Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas

Margarita Kirienko, Gaia Ninatti, Luca Cozzi, Emanuele Voulaz, Nicolò Gennaro, Isabella Barajon, Francesca Ricci, Carmelo Carlo-Stella, Paolo Zucali, Martina Sollini, Luca Balzarini, Arturo Chiti

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVES: We aimed to assess the ability of radiomics, applied to not-enhanced computed tomography (CT), to differentiate mediastinal masses as thymic neoplasms vs lymphomas.

METHODS: The present study was an observational retrospective trial. Inclusion criteria were pathology-proven thymic neoplasia or lymphoma with mediastinal localization, availability of CT. Exclusion criteria were age < 16 years and mediastinal lymphoma lesion < 4 cm. We selected 108 patients (M:F = 47:61, median age 48 years, range 17-79) and divided them into a training and a validation group. Radiomic features were used as predictors in linear discriminant analysis. We built different radiomic models considering segmentation software and resampling setting. Clinical variables were used as predictors to build a clinical model. Scoring metrics included sensitivity, specificity, accuracy and area under the curve (AUC). Wilcoxon paired test was used to compare the AUCs.

RESULTS: Fifty-five patients were affected by thymic neoplasia and 53 by lymphoma. In the validation analysis, the best radiomics model sensitivity, specificity, accuracy and AUC resulted 76.2 ± 7.0, 77.8 ± 5.5, 76.9 ± 6.0 and 0.84 ± 0.06, respectively. In the validation analysis of the clinical model, the same metrics resulted 95.2 ± 7.0, 88.9 ± 8.9, 92.3 ± 8.5 and 0.98 ± 0.07, respectively. The AUCs of the best radiomic and the clinical model not differed.

CONCLUSIONS: We developed and validated a CT-based radiomic model able to differentiate mediastinal masses on non-contrast-enhanced images, as thymic neoplasms or lymphoma. The proposed method was not affected by image postprocessing. Therefore, the present image-derived method has the potential to noninvasively support diagnosis in patients with prevascular mediastinal masses with major impact on management of asymptomatic cases.

Original languageEnglish
Pages (from-to)951-960
Number of pages10
JournalRadiol. Med.
Volume125
Issue number10
DOIs
Publication statusPublished - Oct 2020

Keywords

  • Adolescent
  • Adult
  • Aged
  • Area Under Curve
  • Data Accuracy
  • Diagnosis, Differential
  • Discriminant Analysis
  • Female
  • Humans
  • Lymphoma/diagnostic imaging
  • Male
  • Mediastinal Neoplasms/diagnostic imaging
  • Mediastinum
  • Middle Aged
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity
  • Statistics, Nonparametric
  • Thymus Neoplasms/diagnostic imaging
  • Tomography, X-Ray Computed/methods
  • Young Adult

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