{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T16:45:49Z","timestamp":1789749949701,"version":"4.0.1"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,2]],"date-time":"2023-04-02T00:00:00Z","timestamp":1680393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Ministry of Education and Research","award":["FKZ 13GW0108B"],"award-info":[{"award-number":["FKZ 13GW0108B"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cells"],"abstract":"<jats:p>Liver cancer is one of the most frequently diagnosed and fatal cancers worldwide, with hepatocellular carcinoma (HCC) being the most common primary liver cancer. Hundreds of studies involving thousands of patients have now been analysed across different cancer types, including HCC, regarding the effects of immune infiltrates on the prognosis of cancer patients. However, for these analyses, an unambiguous delineation of the cancer area is paramount, which is difficult due to the strong heterogeneity and considerable inter-operator variability induced by qualitative visual assessment and manual assignment. Nowadays, however, multiplex analyses allow the simultaneous evaluation of multiple protein markers, which, in conjunction with recent machine learning approaches, may offer great potential for the objective, enhanced identification of cancer areas with further in situ analysis of prognostic immune parameters. In this study, we, therefore, used an exemplary five-marker multiplex immunofluorescence panel of commonly studied markers for prognosis (CD3 T, CD4 T helper, CD8 cytotoxic T, FoxP3 regulatory T, and PD-L1) and DAPI to assess which analytical approach is best suited to combine morphological and immunohistochemical data into a cancer score to identify the cancer area that best matches an independent pathologist\u2019s assignment. For each cell, a total of 68 individual cell features were determined, which were used as input for 4 different approaches for computing a cancer score: a correlation-based selection of individual cell features, a MANOVA-based selection of features, a multilayer perceptron, and a convolutional neural network (a U-net). Accuracy was used to evaluate performance. With a mean accuracy of 75%, the U-net was best capable of identifying the cancer area. Although individual cell features showed a strong heterogeneity between patients, the spatial representations obtained with the computed cancer scores delineate HCC well from non-cancer liver tissues. Future analyses with larger sample sizes will help to improve the model and enable direct, in-depth investigations of prognostic parameters, ultimately enabling precision medicine.<\/jats:p>","DOI":"10.3390\/cells12071074","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T01:37:05Z","timestamp":1680485825000},"page":"1074","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Deep-Learning-Computed Cancer Score for the Identification of Human Hepatocellular Carcinoma Area Based on a Six-Colour Multiplex Immunofluorescence Panel"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6677-9801","authenticated-orcid":false,"given":"Axel","family":"Dievernich","sequence":"first","affiliation":[{"name":"Department of General, Visceral and Transplant Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany"},{"name":"Forschungs-und Entwicklungsgesellschaft FEG Textiltechnik, 52070 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Stegmaier","sequence":"additional","affiliation":[{"name":"Institute of Imaging and Computer Vision, RWTH Aachen University, 52074 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pascal","family":"Achenbach","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Hospital RWTH Aachen, 52074 Aachen, Germany"},{"name":"Institute of Neuropathology, University Hospital RWTH Aachen, 52074 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana","family":"Warkentin","sequence":"additional","affiliation":[{"name":"Institute of Pathology, University Hospital RWTH Aachen, 52074 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9166-2912","authenticated-orcid":false,"given":"Till","family":"Braunschweig","sequence":"additional","affiliation":[{"name":"Institute of Pathology, University Hospital RWTH Aachen, 52074 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulf Peter","family":"Neumann","sequence":"additional","affiliation":[{"name":"Department of General, Visceral and Transplant Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany"},{"name":"Department of Surgery, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uwe","family":"Klinge","sequence":"additional","affiliation":[{"name":"Department of General, Visceral and Transplant Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA A Cancer J. 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