Computer-aided diagnosis of hepatocellular carcinoma fusing imaging and structured health data

Menegotto, Alan Baronio; Becker, Carla Diniz Lopes; Cazella, Sílvio César

doi:10.1007/s13755-021-00151-x

Cited by 16 publications

(16 citation statements)

References 42 publications

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“…The multimodal deep learning models used a combination of two different deep learning neural network models trained on processing two different types of datasets modalities (medical imaging and EHRs). The included studies used models like VGG16, VGG19, Inception V3, and ResNet18, to extract and analyze detailed spatial features of medical images in creating multimodal AI techniques (Menegotto et al, 2020 , 2021 ; Zhen et al, 2020 ; Gao et al, 2021 ; Hou et al, 2022 ; Zhang et al, 2022 ). Additionally, multi-task deep learning neural networks, UNet, MTNet, were used to integrate multiple modalities of data in addition to recurrent neural network (RNN) which can utilize, and process text information and numerical figures (clinical parameters and biological markers) derived from EHRs (Fu et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Multimodal deep learning for liver cancer applications: a scoping review

Siam,

Alsaify,

Mohammad

et al. 2023

Front. Artif. Intell.

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BackgroundHepatocellular carcinoma is a malignant neoplasm of the liver and a leading cause of cancer-related deaths worldwide. The multimodal data combines several modalities, such as medical images, clinical parameters, and electronic health record (EHR) reports, from diverse sources to accomplish the diagnosis of liver cancer. The introduction of deep learning models with multimodal data can enhance the diagnosis and improve physicians' decision-making for cancer patients.ObjectiveThis scoping review explores the use of multimodal deep learning techniques (i.e., combining medical images and EHR data) in diagnosing and prognosis of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA).MethodologyA comprehensive literature search was conducted in six databases along with forward and backward references list checking of the included studies. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review guidelines were followed for the study selection process. The data was extracted and synthesized from the included studies through thematic analysis.ResultsTen studies were included in this review. These studies utilized multimodal deep learning to predict and diagnose hepatocellular carcinoma (HCC), but no studies examined cholangiocarcinoma (CCA). Four imaging modalities (CT, MRI, WSI, and DSA) and 51 unique EHR records (clinical parameters and biomarkers) were used in these studies. The most frequently used medical imaging modalities were CT scans followed by MRI, whereas the most common EHR parameters used were age, gender, alpha-fetoprotein AFP, albumin, coagulation factors, and bilirubin. Ten unique deep-learning techniques were applied to both EHR modalities and imaging modalities for two main purposes, prediction and diagnosis.ConclusionThe use of multimodal data and deep learning techniques can help in the diagnosis and prediction of HCC. However, there is a limited number of works and available datasets for liver cancer, thus limiting the overall advancements of AI for liver cancer applications. Hence, more research should be undertaken to explore further the potential of multimodal deep learning in liver cancer applications.

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Section: Resultsmentioning

confidence: 99%

Multimodal deep learning for liver cancer applications: a scoping review

Siam,

Alsaify,

Mohammad

et al. 2023

Front. Artif. Intell.

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“…Recent breakthroughs in machine learning and big data have led to a proliferation of AI applications in biomedical research. These applications range from disease diagnosis [38] , [12] , [58] , [63] and prediction to predicting treatment outcomes [50] , [25] , [4] . Deep learning techniques have shown promise in advancing these efforts by enabling the discovery of complex patterns and relationships in biological systems.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence (AI), with its potential to bridge academic research and clinical practice, promises further advancements. Machine learning algorithms, specifically, can revolutionize medical diagnosis and prediction by accurately analyzing pathology and imaging results [38] , [49] . In the case of HCC, diagnosis and prediction models built on machine learning have improved patients survival and prognosis assessment [9] , [50] , [33] , [26] .…”

Section: Introductionmentioning

confidence: 99%

Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning

Han¹,

Akhtar²,

Liu³

et al. 2023

Computational and Structural Biotechnology Journal

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“…These types of sophisticated and in-depth clinical research and decision-making need to analyze and mine more comprehensive, accurate, and massive data on specific diseases [ 7 – 9 ]. Therefore, it is necessary to collect specific disease data scattered in various hospital business systems to establish DSCDS and fully tap the knowledge contained in medical big data in DSCDS to provide reliable and precise evidence for clinical research and decision-making [ 10 – 12 ].…”

Section: Methodsmentioning

confidence: 99%

Design and development of a disease-specific clinical database system to increase the availability of hospital data in China

Liu

Luo

et al. 2023

Health Inf Sci Syst

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Purpose In order to meet restrictions and difficulties in the development of hospital medical informatization and clinical databases in China, in this study, a disease-specific clinical database system (DSCDS) was designed and built. It provides support for the full utilization of real world medical big data in clinical research and medical services for specific diseases. Methods The development of DSCDS involved (1) requirements analysis on precision medicine, medical big data, and clinical research; (2) design schematics and basic architecture; (3) standard datasets of specific diseases consisting of common data elements (CDEs); (4) collection and aggregation of specific disease data scattered in various medical business systems of the hospital; (5) governance and quality improvement of specific disease data; (6) data storage and computing; and (7) design of data application modules. Results A DSCDS for liver cirrhosis was created in the gastrointestinal department of a 3A grade hospital in China and had more than nine data application modules. Based on this DSCDS, a series of clinical studies are being carried out, such as retrospective or prospective cohorts, prognostic studies using multimodal data, and follow-up studies. Conclusion The development of the DSCDS for liver cirrhosis in this paper provides experience and reference for the design and development of DSCDSs for other specific diseases in China; it can even expand to the development of DSCDSs in other countries if they have the demand for DSCDS and the same or better medical informatization foundation. DSCDS has more accurate, standard, comprehensive, multimodal and usable data of specific diseases than the general clinical database system and clinical data repository (CDR) and provides a credible data foundation for medical research, clinical decision-making and improving the medical service quality of specific diseases.

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Computer-aided diagnosis of hepatocellular carcinoma fusing imaging and structured health data

Cited by 16 publications

References 42 publications

Multimodal deep learning for liver cancer applications: a scoping review

Multimodal deep learning for liver cancer applications: a scoping review

Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning

Design and development of a disease-specific clinical database system to increase the availability of hospital data in China

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