https://scholars.lib.ntu.edu.tw/handle/123456789/623385
標題: | Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning | 作者: | Wang, You-Wei Chen, Chii-Jen Wang, Teh-Chen Huang, Hsu-Cheng HSIN-MING CHEN JIN-YUAN SHIH JIN-SHING CHEN YU-SEN HUANG YEUN-CHUNG CHANG RUEY-FENG CHANG |
關鍵字: | Deep learning; Dual energy CT; Lymph node metastasis; Nodal metastasis; Primary lung tumor | 公開日期: | 2022 | 出版社: | Elsevier Ltd | 卷: | 141 | 來源出版物: | Computers in Biology and Medicine | 摘要: | Lymph node metastasis also called nodal metastasis (Nmet), is a clinically primary task for physicians. The survival and recurrence of lung cancer are related to the Nmet staging from Tumor-Node-Metastasis (TNM) reports. Furthermore, preoperative Nmet prediction is still a challenge for the patient in managing the surgical plan and making treatment decisions. We proposed a multi-energy level fusion model with a principal feature enhancement (PFE) block incorporating radiologist and computer science knowledge for Nmet prediction. The proposed model is custom-designed by gemstone spectral imaging (GSI) with different energy levels on dual-energy computer tomography (CT) from a primary tumor of lung cancer. In the experiment, we take three different energy level fusion datasets: lower energy level fusion (40, 50, 60, 70 keV), higher energy level fusion (110, 120, 130, 140 keV), and average energy level fusion (40, 70, 100, 140 keV). The proposed model is trained by lower energy level fusion that is 93% accurate and the value of Kappa is 86%. When we used the lower energy level images to train the fusion model, there has been a significant difference to other energy level fusion models. Hence, we apply 5-fold cross-validation, which is used to validate the performance result of the multi-keV model with different fusion datasets of energy level images in the pathology report. The cross-validation result also demonstrates that the model with the lower energy level dataset is more robust and suitable in predicting the Nmet of the primary tumor. The lower energy level shows more information of tumor angiogenesis or heterogeneity provided the proposed fusion model with a PFE block and channel attention blocks to predict Nmet from primary tumors. © 2021 Elsevier Ltd |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122043985&doi=10.1016%2fj.compbiomed.2021.105185&partnerID=40&md5=c047cc59aaa66c99895d0c3f401327b4 https://scholars.lib.ntu.edu.tw/handle/123456789/623385 |
ISSN: | 00104825 | DOI: | 10.1016/j.compbiomed.2021.105185 | SDG/關鍵字: | Biological organs; Computerized tomography; Deep learning; Diseases; Forecasting; Image fusion; Pathology; Patient treatment; Spectroscopy; Deep learning; Dual energy computer tomography; Dual-energy; Fusion model; Level fusion; Lowest-energy levels; Lung tumor; Lymph node metastasis; Nodal metastasis; Primary lung tumor; Tumors; Article; cancer classification; cancer staging; computer assisted tomography; controlled study; deep learning; diagnostic accuracy; distant metastasis; dual energy computed tomography; entropy; human; image analysis; lung cancer; lymph node metastasis; prediction; predictive value; primary tumor; radiodiagnosis; radiologist; sensitivity and specificity; spectral imaging; computer; diagnostic imaging; lung tumor; lymph node metastasis; procedures; x-ray computed tomography; Computers; Deep Learning; Humans; Lung Neoplasms; Lymphatic Metastasis; Tomography, X-Ray Computed |
顯示於: | 醫學系 |
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