Table 5.

Pros and cons of the studies in Section 4

Papers	Pros	Cons
Yuan et al. (26)	Allow for joint feature selection across cohorts with different missing modality patterns	Assume little correlation between modalities so same features are selected for each cohort; cannot do out-of-sample prediction
Xiang et al. (27)	Use two separate weights to achieve feature and modality selection; can do out-of-sample prediction	Many parameters to be estimated; assume a product form for modality-wise and feature-wise coefficients
Thung et al. (28)	Use multitask learning to reduce features and samples making computation easier	Conventional missing data imputation algorithms are used on reduced dataset; same features are selected for each cohort.
Liu et al. (29)	Exploiting subject relationship by multi-view hypergraph representation and fusion naturally gets around the issue of missing modalities.	Many parameters to be estimated; hard to identify important features; model needs to be re-learned using all the data every time new test data is available.
Li et al. (30)	Use DL to create “pseudo” PET from MRI; raw images are used for classification not features.	Creating PET from MRI needs justification from imaging physics; Black-box DL model is hard to interpret.