Your browser doesn't support javascript.
loading
Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images.
Zhang, Rui; Wei, Ying; Wang, Denian; Chen, Bojiang; Sun, Huaiqiang; Lei, Yi; Zhou, Qing; Luo, Zhuang; Jiang, Li; Qiu, Rong; Shi, Feng; Li, Weimin.
Afiliación
  • Zhang R; Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
  • Wei Y; General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
  • Wang D; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
  • Chen B; Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
  • Sun H; Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
  • Lei Y; Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Zhou Q; General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
  • Luo Z; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
  • Jiang L; Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
  • Qiu R; Department of Respiratory and Critical Care Medicine, the Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
  • Shi F; Department of Respiratory and Critical Care Medicine, Suining Central Hospital, Suining, Sichuan, China.
  • Li W; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China. feng.shi@uii-ai.com.
Eur Radiol ; 34(7): 4218-4229, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38114849
ABSTRACT

OBJECTIVES:

To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings. MATERIALS AND

METHODS:

Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes. All regions of interest were automatically segmented except that the nodule boxes were additionally manually checked. The performance of models was compared with each other and that of three respiratory clinicians.

RESULTS:

There were 1822 nodules (981 malignant) in the training set, 806 (416 malignant) in the testing set, and 357 (253 malignant) totally in the external sets. The area under the curve (AUC) in the testing set was 0.754, 0.855, 0.928, and 0.942, respectively, for models derived from baseline whole lung, image patch, nodule box, and the baseline plus follow-up nodule boxes. When baseline models externally validated (follow-up images not available), the nodule-box model outperformed the other two with AUC being 0.808, 0.848, and 0.939 respectively in the three external datasets. The resident, junior, and senior clinicians achieved an accuracy of 67.0%, 82.5%, and 90.0%, respectively, in the testing set. The follow-up model performed comparably to the senior clinician.

CONCLUSION:

The deep learning algorithms solely mining nodule information can efficiently predict malignancy of incidental sub-centimeter pulmonary nodules. CLINICAL RELEVANCE STATEMENT The established models may be valuable for supporting clinicians in routine clinical practice, potentially reducing the number of unnecessary examinations and also delays in diagnosis. KEY POINTS • According to different regions of interest, four deep learning models were developed and compared to evaluate the malignancy of sub-centimeter pulmonary nodules by CT images. • The models derived from baseline nodule box or baseline plus follow-up nodule boxes demonstrated sufficient diagnostic accuracy (86.4% and 90.4% in the testing set), outperforming the respiratory resident (67.0%) and junior clinician (82.5%). • The proposed deep learning methods may aid clinicians in optimizing follow-up recommendations for sub-centimeter pulmonary nodules and may lead to fewer unnecessary diagnostic interventions.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Hallazgos Incidentales / Aprendizaje Profundo / Neoplasias Pulmonares Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Hallazgos Incidentales / Aprendizaje Profundo / Neoplasias Pulmonares Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Año: 2024 Tipo del documento: Article