Evidence map›Paper›PMID 36300146›Full record

ReviewTranslational gastroenterology and hepatology2022

Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review.

Michal Kawka, Aleksander Dawidziuk, Long R Jiao, Tamara M H Gall

Open access · diamondAbstract readReview
In one paragraph

Review in Translational gastroenterology and hepatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.8field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 34 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Michal KawkaDepartment of Medicine, Imperial College London, South Kensington Campus, London, UK.
Aleksander DawidziukDepartment of Medicine, Imperial College London, South Kensington Campus, London, UK.
Long R JiaoHPB Surgical Unit, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital Campus, London, UK.
Tamara M H GallHPB Surgical Unit, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital Campus, London, UK.
Hammersmith Hospital · GBImperial College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is a significant cause of morbidity and mortality worldwide. Despite significant advancements in detection and treatment of HCC, its management remains a challenge. Artificial intelligence (AI) has played a role in medicine for several decades, however, clinically applicable AI-driven solutions have only started to emerge, due to gradual improvement in sensitivity and specificity of AI, and implementation of convoluted neural networks. A review of the existing literature has been conducted to determine the role of AI in HCC, and three main domains were identified in the search: detection, characterisation and prediction. Implementation of AI models into detection of HCC has immense potential, as AI excels at analysis and integration of large datasets. The use of biomarkers, with the rise of '-omics', can revolutionise the detection of HCC. Tumour characterisation (differentiation between benign masses, HCC, and other malignant tumours, as well as staging and grading) using AI was shown to be superior to classical statistical methods, based on radiological and pathological images. Finally, AI solutions for predicting treatment outcomes and survival emerged in recent years with the potential to shape future HCC guidelines. These AI algorithms based on a combination of clinical data and imaging-extracted features can also support clinical decision making, especially treatment choice. However, AI research on HCC has several limitations, hindering its clinical adoption; small sample size, single-centre data collection, lack of collaboration and transparency, lack of external validation, and model overfitting all results in low generalisability of the results that currently exist. AI has potential to revolutionise detection, characterisation and prediction of HCC, however, for AI solutions to reach widespread clinical adoption, interdisciplinary collaboration is needed, to foster an environment in which AI solutions can be further improved, validated and included in treatment algorithms. In conclusion, AI has a multifaceted role in HCC across all aspects of the disease and its importance can increase in the near future, as more sophisticated technologies emerge.

Indexed as

artificial intelligence (AI)computer-aided diagnosisHepatocellular carcinoma (HCC)machine learning (ML)neural networks

Identifiers

PMID36300146
PMCPMC9468986
OpenAlexW3093104435

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.