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

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.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

“…In the context of personalized medicine, this is evident as the possibility to predict several prognostic markers allows us to identify the best treatment for a specific patient [41][42][43][44][45][46]. Radiomics analysis could be a promising tool to evaluate a lesion "virtually", with the possibility to analyze the whole tumor during the disease history to obtain those markers which can affect the treatment choice [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]. In addition, this approach is safe and inexpensive since radiomics data are obtained from radiological studies which a patient should be subjected during staging and follow-up [66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84].…”

Colorectal Cancer: Current Updates and Future Perspectives

“…Up to 25% of patients simultaneously experienced primary tumour and colorectal liver metastases (CRLM) diagnosis [ 73 ], while 20% will progress to stage IV. Although the availability of chemotherapy regimen progress in many primary tumours [ 15 ], surgical resection is considered the gold standard treatment for CRLM, with a five-year survival rate from 30% to 60% [ 9 , 40 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 ]. However, about 80% of patients are affected by unresectable CRLM, due to bilobar multiple liver metastases and/or extrahepatic disease.…”

“…In recent years, artificial intelligence has also played a key role in early diagnosis and prediction of cancers [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ], including CRC [ 19 , 20 ], as described by numerous studies in the literature. In this clinical context, knowledge of epidemiological data is of great importance to analyze the trend of incidence and prevalence of pathology and to develop new predictive oncological techniques.…”