{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T05:52:46Z","timestamp":1719467566818},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,14]]},"abstract":"Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.<\/jats:p>","DOI":"10.3233\/shti210942","type":"book-chapter","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T15:57:13Z","timestamp":1642435033000},"source":"Crossref","is-referenced-by-count":1,"title":["Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks"],"prefix":"10.3233","author":[{"given":"Georg","family":"Prokop","sequence":"first","affiliation":[{"name":"Department of Neuropathology, Institute of Pathology, Technical University of Munich, Germany"}]},{"given":"Michael","family":"\u00d6rtl","sequence":"additional","affiliation":[{"name":"DigiHealth Institute, Neu-Ulm University of Applied Sciences, Germany"}]},{"given":"Marina","family":"Fotteler","sequence":"additional","affiliation":[{"name":"DigiHealth Institute, Neu-Ulm University of Applied Sciences, Germany"}]},{"given":"Peter","family":"Sch\u00fcffler","sequence":"additional","affiliation":[{"name":"Department of Computational Pathology, Institute of Pathology, Technical University of Munich, Germany"}]},{"given":"Johannes","family":"Schobel","sequence":"additional","affiliation":[{"name":"DigiHealth Institute, Neu-Ulm University of Applied Sciences, Germany"}]},{"given":"Walter","family":"Swoboda","sequence":"additional","affiliation":[{"name":"DigiHealth Institute, Neu-Ulm University of Applied Sciences, Germany"}]},{"given":"J\u00fcrgen","family":"Schlegel","sequence":"additional","affiliation":[{"name":"Department of Neuropathology, Institute of Pathology, Technical University of Munich, Germany"}]},{"given":"Friederike","family":"Liesche-Starnecker","sequence":"additional","affiliation":[{"name":"Department of Neuropathology, Institute of Pathology, Technical University of Munich, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Informatics and Technology in Clinical Care and Public Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210942","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T15:57:14Z","timestamp":1642435034000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210942"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210942","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,14]]}}}