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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004263naa a2200493 4500
001oai:DiVA.org:uu-469951
003SwePub
008220316s2022 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4699512 URI
0247 a https://doi.org/10.1002/path.59812 DOI
040 a (SwePub)uu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Bekkhus, Toveu Uppsala universitet,Vaskulärbiologi4 aut0 (Swepub:uu)tovbe228
2451 0a Automated detection of vascular remodeling in human tumor draining lymph nodes by the deep learning tool HEV-finder
264 c 2022-07-12
264 1b John Wiley & Sons,c 2022
338 a electronic2 rdacarrier
520 a Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high-throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep-learning model and created a graphical user interface for visualization of the results. The tool, named the HEV-finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV-finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large-scale, fully automated, and user-independent, image-based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Cancer och onkologi0 (SwePub)302032 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Cancer and Oncology0 (SwePub)302032 hsv//eng
653 a HEV-finder
653 a artificial intelligence (AI)
653 a breast cancer
653 a deep learning
653 a high endothelial venules (HEVs)
653 a tumor-draining lymph nodes (TDLNs)
653 a vascular remodeling
7001 a Avenel, Christopheu Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion4 aut0 (Swepub:uu)chrav452
7001 a Hanna, Sabellau Uppsala universitet,Vaskulärbiologi4 aut
7001 a Franzén Boger, Mathiasu Uppsala universitet,Institutionen för immunologi, genetik och patologi4 aut
7001 a Klemm, Anna Hu Uppsala universitet,Avdelningen för visuell information och interaktion,Science for Life Laboratory, SciLifeLab4 aut0 (Swepub:uu)annkl878
7001 a Vasiliu-Bacovia, Danielu Uppsala universitet,Institutionen för immunologi, genetik och patologi4 aut0 (Swepub:uu)danva511
7001 a Wärnberg, Fredriku Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden4 aut0 (Swepub:uu)fredwarn
7001 a Wählby, Carolina,c professor,d 1974-u Uppsala universitet,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab,Avdelningen för visuell information och interaktion4 aut0 (Swepub:uu)cli05194
7001 a Ulvmar, Maria H.u Uppsala universitet,Vaskulärbiologi4 aut0 (Swepub:uu)marul414
7102 a Uppsala universitetb Vaskulärbiologi4 org
7730 t Journal of Pathologyd : John Wiley & Sonsg 258:1, s. 4-11q 258:1<4-11x 0022-3417x 1096-9896
8564 u https://doi.org/10.1002/path.5981y Fulltext
8564 u https://uu.diva-portal.org/smash/get/diva2:1645277/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-469951
8564 8u https://doi.org/10.1002/path.5981

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