Your browser doesn't support javascript.
loading
Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.
Wu, Guangyao; Woodruff, Henry C; Sanduleanu, Sebastian; Refaee, Turkey; Jochems, Arthur; Leijenaar, Ralph; Gietema, Hester; Shen, Jing; Wang, Rui; Xiong, Jingtong; Bian, Jie; Wu, Jianlin; Lambin, Philippe.
Afiliación
  • Wu G; The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands. g.wu@maastrichtuniversity.nl.
  • Woodruff HC; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian, 116001, People's Republic of China. g.wu@maastrichtuniversity.nl.
  • Sanduleanu S; The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
  • Refaee T; The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
  • Jochems A; The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
  • Leijenaar R; The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
  • Gietema H; The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
  • Shen J; Department of Radiology, Maastricht University Medical Center+, Maastricht, The Netherlands.
  • Wang R; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian, 116001, People's Republic of China.
  • Xiong J; Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China.
  • Bian J; Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China.
  • Wu J; Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China.
  • Lambin P; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian, 116001, People's Republic of China. cjr.wujianlin@vip.163.com.
Eur Radiol ; 30(5): 2680-2691, 2020 May.
Article en En | MEDLINE | ID: mdl-32006165
ABSTRACT

OBJECTIVES:

Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM).

METHODS:

This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested.

RESULTS:

The radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume.

CONCLUSIONS:

Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. KEY POINTS • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.
Asunto(s)
Palabras clave

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Nódulo Pulmonar Solitario / Nódulos Pulmonares Múltiples / Adenocarcinoma in Situ / Adenocarcinoma del Pulmón / Neoplasias Pulmonares Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2020 Tipo del documento: Article País de afiliación: Países Bajos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Nódulo Pulmonar Solitario / Nódulos Pulmonares Múltiples / Adenocarcinoma in Situ / Adenocarcinoma del Pulmón / Neoplasias Pulmonares Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2020 Tipo del documento: Article País de afiliación: Países Bajos