Deep learning-assisted literature mining for in vitro radiosensitivity data

Shuichiro Komatsu, Takahiro Oike, Yuka Komatsu, Yoshiki Kubota, Makoto Sakai, Toshiaki Matsui, Endang Nuryadi, Tiara Bunga Mayang Permata, Hiro Sato, Hidemasa Kawamura, Masahiko Okamoto, Takuya Kaminuma, Kazutoshi Murata, Naoko Okano, Yuka Hirota, Tatsuya Ohno, Jun ichi Saitoh, Atsushi Shibata, Takashi Nakano

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Background and purpose: Integrated analysis of existing radiosensitivity data obtained by the gold-standard clonogenic assay has the potential to improve our understanding of cancer cell radioresistance. However, extraction of radiosensitivity data from the literature is highly labor-intensive. To aid in this task, using deep convolutional neural networks (CNNs) and other computer technologies, we developed an analysis pipeline that extracts radiosensitivity data derived from clonogenic assays from the literature. Materials and methods: Three classifiers (C1–3) were developed to identify publications containing radiosensitivity data derived from clonogenic assays. C1 uses Faster Regions CNN with Inception Resnet v2 (fRCNN-IRv2), VGG-16, and Optical Character Recognition (OCR) to identify publications that contain semi-logarithmic graphs showing radiosensitivity data derived from clonogenic assays. C2 uses fRCNN-IRv2 and OCR to identify publications that contain bar graphs showing radiosensitivity data derived from clonogenic assays. C3 is a program that identifies publications containing keywords related to radiosensitivity data derived from clonogenic assays. A program (iSF2) was developed using Mask RCNN and OCR to extract surviving fraction after 2-Gy irradiation (SF2) as assessed by clonogenic assays, presented in semi-logarithmic graphs. The efficacy of C1–3 and iSF2 was tested using seven datasets (1805 and 222 publications in total, respectively). Results: C1–3 yielded sensitivity of 91.2% ± 3.4% and specificity of 90.7% ± 3.6%. iSF2 returned SF2 values that were within 2.9% ± 2.6% of the SF2 values determined by radiation oncologists. Conclusion: Our analysis pipeline is potentially useful to acquire radiosensitivity data derived from clonogenic assays from the literature.

Original languageEnglish
Pages (from-to)87-93
Number of pages7
JournalRadiotherapy and Oncology
Volume139
DOIs
Publication statusPublished - 2019 Oct
Externally publishedYes

Keywords

  • Clonogenic assays
  • Convolutional neural networks
  • Deep learning
  • Radiation oncology
  • Radiosensitivity

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'Deep learning-assisted literature mining for in vitro radiosensitivity data'. Together they form a unique fingerprint.

Cite this