Multitask learning in hepatocellular carcinoma: Integrating diagnosis, prognosis, and clinical decision support

Table 1 Overview of multitask learning models in hepatocellular carcinoma

Ref.	Year	Task(s)	Architecture	Imaging	Key finding
Zhao et al[55]	2025	CK19 positivity + MVI prediction	Expert sharing network with spatial transformation and relation reasoning	Gd-EOB-DTPA-enhanced MRI: Hepatobiliary phase	CK19 AUC = 0.87 internal CV, 0.80 external; MVI AUC: 0.88 internal CV, 0.85 external
You et al[30]	2026	HCC segmentation + histological grading	Integrated MTL framework; segmentation models + radiomics classification; transformer-RFE-fused model	MRI: Arterial-phase T1WI, portal-venous-phase T1WI, wavelet-fused arterial/portal-venous T1WI	Fused MRI test DSC 0.92; training ACC 93.2%, testing ACC 92.5%; testing AUC up to 0.960 in fused modality
Wang et al[52]	2024	OS prediction/survival risk stratification	DeepSurv, NMTLR, RSF, Cox-PH comparison	Structured SEER registry data; no imaging	NMTLR test C-index 0.7353; 1 year, 3 years, 5 years AUC = 0.824, AUC = 0.813, AUC = 0.803
Wang et al[51]	2024	MVI prediction + RFS prediction + PA-TACE benefit analysis	Transformer-based multitask deep learning model	MRI: Fat-saturated T2WI, DWI, arterial-phase T1WI, portal-venous-phase T1WI	MVI AUCs were 0.918 training, 0.800 internal, and 0.837/0.815/0.800 external; RFS C-index was 0.763 training, 0.716 internal, and 0.628/0.675/0.728 external; PA-TACE benefit was observed only in the predicted high-MVI-risk/Low-survival-score subgroup
Song et al[16]	2024	Early recurrence prediction + FLL classification	Multitask self-supervised pre-training with phase-shuffle prediction and case discrimination; ResNet18 backbone	Multi-phase CT: NC, ART, PV	ER prediction ACC 74.65%, AUC = 0.739; FLL classification ACC 88.06%, AUC = 0.791; outperformed ImageNet transfer learning, single-task self-supervised pre-training, and DINOv2
Wen et al[36]	2023	HCC segmentation + pathological differentiation prediction	MTL model with segmentation subnet and classification subnet; boundary-aware attention; multi-scale feature fusion; dynamic weight averaging	Contrast-enhanced MRI: Arterial, portal venous, delayed phases	The model outperformed comparator multitasks methods; Dice 83.90%; classification ACC 80.67%, AUC = 0.791, recall 86.08%, and F1 score 80.05%
Xie et al[37]	2023	MVI prediction + 3D tumor segmentation	Triplet-uncertainty MTL; 3D U-Net/VGG-based branches	Contrast-enhanced MRI: NR phase/sequence; 3D tumor ROI	TU-MTL achieved the best joint performance, with MVI ACC 80.96%, SEN 74.03%, SPE 88.94%, AUC = 0.8208, segmentation Dice 82.07%, and JC 82.13%
He et al[18]	2023	MTM subtype prediction + OS prediction after HAIC	Multitask deep learning radiomics model; 3D MobileNetV1 + radiomics + clinical integration	Dual-phase contrast-enhanced CT: Arterial + venous phases	MTM AUCs were 0.968/0.912/0.773; MDLR prognostic AUCs were 0.855/0.805/0.792 across cohorts
Xiao et al[54]	2022	HCC detection + size grading + multi-index quantification	TrdAL: CNN encoder + modality-aware Transformer + radiomics-guided discriminator + task-interaction loss	Multi-modality MRI: In-phase, out-phase, T2FS, DWI	Detection ACC 93.33%, SEN 93.15%, SPE 93.71%, IoU 82.93%; size-grading ACC 77.78%-96.87%; center-point MAE 2.74 mm; max-diameter MAE 3.17 mm; area MAE 144.51 mm2
Li et al[10]	2022	Objective response prediction after TACE + tumor segmentation	Tumor-aware multitask deep learning network with shared encoder, OR prediction pathway, and segmentation pathway	Contrast-enhanced CT: Arterial phase + portal venous phase	Multi-DL achieved AUC 0.871, ACC 83.9%, SEN 85.7%, SPE 82.9%, and Dice 73.6%; also stratified survival risk
Li et al[40]	2022	MVI prediction + auxiliary tumor segmentation	Split-attention ResNeSt encoder + U-Net decoder MTL	Gd-EOB-DTPA-enhanced MRI: NR phase/sequence	For HCC MVI prediction, internal ACC 74.2%, SEN 80.0%, SPC 70.7%, AUC 0.803; external ACC 89.4%, SEN 83.3%, SPC 82.3%, AUC 0.885
Chu et al[49]	2022	MVI + VETC prediction; prognostic stratification	3D CNN-based multitask learning	Multiphase Gd-EOB-DTPA-enhanced MRI: Late arterial, portal venous, hepatobiliary phases	MTL improved MVI prediction from AUC 0.896 to 0.917 with ACC 90.0%; VETC was predicted with AUC = 0.8604 and ACC 82.5%; MVI/VETC-based stratification was associated with OS/RFS
Wang et al[53]	2021	HCC segmentation + auxiliary patch classification	Hybrid MTL network: Shared SE-ResNeXt-101 encoder + two pixel-wise segmentation branches + auxiliary patch-level classification branch + SKM/scSEM ensemble	H&E-stained whole-slide images	The ensemble model achieved PAIP Jaccard index 79.7%, CRAG Dice 92.3%, and UHCMC&CWRU Dice 76.5%
Fu et al[7]	2021	Future macrovascular invasion prediction + OS stratification	MTnet with segmentation subnet + clinical/radiological/radiomic fusion	Portal venous phase CT	Combined model CR-DR achieved AUC 0.877 in training and 0.836 in external validation; model-defined risk groups stratified time to macrovascular invasion and OS
Liu et al[50]	2020	MVI prediction + Edmondson-Steiner grading + survival prediction after TACE	Hybrid ML/DL framework; RF + SVM histologic scores + DL-score + Cox-PH model	Contrast-enhanced CT: Late arterial + portal venous phases	Test AUCs were 0.79 for MVI and 0.72 for Edmondson-Steiner grade; OS C-index was 0.73, with 3 years, 5 years, and 10 years AUCs of 0.85, 0.90, and 0.89
Huang et al[56]	2022	IVIM parameter fitting + MVI prediction	Transformer-based multitask deep learning (CCT-inspired)	IVIM-DWI MRI: 9 b-value images	Joint learning improved both IVIM fitting and MVI prediction; AUC = 0.855, outperforming single-task and other MTL models

CK19: Cytokeratin 19; MVI: Microvascular invasion; RFS: Recurrence-free survival; HCC: Hepatocellular carcinoma; OS: Overall survival; FS: Recurrence-free survival; PA: Postoperative adjuvant; TACE: Transarterial chemoembolization; FLL: Focal liver lesion; 3D: Three-dimensional; MTM: Macrotrabecular-massive; HAIC: Hepatic arterial infusion chemotherapy; VETC: Vessels encapsulating tumor clusters; H&E: Hematoxylin and eosin; WSI: Whole-slide image; IVIM: Intravoxel incoherent motion; MTL: Multitask learning; RFE: Recursive feature elimination; NMTLR: Neural multitask logistic regression; RSF: Random survival forest; Cox-PH: Cox proportional hazards; VGG: Visual geometry group; TrdAL: Task relevance driven adversarial learning; CNN: Convolutional neural network; OR: Objective response; SKM: Selective kernel module; scSEM: Spatial channel squeeze-and-excitation module; MTnet: Multitask network; ML: Machine learning; DL: Deep learning; RF: Random forest; SVM: Support vector machine; CCT: Compact convolutional transformer; Gd-EOB-DTPA: Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid; MRI: Magnetic resonance imaging; T1WI: Arterial-phase T1-weighted imaging; SEER: Surveillance, Epidemiology, and End Results; T2WI: Fat-saturated T2-weighted imaging; DWI: Diffusion-weighted imaging; CT: Computed tomography; NC: Non-contrast; ART: Arterial phase; PV: Portal venous phase; NR: Not reported; ROI: Region of interest; T2FS: T2 fat-saturated imaging; AUC: Area under the receiver operating characteristic curve; CV: Cross-validation; DSC: Dice similarity coefficient; ACC: Accuracy; ER: Early recurrence; FLL: Focal liver lesion; DINOv2: Distillation with no labels version 2; TU-MTL: Triplet-uncertainty multitask learning; SEN: Sensitivity; SPE: Specificity; JC: Jaccard coefficient; MDLR: Multitask deep learning radiomics; IoU: Intersection over union; MAE: Mean absolute error; SPC: Specificity; PAIP: Pathology artificial intelligence platform; CRAG: Computational pathology research group; UHCMC: University Hospitals Cleveland Medical Center; CWRU: Case Western Reserve University; CR-DR: Clinical-radiological deep radiomics.

Table 2 Study design, validation, and limitations of included multitask learning studies

Ref.	Primary task category	Sample size	Study design	Ext valid	Key study-level limitations
Zhao et al[55]	Diagnostic/biologic aggressiveness prediction	145	Retrospective multicenter study; 10-fold CV in center 1 + external independent test set from centers 2 and 3	Yes	Limited sample size; single-center training with potential inter-center domain bias; HBP-only model; no clinical-variable integration; no prospective validation
You et al[30]	Structural + diagnostic grading	1673	Retrospective single-center study; 80/10/10 train-validation-test split + five-fold CV	No	Single-center retrospective design; no external or prospective validation; arterial/portal-venous MRI only; clinical/genomic variables not integrated; sequential pipeline without demonstrated joint multitask optimization or shared-representation learning
Wang et al[52]	Prognostic survival prediction	2197	Retrospective SEER database study; 7:3 train/test split + five-fold CV	No	Registry-based design; no imaging data; no independent external validation; limited clinical granularity; black-box interpretability remains limited
Wang et al[51]	Biologic + prognostic + treatment-benefit prediction	725	Retrospective multicenter study; training/internal test split + three external test sets	Yes	Retrospective design; predominantly HBV-related Chinese cohort; moderate RFS performance in some external sets; nonrandomized PA-TACE benefit analysis; no prospective validation
Song et al[16]	Prognostic recurrence prediction	167 patients/765 CT slices; FLL classification: 85 CT volumes/489 slices	Retrospective single-center study; 10-fold CV for ER prediction + 5-fold CV for FLL classification	No	Small single-center HCC cohort; no external or prospective validation; ROI-based 2D slice analysis; multitask component limited to pre-training; method restricted to multi-phase CT images
Wen et al[36]	Structural + pathological differentiation prediction	295 patients/885 CE-MRI	Retrospective single-center study; train-test split + five-fold CV within the training set	No	Single-center design; no external or prospective validation; modest cohort size; simplified pathological differentiation categories
Xie et al[37]	Structural + biologic aggressiveness prediction	137	Single-institution study; random train/validation-test split	No	Small cohort; no external or prospective validation; limited MRI phase/sequence reporting; internal split only
He et al[18]	Biologic subtype + prognostic prediction	MTM cohort: 159; HAIC cohort: 752	Retrospective multi-institutional study; training/internal test cohorts from one institution + external test cohort from four centers	Yes	Retrospective design; MTM cohort mainly surgical; HAIC-only prognostic cohort; limited generalizability to other treatment contexts; no prospective validation; whole-liver ROI approach not compared with tumor-specific ROI; complications during and after HAIC or TKI treatment were not analyzed
Xiao et al[54]	Structural detection + quantitative assessment	135 subjects/10800 MRI slices	Single-dataset experimental study; five-fold CV	No	Small dataset; no external or prospective validation; single-source MRI dataset; protocol-specific multimodality inputs
Li et al[10]	Treatment response prediction + segmentation	248	Retrospective two-center study; training/internal validation cohort + external testing cohort	Yes	Retrospective design; two-center dataset; 2D-only network with limited spatial-context utilization; small DEB-TACE subgroup; no prospective validation
Li et al[40]	Biologic aggressiveness prediction + segmentation	85	Two-center study; internal CV in center 1 + external validation in center 2	Yes	Small HCC cohort; limited external cohort size; MRI phase/sequence reporting incomplete; no prospective validation
Chu et al[49]	Biologic aggressiveness prediction + prognostic stratification	133	Single-center retrospective cohort; 7:3 train/internal validation split	No	Limited sample size; single-center design; strict exclusion criteria; no external validation
Wang et al[53]	Pathology-based structural segmentation	PAIP: 100 HCC WSIs; CRAG: 213 images; UHCMC & CWRU: 110 images	Public benchmark-based experimental study; PAIP train/validation/test split + CRAG and UHCMC&CWRU train/test splits	No	HCC validation limited to PAIP; external benchmark datasets were non-HCC pathology images; pathology-only input; no prospective clinical validation
Fu et al[7]	Future macrovascular invasion prediction + survival stratification	366	Retrospective multicenter study; training dataset from four hospitals + external validation dataset from one hospital	Yes	Retrospective design; strict inclusion criteria; HBV-predominant Chinese cohort; no prospective validation; black-box interpretability remains limited
Liu et al[50]	Histologic surrogate prediction + post-TACE survival prediction	494 surgical cohort; 243 TACE cohort	Retrospective single-center study; surgical cohort for histologic-score development + TACE cohort for survival modeling	No	Survival model limited to TACE-treated patients; retrospective design; no external validation; complex multi-stage pipeline introduces risk of compounding overfitting; label reliability for histological surrogates not formally assessed
Huang et al[56]	Structural + biologic aggressiveness prediction	114 HCC samples	Single-center study; 4-fold CV repeated five times	No	Small single-center cohort; no external validation; slice-based analysis; IVIM parameter fitting evaluated indirectly using reconstructed b-value images

TACE: Transarterial chemoembolization; IVIM: Intravoxel incoherent motion; MVI: Microvascular invasion; CT: Computed tomography; CE-MRI: Contrast-enhanced magnetic resonance imaging; MTM: Macrotrabecular-massive; HAIC: Hepatic arterial infusion chemotherapy; MRI: Magnetic resonance imaging; PAIP: Pathology artificial intelligence platform; HCC: Hepatocellular carcinoma; WSI: Whole-slide image; CRAG: Computational pathology research group; UHCMC: University Hospitals Cleveland Medical Center; CWRU: Case Western Reserve University; CV: Cross-validation; SEER: Surveillance, Epidemiology, and End Results; ER: Early recurrence; FLL: Focal liver lesion; HBP: Hepatobiliary phase; HBV: Hepatitis B virus; RFS: Recurrence-free survival; PA: Postoperative adjuvant; ROI: Region of interest; 2D: Two-dimensional; 3D: Three-dimensional; CNN: Convolutional neural network.