Contrast-enhanced computed tomography radiomics and multilayer perceptron network classifier: an approach for predicting CD20+ B cells in patients with pancreatic ductal adenocarcinoma.
Purpose: To develop and validate a machine-learning classifier based on contrast-enhanced computed tomography (CT) for the preoperative prediction of CD20+ B lymphocyte expression in patients with pancreatic ductal adenocarcinoma (PDAC). Methods: Overall, 189 patients with PDAC (n = 132 and n = 57 i...
| Publicado en: | Abdominal Radiology Vol. 47; no. 1; pp. 242 - 254 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2022
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| 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=154792859&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154792859 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jan2022 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154792859 153259479 154792859 154792859 10.1007/s00261-021-03285-4 154792859 ppf: 242 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Contrast-enhanced computed tomography radiomics and multilayer perceptron network classifier: an approach for predicting CD20+ B cells in patients with pancreatic ductal adenocarcinoma. aug: au: Yu, Jieyu Li, Qi Zhang, Hao Meng, Yinghao Liu, Yan Fang Jiang, Hui Ma, Chao Liu, Fang Fang, Xu Li, Jing Feng, Xiaochen Shao, Chengwei Bian, Yun Lu, Jianping affil: Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, 200434, Shanghai, China sug: subj: Tomography, X-Ray Computed Pancreatic Neoplasms Prognosis Carcinoma, Ductal Prognosis Adenocarcinoma Prognosis Cancer Patients Contrast Media Diagnostic Use B Lymphocytes Multilayer Perceptrons Human Immunohistochemistry Spearman's Rank Correlation Coefficient Log-Rank Test Confidence Intervals Descriptive Statistics ab: Purpose: To develop and validate a machine-learning classifier based on contrast-enhanced computed tomography (CT) for the preoperative prediction of CD20+ B lymphocyte expression in patients with pancreatic ductal adenocarcinoma (PDAC). Methods: Overall, 189 patients with PDAC (n = 132 and n = 57 in the training and validation sets, respectively) underwent immunohistochemistry and radiomics feature extraction. The X-tile software was used to stratify them into groups with 'high' and 'low' CD20+ B lymphocyte expression levels. For each patient, 1409 radiomic features were extracted from volumes of interest and reduced using variance analysis and Spearman correlation analysis. A multilayer perceptron (MLP) network classifier was developed using the training and validation set. Model performance was determined by its discriminative ability, calibration, and clinical utility. Results: A log-rank test showed that the patients with high CD20+ B expression had significantly longer survival than those with low CD20+ B expression. The prediction model showed good discrimination in both the training and validation sets. For the training set, the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.82 (95% CI 0.74–0.89), 92.42%, 57.58%, 0.75, 0.69, and 0.88, respectively; whereas these values for the validation set were 0.84 (95% CI 0.72–0.93), 86.21%, 78.57%, 0.83, 0.81, and 0.85, respectively. Conclusion: The MLP network classifier based on contrast-enhanced CT can accurately predict CD20+ B expression in patients with PDAC. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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