FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328112729> ?p ?o ?g. }

• W4328112729 abstract "Radiotherapy (RT) is increasingly being used on dogs with spontaneous head and neck cancer (HNC), which account for a large percentage of veterinary patients treated with RT. Accurate definition of the gross tumor volume (GTV) is a vital part of RT planning, ensuring adequate dose coverage of the tumor while limiting the radiation dose to surrounding tissues. Currently the GTV is contoured manually in medical images, which is a time-consuming and challenging task.The purpose of this study was to evaluate the applicability of deep learning-based automatic segmentation of the GTV in canine patients with HNC.Contrast-enhanced computed tomography (CT) images and corresponding manual GTV contours of 36 canine HNC patients and 197 human HNC patients were included. A 3D U-Net convolutional neural network (CNN) was trained to automatically segment the GTV in canine patients using two main approaches: (i) training models from scratch based solely on canine CT images, and (ii) using cross-species transfer learning where models were pretrained on CT images of human patients and then fine-tuned on CT images of canine patients. For the canine patients, automatic segmentations were assessed using the Dice similarity coefficient (Dice), the positive predictive value, the true positive rate, and surface distance metrics, calculated from a four-fold cross-validation strategy where each fold was used as a validation set and test set once in independent model runs.CNN models trained from scratch on canine data or by using transfer learning obtained mean test set Dice scores of 0.55 and 0.52, respectively, indicating acceptable auto-segmentations, similar to the mean Dice performances reported for CT-based automatic segmentation in human HNC studies. Automatic segmentation of nasal cavity tumors appeared particularly promising, resulting in mean test set Dice scores of 0.69 for both approaches.In conclusion, deep learning-based automatic segmentation of the GTV using CNN models based on canine data only or a cross-species transfer learning approach shows promise for future application in RT of canine HNC patients." @default.
• W4328112729 created "2023-03-22" @default.
• W4328112729 creator A5021797936 @default.
• W4328112729 creator A5029866984 @default.
• W4328112729 creator A5035478857 @default.
• W4328112729 creator A5037561171 @default.
• W4328112729 creator A5051567901 @default.
• W4328112729 creator A5063872512 @default.
• W4328112729 creator A5072650479 @default.
• W4328112729 creator A5081311927 @default.
• W4328112729 date "2023-03-21" @default.
• W4328112729 modified "2023-09-29" @default.
• W4328112729 title "Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning" @default.
• W4328112729 cites W1597938904 @default.
• W4328112729 cites W1909740415 @default.
• W4328112729 cites W1981452537 @default.
• W4328112729 cites W1987869189 @default.
• W4328112729 cites W2014931987 @default.
• W4328112729 cites W2024619586 @default.
• W4328112729 cites W2026616100 @default.
• W4328112729 cites W2077749967 @default.
• W4328112729 cites W2091020575 @default.
• W4328112729 cites W2108836663 @default.
• W4328112729 cites W2135428178 @default.
• W4328112729 cites W2149942242 @default.
• W4328112729 cites W2160754664 @default.
• W4328112729 cites W2165698076 @default.
• W4328112729 cites W2174661749 @default.
• W4328112729 cites W2301358467 @default.
• W4328112729 cites W2324081306 @default.
• W4328112729 cites W2464708700 @default.
• W4328112729 cites W2509234424 @default.
• W4328112729 cites W2531931036 @default.
• W4328112729 cites W2549734211 @default.
• W4328112729 cites W2564809246 @default.
• W4328112729 cites W2758671230 @default.
• W4328112729 cites W2769556951 @default.
• W4328112729 cites W2773727367 @default.
• W4328112729 cites W2777439179 @default.
• W4328112729 cites W2804047627 @default.
• W4328112729 cites W2896909802 @default.
• W4328112729 cites W2898336166 @default.
• W4328112729 cites W2900237898 @default.
• W4328112729 cites W2922812404 @default.
• W4328112729 cites W2925142108 @default.
• W4328112729 cites W2927301085 @default.
• W4328112729 cites W2937349975 @default.
• W4328112729 cites W2942456739 @default.
• W4328112729 cites W2945683845 @default.
• W4328112729 cites W2946394741 @default.
• W4328112729 cites W2954549797 @default.
• W4328112729 cites W2962914239 @default.
• W4328112729 cites W2964317695 @default.
• W4328112729 cites W2972640676 @default.
• W4328112729 cites W2975885948 @default.
• W4328112729 cites W2979318050 @default.
• W4328112729 cites W2979754953 @default.
• W4328112729 cites W2983341131 @default.
• W4328112729 cites W3022950150 @default.
• W4328112729 cites W3033944192 @default.
• W4328112729 cites W3045304483 @default.
• W4328112729 cites W3090446625 @default.
• W4328112729 cites W3119087862 @default.
• W4328112729 cites W3126864501 @default.
• W4328112729 cites W3127671756 @default.
• W4328112729 cites W3128646645 @default.
• W4328112729 cites W3129816016 @default.
• W4328112729 cites W3133722793 @default.
• W4328112729 cites W3142364075 @default.
• W4328112729 cites W3157259822 @default.
• W4328112729 cites W3164924744 @default.
• W4328112729 cites W3178300374 @default.
• W4328112729 cites W3179097742 @default.
• W4328112729 cites W3180629942 @default.
• W4328112729 cites W3198880612 @default.
• W4328112729 cites W3206830804 @default.
• W4328112729 cites W3216040544 @default.
• W4328112729 cites W4200247705 @default.
• W4328112729 cites W4226084777 @default.
• W4328112729 cites W4244369342 @default.
• W4328112729 cites W4287509914 @default.
• W4328112729 cites W609314357 @default.
• W4328112729 doi "https://doi.org/10.3389/fvets.2023.1143986" @default.
• W4328112729 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37026102" @default.
• W4328112729 hasPublicationYear "2023" @default.
• W4328112729 type Work @default.
• W4328112729 citedByCount "1" @default.
• W4328112729 countsByYear W43281127292023 @default.
• W4328112729 crossrefType "journal-article" @default.
• W4328112729 hasAuthorship W4328112729A5021797936 @default.
• W4328112729 hasAuthorship W4328112729A5029866984 @default.
• W4328112729 hasAuthorship W4328112729A5035478857 @default.
• W4328112729 hasAuthorship W4328112729A5037561171 @default.
• W4328112729 hasAuthorship W4328112729A5051567901 @default.
• W4328112729 hasAuthorship W4328112729A5063872512 @default.
• W4328112729 hasAuthorship W4328112729A5072650479 @default.
• W4328112729 hasAuthorship W4328112729A5081311927 @default.
• W4328112729 hasBestOaLocation W43281127291 @default.
• W4328112729 hasConcept C108583219 @default.
• W4328112729 hasConcept C124504099 @default.

• next