Your browser doesn't support javascript.
loading
Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.
Mayoral, Maria; Pagano, Andrew M; Araujo-Filho, Jose Arimateia Batista; Zheng, Junting; Perez-Johnston, Rocio; Tan, Kay See; Gibbs, Peter; Fernandes Shepherd, Annemarie; Rimner, Andreas; Simone II, Charles B; Riely, Gregory; Huang, James; Ginsberg, Michelle S.
Afiliação
  • Mayoral M; Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Medical Imaging Department. Hospital Clinic of Barcelona, 170 Villarroel street, Barcelona 08036, Spain. Electronic address: mmayoral@clinic.cat.
  • Pagano AM; Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Araujo-Filho JAB; Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Department of Radiology. Hospital Sirio-Libanes, 91 Dona Adma Jafet street, São Paulo 01308-050, Brazil.
  • Zheng J; Department of Epidemiology and Biostatistics. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Perez-Johnston R; Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Tan KS; Department of Epidemiology and Biostatistics. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Gibbs P; Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Fernandes Shepherd A; Department of Radiation Oncology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Rimner A; Department of Radiation Oncology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Simone II CB; Department of Radiation Oncology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Riely G; Department of Surgery. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Huang J; Department of Surgery. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  • Ginsberg MS; Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Lung Cancer ; 178: 206-212, 2023 04.
Article em En | MEDLINE | ID: mdl-36871345
OBJECTIVES: The aim of this study was to differentiate benign from malignant tumors in the anterior mediastinum based on computed tomography (CT) imaging characteristics, which could be useful in preoperative planning. Additionally, our secondary aim was to differentiate thymoma from thymic carcinoma, which could guide the use of neoadjuvant therapy. MATERIALS AND METHODS: Patients referred for thymectomy were retrospectively selected from our database. Twenty-five conventional characteristics were evaluated by visual analysis, and 101 radiomic features were extracted from each CT. In the step of model training, we applied support vector machines to train classification models. Model performance was assessed using the area under the receiver operating curves (AUC). RESULTS: Our final study sample comprised 239 patients, 59 (24.7 %) with benign mediastinal lesions and 180 (75.3 %) with malignant thymic tumors. Among the malignant masses, there were 140 (58.6 %) thymomas, 23 (9.6 %) thymic carcinomas, and 17 (7.1 %) non-thymic lesions. For the benign versus malignant differentiation, the model that integrated both conventional and radiomic features achieved the highest diagnostic performance (AUC = 0.715), in comparison to the conventional (AUC = 0.605) and radiomic-only (AUC = 0.678) models. Similarly, regarding thymoma versus thymic carcinoma differentiation, the model that integrated both conventional and radiomic features also achieved the highest diagnostic performance (AUC = 0.810), in comparison to the conventional (AUC = 0.558) and radiomic-only (AUC = 0.774) models. CONCLUSION: CT-based conventional and radiomic features with machine learning analysis could be useful for predicting pathologic diagnoses of anterior mediastinal masses. The diagnostic performance was moderate for differentiating benign from malignant lesions and good for differentiating thymomas from thymic carcinomas. The best diagnostic performance was achieved when both conventional and radiomic features were integrated in the machine learning algorithms.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Timoma / Neoplasias do Timo / Neoplasias Pulmonares Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Timoma / Neoplasias do Timo / Neoplasias Pulmonares Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article