Margarita Kirienko1, Gaia Ninatti1, Luca Cozzi1,2, Emanuele Voulaz1,3, Nicolò Gennaro4, Isabella Barajon1, Francesca Ricci5, Carmelo Carlo-Stella1,5, Paolo Zucali5, Martina Sollini6,7, Luca Balzarini8, Arturo Chiti1,9. 1. Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini, 4, 20090, Pieve Emanuele, Milano, Italy. 2. Radiotherapy, Humanitas Cancer Center, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy. 3. Thoracic Surgery, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy. 4. Training Program in Radiology, Humanitas University, via Rita Levi Montalcini, 4, 20090, Pieve Emanuele, Milano, Italy. 5. Department of Oncology and Hematology, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy. 6. Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini, 4, 20090, Pieve Emanuele, Milano, Italy. martina.sollini@cancercenter.humanitas.it. 7. Nuclear Medicine, Diagnostic Imaging Department, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy. martina.sollini@cancercenter.humanitas.it. 8. Radiology, Diagnostic Imaging Department, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy. 9. Nuclear Medicine, Diagnostic Imaging Department, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy.
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.
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.