Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images.
Objective: To predict the local recurrence of giant cell bone tumors (GCTB) on MR features and the clinical characteristics after curettage using a deep convolutional neural network (CNN).Methods: MR images were collected from 56 patients with histopathologically confirmed GCTB after curettage who w...
| Publicado en: | European Radiology Vol. 29; no. 10; pp. 5441 - 5452 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2019
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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=138395931&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138395931 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Oct2019 vid: 29 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138395931 138395931 143916534 NLM30859281 10.1007/s00330-019-06082-2 NLM30859281 138395931 ppf: 5441 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images. aug: au: He, Yifeng Guo, Jiapan Ding, Xiaoyi van Ooijen, Peter M. A. Zhang, Yaping Chen, An Oudkerk, Matthijs Xie, Xueqian affil: Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, HaiNing Rd.100, 200080, Shanghai, China sug: subj: Bone Neoplasms Giant Cell Tumor of Bone Neoplasm Recurrence, Local Prognosis Algorithms Curettage Magnetic Resonance Imaging Methods Female Middle Age Adult Adolescence Young Adult Giant Cell Tumor of Bone Surgery Bone and Bones Pathology Neoplasm Staging Preoperative Period Male Logistic Regression Image Interpretation, Computer Assisted Methods Bone Neoplasms Surgery Prospective Studies Scales Middle Aged: 45-64 years Adult: 19-44 years Adolescent: 13-18 years Female Male ab: Objective: To predict the local recurrence of giant cell bone tumors (GCTB) on MR features and the clinical characteristics after curettage using a deep convolutional neural network (CNN).Methods: MR images were collected from 56 patients with histopathologically confirmed GCTB after curettage who were followed up for 5.8 years (range, 2.0 to 9.5 years). The inception v3 CNN architecture was fine-tuned by two categories of the MR datasets (recurrent and non-recurrent GCTB) obtained through data augmentation and was validated using fourfold cross-validation to evaluate its generalization ability. Twenty-eight cases (50%) were chosen as the training dataset for the CNN and four radiologists, while the remaining 28 cases (50%) were used as the test dataset. A binary logistic regression model was established to predict recurrent GCTB by combining the CNN prediction and patient features (age and tumor location). Accuracy and sensitivity were used to evaluate the prediction performance.Results: When comparing the CNN, CNN regression, and radiologists, the accuracies of the CNN and CNN regression models were 75.5% (95% CI 55.1 to 89.3%) and 78.6% (59.0 to 91.7%), respectively, which were higher than the 64.3% (44.1 to 81.4%) accuracy of the radiologists. The sensitivities were 85.7% (42.1 to 99.6%) and 87.5% (47.3 to 99.7%), respectively, which were higher than the 58.3% (27.7 to 84.8%) sensitivity of the radiologists (p < 0.05).Conclusion: The CNN has the potential to predict recurrent GCTB after curettage. A binary regression model combined with patient characteristics improves its prediction accuracy.Key Points: • Convolutional neural network (CNN) can be trained successfully on a limited number of pre-surgery MR images, by fine-tuning a pre-trained CNN architecture. • CNN has an accuracy of 75.5% to predict post-surgery recurrence of giant cell tumors of bone, which surpasses the 64.3% accuracy of human observation. • A binary logistic regression model combining CNN prediction rate, patient age, and tumor location improves the accuracy to predict post-surgery recurrence of giant cell bone tumors to 78.6%. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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