Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Brunese, Maria Chiara; Fantozzi, Maria Rita; Fusco, Roberta; Muzio, Federica De; Gabelloni, Michela; Danti, Ginevra; Borgheresi, Alessandra; Palumbo, Pierpaolo; Bruno, Federico; Gandolfo, Nicoletta; Giovagnoni, Andrea; Miele, Vittorio; Barile, Antonio; Granata, Vincenza

doi:10.3390/diagnostics13081488

Cited by 9 publications

(5 citation statements)

References 201 publications

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“…Multiple algorithms have been developed to characterize focal liver lesions as benign or malignant on US and CT, showing an AUC comparable to radiologist performance [ 44 , 45 , 46 ]. Other research focused on the automated differentiation of hepatocellular carcinoma from other malignant tumors, such as intrahepatic cholangiocarcinoma [ 47 , 48 ]. Yin et al developed a deep learning convoluted neural network (CNN) capable of distinguishing gallbladder cancer from benign gallbladder diseases on CT (AUC, 0.81; 95% CI 0.71–0.92) [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Mervak,

Fried,

Wasnik

2023

Diagnostics

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Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.

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Section: Discussionmentioning

confidence: 99%

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Mervak,

Fried,

Wasnik

2023

Diagnostics

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“…In gastrointestinal imaging, it aids in the identification of biomarkers associated with disease progression and treatment response. Some softwares have a promised impact on evaluation of prostate cancer multiparametric MRI values, which need a preliminary long, spending time, images elaboration before Radiologist report with p-rads criteria [32][33][34][35][36][37][38][39][40][41].…”

Section: Organs Software Applicationsmentioning

confidence: 99%

Radiomics in Radiology: What the Radiologist Need to Know about Technical Aspects and Clinical Impact

Ferrari,

Trinci,

Casinelli

et al. 2024

Preprint

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Radiomics represents the science of extraction and analysis of a multitude of quantitative features from medical imaging, unrevealing quantitative potentiality of Radiologic images. This scientific review aims to provide radiologists a comprehensive understanding of radiomics, emphasizing its principles, applications, challenges, limits, and future prospects. Limits of standardization of actual scientific production are analyzed with the possible solutions proposed by some papers reported and commented. As the continuous evolution of medical images is ongoing, radiologists must be aware of new perspective to perform a central role in patients management.

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“…Several studies have reported that AI tools can also predict outcomes and recurrence risk based on various clinical and genetic factors [24,38,53,108]. In this clinical scenario, AI should guide the physician during short-and long-term follow-up and surveillance of patients affected by CRC and LM.…”

Section: Current Status and Future Perspectivesmentioning

confidence: 99%

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

Avella,

Cappuccio,

Cappuccio

et al. 2023

Life

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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.

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Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Cited by 9 publications

References 201 publications

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Radiomics in Radiology: What the Radiologist Need to Know about Technical Aspects and Clinical Impact

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

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