Table 2. Overview of research works on fusion of pathomics with genomics.

References	Aims	Approach	Data used	Results
CNN, convolutional neural networks; GCN, graph convolutional networks; H&E, hematoxylin and eosin; IHC, immunohistochemistry; CNV, copy number variant; CCRCC, clear cell renal cell carcinoma; CV, cross-validation; TCGA, The Cancer Genomic Atlas; WSI, whole-section image; HR, hazard ratio; 95% CI, 95% confidence interval; ROI, region of interest; HPF, high power field; DNN, deep neural network; SVM, support vector machine; BCa, breast cancer; ER, estrogen receptor.
Chen et al. (27)	Constructing a prognostic models for glioma and CCRCC	Histologic image-based features extracted by CNN, and graph-based image features extracted by GCN, and genomic features learned by Feed Forward Network. All above mentioned data were integrated by a multimodal learning paradigm, which modeled on pairwise feature interactions across modalities by taking the Kronecker product of unimodal feature representations and gating attention mechanism, for prognostication.	Glioma: 1,505 H&E-stained images from 769 patient with 320 genomic features from CNV, mutation status and bulk RNA-Seq expression; 1,251 H&E-stained CCRCC images from 417 patients with 357 genomic features from CNV and RNA-Seq.	C-index=0.826 for Glioma; C-index=0.720 for CCRCC. Both models’ performance are higher than the corresponding unimodal models. Results reported under CV scheme.
Shao et al. (26)	Proposing a framework combining pathological images and multi-modal genomic data for the prognosis of early-stage cancer patients.	1) A generalized sparse canonical correlation analysis, named ordinal multi-modality feature selection (OMMFS) that captures the intrinsic relationship among multiple views, to identify important features from WSI and multi-modal data; 2) cox proportional hazard model was applied for prognosticating patients.	Kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, and lung squamous cell carcinoma cohorts with WSI and multi-modal genomic data from TCGA.	The identified image and multi-modal features were strongly correlated with patients survival outcome, thus enable effective stratification of patients.
Cheerla et al. (22)	Constructing a deep learning based pancancer model for predicting survival of patients.	Auto encoder to extract four data modalities (gene expression, miRNA data, clinical data, and WSI) into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method.	Gene expression (n=10,198), miRNA data (n=10,125), clinical data (n=7,512), and WSI (n=10,914) from TGCA (20 different cancer types).	The pan-cancer prognostic model yielded a C-index of 0.78 overall.
Cheng et al. (23)	Constructing a prognostic model for clear cell renal cell carcinoma	1) Nuclear features (nucleus size, shape, texture, and distance to neighbors) were aggregated statistically into patient-level features; 2) gene co-expression network analysis (GCNA) to cluster genes into co-expressed modules (clusters of highly interconnected/correlated genes); 3) lasso-regularized Cox proportional hazards model was used to calculate the risk scores based on the feature from 1 and 2.	WSI, transcriptome, and somatic mutation. N=410 from TCGA.	1) Patients with high percentage of stromal tissue are related to poor prognosis; 2) risk index is independent of known prognostic factors with HR (95% CI)=3.06 (2.10−4.45) P<0.005. Note: Results reported under CV scheme.
Mobadersany et al. (24)	Predicting the overall survival of patients diagnosed with glioma	Hybrid architecture combing abstracted histologic image features from convolutional layers and genomic variables (IDH mutation status and 1p/19q codeletion) to fully connected layers. When predicting of a newly diagnosed patient, 9 HPFs were sampled from each ROI, and the median risk score was selected to represent that ROI. Second highest risk score among all ROIs of a WSI was used as the final risk score.	N=1,061 WSIs from 769 patients from TCGA. Genomic variables (IDH mutation status and 1p/19q codeletion).	Model achieved prognostic power with c index of 0.754 and correlate with molecular subtypes and histologic grade; the c-index boosted to 0.801 while integrating with genomic variables. Note: Results reported under CV scheme.
Ren et al. (25)	Constructing a survival model for predicting the recurrent of prostate cancer patients with Gleason score 7	1) Pathway activities were quantified by pathway scores using RNA sequences; 2) image patches from WSI and pathway scores were integrated into DNN to extract “deep features”; 3) “deep features” and clinical prognostic factors were fed into a Cox model.	N=339 WSIs and RNA (Illumina HiSeq) sequencing data from TCGA.	Integrated model yielded C-index=0.74, and C-index=0.71 for histology image only.
Yuan et al. (7)	Correlation between histology image features and genomic data; Prognosticating early-stage ER-BCa patients	Cancer cells, stroma cells and lymphocytes were detected from the histology image and the proportions of these cells are used as image features to correlate and combine with genomic data.	N=564 early-stage BCa patients with H&E-stained WSIs and genomic data.	A SVM predictor integrating gene expression and image features achieved 86%±3.0% cross-validation accuracy and improved stratification of the patient cohort.