Nonlinear optics with near-infrared excitation enable real-time quantitative diagnosis of human cervical cancers

Takahiro Matsui, Ryo Tamoto, Akio Iwasa, Masafumi Mimura, Seiji Taniguchi, Tetsuo Hasegawa, Takao Sudo, Hiroki Mizuno, Junichi Kikuta, Ichiro Onoyama, Kaoru Okugawa, Mayu Shiomi, Shinya Matsuzaki, Eiichi Morii, Tadashi Kimura, Kiyoko Kato, Yasujiro Kiyota, Masaru Ishii

Research output: Contribution to journalArticlepeer-review

Abstract

Histopathologic analysis through biopsy has been one of the most useful methods for the assessment of malignant neoplasms. However, some aspects of the analysis such as invasiveness, evaluation range, and turnaround time from biopsy to report could be improved. Here, we report a novel method for visualizing human cervical tissue three-dimensionally, without biopsy, fixation, or staining, and with sufficient quality for histologic diagnosis. Near-infrared excitation and nonlinear optics were employed to visualize unstained human epithelial tissues of the cervix uteri by constructing images with third-harmonic generation (THG) and second-harmonic generation (SHG). THG images enabled evaluation of nuclear morphology in a quantitative manner with six parameters after image analysis using deep learning. It was also possible to quantitatively assess intraepithelial fibrotic changes based on SHG images and another deep learning analysis. Using each analytical procedure alone, normal and cancerous tissue were classified quantitatively with an AUC ≥0.92. Moreover, a combinatory analysis of THG and SHG images with a machine learning algorithm allowed accurate classification of three-dimensional image files of normal tissue, intraepithelial neoplasia, and invasive carcinoma with a weighted kappa coefficient of 0.86. Our method enables real-time noninvasive diagnosis of cervical lesions, thus constituting a potential tool to dramatically change early detection.

Original languageEnglish
Pages (from-to)3745-3754
Number of pages10
JournalCancer Research
Volume80
Issue number17
DOIs
Publication statusPublished - 01-09-2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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