Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography.
Background: We designed a deep learning model for assessing 18F-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer.Methods: All 142 patients with cervical cancer underwent 18F-FDG PET/CT for pretreatment staging and received allocated tre...
| Publicado en: | European Radiology Vol. 29; no. 12; pp. 6741 - 6750 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2019
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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=139479360&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139479360 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Dec2019 vid: 29 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139479360 139479360 143955182 NLM31134366 139479360 10.1007/s00330-019-06265-x NLM31134366 139479360 ppf: 6741 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography. aug: au: Shen, Wei-Chih Chen, Shang-Wen Wu, Kuo-Chen Hsieh, Te-Chun Liang, Ji-An Hung, Yao-Ching Yeh, Lian-Shung Chang, Wei-Chun Lin, Wu-Chou Yen, Kuo-Yang Kao, Chia-Hung affil: Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan sug: subj: Neoplasm Recurrence, Local Fludeoxyglucose F 18 Cervix Neoplasms Cervix Neoplasms Therapy Cervix Neoplasms Pathology Radiopharmaceuticals Retrospective Design Adult Recurrence Cervix Pathology Cervix Sensitivity and Specificity Prospective Studies Prognosis Middle Age Aged, 80 and Over Aged Treatment Outcomes Female Adult: 19-44 years Middle Aged: 45-64 years Aged, 80 & over Aged: 65+ years Female ab: Background: We designed a deep learning model for assessing 18F-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer.Methods: All 142 patients with cervical cancer underwent 18F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. In each round of k-fold cross-validation, a well-trained proposed model and a slice-based optimal threshold were derived from a training set and used to classify each slice set in the test set into the categories of with or without local or distant failure. The classification results of each tumor were aggregated to summarize a tumor-based prediction result.Results: In total, 21 and 26 patients experienced local and distant failures, respectively. Regarding local recurrence, the tumor-based prediction result summarized from all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively.Conclusion: This is the first study to use deep learning model for assessing 18F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients.Key Points: • This is the first study to use deep learning model for assessing 18 F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients. • All 142 patients with cervical cancer underwent 18 F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. • For local recurrence, all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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