000290382 001__ 290382
000290382 005__ 20241124013910.0
000290382 0247_ $$2doi$$a10.1038/s41698-024-00592-z
000290382 0247_ $$2pmid$$apmid:38783059
000290382 0247_ $$2altmetric$$aaltmetric:163916204
000290382 037__ $$aDKFZ-2024-01095
000290382 041__ $$aEnglish
000290382 082__ $$a610
000290382 1001_ $$00009-0009-3598-5720$$aGustav, Marco$$b0
000290382 245__ $$aDeep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology.
000290382 260__ $$a[London]$$bSpringer Nature$$c2024
000290382 3367_ $$2DRIVER$$aarticle
000290382 3367_ $$2DataCite$$aOutput Types/Journal article
000290382 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1716560580_31913
000290382 3367_ $$2BibTeX$$aARTICLE
000290382 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000290382 3367_ $$00$$2EndNote$$aJournal Article
000290382 520__ $$aIn the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as 'positive' by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.
000290382 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0
000290382 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
000290382 7001_ $$00000-0002-0070-3158$$aReitsam, Nic Gabriel$$b1
000290382 7001_ $$aCarrero, Zunamys I$$b2
000290382 7001_ $$00009-0002-9659-5194$$aLoeffler, Chiara M L$$b3
000290382 7001_ $$avan Treeck, Marko$$b4
000290382 7001_ $$0P:(DE-He78)b9e439a1aa1244925f92d547c0919349$$aYuan, Tanwei$$b5$$udkfz
000290382 7001_ $$aWest, Nicholas P$$b6
000290382 7001_ $$00000-0002-3597-5444$$aQuirke, Philip$$b7
000290382 7001_ $$0P:(DE-He78)1e33961c8780aca9b76d776d1fdc1ebb$$aBrinker, Titus$$b8$$udkfz
000290382 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b9$$udkfz
000290382 7001_ $$aFavre, Loëtitia$$b10
000290382 7001_ $$aMärkl, Bruno$$b11
000290382 7001_ $$00000-0003-1001-103X$$aStenzinger, Albrecht$$b12
000290382 7001_ $$aBrobeil, Alexander$$b13
000290382 7001_ $$aHoffmeister, Michael$$b14
000290382 7001_ $$aCalderaro, Julien$$b15
000290382 7001_ $$00000-0002-3452-7420$$aPujals, Anaïs$$b16
000290382 7001_ $$aKather, Jakob Nikolas$$b17
000290382 773__ $$0PERI:(DE-600)2891458-2$$a10.1038/s41698-024-00592-z$$gVol. 8, no. 1, p. 115$$n1$$p115$$tnpj precision oncology$$v8$$x2397-768X$$y2024
000290382 8564_ $$uhttps://inrepo02.dkfz.de/record/290382/files/s41698-024-00592-z.pdf
000290382 8564_ $$uhttps://inrepo02.dkfz.de/record/290382/files/s41698-024-00592-z.pdf?subformat=pdfa$$xpdfa
000290382 909CO $$ooai:inrepo02.dkfz.de:290382$$pVDB
000290382 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)b9e439a1aa1244925f92d547c0919349$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ
000290382 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)1e33961c8780aca9b76d776d1fdc1ebb$$aDeutsches Krebsforschungszentrum$$b8$$kDKFZ
000290382 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aDeutsches Krebsforschungszentrum$$b9$$kDKFZ
000290382 9131_ $$0G:(DE-HGF)POF4-313$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vKrebsrisikofaktoren und Prävention$$x0
000290382 9141_ $$y2024
000290382 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNPJ PRECIS ONCOL : 2022$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2023-04-12T15:13:05Z
000290382 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2023-04-12T15:13:05Z
000290382 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2023-04-12T15:13:05Z
000290382 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2023-04-12T15:13:05Z
000290382 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bNPJ PRECIS ONCOL : 2022$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2023-10-27
000290382 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2023-10-27
000290382 9201_ $$0I:(DE-He78)C070-20160331$$kC070$$lC070 Klinische Epidemiologie und Alternf.$$x0
000290382 9201_ $$0I:(DE-He78)C140-20160331$$kC140$$lNWG Digitale Biomarker in der Onkologie$$x1
000290382 9201_ $$0I:(DE-He78)C120-20160331$$kC120$$lPräventive Onkologie$$x2
000290382 9201_ $$0I:(DE-He78)HD01-20160331$$kHD01$$lDKTK HD zentral$$x3
000290382 980__ $$ajournal
000290382 980__ $$aVDB
000290382 980__ $$aI:(DE-He78)C070-20160331
000290382 980__ $$aI:(DE-He78)C140-20160331
000290382 980__ $$aI:(DE-He78)C120-20160331
000290382 980__ $$aI:(DE-He78)HD01-20160331
000290382 980__ $$aUNRESTRICTED