Table 3. Overview of research works on fusion of pathomics with radiomics.

References	Aims	Approach	Data used	Results
MRI, magnetic resonance imaging; H&E, hematoxylin and eosin; AUC, area under the curve; MRF, magnetic resonance fingerprinting; ADC, apparent diffusion coefficient; ROI, region of interest; CT, computed tomography; NPC, nasopharyngeal cancer; NSCLC, non-small cell lung cancer; nCRT, neoadjuvant chemoradiotherapy; SVM, support vector machine; GBM, glioblastoma; TCIA, The Cancer Imaging Archive; TCGA, The Cancer Genomic Atlas.
Penzias et al. (29)	Identify morphologic basis of radiomic features for prostate cancer risk stratification	Radiomic features from T2W MRI that were associated with low- and high-risk prostate cancer were identified, pathomic features that were best correlated with these features were explored	A single institution cohort of 36 patient studies was used with T2W MRI, post-surgical H&E slides	Gabor features on T2W MRI performance (AUC=0.69) and gland lumen shape features (AUC=0.75) resulted in best classification performance
Shiradkar et al. (30)	Establish the morphologic basis of MR fingerprinting values on the prostate.	Co-registration of whole mount pathology with MRI, MRF followed by correlation of tissue compartments with MR measurements within prostate cancer, prostatitis and normal prostate ROI	A set of 14 patient studies who underwent MRI, MRF scans followed by radical prostatectomy	Tissue compartments of epithelium, lumen and stroma were significantly correlated with T1, T2 MRF, ADC values (P<0.05)
Alvarez-Jimenez et al. (28)	Association between radiomic and pathomic features that distinguish adenocarcinoma and squamous cell carcinoma	Pathomic features from digitized H&E slides of lung cancer; radiomic features from lung cancer CT scans; Cross scale associations were computed between radiomic and pathomic features to compare with individual feature classes	N=171 pathology studies, n=101 lung CT studies acquired from publicly available databases.	Cross-scale associated features resulted in better discrimination (AUC=0.78) of NSCLC subtypes compared to using individual feature classes
Zhang et al. (34)	A prognostic nomogram integrating radiomics and pathology signature to prognosticate NPC	Radiomics from MRI images are combined with a pathomic signature obtained from a deep learning model along with clinical factors to build a multi-scale prognostic nomogram for nasopharyngeal cancer	N=220 NPC patients were divided into n=132 for training, n=88 for internal and external validation.	Multi-scale nomogram resulted in an improved predictor of survival (C-index 0.82 vs. 0.73) compared with clinical model and individual signatures.
Vaidya et al. (33)	Integrating radiomic and pathomic signatures of NSCLC to predict cancer recurrence	Radiomic features from ROIs on lung CT were combined with pathomic eatures from H&E slides of resected tissue to build an integrated supervised machine learning classifier.	50 NSCLC patients were used for training and 43 patients for external validation	The combined classifier resulted in higher AUC=0.78 compared to radiomic (AUC=0.74) and pathomic classifier (AUC=0.67) alone
Braman et al. (36)	Deep learning prognostic model for gliomas integrating radiology, pathology, genomics and clinical data	Deep learning model where each modality embeddings are combined via attention gated tensor fusion. A multimodal orthogonalization loss is presented to maximize information from each modality so they are complementary.	176 patients witn T1w and T2w-FLAIR sequences annotated by 7 radiologists, H& slides and DNA sequencing info	Presented model results in C-index of 0.788±0.067, significantly outperforming (P=0.023) the best performing unimodal (C-index of 0.718±0.064)
Shao et al. (32)	Integrating radiological and pathological information on pre-treatment info to predict pathological response in rectal cancer	Computational features were derived from rectal pre-treatment MRI and digitized H&E slides, combined to create a radiopathomic signature (RPS) to predict treatment response	N=981 patients who received nCRT along with pretreatment MRI and biopsy whole slide images.	RPS resulted in AUC of 0.84−0.98 at each grade of pathological response with significantly higher performance compared to without integration.
Rathore et al. (31)	Integrating radiomic and pathomic features for prognosis of GBM	Radoimic features from T1, T1-Gd, T2, T2-FLAIR, were combined with pathomic s of H&E slides to build a SVM classifier for differentiating long and short term survivors	N=107 GBM patients with MRI and pathology images obtained from TCIA and TCGA	AUC=0.74, 0.76 and 0.8 for radiomics, pathomics and combined model in predicting survival outcome