Dataset Information

Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.

ABSTRACT: The effectiveness of ultrasonography (USG) in liver cancer screening is partly constrained by the operator's expertise. We aimed to develop and evaluate an AI-assisted system for detecting and classifying focal liver lesions (FLLs) from USG images. This retrospective study incorporated 26,288 USG images from 5444 patients to train YOLOv5 model for FLLs detection and classification of seven different types of FLLs, including hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), focal fatty infiltration, focal fatty sparing (FFS), cyst, hemangioma, and regenerative nodules. AI model performance was assessed for detection and diagnosis of the FLLs on a per-image and per-lesion basis. The AI achieved an overall FLLs detection rate of 84.8% (95%CI:83.3-86.4), with consistent performance for FLLs ≤ 1 cm and > 1 cm. It also exhibited sensitivity and specificity for distinguishing malignant FLLs from other benign FLLs at 97.0% (95%CI:95. 9-98.2) and 97.0% (95%CI:95.9-98.1), respectively. Among specific FLL types, CCA detection rate was at 92.2% (95%CI:88.0-96.4), followed by FFS at 89.7% (95%CI:87.1-92.3), and HCC at 82.3% (95%CI:77.1-87.5). The specificities and NPVs for regenerative nodules were 100% and 99.9% (95%CI:99.8-100.0), respectively. Our AI model can potentially assist physicians in FLLs detection and diagnosis during USG examinations. Further external validation is needed for clinical application.

SUBMITTER: Chaiteerakij R

PROVIDER: S-EPMC11375009 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

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Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.

Chaiteerakij Roongruedee R Ariyaskul Darlene D Kulkraisri Kittipat K Apiparakoon Terapap T Sukcharoen Sasima S Chaichuen Oracha O Pensuwan Phaiboon P Tiyarattanachai Thodsawit T Rerknimitr Rungsun R Marukatat Sanparith S

Scientific reports 20240904 1

The effectiveness of ultrasonography (USG) in liver cancer screening is partly constrained by the operator's expertise. We aimed to develop and evaluate an AI-assisted system for detecting and classifying focal liver lesions (FLLs) from USG images. This retrospective study incorporated 26,288 USG images from 5444 patients to train YOLOv5 model for FLLs detection and classification of seven different types of FLLs, including hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), focal fatty in ...[more]

PMID: 39232086

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Project description: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.

Project description:BackgroundHepatitis B virus (HBV) is the dominant pathogenic factor of hepatocellular carcinoma (HCC) in Asia and Africa. Early identification and clinical diagnosis are crucial for HBV-related HCC. Random forest (RF) and artificial neural network (ANN) were an innovative and highly effective supervised machine learning (ML) algorithm for the early diagnosis and screening of HBV-related HCC. This study aims to identify significant biomarkers and develop a novel genetic model for the efficient diagnosis of HBV-related HCC.MethodsGene Expression Omnibus (GEO) Series (GSE)19665, GSE55092, and GSE121248 were used to identify significant differentially expressed genes (DEGs). The enrichment analysis was performed on Metascape online tool. The RF algorithm and ANN were used to select the potential predictive gene panels and construct an HBV-related HCC diagnostic model. Subsequently, GSE17548, GSE104310, GSE44074, and GSE136247 were used to test the accuracy of the ANN model. Finally, the CIBERSORT algorithm was used to assess the abundance of immune infiltrates in all samples.ResultsFirst, 116 genes were identified as DEGs, and the DEGs were particularly enriched in cellular hormone metabolic process, monocarboxylic acid metabolic process, NABA extracellular matrix (ECM) AFFILIATED steroid metabolic process and metabolism of bile acid and bile salt. DNA topoisomerase II alpha (TOP2A), C-type lectin domain family 1 member B (CLEC1B), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), ficolin 2 (FCN2), C-X-C motif chemokine ligand 14 (CXCL14), cyclase associated actin cytoskeleton regulatory protein 2 (CAP2), ficolin 3 (FCN3), kynurenine 3-monooxygenase (KMO) and cadherin related family member 2 (CDHR2) were available to develop an HBV-related HCC diagnostic model. After validation, the diagnostic model showed high sensitivity (88.5%, 90%, 88.5%, 76.5%) and specificity (100%, 81.8%, 89.5%, 72.2%), and the areas under the receiver operating characteristic (ROC) curves showed excellent efficiency (1, 0.927, 0.921, 0.833). Finally, the percentage of infiltrating immune cell types [B cells naïve, B cells memory, plasma cells, T cells CD8, T cells CD4 memory resting, T cells regulatory (Tregs), T cells gamma delta, natural killer (NK) cells resting, NK cells activated, Macrophages M0, Dendritic cells activated, Mast cells activated] for hepatitis B-related HCC were significantly different from that of non-cancerous liver tissue with HBV.ConclusionsA novel early diagnostic model of HBV-related HCC was established, and the model showed better efficiency in distinguishing HBV-related HCC from other non-cancerous with HBV individuals.

Dataset Information

Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.

Publications

Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.

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