FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2897434820> ?p ?o ?g. }

• W2897434820 endingPage "1646" @default.
• W2897434820 startingPage "1636" @default.
• W2897434820 abstract "Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology to improve histopathologic interpretation and clinical care. To evaluate the potential impact of digital assistance on interpretation of digitized slides, we conducted a multireader multicase study utilizing our deep learning algorithm for the detection of breast cancer metastasis in lymph nodes. Six pathologists reviewed 70 digitized slides from lymph node sections in 2 reader modes, unassisted and assisted, with a wash-out period between sessions. In the assisted mode, the deep learning algorithm was used to identify and outline regions with high likelihood of containing tumor. Algorithm-assisted pathologists demonstrated higher accuracy than either the algorithm or the pathologist alone. In particular, algorithm assistance significantly increased the sensitivity of detection for micrometastases (91% vs. 83%, P=0.02). In addition, average review time per image was significantly shorter with assistance than without assistance for both micrometastases (61 vs. 116 s, P=0.002) and negative images (111 vs. 137 s, P=0.018). Lastly, pathologists were asked to provide a numeric score regarding the difficulty of each image classification. On the basis of this score, pathologists considered the image review of micrometastases to be significantly easier when interpreted with assistance (P=0.0005). Utilizing a proof of concept assistant tool, this study demonstrates the potential of a deep learning algorithm to improve pathologist accuracy and efficiency in a digital pathology workflow." @default.
• W2897434820 created "2018-10-26" @default.
• W2897434820 creator A5003475212 @default.
• W2897434820 creator A5005201087 @default.
• W2897434820 creator A5021041237 @default.
• W2897434820 creator A5029943624 @default.
• W2897434820 creator A5055588368 @default.
• W2897434820 creator A5063867870 @default.
• W2897434820 creator A5065027466 @default.
• W2897434820 creator A5078784976 @default.
• W2897434820 creator A5090319728 @default.
• W2897434820 date "2018-12-01" @default.
• W2897434820 modified "2023-10-17" @default.
• W2897434820 title "Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer" @default.
• W2897434820 cites W149802270 @default.
• W2897434820 cites W1771499318 @default.
• W2897434820 cites W1978814871 @default.
• W2897434820 cites W1989119747 @default.
• W2897434820 cites W2003339792 @default.
• W2897434820 cites W2017036902 @default.
• W2897434820 cites W2083102593 @default.
• W2897434820 cites W2096785769 @default.
• W2897434820 cites W2101979288 @default.
• W2897434820 cites W2113874502 @default.
• W2897434820 cites W2117539524 @default.
• W2897434820 cites W2122441470 @default.
• W2897434820 cites W2146745078 @default.
• W2897434820 cites W2147541126 @default.
• W2897434820 cites W2148198874 @default.
• W2897434820 cites W2155090854 @default.
• W2897434820 cites W2167642653 @default.
• W2897434820 cites W2234404887 @default.
• W2897434820 cites W2401520370 @default.
• W2897434820 cites W2738975713 @default.
• W2897434820 cites W2753233764 @default.
• W2897434820 cites W2759004613 @default.
• W2897434820 cites W2772723798 @default.
• W2897434820 cites W2793781442 @default.
• W2897434820 cites W2919115771 @default.
• W2897434820 doi "https://doi.org/10.1097/pas.0000000000001151" @default.
• W2897434820 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6257102" @default.
• W2897434820 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30312179" @default.
• W2897434820 hasPublicationYear "2018" @default.
• W2897434820 type Work @default.
• W2897434820 sameAs 2897434820 @default.
• W2897434820 citedByCount "296" @default.
• W2897434820 countsByYear W28974348202018 @default.
• W2897434820 countsByYear W28974348202019 @default.
• W2897434820 countsByYear W28974348202020 @default.
• W2897434820 countsByYear W28974348202021 @default.
• W2897434820 countsByYear W28974348202022 @default.
• W2897434820 countsByYear W28974348202023 @default.
• W2897434820 crossrefType "journal-article" @default.
• W2897434820 hasAuthorship W2897434820A5003475212 @default.
• W2897434820 hasAuthorship W2897434820A5005201087 @default.
• W2897434820 hasAuthorship W2897434820A5021041237 @default.
• W2897434820 hasAuthorship W2897434820A5029943624 @default.
• W2897434820 hasAuthorship W2897434820A5055588368 @default.
• W2897434820 hasAuthorship W2897434820A5063867870 @default.
• W2897434820 hasAuthorship W2897434820A5065027466 @default.
• W2897434820 hasAuthorship W2897434820A5078784976 @default.
• W2897434820 hasAuthorship W2897434820A5090319728 @default.
• W2897434820 hasBestOaLocation W28974348201 @default.
• W2897434820 hasConcept C108583219 @default.
• W2897434820 hasConcept C121608353 @default.
• W2897434820 hasConcept C126322002 @default.
• W2897434820 hasConcept C126838900 @default.
• W2897434820 hasConcept C142724271 @default.
• W2897434820 hasConcept C154945302 @default.
• W2897434820 hasConcept C19527891 @default.
• W2897434820 hasConcept C2777522853 @default.
• W2897434820 hasConcept C2780849966 @default.
• W2897434820 hasConcept C41008148 @default.
• W2897434820 hasConcept C530470458 @default.
• W2897434820 hasConcept C71924100 @default.
• W2897434820 hasConceptScore W2897434820C108583219 @default.
• W2897434820 hasConceptScore W2897434820C121608353 @default.
• W2897434820 hasConceptScore W2897434820C126322002 @default.
• W2897434820 hasConceptScore W2897434820C126838900 @default.
• W2897434820 hasConceptScore W2897434820C142724271 @default.
• W2897434820 hasConceptScore W2897434820C154945302 @default.
• W2897434820 hasConceptScore W2897434820C19527891 @default.
• W2897434820 hasConceptScore W2897434820C2777522853 @default.
• W2897434820 hasConceptScore W2897434820C2780849966 @default.
• W2897434820 hasConceptScore W2897434820C41008148 @default.
• W2897434820 hasConceptScore W2897434820C530470458 @default.
• W2897434820 hasConceptScore W2897434820C71924100 @default.
• W2897434820 hasIssue "12" @default.
• W2897434820 hasLocation W28974348201 @default.
• W2897434820 hasLocation W28974348202 @default.
• W2897434820 hasLocation W28974348203 @default.
• W2897434820 hasLocation W28974348204 @default.
• W2897434820 hasLocation W28974348205 @default.
• W2897434820 hasOpenAccess W2897434820 @default.
• W2897434820 hasPrimaryLocation W28974348201 @default.
• W2897434820 hasRelatedWork W2731899572 @default.
• W2897434820 hasRelatedWork W2748952813 @default.
• W2897434820 hasRelatedWork W2762006829 @default.

• next