Artificial intelligence in colorectal liver metastases: From detection and resectability to treatment response and recurrence prediction

Table 1 Representative artificial intelligence applications across the colorectal liver metastasis continuum

Ref.	Model and modality	Key metric	Principal finding or limitation	Maturity
Detection and characterization
Kim et al[6], 2021	Deep learning lesion detection; contrast-enhanced CT	Sensitivity approximately 82% per lesion	Comparable to radiologists but with more false positives; suited to an assistive role	Mature
Höppener et al[10], 2024	Deep learning (neural image compression); H&E whole-slide images	AUC: 0.93/0.95 (dev/external)	Reproducible desmoplastic vs non-desmoplastic growth-pattern classification	Emerging
Resectability and surgical planning
Xie et al[11], 2023	3D deep learning segmentation; contrast-enhanced CT	DSC 0.93-0.95; FLR approximately manual	Automated couinaud-segment and future-liver-remnant volumetry reproducing hepatectomy indications	Mature
Chen et al[13], 2024	Random forest; clinical and genetic variables	AUC approximately 0.70-0.74 (external)	Predicts complications and survival after simultaneous resection; deployed as a web tool	Emerging
Intraoperative guidance
Hardy et al[16], 2023	Computer vision; real-time ICG fluorescence video	Malignant vs benign discrimination	Intraoperative delineation of CRLM from surrounding parenchyma; small exploratory series	Early
Nakano et al[18], 2025	AI-enhanced navigation; robotic hepatectomy video	Technical report; no comparative metric	Earlier intraoperative identification of the IVC and major hepatic vein roots	Early
Treatment-response prediction
Wei et al[19], 2021	Deep learning radiomics (ResNet); contrast-enhanced CT	AUC 0.82 (validation)	Outperformed handcrafted radiomics and CEA for chemotherapy-response prediction	Emerging
Taghavi et al[22], 2021 and van der Reijd et al[23], 2024	CT radiomics; pre-ablation	C-index 0.79 vs approximately 0.47-0.50 (dev vs external)	Strong internal performance not reproduced externally; illustrates the generalizability gap	Early
Recurrence and survival prediction
Tang et al[25], 2024	Multi-sequence MRI deep learning	AUC 0.84; c-index 0.73	Predicts 1-year recurrence and recurrence-free survival after resection	Emerging
Saber et al[27], 2023	Interpretable ML radiomics; contrast-enhanced CT	Independent predictor of TTR and DSS	Noninvasive imaging surrogate of CD73 expression with independent prognostic value	Early
Lam et al[24], 2023	Machine learning clinical model	c-index 0.65 vs Fong 0.57	Outperforms a classical clinical risk score for post-hepatectomy prognostication	Emerging

Maturity categories: Mature, clinically practicable and near expert-level; Emerging, promising but requiring broader validation; Early, proof of concept or failed external validation. 3D: Three-dimensional; AI: Artificial intelligence; AUC: Area under the receiver operating characteristic curve; CEA: Carcinoembryonic antigen; c-index: Concordance index; CRLM: Colorectal liver metastases; CT: Computed tomography; dev: Development cohort; DSC: Dice similarity coefficient; DSS: Disease-specific survival; FLR: Future liver remnant; H&E: Haematoxylin and eosin; ICG: Indocyanine green; IVC: Inferior vena cava; ML: Machine learning; MRI: Magnetic resonance imaging; ResNet: Residual convolutional neural network; TTR: Time to recurrence.

Table 2 Model inputs, performance, and validation status of representative artificial intelligence models in colorectal liver metastases

Ref.	Task	Input data	Performance	Validation status
Kim et al[6], 2021	Lesion detection	Contrast-enhanced CT images	Sensitivity approximately 82% per lesion	Internal only; retrospective, single-centre
Höppener et al[10], 2024	Growth-pattern classification	H&E whole-slide images	AUC 0.93 (dev); 0.95 (external)	External validation performed
Xie et al[11], 2023	Couinaud segment and FLR segmentation	Contrast-enhanced CT images	DSC 0.93-0.95; FLR approximately manual	External validation performed
Xie et al[12], 2023	Blood-free FLR assessment	Contrast-enhanced CT images; hepatic and portal veins	Automated FLR approximately manual reference	Internal validation; single-centre
Chen et al[13], 2024	Postoperative complications and survival	Demographic, clinical, laboratory and genetic variables (KRAS, BRAF, mismatch repair)	AUC approximately 0.70-0.74	External, multicentre validation
Hardy et al[16], 2023	Intraoperative lesion delineation	Real-time ICG fluorescence video	Malignant vs benign discrimination reported	Exploratory; single-centre; no external cohort
Wei et al[19], 2021	Chemotherapy-response prediction	Contrast-enhanced CT images	AUC = 0.82	Internal validation; single-centre
Giannini et al[20], 2022	Per-lesion chemotherapy response	Baseline and early follow-up CT (delta features)	Reclassified lesions misclassified by RECIST	Internal validation; multicentre cohort
Chiu et al[21], 2024	Morphological response and survival	Contrast-enhanced CT images	Reclassified non-responders had longer overall survival	Internal validation; single-centre
Taghavi et al[22], 2021	Local tumour progression after ablation	Pre-ablation CT radiomic features	c-index 0.79	Internal validation only
van der Reijd et al[23], 2024	Local tumour progression after ablation	Pre-ablation CT radiomic features	c-index approximately 0.47-0.50	Independent internal and external validation; discrimination lost
Lam et al[24], 2023	Overall and recurrence-free survival	Clinical, laboratory and tumour variables	c-index 0.65 (Fong score 057)	Internal validation; multicentre
Tang et al[25], 2024	One-year recurrence and RFS	Multi-sequence MRI	AUC = 0.84; c-index 0.73	Internal validation
Saber et al[27], 2023	CD73 expression, TTR and DSS	Preoperative contrast-enhanced CT	Independent predictor of TTR and DSS	Internal validation; single-centre
Lin et al[28], 2025	Growth-pattern classification and risk stratification	H&E whole-slide images	Improved junior pathologist accuracy; reduced reading time	Prospective, multicentre

AUC: Area under the receiver operating characteristic curve; c-index: Concordance index; CT: Computed tomography; dev: Development cohort; DSC: Dice similarity coefficient; DSS: Disease-specific survival; FLR: Future liver remnant; H&E: Haematoxylin and eosin; ICG: Indocyanine green; MRI: Magnetic resonance imaging; RECIST: Response Evaluation Criteria in Solid Tumors; RFS: Recurrence-free survival; TTR: Time to recurrence.