Deep learning for the preoperative diagnosis of metastatic cervical lymph nodes on contrast-enhanced computed tomography in patients with oral squamous cell carcinoma

Hayato Tomita, Tsuneo Yamashiro, Joichi Heianna, Toshiyuki Nakasone, Tatsuaki Kobayashi, Sono Mishiro, Daisuke Hirahara, Eichi Takaya, Hidefumi Mimura, Sadayuki Murayama, Yasuyuki Kobayashi

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number600
Pages (from-to)1-11
Number of pages11
JournalCancers
Volume13
Issue number4
DOIs
Publication statusPublished - 2021 Feb 2
Externally publishedYes

Keywords

  • Cervical lymph node
  • Convolutional neural network
  • Deep learning
  • Level
  • Squamous cell carcinoma

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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