FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387208128> ?p ?o ?g. }

• W4387208128 endingPage "S55" @default.
• W4387208128 startingPage "S55" @default.
• W4387208128 abstract "The diagnosis of lymph node (LN) metastasis in computed tomography (CT) is an essential yet challenging task in esophageal cancer staging and treatment planning. Although criteria (e.g., RECIST, morphological/texture features) are proposed to predict LN metastasis, the diagnostic accuracy remains low with sensitivity <50% and specificity <75%, as reported in previous studies. Deep learning (DL) has the potential to address this issue by learning from large-scale labeled data. However, due to the practical surgery procedure in lymph node dissection, it is difficult to pair the metastasis of individual LN reported in the pathology report to the LN instance found in the CT image. Hence, in this study, we first use pathology reports to determine the LNS metastasis, then develop a multiple instance deep learning (MIDL) model to predict lymph node station (LNS) metastasis.We collected 1200 esophageal cancer patients with preoperative contrast-enhanced CT before surgery. A recently developed automatic mediastinal LNS segmentation model was first applied to segment LNS of 1 to 8 based on the IASLC protocol. For each LNS, the local CT region of interest (ROI) was cropped to generate a station-wise CT patch, where the LNS was labeled as metastatic if at least one metastatic LN was indicated in the pathology report. Using the station-wise CT patch and LNS label, we train a 3D MIDL model, MobileNetV3, to predict LNS metastasis. To better provide the LN position priors in MIDL, LN instances (with a short axis >4mm) were also segmented using an automatic LN detection algorithm and were added to the MIDL model as an auxiliary input. Five-fold cross-validation was conducted to evaluate the MIDL performance.The MIDL model's performance is summarized in Table 1. The MIDL model incorporating an additional LN instance mask demonstrated a superior overall AUC of 0.7539, surpassing the model without the LN mask input by 2.93%. The specificity was evaluated at a threshold resulting in a recall of 0.7, and the best model outperformed the CT input model in terms of specificity by 2.11%. This highlights the value of including the LN position prior to the MIDL model. Notably, when a threshold was set to result in a specificity of 75%, the best MIDL model demonstrated a significantly higher recall compared to the previously reported clinical diagnostic recall (39.7% vs. 63.21%).We developed a MIDL classification model to predict LNS metastasis using CT scans of 1200 patients. Our findings suggest that the MIDL model can substantially improve LNS metastasis prediction and has the potential to play an essential role in cancer staging, treatment planning, and prognostic analysis." @default.
• W4387208128 created "2023-09-30" @default.
• W4387208128 creator A5005452571 @default.
• W4387208128 creator A5012737536 @default.
• W4387208128 creator A5016814335 @default.
• W4387208128 creator A5022950764 @default.
• W4387208128 creator A5031855019 @default.
• W4387208128 creator A5034104556 @default.
• W4387208128 creator A5042184569 @default.
• W4387208128 creator A5076749867 @default.
• W4387208128 creator A5091617684 @default.
• W4387208128 date "2023-10-01" @default.
• W4387208128 modified "2023-10-17" @default.
• W4387208128 title "Deep Learning for Automatic Prediction of Lymph Node Station Metastasis in Esophageal Cancer Patients from Contrast-Enhanced CT" @default.
• W4387208128 doi "https://doi.org/10.1016/j.ijrobp.2023.06.347" @default.
• W4387208128 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37784523" @default.
• W4387208128 hasPublicationYear "2023" @default.
• W4387208128 type Work @default.
• W4387208128 citedByCount "0" @default.
• W4387208128 crossrefType "journal-article" @default.
• W4387208128 hasAuthorship W4387208128A5005452571 @default.
• W4387208128 hasAuthorship W4387208128A5012737536 @default.
• W4387208128 hasAuthorship W4387208128A5016814335 @default.
• W4387208128 hasAuthorship W4387208128A5022950764 @default.
• W4387208128 hasAuthorship W4387208128A5031855019 @default.
• W4387208128 hasAuthorship W4387208128A5034104556 @default.
• W4387208128 hasAuthorship W4387208128A5042184569 @default.
• W4387208128 hasAuthorship W4387208128A5076749867 @default.
• W4387208128 hasAuthorship W4387208128A5091617684 @default.
• W4387208128 hasConcept C108583219 @default.
• W4387208128 hasConcept C121608353 @default.
• W4387208128 hasConcept C126322002 @default.
• W4387208128 hasConcept C126838900 @default.
• W4387208128 hasConcept C142724271 @default.
• W4387208128 hasConcept C154945302 @default.
• W4387208128 hasConcept C2779013556 @default.
• W4387208128 hasConcept C2779720271 @default.
• W4387208128 hasConcept C2779742542 @default.
• W4387208128 hasConcept C2780849966 @default.
• W4387208128 hasConcept C41008148 @default.
• W4387208128 hasConcept C71924100 @default.
• W4387208128 hasConceptScore W4387208128C108583219 @default.
• W4387208128 hasConceptScore W4387208128C121608353 @default.
• W4387208128 hasConceptScore W4387208128C126322002 @default.
• W4387208128 hasConceptScore W4387208128C126838900 @default.
• W4387208128 hasConceptScore W4387208128C142724271 @default.
• W4387208128 hasConceptScore W4387208128C154945302 @default.
• W4387208128 hasConceptScore W4387208128C2779013556 @default.
• W4387208128 hasConceptScore W4387208128C2779720271 @default.
• W4387208128 hasConceptScore W4387208128C2779742542 @default.
• W4387208128 hasConceptScore W4387208128C2780849966 @default.
• W4387208128 hasConceptScore W4387208128C41008148 @default.
• W4387208128 hasConceptScore W4387208128C71924100 @default.
• W4387208128 hasIssue "2" @default.
• W4387208128 hasLocation W43872081281 @default.
• W4387208128 hasLocation W43872081282 @default.
• W4387208128 hasOpenAccess W4387208128 @default.
• W4387208128 hasPrimaryLocation W43872081281 @default.
• W4387208128 hasRelatedWork W2041343500 @default.
• W4387208128 hasRelatedWork W2312841343 @default.
• W4387208128 hasRelatedWork W2365411912 @default.
• W4387208128 hasRelatedWork W2370473007 @default.
• W4387208128 hasRelatedWork W2385555853 @default.
• W4387208128 hasRelatedWork W2401867440 @default.
• W4387208128 hasRelatedWork W2415741342 @default.
• W4387208128 hasRelatedWork W3029099239 @default.
• W4387208128 hasRelatedWork W3139189715 @default.
• W4387208128 hasRelatedWork W4200527604 @default.
• W4387208128 hasVolume "117" @default.
• W4387208128 isParatext "false" @default.
• W4387208128 isRetracted "false" @default.
• W4387208128 workType "article" @default.