Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification

Table 1 Studies included in the review

Ref.	References	Purpose of the study	Data source	Sample size	Major findings	External validation
The use of pathomics in the diagnosis and classification of liver cancer
Cheng et al[46], 2022	35202643	Develop a deep learning system to improve histopathologic diagnosis of various liver lesions and tissues	Surgical and biopsy samples from 6 hospitals	738 patients (providing 1115 whole-slide images)	The HnAIM system integrates ResNet50, InceptionV3, and Xception architectures, achieving an AUC of 93.5% for hepatic lesion classification	Yes (independent external validation cohort)
Aatresh et al[47], 2021	34053009	Develop a deep learning framework for multi-subtype classification of hepatic carcinoma histopathology	KMC liver dataset (257 annotated slides) and TCGA-LIHC public cohort (300+ HCC samples)	Not mentioned	LiverNet: Lightweight ASPP architecture with 0.573M parameters achieving 90.93% accuracy in HCC grading	Not mentioned
Sun et al[48], 2020	31670686	Develop a deep learning model for liver cancer histopathological image classification	Not mentioned	Not mentioned	Combine transfer learning and multi-instance learning to achieve high-precision classification of normal/abnormal liver histopathological images, addressing challenges of limited training samples and large-scale image processing	Not mentioned
Liao et al[49], 2020	32536036	Deep learning model for HCC classification and mutation prediction	TCGA datasets (WSIs) and the West China Hospital Biospecimen Repository	TCGA (393 HCC vs 88 normal) and West China Hospital Biobank (455 HCC vs. 264 normal)	CNN model: Achieved AUC = 1.000 in WSI analysis, linking morphological features to gene mutations	Yes (Successfully tested on West China Hospital data)
Beaufrère et al[50], 2024	38379584	Automated classification of primary liver cancer biopsy types	Biopsy samples (HE stained WSI)	166 HE stained WSIs (90 training, 29 internal validation, 47 external validation)	Weakly supervised algorithm distinguishing HCC/iCCA subtypes and quantifying cHCC-CCA heterogeneity	Yes
Dong et al[51], 2022	35509058	Classification of liver cancer differentiation grades	Histopathologic images of liver cancer	73 hepatocellular carcinoma histopathological images from patients with varying differentiation grades	FuNet fusion strategy: Multimodal feature fusion with channel-space attention mechanism to enhance feature characterization	Not mentioned
Kiani et al[52], 2020	32140566	Evaluating the Impact of AI assistants on the diagnosis of subtypes of hepatocellular carcinoma	HE stained WSI	Validation set 26 cases, test set 80 cases	Deep learning-assisted systems: Improving diagnostic consistency for pathologists, but false prediction bias needs to be addressed	Yes (independent test set of 80 cases validated)
Liu et al[53], 2023	37882066	Building a faster RCNN model to identify PCCCL and CHCC	Case data of Beijing You'an Hospital	151 cases	Faster RCNN: For rare PCCCL subtypes, the diagnostic accuracy is 96.2%, and a single case takes only 4 seconds to process	Not mentioned
The use of pathomics in the prediction of recurrence in liver cancer
Zhang et al[58], 2024	38488408	Develop a deep learning model to improve the efficiency of MVI diagnosis	First affiliated hospital of Zhejiang University and TCGA database	753 cases (internal) and 358 cases (external)	MVI-AIDM: Three-step simulation of pathology diagnostic process (localization, segmentation, classification) with 94.25% accuracy of MVI detection	Yes
Laurent-Bellue et al[59], 2024	38879083	Construction of a deep learning model for predicting HCC recurrence after surgery	WSI of hepatic resection specimens and external hospital data	Internal 107 cases (680 WSIs), external 29 cases	ResNet34 model: Automatic quantification of invasive structures such as MVI and validation of correlation with recurrence risk	Yes
Chen et al[60], 2022	35349075	Develop a deep learning model to assess microvascular invasion in HCC	Zhongshan first hospital, Dongguan People's Hospital and Southern Medical University Shunde Hospital	Internal 350 cases (2917 WSIs), external 120 cases (504 WSIs)	MVI-DL: Weakly supervised multiple exemplar learning framework with high AUC (0.871) despite single section or biopsy sample	Yes
Feng et al[61], 2021	34926264	Develop a deep learning model for HCC diagnosis and classification	First Affiliated Hospital of Zhejiang University and TCGA database	592 cases (training 137, testing 455), external validation 157 cases	Annotated noise optimization framework: Two-stage training strategy (feature filtering + dynamic label smoothing), segmentation accuracy 87.81%, diagnosis accuracy 98.77%	Yes
Qu et al[62], 2023	37031334	Development of DPS to predict HCC recurrence after liver	HCC patient dataset	380 cases	DPS: Based on ResNet-50 and DeepSurv network, C-index up to 0.827, associated NK cell infiltration	Not mentioned
The use of pathomics in prognosis and survival prediction of patients with liver cancer
Zhou et al[64], 2024	39640777	Exploring the relationship between pathomics characteristics and EZH2 expression to predict HCC survival	TCGA database	267 cases	Pathomics modeling: Prediction of EZH2 expression, high scores independently associated with poorer OS	Not mentioned
Jia et al[65], 2023	37450030	Constructing a deep learning model to assess immune infiltration and prognosis of liver cancer	Xijing Hospital cohort, TCGA database	100 WSIs (training set)	ResNet 101V2 model: Quantify TILs with AUC > 0.95, construct prognostic nomogram	Yes (TCGA and Xijing Hospital cohort cross-validation)
Saillard et al[66], 2020	32108950	Constructing deep learning models to predict survival in HCC patients	Henri Mondor Hospital, TCGA database	Discovery set of 194 cases, validation set of 328 cases	CHOWDER model: Predicts HCC survival C-index up to 0.75-0.78 without annotation, identifies vascular and immunodeficiency features	Yes
Ding et al[67], 2024	38972973	Interpretable modeling to resolve iCCA prognosis and morphological-molecular associations	Internal and external cohorts at research institutions	373 cases (development set), validation set 381 cases (213 internal + 168 external)	Interpretable framework: Identify key prognostic markers such as tertiary lymphoid structure distribution and abnormal nuclear morphology, and validate the biological mechanism by multi-omics	Yes
Xie et al[68], 2022	35537220	Development of quantitative morphological features to stratify ICC patient survival	Postoperative ICC patients (H&E stained whole-slide images)	127 cases (78 in modeling set, 49 in test set)	Morphometric analysis framework: Construct IHC-free dependent prognostic model based on tumor architectural complexity and lymphocyte spatial topology (AUC = 0.68)	Not mentioned
Shi et al[69], 2021	32998878	Development of deep learning model for HCC risk stratification	Zhongshan Hospital cohort (WSIs), TCGA database	Zhongshan cohort 1125 cases (2451 WSIs), TCGA 320 cases (320 WSIs)	TRS independently predicts prognosis, associates hepatic sinusoidal capillarization, FAT3 mutations and immune infiltration	Yes (TCGA cohort validation)
The use of pathomics in liver metastases
Chen et al[79], 2024	37822044	Development of features to identify primary sites of liver metastases	WSIs of patients with liver metastases	114 patients (175 WSIs)	Fusion model improves primary site identification with similar morphological features of primary and metastatic tumors	Not mentioned
Albrecht et al[80], 2023	37562657	Development of HEPNET to differentiate ICC from colorectal liver metastases	Heidelberg University Hospital and University Medical Center Mainz	456 cases (training), 115 cases (internal testing), 159 cases (external validation)	HEPNET system: Differentiation of iCCA from colorectal liver metastases, AUC = 0.997, reduced IHC dependence	Yes
Jang et al[81], 2023	38001649	Development of deep learning models to distinguish HCC, CC and mCRC	WSIs for HCC, CC, mCRC (specific source not specified)	Not mentioned	Triple classification framework: Differentiate between HCC, iCCA and colorectal cancer metastasis with AUC > 0.995	Yes (tested using external datasets)
Höppener et al[82], 2024	39471410	Development of a deep learning algorithm to classify growth patterns of colorectal liver metastases	Erasmus MC and Radboud University Medical Center, The Netherlands	Development group 932 cases (3641 images), external validation 870 images	NIC algorithm: Automatic differentiation of fibrotic/non-fibrotic subtypes of colorectal liver metastases, AUC = 0.93-0.95, associated prognosis	Yes
Qi et al[83], 2023	37701575	Development of a deep learning framework for automated analysis of CRLM tissue features	Not mentioned	Not mentioned	SOF: Quantification of 17 microenvironmental features including tumor necrosis rate, 5-year survival stratification difference improved to 22.5%	Yes (independent clinical cohort validation)
Xiao et al[84], 2022	35433467	Development of a deep learning model to predict liver metastasis in colorectal cancer	Retrospectively collected data on colorectal cancer patients (unspecified institution)	611 cases (428 in training group, 183 in validation group)	Deep learning nomogram: Combining pT/pN staging to predict the risk of liver metastasis (C-index = 0.81) and dynamically optimize the timing of chemotherapy	Not mentioned
The use of pathomics in image segmentation
Lal et al[86], 2021	33190012	Development of deep learning network for Nuclei Segmentation of Liver Cancer Pathology Images	KMC liver dataset (Kasturba Medical College, India)	80 images (KMC dataset)	NucleiSegNet: Residual block + attention decoder for efficient segmentation of complex adherent cell nuclei	Not mentioned
Rong et al[87], 2023	37100227	Development of HD-Yolo for accelerated nuclear segmentation and tumor microenvironment quantification	Lung, liver, breast cancer tissue samples (unspecified specific institution/database)	Not mentioned	HD-Yolo algorithm: Optimizes the detection process, accelerates nuclear segmentation and enhances tumor microenvironment analysis	Not mentioned
Gu et al[88], 2025	39528162	Development of a deep learning process to achieve whole cell segmentation of HE stained tissues	Hepatocellular carcinoma and normal liver tissue samples, 5 external datasets (liver/lung/oral disease)	Training set of 18 cases (7 cancerous + 11 normal), external test set of 5 datasets	CSGO framework: Nuclear membrane segmentation + post-processing algorithm, superior to Cellpose, supports TME whole cell morphology analysis	Yes (5 external datasets validation)
Jehanzaib et al[89], 2025	39265361	Development of PathoSeg model to improve cancer tissue segmentation performance	Internal dataset (liver, prostate, breast cancer patients)	82 full slices (from 62 patients)	PathoSeg model: Combined with synthetic data generation (PathopixGAN) to mitigate category imbalance.	Not mentioned
Hägele et al[90], 2024	39443575	Development of a deep learning model to distinguish HCC and ICC	HE stained whole section images	165 examples	Complementary labeling strategy: Weakly supervised segmentation of HCC and iCCA with balanced accuracy of 0.91	Not mentioned
Chen et al[91], 2022	35787805	Intelligent classification of differentiated liver cancer pathology images	Hepatocellular carcinoma samples from 74 patients in Xinjiang Medical University Cancer Hospital	444 liver cancer pathology images	SENet model classifies multidifferentiated liver cancer pathology images with 95.27% accuracy	Not mentioned
Roy et al[92], 2021	33420322	Development of a whole-slide tumor segmentation model for the liver	Not mentioned	Not mentioned	HistoCAE: Self-Encoder Reconstruction Strategy with Multi-Resolution Scaling to Improve Liver Tumor Segmentation Accuracy	Not mentioned
Khened et al[93], 2021	34078928	Development of generic deep learning tissue analysis framework	Liver cancer public dataset	Not mentioned	DigiPathAI: Multi-model integration + uncertainty estimation, open source process supports segmentation to tumor load calculation.	Not mentioned
Combination of pathomics and transcriptomic data
Calderaro et al[94], 2023	38092727	Improving diagnostic accuracy of mixed liver cancer	Not mentioned	405 patients with cHCC-CCA	Deep learning framework: Self-supervised ResNet50+ attention MIL, AUROC = 0.99, spatial transcriptome validation of molecular subtypes	Not mentioned
Zeng et al[95], 2022	35143898	Predicting activation of immune gene signatures in hepatocellular carcinoma	Not mentioned	Not mentioned	CLAM architecture: Multiscale analysis to predict immunogene activation status, AUC = 0.81-0.92, associated immune infiltration hotspots	Not mentioned

HnAIM: Hepatocellular-nodular artificial intelligence model; ResNet50: Residual network 50; InceptionV3: Inception Version 3; Xception: Extreme Inception; AUC: Area under the curve; KMC: Kasturba medical college; TCGA-LIHC: The cancer genome atlas–liver hepatocellular carcinoma; HCC: Hepatocellular carcinoma; ASPP: Atrous spatial pyramid pooling; TCGA: The cancer genome atlas; WSIs: Whole-slide images; CNN: Convolutional neural network; WSI: Whole-slide image; HE stained: Hematoxylin and eosin stained; iCCA: Intrahepatic cholangiocarcinoma; cHCC-CCA: Combined hepatocellular-cholangiocarcinoma; FuNet: Fusion networks; AI: Artificial intelligence; RCNN: Region-based convolutional neural network; PCCCL: Primary clear cell carcinoma of the liver; CHCC: Common hepatocellular carcinoma; MVI: Microvascular invasion; MVI-AIDM: MVI artificial intelligence diagnostic model; ResNet34: Residual Network 34; MVI-DL: MVI deep-learning; DPS: Deep pathomics score; C-index: Conformity index; NK cell: Natural Killer cell; EZH2: Enhancer of zeste 2 polycomb repressive complex 2 subunit; OS: Overall survival; ResNet 101V2: Residual Network 101V2; TILs: Tumor infiltrating lymphocytes; ICC: Intrahepatic cholangiocarcinoma; IHC-free: Immunohistochemistry-free; TRS: Tumour risk score; CC: Cholangiocarcinoma; mCRC: Metastatic colorectal cancer; NIC: Neural image compression approach; CRLM: Colorectal liver metastases; SOFs: Spatial organization features; pT/pN staging: Pathologic tumor/pathologic lymph node staging; CSGO: Cell segmentation with globally optimized boundaries; TME: Tumor microenvironment; GAN: Generative adversarial network; DigiPathAI: Digital pathology artificial intelligence; MIL: Multiple instance learning; CLAM: Clustering-constrained attention multiple instance learning.