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Table 1 Summary of deep learning methods, their relevant applications and brief technical descriptions of each DL model

From: Deep learning in cancer diagnosis, prognosis and treatment selection

Application DL method Reference Description
Microscopy-based assessment of cancer CNN Ruy et al. [40]
Nir et al. [41]
Ström et al. [42]
Ehteshami Bejnordi et al. [43]
Vuong et al. [44]
El Achi and Khoury [45]
Trained CNNs on pathology images to predict grading of prostate [40,41,42], breast [43], colon cancer [44] and lymphoma [45]
CNN & explainability Hägele et al. [46] LRP used to assigned feature contribution for cancer grade for each pixel of WSIs
Semantic segmentation Poojitha and Lal Sharma [47] A semantic segmentation technique called GAN was used to segment tissue maps for prostate cancer grade prediction
Molecular subtyping MLP DeepCC [48] Gene set enrichment analysis used to transform gene expression input into functional spectra
CNN imCMS [49],
Sirinukuwattana et al. [50], Stalhammar et al. [51], Couture et al. [52]
Woerl et al. [53]
Models trained on histopathology images to classify molecular subtypes of of lung [49], colorectal [50], breast [51, 52] and bladder cancer [53]
GCNN Rhee et al. [18] Utilised a hybrid GCNN model to organise input gene expression profiles into STRING PPI network [16] and predict breast cancer molecular subtypes
Multimodal learning Islam et al. [54] Two CNN models used to predict breast cancer molecular subtypes from CNAs and gene expression;
Outputs of the last fully connected layer of each model concatenated for a final subtype prediction
Cancer of unknown primary MLP Jiao et al. [55] Model trained to predict origins of 24 cancer types using somatic mutation patterns and driver genes
CNN SCOPE [56],
CUP-AI-Dx [57]
Both studies trained models to predict different cancer types from gene expression
RNN & explainability TOAD [58] RNN-based model called Attention was trained on WSIs to predict metastasis and cancer origin;
Attention algorithm reveal image regions contributing most to predictions were mostly cancer cells
Prognosis prediction MLP Cox-nnet [59],
DeepSurv [60], RankedDeepSurv [61]
Cox regression used as the last layer of MLP models for prognosis prediction
MLP & AEs AECOX [62] AE used to “compress” gene expression into low-dimensional embedding vector and used as an input for Cox-regression
Explainability PASNET [63],
Cox-PASNET [64]
A pathway layer used between the input and the hidden layers with each node representing a known pathway;
Analysis of weight differences in pathway layers reveal clinically actionable genetic traits
MesoNet [65] Histopathology images split into tiles and scored by survival prediction contributions;
Scores used to identify top-contributing regions, reviewed by pathologists
GCNN & explainability Chereda et al. [19] Combine GCNN and explainability method LRP to identify biologically and therapeutically relevant genes in predicting metastasis of breast cancer
Explainability with multimodal learning PAGE-Net [66] CNN used to compress features from WSIs;
Cox-PASNet used to incorporate gene pathway and provide cross-modal analysis with image features extracted by CNN
PathME [67] AEs used to compress features from four omics modalities, which are combined to predict survival;
SHAP used to assign each omics feature survival prediction contribution score
Precision Oncology MLP HER2RNA [68] Transcriptomic profiles inferred from histopathology images divided into tiles;
Predictions added up for all tiles and compared with ‘ground truth’ transcriptomic profiles
CNN Image2TMB [69] Ensemble of three CNNs to extract features from histopathological images at different resolutions (x5, x10 and x20);
Extracted features are combined to infer TMB
Kather et al. [70] TCGA histopathology images used to predict mutational status of key genes, molecular subtypes and gene expression of standard biomarkers
Tumour microenvironment MLP Scaden [71] Ensemble of three models with different filter sizes to predict TME composition from gene expression;
Predictions from the models are averaged into a final prediction
Explainability with MLP MethylNet [72] MLP and AE used to ‘compress’ CpG beta values into an embedding vector for predicting TME composition;
SHAP used to assign feature contribution to each CpG site
Semantic segmentation Saltz et al. [20] Semantic segmentation model used on H&E images to localise spatial heterogeneity patterns of TIL and necrosis
Spatial transcriptomics CNN ST-Net [73] Images split into tiles centred on spatial transcriptomics spots;
Tiles used to train a CNN to predict expression of 250 target genes
Pharmacogenomics CNN CDRscan [74] Two models used to extract features from somatic mutational fingerprints and molecular profiles of drugs (cell lines);
Feature vectors combined to predict efficacy of drugs based on genomic profiles
MLP DeepSynergy [75] Cell line gene expression and chemical features of drugs in drug combinations used as input;
Predicts ‘synergy score’ between the drug combinations and transcriptomic profiles
GCNN Jiang et al. [76] Utilised graph structure to integrate protein-protein, drug-drug and drug-protein interactions to predict synergistic drug combination for specific cell lines
Multimodal learning DeepDR [77] Collection of ten AEs to integrate ten drug-disease networks, which predict drug-disease associations
CNN DeepDTI [78] Protein sequence and drug fingerprint as input to predict drug protein-binding sites
  1. AE: autoencoder, CNA: copy number alterations, CNN: convolutional neural network, DL: deep learning, GCNN: graph convolutional neural network, H&E: haematoxylin and eosin, LRP: layer-wise relevance propagation, MLP: multilayer perceptron, RNN: recurrent neural netowrk, SHAP: SHapley Additive exPlanations, TIL: tumour-infiltrating lymphocytes, TMB: tumour mutational burden, WSI: whole slide image