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.