Multimodal deep learning for liver cancer applications: a scoping review
<h3>Background</h3><p dir="ltr">Hepatocellular 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...
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2023
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| _version_ | 1864513509199446016 |
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| author | Aisha Siam (17293957) |
| author2 | Abdel Rahman Alsaify (17293960) Bushra Mohammad (17293963) Md. Rafiul Biswas (17293966) Hazrat Ali (421019) Zubair Shah (231886) |
| author2_role | author author author author author |
| author_facet | Aisha Siam (17293957) Abdel Rahman Alsaify (17293960) Bushra Mohammad (17293963) Md. Rafiul Biswas (17293966) Hazrat Ali (421019) Zubair Shah (231886) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aisha Siam (17293957) Abdel Rahman Alsaify (17293960) Bushra Mohammad (17293963) Md. Rafiul Biswas (17293966) Hazrat Ali (421019) Zubair Shah (231886) |
| dc.date.none.fl_str_mv | 2023-10-27T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/frai.2023.1247195 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multimodal_deep_learning_for_liver_cancer_applications_a_scoping_review/26535472 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Machine learning multimodal deep learning liver cancer EHR imaging modality |
| dc.title.none.fl_str_mv | Multimodal deep learning for liver cancer applications: a scoping review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Hepatocellular 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.</p><h3>Objective</h3><p dir="ltr">This 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).</p><h3>Methodology</h3><p dir="ltr">A 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.</p><h3>Results</h3><p dir="ltr">Ten 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.</p><h3>Conclusion</h3><p dir="ltr">The 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Artificial Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/frai.2023.1247195" target="_blank">https://dx.doi.org/10.3389/frai.2023.1247195</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_17ba14511db0e65460ff27bb12edc3fa |
| identifier_str_mv | 10.3389/frai.2023.1247195 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26535472 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multimodal deep learning for liver cancer applications: a scoping reviewAisha Siam (17293957)Abdel Rahman Alsaify (17293960)Bushra Mohammad (17293963)Md. Rafiul Biswas (17293966)Hazrat Ali (421019)Zubair Shah (231886)Biomedical and clinical sciencesOncology and carcinogenesisHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningmultimodaldeep learningliver cancerEHRimaging modality<h3>Background</h3><p dir="ltr">Hepatocellular 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.</p><h3>Objective</h3><p dir="ltr">This 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).</p><h3>Methodology</h3><p dir="ltr">A 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.</p><h3>Results</h3><p dir="ltr">Ten 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.</p><h3>Conclusion</h3><p dir="ltr">The 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Artificial Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/frai.2023.1247195" target="_blank">https://dx.doi.org/10.3389/frai.2023.1247195</a></p>2023-10-27T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/frai.2023.1247195https://figshare.com/articles/journal_contribution/Multimodal_deep_learning_for_liver_cancer_applications_a_scoping_review/26535472CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/265354722023-10-27T09:00:00Z |
| spellingShingle | Multimodal deep learning for liver cancer applications: a scoping review Aisha Siam (17293957) Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Machine learning multimodal deep learning liver cancer EHR imaging modality |
| status_str | publishedVersion |
| title | Multimodal deep learning for liver cancer applications: a scoping review |
| title_full | Multimodal deep learning for liver cancer applications: a scoping review |
| title_fullStr | Multimodal deep learning for liver cancer applications: a scoping review |
| title_full_unstemmed | Multimodal deep learning for liver cancer applications: a scoping review |
| title_short | Multimodal deep learning for liver cancer applications: a scoping review |
| title_sort | Multimodal deep learning for liver cancer applications: a scoping review |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Machine learning multimodal deep learning liver cancer EHR imaging modality |