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Article

Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma

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Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa 216-8511, Japan
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Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan
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Department of Oral and Maxillofacial Surgery, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan
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Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa 216-8511, Japan
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Department of AI Research Lab, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, Kagoshima 891-0113, Japan
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School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Khaled M. Elsayes and Tanya W. Moseley
Cancers 2021, 13(4), 600; https://doi.org/10.3390/cancers13040600
Received: 22 December 2020 / Revised: 23 January 2021 / Accepted: 31 January 2021 / Published: 3 February 2021
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Cervical lymph node (LN) metastasis in patients with oral squamous cell carcinoma is one of the important prognostic factors. Pretreatment cervical nodal staging is performed using computed tomography (CT) as the first-line examination. However, imaging findings focused on morphology are not specific for detecting cervical LN metastasis. In this study, deep learning (DL) analysis of pretreatment contrast-enhanced CT was evaluated and compared with radiologists’ assessments at levels I–II, I, and II using the independent test set. The DL model achieved higher diagnostic performance in discriminating between benign and metastatic cervical LNs at levels I–II, I, and II. Significant difference in the area under the curves of the DL model and the radiologists’ assessments at levels I–II and II were observed. Our findings suggest that this approach can provide additional value to treatment strategies.
We investigated the value of deep learning (DL) in differentiating between benign and metastatic cervical lymph nodes (LNs) using pretreatment contrast-enhanced computed tomography (CT). This retrospective study analyzed 86 metastatic and 234 benign (non-metastatic) cervical LNs at levels I–V in 39 patients with oral squamous cell carcinoma (OSCC) who underwent preoperative CT and neck dissection. LNs were randomly divided into training (70%), validation (10%), and test (20%) sets. For the validation and test sets, cervical LNs at levels I–II were evaluated. Convolutional neural network analysis was performed using Xception architecture. Two radiologists evaluated the possibility of metastasis to cervical LNs using a 4-point scale. The area under the curve of the DL model and the radiologists’ assessments were calculated and compared at levels I–II, I, and II. In the test set, the area under the curves at levels I–II (0.898) and II (0.967) were significantly higher than those of each reader (both, p < 0.05). DL analysis of pretreatment contrast-enhanced CT can help classify cervical LNs in patients with OSCC with better diagnostic performance than radiologists’ assessments alone. DL may be a valuable diagnostic tool for differentiating between benign and metastatic cervical LNs. View Full-Text
Keywords: deep learning; cervical lymph node; convolutional neural network; level; squamous cell carcinoma deep learning; cervical lymph node; convolutional neural network; level; squamous cell carcinoma
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MDPI and ACS Style

Tomita, H.; Yamashiro, T.; Heianna, J.; Nakasone, T.; Kobayashi, T.; Mishiro, S.; Hirahara, D.; Takaya, E.; Mimura, H.; Murayama, S.; Kobayashi, Y. Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma. Cancers 2021, 13, 600. https://doi.org/10.3390/cancers13040600

AMA Style

Tomita H, Yamashiro T, Heianna J, Nakasone T, Kobayashi T, Mishiro S, Hirahara D, Takaya E, Mimura H, Murayama S, Kobayashi Y. Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma. Cancers. 2021; 13(4):600. https://doi.org/10.3390/cancers13040600

Chicago/Turabian Style

Tomita, Hayato, Tsuneo Yamashiro, Joichi Heianna, Toshiyuki Nakasone, Tatsuaki Kobayashi, Sono Mishiro, Daisuke Hirahara, Eichi Takaya, Hidefumi Mimura, Sadayuki Murayama, and Yasuyuki Kobayashi. 2021. "Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma" Cancers 13, no. 4: 600. https://doi.org/10.3390/cancers13040600

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