Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.
Rationale and Objectives: This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis.Materials and Methods: Seventy-nine patients with 84 solid renal masses (21 benign; 63 malign...
| Published in: | Academic Radiology Vol. 27; no. 10; pp. 1422 - 1430 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Elsevier B.V.
Oct2020
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146171560&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146171560 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10766332 T4X jtl: Academic Radiology issn: 10766332 maglogo: N pubinfo: dt: Oct2020 vid: 27 iid: 10 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 146171560 146171560 NLM32014404 146171560 10.1016/j.acra.2019.12.015 NLM32014404 146171560 ppf: 1422 ppct: 8 formats: tig: atl: Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis. aug: au: Erdim, Cagri Yardimci, Aytul Hande Bektas, Ceyda Turan Kocak, Burak Koca, Sevim Baykal Demir, Hale Kilickesmez, Ozgur affil: Department of Radiology, Sultangazi Haseki Training and Research Hospital, Sultangazi, Istanbul, Turkey sug: subj: Carcinoma, Renal Cell Kidney Neoplasms Tomography, X-Ray Computed Reproducibility of Results Diagnosis, Differential Probability Retrospective Design Human ab: Rationale and Objectives: This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis.Materials and Methods: Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC. Benign masses are represented by oncocytomas and fat-poor angiomyolipomas. Following preprocessing steps, a total of 271 texture features were extracted from unenhanced and contrast-enhanced CT images. Dimension reduction was done with a reliability analysis and then with a feature selection algorithm. A nested-approach was used for feature selection, model optimization, and validation. Eight ML algorithms were used for the classifications: decision tree, locally weighted learning, k-nearest neighbors, naive Bayes, logistic regression, support vector machine, neural network, and random forest.Results: The number of features with good reproducibility was 198 for unenhanced CT and 244 for contrast-enhanced CT. Random forest algorithm demonstrated the best predictive performance using five selected contrast-enhanced CT texture features. The accuracy and area under the curve metrics were 90.5% and 0.915, respectively. Having eliminated the highly collinear features from the analysis, the accuracy and area under the curve values slightly increased to 91.7% and 0.916, respectively.Conclusion: ML-based contrast-enhanced CT texture analysis might be a potential method for distinguishing benign and malignant solid renal masses with satisfactory performance. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|