Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibroblast Activation Protein Expression in Patients With Pancreatic Ductal Adenocarcinoma.
Background: Fibroblast activation protein (FAP) in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment of patients. Accurate preoperative FAP expression can better identify the population benefitting from FAP-targeting drugs.Purpose: To develop and validate a ma...
| Publicado en: | Journal of Magnetic Resonance Imaging Vol. 54; no. 5; pp. 1432 - 1444 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Nov2021
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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=153010062&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153010062 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10531807 O63 jtl: Journal of Magnetic Resonance Imaging issn: 10531807 maglogo: Y pubinfo: dt: Nov2021 vid: 54 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 153010062 153010062 NLM33890347 153010062 10.1002/jmri.27648 NLM33890347 153010062 ppf: 1432 ppct: 12 formats: tig: atl: Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibroblast Activation Protein Expression in Patients With Pancreatic Ductal Adenocarcinoma. aug: au: Meng, Yinghao Zhang, Hao Li, Qi Xing, Pengyi Liu, Fang Cao, Kai Fang, Xu Li, Jing Yu, Jieyu Feng, Xiaochen Ma, Chao Wang, Li Jiang, Hui Lu, Jianping Bian, Yun Shao, Chengwei affil: Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China sug: subj: Pancreatic Neoplasms Carcinoma, Ductal Multilayer Perceptrons Magnetic Resonance Imaging Adenocarcinoma Retrospective Design Human Magnetic Resonance Spectroscopy Fibroblasts Comparative Studies Multicenter Studies Evaluation Research Validation Studies Funding Source ab: Background: Fibroblast activation protein (FAP) in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment of patients. Accurate preoperative FAP expression can better identify the population benefitting from FAP-targeting drugs.Purpose: To develop and validate a machine learning classifier based on noncontrast MRI for the preoperative prediction of FAP expression in patients with PDAC.Study Type: Retrospective cohort study.Population: Altogether, 129 patients with pathology-confirmed PDAC undergoing MR scan and surgical resection; 90 patients in a training cohort, and 39 patients in a validation cohort. FIELD STRENGTH/SEQUENCE/3T: Breath-hold single-shot fast-spin echo T2-weighted sequence and unenhanced and noncontrast T1-weighted fat-suppressed sequences.Assessment: FAP expression was quantified using immunohistochemistry. For each patient, 1409 radiomics features were extracted from T1- and T2-weighted images and reduced using the least absolute shrinkage and selection operator logistic regression algorithm. A multilayer perceptron (MLP) network classifier was developed using the training and validation set. The MLP network classifier performance was determined by its discriminative ability, calibration, and clinical utility.Statistical Tests: Kaplan-Meier estimates, student's t-test, the Kruskal-Wallis H test, and the chi-square test, univariable regression analysis, receiver operating characteristic curve, and decision curve analysis were used.Results: A log-rank test showed that the survival of patients with low FAP expression (24.43 months) was significantly longer (P < 0.05) than that in the FAP-high group (13.50 months). The prediction model showed good discrimination in the training set (area under the curve [AUC], 0.84) and the validation set (AUC, 0.77). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 75.00%, 79.41%, 0.77, 0.86, and 0.66, respectively, whereas those for the validation set were 85.00%, 63.16%, 0.74, 0.71, and 0.80, respectively.Data Conclusions: The MLP network classifier based on noncontrast MRI can accurately predict FAP expression in patients with PDAC.Evidence Level: 2 TECHNICAL EFFICACY: Stage 2. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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