CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.
Objectives: To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV).Methods: A total of 587 subjects (mean age 60....
| Publicado en: | European Radiology Vol. 32; no. 4; pp. 2552 - 2564 |
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| Autores principales: | , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
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=155758969&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155758969 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2022 vid: 32 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155758969 155758969 NLM34757449 10.1007/s00330-021-08344-4 NLM34757449 155758969 ppf: 2552 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma. aug: au: Demirjian, Natalie L. Varghese, Bino A. Cen, Steven Y. Hwang, Darryl H. Aron, Manju Siddiqui, Imran Fields, Brandon K. K. Lei, Xiaomeng Yap, Felix Y. Rivas, Marielena Reddy, Sharath S. Zahoor, Haris Liu, Derek H. Desai, Mihir Rhie, Suhn K. Gill, Inderbir S. Duddalwar, Vinay affil: College of Medicine – Tucson, University of Arizona, Tucson, AZ, USA sug: subj: Carcinoma, Renal Cell Pathology Kidney Neoplasms Pathology Kidney Neoplasms Carcinoma, Renal Cell Aged Adult Middle Age Tomography, X-Ray Computed Methods Retrospective Design Young Adult Aged, 80 and Over Pharmacokinetics Scales Aged: 65+ years Adult: 19-44 years Middle Aged: 45-64 years Aged, 80 & over ab: Objectives: To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV).Methods: A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC).Results: The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification.Conclusion: Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC.Key Points: • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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