Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC).
Purpose: The purpose of this study was to evaluate the use of CT radiomics features and machine learning analysis to identify aggressive tumor features, including high nuclear grade (NG) and sarcomatoid (sarc) features, in large renal cell carcinomas (RCCs). Methods: CT-based volumetric radiomics an...
| Published in: | Abdominal Radiology Vol. 46; no. 9; pp. 4278 - 4289 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Sep2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151776208&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151776208 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Sep2021 vid: 46 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151776208 149800615 10.1007/s00261-021-03083-y 151776208 ppf: 4278 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC). aug: au: Gurbani, Sidharth Morgan, Dane Jog, Varun Dreyfuss, Leo Shen, Mingren Das, Arighno Abel, E. Jason Lubner, Meghan G. affil: Department of Electrical and Computer Engineering, University of Wisconsin College of Engineering, Madison, WI, USA sug: ab: Purpose: The purpose of this study was to evaluate the use of CT radiomics features and machine learning analysis to identify aggressive tumor features, including high nuclear grade (NG) and sarcomatoid (sarc) features, in large renal cell carcinomas (RCCs). Methods: CT-based volumetric radiomics analysis was performed on non-contrast (NC) and portal venous (PV) phase multidetector computed tomography images of large (> 7 cm) untreated RCCs in 141 patients (46W/95M, mean age 60 years). Machine learning analysis was applied to the extracted radiomics data to evaluate for association with high NG (grade 3–4), with multichannel analysis for NG performed in a subset of patients (n = 80). A similar analysis was performed in a sarcomatoid rich cohort (n = 43, 31M/12F, mean age 63.7 years) using size-matched non-sarcomatoid controls (n = 49) for identification of sarcomatoid change. Results: The XG Boost Model performed best on the tested data. After manual and machine feature extraction, models consisted of 3, 7, 5, 10 radiomics features for NC sarc, PV sarc, NC NG and PV NG, respectively. The area under the receiver operating characteristic curve (AUC) for these models was 0.59, 0.65, 0.69 and 0.58 respectively. The multichannel NG model extracted 6 radiomic features using the feature selection strategy and showed an AUC of 0.67. Conclusions: Statistically significant but weak associations between aggressive tumor features (high nuclear grade, sarcomatoid features) in large RCC were identified using 3D radiomics and machine learning analysis pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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