Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer.
* Context.--Pancreatic ductal adenocarcinoma has some of the worst prognostic outcomes among various cancer types. Detection of histologic patterns of pancreatic tumors is essential to predict prognosis and decide the treatment for patients. This histologic classification can have a large degree of...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 147; no. 11; pp. 1251 - 1261 |
|---|---|
| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Nov2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173208912&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173208912 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Nov2023 vid: 147 iid: 11 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 173208912 173208912 173208912 10.5858/arpa.2022-0035-OA 173208912 ppf: 1251 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer. aug: au: Weiyi Wu Xiaoying Liu Hamilton, Robert B. Suriawinata, Arief A. Hassanpour, Saeed affil: Department of Biomedical Data Science, Geisel School of Medicine, Hanover, New Hampshire sug: subj: Pancreatic Neoplasms Diagnosis Carcinoma, Ductal Diagnosis Pancreatic Neoplasms Pathology Carcinoma, Ductal Pathology Convolutional Neural Networks Deep Learning Diagnosis, Computer Assisted Human Prediction Models Descriptive Statistics Histological Techniques Histocytological Preparation Techniques Equipment and Supplies Pathologists ab: * Context.--Pancreatic ductal adenocarcinoma has some of the worst prognostic outcomes among various cancer types. Detection of histologic patterns of pancreatic tumors is essential to predict prognosis and decide the treatment for patients. This histologic classification can have a large degree of variability even among expert pathologists. Objective.--To detect aggressive adenocarcinoma and less aggressive pancreatic tumors from nonneoplasm cases using a graph convolutional network--based deep learning model. Design.--Our model uses a convolutional neural network to extract detailed information from every small region in a whole slide image. Then, we use a graph architecture to aggregate the extracted features from these regions and their positional information to capture the whole slide--level structure and make the final prediction. Results.--We evaluated our model on an independent test set and achieved an F1 score of 0.85 for detecting neoplastic cells and ductal adenocarcinoma, significantly outperforming other baseline methods. Conclusions.--If validated in prospective studies, this approach has a great potential to assist pathologists in identifying adenocarcinoma and other types of pancreatic tumors in clinical settings. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|