Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography-Reference-Cited by-同舟云学术

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Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography

Published:2022-07-05 Issue:12 Volume:29 Page:7473-7482
ISSN:1068-9265
Container-title:Annals of Surgical Oncology
language:en
Short-container-title:Ann Surg Oncol

Author:

Chen Li-Wei,Yang Shun-Mao,Chuang Ching-Chia,Wang Hao-Jen,Chen Yi-Chang,Lin Mong-Wei,Hsieh Min-Shu,Antonoff Mara B.,Chang Yeun-Chung,Wu Carol C.,Pan Tinsu,Chen Chung-Ming

Funder

National Taiwan University Hospital, Hsin-Chu Branch, Taiwan

Ministry of Science and Technology, Taiwan

Publisher

Springer Science and Business Media LLC

Subject

Oncology,Surgery

Link

https://link.springer.com/content/pdf/10.1245/s10434-022-12055-5.pdf

Reference41 articles.

1. Tsao MS, Marguet S, Le Teuff G, et al. Subtype classification of lung adenocarcinoma predicts benefit from adjuvant chemotherapy in patients undergoing complete resection. J Clin Oncol. 2015;33:3439–46.

2. Travis WD, Brambilla E, Noguchi M, et al. International Association for the study of lung cancer/American thoracic society/European respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol. 2011;6:244–85.

3. Russell PA, Wainer Z, Wright GM, et al. Does lung adenocarcinoma subtype predict patient survival? A clinicopathologic study based on the new international association for the study of lung cancer/American Thoracic Society/European Respiratory Society international multidisciplinary lung adenocarcinoma classification. J Thorac Oncol. 2011;6:1496–504.

4. Zhang J, Wu J, Tan Q, Zhu L, Gao W. Why do pathological stage IA lung adenocarcinomas vary from prognosis? A clinicopathologic study of 176 patients with pathological stage IA lung adenocarcinoma based on the IASLC/ATS/ERS classification. J Thorac Oncol. 2013;8:1196–202.

5. Lee HY, Lee SW, Lee KS, et al. Role of CT and PET imaging in predicting tumor recurrence and survival in patients with lung adenocarcinoma: a comparison with the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification of lung adenocarcinoma. J Thorac Oncol. 2015;10:1785–94.

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2. Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma;La radiologia medica;2024-03-21

3. CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma;Annals of Surgical Oncology;2023-11-13

4. Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography;Academic Radiology;2023-10

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