The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers
Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classif...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022004159 |
| _version_ | 1797978158452965376 |
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| author | Corina-Elena Minciuna Mihai Tanase Teodora Ecaterina Manuc Stefan Tudor Vlad Herlea Mihnea P. Dragomir George A. Calin Catalin Vasilescu |
| author_facet | Corina-Elena Minciuna Mihai Tanase Teodora Ecaterina Manuc Stefan Tudor Vlad Herlea Mihnea P. Dragomir George A. Calin Catalin Vasilescu |
| author_sort | Corina-Elena Minciuna |
| collection | DOAJ |
| description | Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classification revealed new prognostic markers and may aid the therapy selection. Because of the high-costs to perform a molecular classification, in recent years immunohistochemistry-based surrogate classification were developed which permit the stratification of patients, and in parallel multiple groups developed hematoxylin and eosin whole slide image analysis for sub-classifying these entities. Hence, we are witnessing a return to an image-based classification with the purpose to infer hidden information from routine histology images that would permit to detect the patients that respond to specific therapies and would be able to predict their outcome. In this review paper, we will discuss the current histological, molecular, and immunohistochemical classifications of the most common gastrointestinal cancers, gastric adenocarcinoma, and colorectal adenocarcinoma, and will present key aspects for developing a new artificial intelligence aided image-based classification of these malignancies. |
| first_indexed | 2024-04-11T05:18:29Z |
| format | Article |
| id | doaj.art-8203f41708cc4a668c5804fdf2e483c8 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:18:29Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-8203f41708cc4a668c5804fdf2e483c82022-12-24T04:54:24ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012050655075The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancersCorina-Elena Minciuna0Mihai Tanase1Teodora Ecaterina Manuc2Stefan Tudor3Vlad Herlea4Mihnea P. Dragomir5George A. Calin6Catalin Vasilescu7Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania; Carol Davila University of Medicine and Pharmacy, Bucharest, RomaniaDepartment of Automatic Control and Computers, Politehnica University of Bucharest, Bucharest, Romania; University of Bucharest, Bucharest, RomaniaCarol Davila University of Medicine and Pharmacy, Bucharest, Romania; Department of Gastroenterology, Fundeni Clinical Institute, Bucharest, RomaniaDepartment of Surgery, Fundeni Clinical Institute, Bucharest, Romania; Carol Davila University of Medicine and Pharmacy, Bucharest, RomaniaDepartment of Pathology, Fundeni Clinical Institute, 022328 Bucharest, Romania; “Titu Maiorescu” University, Bucharest, RomaniaGerman Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Berlin Institute of Health (BIH), Berlin, Germany; Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany; Corresponding authors at: Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin (M.P. Dragomir). Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (G.A. Calin). Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania (C. Vasilescu).Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Center for RNA Interference and Non-Coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; Corresponding authors at: Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin (M.P. Dragomir). Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (G.A. Calin). Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania (C. Vasilescu).Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania; Carol Davila University of Medicine and Pharmacy, Bucharest, Romania; Corresponding authors at: Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin (M.P. Dragomir). Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (G.A. Calin). Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania (C. Vasilescu).Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classification revealed new prognostic markers and may aid the therapy selection. Because of the high-costs to perform a molecular classification, in recent years immunohistochemistry-based surrogate classification were developed which permit the stratification of patients, and in parallel multiple groups developed hematoxylin and eosin whole slide image analysis for sub-classifying these entities. Hence, we are witnessing a return to an image-based classification with the purpose to infer hidden information from routine histology images that would permit to detect the patients that respond to specific therapies and would be able to predict their outcome. In this review paper, we will discuss the current histological, molecular, and immunohistochemical classifications of the most common gastrointestinal cancers, gastric adenocarcinoma, and colorectal adenocarcinoma, and will present key aspects for developing a new artificial intelligence aided image-based classification of these malignancies.http://www.sciencedirect.com/science/article/pii/S2001037022004159Artificial intelligenceMolecular classificationImage-based classificationGastric adenocarcinoma |
| spellingShingle | Corina-Elena Minciuna Mihai Tanase Teodora Ecaterina Manuc Stefan Tudor Vlad Herlea Mihnea P. Dragomir George A. Calin Catalin Vasilescu The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers Computational and Structural Biotechnology Journal Artificial intelligence Molecular classification Image-based classification Gastric adenocarcinoma |
| title | The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers |
| title_full | The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers |
| title_fullStr | The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers |
| title_full_unstemmed | The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers |
| title_short | The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers |
| title_sort | seen and the unseen molecular classification and image based analysis of gastrointestinal cancers |
| topic | Artificial intelligence Molecular classification Image-based classification Gastric adenocarcinoma |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022004159 |
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