Computer-aided classification of MRI for pathological complete response to neoadjuvant chemotherapy in breast cancer.
Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoad...
| Publicado en: | Future Oncology Vol. 18; no. 8; pp. 991 - 1002 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Taylor & Francis Ltd
Apr2022
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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=155515029&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155515029 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14796694 3CMQ jtl: Future Oncology issn: 14796694 maglogo: N pubinfo: dt: Apr2022 vid: 18 iid: 8 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 155515029 155515029 NLM34894719 10.2217/fon-2021-1212 NLM34894719 155515029 ppf: 991 ppct: 11 formats: tig: atl: Computer-aided classification of MRI for pathological complete response to neoadjuvant chemotherapy in breast cancer. aug: au: Yan, Shaolei Peng, Haiyong Yu, Qiujie Chen, Xiaodan Liu, Yue Zhu, Ye Chen, Kaige Wang, Ping Li, Yujiao Zhang, Xiushi Meng, Wei affil: Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China sug: subj: Neoadjuvant Therapy Magnetic Resonance Imaging Breast Neoplasms Image Processing, Computer Assisted Retrospective Design Middle Age Aged Breast Neoplasms Drug Therapy Female Aged, 80 and Over Predictive Value of Tests Adult ROC Curve Questionnaires Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Adult: 19-44 years Female ab: Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoadjuvant chemotherapy. Methods: We analyzed a total of 455 masses and used the U-Net network and ResNet to execute MRI segmentation and pCR classification. The diagnostic performance of radiologists, the computer-aided system and a combination of radiologists and computer-aided system were compared using receiver operating characteristic curve analysis. Results: The combination of radiologists and computer-aided system had the best performance for predicting pCR with an area under the curve (AUC) value of 0.899, significantly higher than that of radiologists alone (AUC: 0.700) and computer-aided system alone (AUC: 0.835). Conclusion: An automated classification system is feasible to predict the pCR to neoadjuvant chemotherapy in patients with breast cancer and can complement MRI. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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