Texture analysis as a radiomic marker for differentiating renal tumors.
Purpose: To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma. Materials and methods: Following IRB approval, a retrospective analysis was performed, including all patients with pathology-proven renal...
| Publicado en: | Abdominal Radiology Vol. 42; no. 10; pp. 2470 - 2479 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2017
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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=125293609&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125293609 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Oct2017 vid: 42 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125293609 143956322 10.1007/s00261-017-1144-1 125293609 ppf: 2470 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Texture analysis as a radiomic marker for differentiating renal tumors. aug: au: Yu, HeiShun Scalera, Jonathan Khalid, Maria Touret, Anne-Sophie Bloch, Nicolas Li, Baojun Qureshi, Muhammad Soto, Jorge Anderson, Stephan affil: Department of Radiology , Boston Medical Center , 820 Harrison Avenue, FGH Building, 3rd Floor Boston 02118 USA sug: ab: Purpose: To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma. Materials and methods: Following IRB approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed. A support vector machine (SVM) method was also applied to classify tumor types. Texture analysis results were compared to the various tumors and areas under the curve (AUC) were calculated. Similar calculations were performed with the SVM data. Results: One hundred nineteen patients were included. Excellent discriminators of tumors were identified among the histogram-based features noting features skewness and kurtosis, which demonstrated AUCs of 0.91 and 0.93 ( p < 0.0001), respectively, for differentiating clear cell subtype from oncocytoma. Histogram feature median demonstrated an AUC of 0.99 ( p < 0.0001) for differentiating papillary subtype from oncocytoma and an AUC of 0.92 for differentiating oncocytoma from other tumors. Machine learning further improved the results achieving very good to excellent discrimination of tumor subtypes. The ability of machine learning to distinguish clear cell subtype from other tumors and papillary subtype from other tumors was excellent with AUCs of 0.91 and 0.92, respectively. Conclusion: Texture analysis is a promising non-invasive tool for distinguishing renal tumors on CT images. These results were further improved upon application of machine learning, and support the further development of texture analysis as a quantitative biomarker for distinguishing various renal tumors. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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