Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT.
Objective: To compare machine learning (ML) of texture analysis (TA) features for classification of solid renal masses on non-contrast-enhanced CT (NCCT), corticomedullary (CM) and nephrographic (NG) phase contrast-enhanced (CE) CT. Materials and methods: With IRB approval, we retrospectively identi...
| Publicado en: | Abdominal Radiology Vol. 45; no. 9; pp. 2786 - 2797 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2020
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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=145053709&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145053709 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Sep2020 vid: 45 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145053709 144442570 10.1007/s00261-020-02632-1 145053709 ppf: 2786 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT. aug: au: Schieda, Nicola Nguyen, Kathleen Thornhill, Rebecca E. McInnes, Matthew D. F. Wu, Mark James, Nick affil: Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, K1Y 4E9, Ottawa, ON, Canada sug: ab: Objective: To compare machine learning (ML) of texture analysis (TA) features for classification of solid renal masses on non-contrast-enhanced CT (NCCT), corticomedullary (CM) and nephrographic (NG) phase contrast-enhanced (CE) CT. Materials and methods: With IRB approval, we retrospectively identified 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe and 61 benign tumors; 49 oncocytomas and 12 fat-poor angiomyolipomas) with renal protocol CT between 2012 and 2017. Tumors were independently segmented by two blinded radiologists. Twenty-five 2-dimensional TA features were extracted from each phase. Diagnostic accuracy for 1) RCC versus benign tumor and 2) cc-RCC versus other tumor was assessed using XGBoost. Results: ML of texture analysis features on different phases achieved mean area under the ROC curve (AUC [SD]), sensitivity/specificity for 1) RCC vs benign = 0.70(0.19), 96%/32% on CM-CECT and 0.71(0.14), 83%/58% on NG-CECT and; 2) cc-RCC vs other = 0.77(0.12), 49%/90% on CM-CECT and 0.71(0.16), 22%/94% on NG-CECT. There was no difference in AUC comparing CECT to NCCT (p = 0.058–0.54) and no improvement when combining data across all three phases compared single-phase assessment (p = 0.39–0.68) for either outcome. AUCs decreased when ML models were trained with one phase and tested on a different phase for both outcomes (RCC;p = 0.045–0.106, cc-RCC; < 0.001). Conclusion: Accuracy of machine learning classification of renal masses using texture analysis features did not depend on phase; however, models trained using one phase performed worse when tested on another phase particularly when associating NCCT and CECT. These findings have implications for large registries which use varying CT protocols to study renal masses. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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