …; X D, i ∈ 1, D$

Output: fused representation $V n e w = P X ∘ U$ , where $U ∈ R k × n$ , $u i j = 1 / D j$ , $i ∈ 1, k, j ∈ 1, n,$ $D j$ is the number of complete modalities in the $j t h$ sample.

Tab.3 Pseudocode of MRI multi-sequence feature imputation and fusion mutual aid model based on sequence deletion

Algorithm 1 Pseudocode of the Proposed Method

Training stage

Input: multimodal feature matrix $X = X 1; …; X i; …; X D, i ∈ 1, D$ ,feature dimension $m i$ and hyperparameters $k, λ i$ and $γ i$ .

Output: $V$ , $P$

Begin

Initialize $Q, V, P, H$ as random matrices;

For $t = 1$ to $T$ do

Update $Q$ by using $Q - α 1 ∇ Φ Q$

Update $V$ by using $V - α 2 ∇ Φ V$

Update $P$ column by column by using $p j = Z f j T f j f j T + γ 3 / p j 2 - 1$

Update $H$ by using $H - α 3 ∇ Φ H$

End

Testing stage

Input: new unseen multimodal feature $X = X 1; …; X i; …; X D, i ∈ 1, D$

Output: fused representation $V n e w = P X ∘ U$ , where $U ∈ R k × n$ , $u i j = 1 / D j$ , $i ∈ 1, k, j ∈ 1, n,$ $D j$ is the number of complete modalities in the $j t h$ sample.

Tab.4 Confusion Matrix

Actual\\Predicted	Predicted Negative (PN)	Predicted Positive (PP)
Actual Negative (AN)	True Negative (TN)	False Positive (FP)
Actual Positive (AP)	False Negative (FN)	True Positive (TP)

Tab.5 Performance comparison after feeding the fusion multimodal data into various classifiers

Classifier	ACC/BAcc	AUC	SPE	SEN
SVM	0.770/0.758	0.824	0.724	0.792
LDA	0.761/0.743	0.797	0.684	0.802
KNN	0.767/0.751	0.761	0.709	0.792
Bagging	0.744/0.731	0.790	0.689	0.772
XGBoost	0.764/0.743	0.794	0.668	0.819
AdaBoost	0.731/0.711	0.711	0.651	0.771
Gaussian NB	0.767/0.756	0.790	0.737	0.774
Logistic regression	0.777/0.768	0.826	0.754	0.780

Tab.6 Classification performance of this model and other models for discriminating HGG from LGG at different missing rates

Model	10%			30%			50%
Model	ACC/BAcc	SPE	SEN	ACC/BAcc	SPE	SEN	ACC/BAcc	SPE	SEN
BSV	0.639/0.611	0.491	0.730	0.662/0.613	0.405	0.820	0.646/0.569	0.250	0.889
Concat	0.620/0.592	0.474	0.709	0.620/0.587	0.448	0.725	0.616/0.574	0.397	0.751
DAIMC	0.616/0.589	0.474	0.704	0.607/0.583	0.483	0.683	0.600/0.499	0.078	0.921
AWGF	0.646/0.644	0.638	0.651	0.554/0.549	0.526	0.571	0.580/0.561	0.483	0.640
PIC	0.600/0.532	0.250	0.815	0.620/0.528	0.147	0.910	0.574/0.605	0.733	0.476
CDIMC	0.607/0.456	0.196	0.716	0.593/0.524	0.815	0.233	0.534/0.461	0.482	0.440
MKKM_IK	0.603/0.627	0.724	0.529	0.597/0.620	0.716	0.524	0.590/0.589	0.586	0.593
Proposed	0.777/0.768	0.754	0.780	0.754/0.742	0.702	0.783	0.734/0.734	0.708	0.759

Tab.7 Confusion matrix results of the model for the classification task of HGG and LGG

Actual\\Predicted	10%		30%		50%
Actual\\Predicted	PN	PP	PN	PP	PN	PP
AN	88	28	81	35	81	35
AP	40	149	40	149	46	143

Fig.2 Scatter plot visualization experiment of fused features (A) and original features (B) samples.

Fig.3 Convergence curve of the proposed method on the dataset.

References 46

1	Tan AC, Ashley DM, López GY, et al. Management of glioblastoma: state of the art and future directions[J]. CA Cancer J Clin, 2020, 70(4): 299-312.
2	Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro Oncol, 2021, 23(8): 1231-51.
3	Banerjee S, Mitra S, Masulli F, et al. Deep radiomics for brain tumor detection and classification from multi-sequence MRI[J]. arXiv preprint arXiv: 1903. 09240, 2019.
4	Ellingson BM, Bendszus M, Boxerman J, et al. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials[J]. Neuro Oncol, 2015, 17(9): 1188-98.
5	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-77.
6	Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-62.
7	Gore S, Chougule T, Jagtap J, et al. A review of radiomics and deep predictive modeling in glioma characterization[J]. Acad Radiol, 2021, 28(11): 1599-621.
8	Wang XY, Wang DQ, Yao ZG, et al. Machine learning models for multiparametric glioma grading with quantitative result interpretations[J]. Front Neurosci, 2018, 12: 1046.
9	Bisdas S, Shen HC, Thust S, et al. Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study[J]. Sci Rep, 2018, 8(1): 6108-17.
10	Cho HH, Park H. Classification of low-grade and high-grade glioma using multi-modal image radiomics features[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2017, 2017: 3081-4.
11	Tian Q, Yan LF, Zhang X, et al. Radiomics strategy for glioma grading using texture features from multiparametric MRI[J]. J Magn Reson Imaging, 2018, 48(6): 1518-28.
12	Sudre CH, Panovska-Griffiths J, Sanverdi E, et al. Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status[J]. BMC Med Inform Decis Mak, 2020, 20(1): 149-62.
13	Yang Y, Yan LF, Zhang X, et al. Glioma grading on conventional MR images: a deep learning study with transfer learning[J]. Front Neurosci, 2018, 12: 804.
14	Xu C, Tao D, Xu C. A Survey on Multi-view Learning[J]. Computer Science, 2013,4. arXiv.org.
15	Wen J, Zhang Z, Fei L, et al. A Survey on Incomplete Multiview Clustering[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(2): 1136-49.
16	Ng A, Jordan MI, Weiss Y. On Spectral Clustering: analysis and an algorithm[J]. Adv Neural Inf Process Syst, 2001, 14.
17	Hu ML, Chen SC. Doubly aligned incomplete multi-view clustering[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. July 13-19, 2018. Stockholm, Sweden. California: International Joint Conferences on Artificial Intelligence Organization, 2018: 2262-8.
18	Shao WX, He LF, Lu CT, et al. Online multi-view clustering with incomplete views[C]//2016 IEEE International Conference on Big Data (Big Data). December 5-8, 2016. Washington DC, USA. IEEE, 2016: 1012-7.
19	Zhao H, Liu H, Fu Y. Incomplete multi-modal visual data grouping[C]// Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. New York: AAAI Press, 2016: 2392-8.
20	Wen J, Zhang Z, Xu Y, et al. Incomplete multi-view clustering via graph regularized matrix factorization[C]//European Conference on Computer Vision. Cham: Springer, 2019: 593-608.
21	Hu M, Chen S. One-pass incomplete multi-view clustering[C]//. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu, Hawaii, USA: AAAI Press, 2019: 471.
22	Wen J, Zhang Z, Zhang Z, et al. Unified tensor framework for incomplete multi-view clustering and missing-view inferring[J]. Proc AAAI Conf Artif Intell, 2021, 35(11): 10273-81.
23	Zhang P, Wang SW, Hu JT, et al. Adaptive weighted graph fusion incomplete multi-view subspace clustering[J]. Sensors, 2020, 20(20): 5755.
24	Wen J, Xu Y, Liu H. Incomplete multiview spectral clustering with adaptive graph learning[J]. IEEE Trans Cybern, 2020, 50(4): 1418-29.
25	Liu XW, Zhu XZ, Li MM, et al. Efficient and effective incomplete multi-view clustering[C]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
26	Liu XW, Li MM, Tang C, et al. Efficient and effective regularized incomplete multi-view clustering[J]. IEEE Trans Pattern Anal Mach Intell, 2021, 43(8): 2634-46.
27	Liu XW, Wang L, Yin JP, et al. Absent multiple kernel learning[J]. Proc AAAI Conf Artif Intell, 2015, 29(1): 2807-13.
28	Liu XW, Zhu XZ, Li MM, et al. Multiple kernel k-means with incomplete kernels[J]. IEEE Trans Pattern Anal Mach Intell, 2020, 42(5): 1191-204.
29	Xu C, Guan ZY, Zhao W, et al. Adversarial incomplete multi-view clustering[C]. IEEE Transactions on Cybernetics, 2019.
30	Wen J, Zhang Z, Xu Y, et al. CDIMC-net: cognitive deep incomplete multi-view clustering network[C]. Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 2021: 3230-6.
31	Wang QQ, Ding ZM, Tao ZQ, et al. Partial multi-view clustering via consistent GAN[C]//2018 IEEE International Conference on Data Mining (ICDM). November 17-20, 2018. Singapore. IEEE, 2018: 1290-5.
32	Li SY, Jiang Y, Zhou ZH. Partial multi-view clustering[J]. Proc AAAI Conf Artif Intell, 2014, 28(1): 1968-74.
33	van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype[J]. Cancer Res, 2017, 77(21): e104-7.
34	Elmannai H, El-Rashidy N, Mashal I, et al. Polycystic ovary syndrome detection machine learning model based on optimized feature selection and explainable artificial intelligence[J]. Diagnostics, 2023, 13(8): 1506.
35	Wang H, Zong LL, Liu B, et al. Spectral perturbation meets incomplete multi-view data[J/OL]. Computer Science, 2019：.
36	Maaten L, Hinton GE. Visualizing Data using t-SNE[J]. J Mach Learn Res, 2008, 9(11).
37	Venkatesh B, Anuradha J. A review of feature selection and its methods[J]. Cybern Inf Technol, 2019, 19(1): 3-26.
38	Zebari R, Abdulazeez A, Zeebaree D, et al. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction[J]. J Appl Sci Technol Trends, 2020, 1(1): 56-70.
39	Blackwell M, Honaker J, King G. A unified approach to measurement error and missing data: overview and applications[J]. Sociol Meth Res, 2017, 46(3): 303-41.
40	Emmanuel T, Maupong T, Mpoeleng D, et al. A survey on missing data in machine learning[J]. J Big Data, 2021, 8(1): 140.
41	Zhou T, Liu MX, Thung KH, et al. Latent representation learning for Alzheimer's disease diagnosis with incomplete multi-modality neuroimaging and genetic data[J]. IEEE Trans Med Imaging, 2019, 38(10): 2411-22.
42	Kopf A, Claassen M. Latent representation learning in biology and translational medicine[J]. Patterns, 2021, 2(3): 100198.
43	Ning ZY, Xiao Q, Feng QJ, et al. Relation-induced multi-modal shared representation learning for Alzheimer's disease diagnosis[J]. IEEE Trans Med Imaging, 2021, 40(6): 1632-45.
44	Shen DG, Wu GR, Suk HI. Deep learning in medical image analysis[J]. Annu Rev Biomed Eng, 2017, 19: 221-48.
45	Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-8.
46	Wang GT, Li WQ, Zuluaga MA, et al. Interactive medical image segmentation using deep learning with image-specific fine tuning[J]. IEEE Trans Med Imaging, 2018, 37(7): 1562-73.

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