Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms

Xing Li; Lianyu Li; Qing Sun; Bo Chen; Chenjie Zhao; Yuting Dong; Zhihui Zhu; Ruiqi Zhao; Xinsong Ma; Mingxin Yu; Tao Zhang

doi:10.3389/fonc.2023.1272305

Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms

Front Oncol. 2023 Oct 10:13:1272305. doi: 10.3389/fonc.2023.1272305. eCollection 2023.

Authors

Xing Li¹, Lianyu Li², Qing Sun³, Bo Chen⁴, Chenjie Zhao⁵, Yuting Dong⁵, Zhihui Zhu¹, Ruiqi Zhao¹, Xinsong Ma², Mingxin Yu², Tao Zhang¹

Affiliations

¹ Department of Stomatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
² Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China.
³ Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁴ Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁵ Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Abstract

Introduction: Oral cancer, a predominant malignancy in developing nations, represents a global health challenge with a five-year survival rate below 50%. Nonetheless, substantial reductions in both its incidence and mortality rates can be achieved through early detection and appropriate treatment. Crucial to these treatment plans and prognosis predictions is the identification of the pathological type of oral cancer.

Methods: Toward this end, fiber-optic Raman spectroscopy emerges as an effective tool. This study combines Raman spectroscopy technology with deep learning algorithms to develop a portable intelligent prototype for oral case analysis. We propose, for the first time, a multi-task network (MTN) Raman spectroscopy classification model that utilizes a shared backbone network to simultaneously achieve different clinical staging and histological grading diagnoses.

Results: The developed model demonstrated accuracy rates of 94.88%, 94.57%, and 94.34% for tumor staging, lymph node staging, and histological grading, respectively. Its sensitivity, specificity, and accuracy compare closely with the gold standard: routine histopathological examination.

Discussion: Thus, this prototype proposed in this study has great potential for rapid, non-invasive, and label-free pathological diagnosis of oral cancer.

Keywords: Raman spectroscopy; TNM classification; histological diagnosis; machine learning algorithm; oral cancer.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No.2022-JKCS-17), the National High Level Hospital Clinical Research Funding (Grant No.2022-PUMCH-B-036) and the Beijing Natural Science Foundation (Grant No. 4222040).