Evidence map›Paper›PMID 32803417›Full record

ArticleEuropean radiology2021

Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

Felix Y Yap, Bino A Varghese, Steven Y Cen, Darryl H Hwang, Xiaomeng Lei, Bhushan Desai, Christopher Lau, Lindsay L Yang, Austin J Fullenkamp, Simin Hajian and 9 more

Abstract read
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In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Felix Y YapDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Bino A VargheseDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA. bino.varghese@med.usc.edu.
Steven Y CenDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Darryl H HwangDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Xiaomeng LeiDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Bhushan DesaiDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Christopher LauDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Lindsay L YangDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Austin J FullenkampDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Simin HajianDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Marielena RivasDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Megha Nayyar GuptaDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Brian D QuinnDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.
Manju AronDepartment of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Mihir M DesaiInstitute of Urology and the Catherine & Joseph Aresty, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Monish AronInstitute of Urology and the Catherine & Joseph Aresty, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Assad A OberaiDepartment of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Inderbir S GillInstitute of Urology and the Catherine & Joseph Aresty, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Vinay A DuddalwarDepartment of Radiology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA.

Funding

Radiological Society of North America #RF1821
6 · The paper itself

Abstract

objectivesUsing a radiomics framework to quantitatively analyze tumor shape and texture features in three dimensions, we tested its ability to objectively and robustly distinguish between benign and malignant renal masses. We assessed the relative contributions of shape and texture metrics separately and together in the prediction model. MATERIALS AND

methodsComputed tomography (CT) images of 735 patients with 539 malignant and 196 benign masses were segmented in this retrospective study. Thirty-three shape and 760 texture metrics were calculated per tumor. Tumor classification models using shape, texture, and both metrics were built using random forest and AdaBoost with tenfold cross-validation. Sensitivity analyses on five sub-cohorts with respect to the acquisition phase were conducted. Additional sensitivity analyses after multiple imputation were also conducted. Model performance was assessed using AUC.

resultsRandom forest classifier showed shape metrics featuring within the top 10% performing metrics regardless of phase, attaining the highest variable importance in the corticomedullary phase. Convex hull perimeter ratio is a consistently high-performing shape feature. Shape metrics alone achieved an AUC ranging 0.64-0.68 across multiple classifiers, compared with 0.67-0.75 and 0.68-0.75 achieved by texture-only and combined models, respectively.

conclusionShape metrics alone attain high prediction performance and high variable importance in the combined model, while being independent of the acquisition phase (unlike texture). Shape analysis therefore should not be overlooked in its potential to distinguish benign from malignant tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics. KEY POINTS: • Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors. • Shape metrics alone can attain high prediction performance and demonstrate high variable importance in the combined shape and texture radiomics model. • Any future radiomics platform powered by machine learning should harness both shape and texture metrics, especially since tumor shape (unlike texture) is independent of the acquisition phase and more robust from the imaging variations.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsDiagnosis, DifferentialHumansRetrospective StudiesTomography, X-Ray ComputedData analysisImage Processing, radiomicsKidney neoplasmsMachine learningTomography, X-ray computed

Identifiers

PMID32803417

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.