LiSNet: An artificial intelligence ‐based tool for liver imaging staging of hepatocellular carcinoma aggressiveness

Sun, Shu; Xu, Xun; Liu, Qiu Ping; Chen, Jie Neng; Zhu, Fei; Liu, Xi Sheng; Zhang, Yudong; Wang, Jie

doi:10.1002/mp.15972

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…In addition to detecting relationships within large multi-omic data sets to improve prognostication, AI techniques can help us identify biomarkers in the preoperative setting typically only identified through pathologic evaluation such as microvascular invasion (MVI) [28]. For example, multiple studies have shown the feasibility of using machine learning algorithms to accurately predict the presence of MVI based on preoperative axial imaging characteristics [29][30][31]. Chong et al built a radiomic-based nomogram to assess the risk of MVI [32].…”

Section: Hcc Prognosis and Risk Of Recurrencementioning

confidence: 99%

Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma

Larrain,

Torres-Hernandez,

Hewitt

2024

Livers

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Artificial Intelligence (AI) can be a useful tool in the management of disease processes such as hepatocellular carcinoma (HCC) as treatment decisions are often complex and multifaceted. AI applications in medicine are expanding with the ongoing advances in AI including more sophisticated machine learning and deep learning processes. In preliminary studies, AI algorithms have demonstrated superiority in predicting the development of HCC compared with standard models. Radiomics, a quantitative method used to extract features from medical imaging, has been applied to numerous liver imaging modalities to aid in the diagnosis and prognostication of HCC. Deep learning methodologies can help us to identify patients at higher likelihood of disease progression and improve risk stratification. AI applications have expanded into the field of surgery as models not only help us to predict surgical outcomes but AI methodologies are also used intra-operatively, in real time, to help us to define anatomic structures and aid in the resection of complex lesions. In this review, we discuss promising applications of AI in the management of HCC. While further clinical validation is warranted to improve generalizability through the inclusion of larger and more diverse populations, AI is expected to play a central role in assisting clinicians with the management of complex disease processes such as HCC.

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Section: Hcc Prognosis and Risk Of Recurrencementioning

confidence: 99%

Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma

Larrain,

Torres-Hernandez,

Hewitt

2024

Livers

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“…As a powerful tool, deep learning can provide objective references for doctors and further improve their work efficiency [ 18 , 19 ]. Therefore, it is widely used in thyroid cancer [ 20 ], cervical cancer [ 21 ], liver cancer [ 22 , 23 ], and other diseases to improve the diagnostic efficiency [ 24 ]. And in endometrial diagnosis, deep learning is usually used for segmentation and classification tasks.…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles

Wang,

Li,

Shi

et al. 2024

PLoS ONE

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Endometrial cancer screening is crucial for clinical treatment. Currently, cytopathologists analyze cytopathology images is considered a popular screening method, but manual diagnosis is time-consuming and laborious. Deep learning can provide objective guidance efficiency. But endometrial cytopathology images often come from different medical centers with different staining styles. It decreases the generalization ability of deep learning models in cytopathology images analysis, leading to poor performance. This study presents a robust automated screening framework for endometrial cancer that can be applied to cytopathology images with different staining styles, and provide an objective diagnostic reference for cytopathologists, thus contributing to clinical treatment. We collected and built the XJTU-EC dataset, the first cytopathology dataset that includes segmentation and classification labels. And we propose an efficient two-stage framework for adapting different staining style images, and screening endometrial cancer at the cellular level. Specifically, in the first stage, a novel CM-UNet is utilized to segment cell clumps, with a channel attention (CA) module and a multi-level semantic supervision (MSS) module. It can ignore staining variance and focus on extracting semantic information for segmentation. In the second stage, we propose a robust and effective classification algorithm based on contrastive learning, ECRNet. By momentum-based updating and adding labeled memory banks, it can reduce most of the false negative results. On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. Our method robustly predicts endometrial cancer on cytopathologic images with different staining styles, which will further advance research in endometrial cancer screening and provide early diagnosis for patients. The code will be available on GitHub.

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“…In recent years, advancements in artificial intelligence (AI) capabilities have shown great potential to redefine the way we navigate clinical care for HCC patients. AI has the capacity to improve risk prediction in chronic hepatitis patients [13], accelerate the diagnostic process with early identification of HCC [14][15][16] , increase accuracy in the classification of liver lesions and HCC subtypes [17][18][19][20] , tumor staging [21] , and survival prediction [22,23] . Decisions regarding candidate selection and optimal treatment methods may also utilize AI in the prediction of treatment response, progression-free and overall survival [24,25] and risk of HCC recurrence [26] .…”

Section: Introductionmentioning

confidence: 99%

Augmenting care in hepatocellular carcinoma with artificial intelligence

Xu¹,

Tang²,

Soh³

et al. 2023

Art Int Surg

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Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide and prognosis remains poor. The recent paradigm shifts in management algorithms of such patients have resulted in unique challenges in the early identification of HCC, prognosis, surgical outcomes, prioritization of potential transplant recipients, donor-recipient matching, and so on. In recent years, advancements in artificial intelligence (AI) capabilities have shown potential in HCC treatment. In this narrative review, we outline first the different types of AI models that are applied in clinical practice and then focus on the frontiers of AI research in the diagnosis, prognostication, and treatment of HCC, particularly in classification of indeterminate liver lesions, tumor staging, survival prediction, improving equity in transplant recipient selection, prediction of treatment response and prognosis. We show that US coupled with AI-driven predictive models can provide accurate noninvasive screening tools for early disease. While AI models applied to contrast-enhanced CT, MRI and PET studies may appear to have limited clinical utility in disease diagnosis and differentials, owing to their accuracy, we highlighted the importance of such models in predicting pathological findings preoperatively. Despite the availability of many accurate, sensitive, and specific AI algorithms that outperform traditional scoring systems, they have not been widely used in clinical practice. The challenges in AI application, including distributional shift and imbalanced data, lack of standardization, and the ‘black box’ phenomenon, are addressed here. The importance of AI in the future of HCC makes it important for clinicians to have a good understanding of different AI techniques, their benefits, and potential pitfalls.

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LiSNet: An artificial intelligence ‐based tool for liver imaging staging of hepatocellular carcinoma aggressiveness

Cited by 3 publications

References 26 publications

Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma

Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma

A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles

Augmenting care in hepatocellular carcinoma with artificial intelligence

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