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Review
. 2023 Oct 9;13(10):2027.
doi: 10.3390/life13102027.

Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives

Pasquale Avella  1   2 , Micaela Cappuccio  2 , Teresa Cappuccio  3 , Marco Rotondo  3 , Daniela Fumarulo  3 , Germano Guerra  3 , Guido Sciaudone  3 , Antonella Santone  3 , Francesco Cammilleri  4 , Paolo Bianco  1 , Maria Chiara Brunese  3
Affiliations
Review

Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives

Pasquale Avella et al. Life (Basel). .

Abstract

Background: Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM.

Methods: A narrative review of the literature was conducted on PubMed. A total of 336 studies were screened.

Results: We selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT).

Conclusions: Routinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario.

Keywords: artificial intelligence; colorectal liver metastases; early prediction; liver metastases; liver neoplasm.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study flow diagram.
Figure 2
Figure 2
Regions of Interest obtained by Contrasted-Enhanced Computed Tomography slices of patients affected by Colorectal Cancer. (A), liver parenchyma marked green, gallbladder (*) and portal vein (⊥) exclusion; (B), liver parenchyma marked green, suprahepatic veins (→) and inferior vena cava (*) excluded.
Figure 3
Figure 3
Artificial intelligence, machine learning and deep learning definitions. AI: Artificial Intelligence.
See this image and copyright information in PMC

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