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學年
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109 |
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學期
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2 |
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出版（發表）日期
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2021-07-01 |
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作品名稱
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Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning |
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作品名稱（其他語言）
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著者
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You-Wei Wang; Chii-Jen Chen; Hsu-Cheng Huang; Teh-Chen Wang; Hsin-Ming Chen; Jin-Yuan Shih; Jin-Shing Chen; Yu‐Sen Huang; Yeun‑Chung Chang; Ruey-Feng Chang |
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單位
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出版者
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著錄名稱、卷期、頁數
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Computerized Medical Imaging and Graphics 91, pp.1-13 (101953) |
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摘要
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Lymph node metastasis (LNM) identification is the most clinically important tasks related to survival and recurrence from lung cancer. However, the preoperative prediction of nodal metastasis remains a challenge to determine surgical plans and pretreatment decisions in patients with cancers. We proposed a novel deep prediction method with a size-related damper block for nodal metastasis (Nmet) identification from the primary tumor in lung cancer generated by gemstone spectral imaging (GSI) dual-energy computer tomography (CT). The best model is the proposed method trained by the 40 keV dataset achieves an accuracy of 86 % and a Kappa value of 72 % for Nmet prediction. In the experiment, we have 11 different monochromatic images from 40~140 keV (the interval is 10 keV) for each patient. When we used the model of 40 keV dataset, there has significant difference in other energy levels (unit of keV). Therefore, we apply in 5-fold cross-validation to explain the lower keV is more efficient to predict Nmet of the primary tumor. The result shows that tumor heterogeneity and size contributed to the proposed model to estimate whether absence or presence of nodal metastasis from the primary tumor. |
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關鍵字
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Lymph node metastasis;Nodal metastasis;Dual energy CT;Deep learning |
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語言
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en |
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ISSN
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0895-6111;1879-0771 |
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期刊性質
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國外 |
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收錄於
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SCI
Scopus
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產學合作
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通訊作者
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審稿制度
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是 |
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國別
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GBR |
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公開徵稿
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出版型式
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,電子版 |