عنوان انگلیسی مقاله:
Deep learning-assisted literature mining for in vitro radiosensitivity data
ترجمه فارسی عنوان مقاله:
استخراج ادبیات با کمک یادگیری عمیق برای داده های تابش آزمایشگاهی
Sciencedirect - Elsevier - Radiotherapy and Oncology, 139 (2019) 87-93: doi:10:1016/j:radonc:2019:07:003
Shuichiro Komatsu a, Takahiro Oike a,⇑, Yuka Komatsu a, Yoshiki Kubota b, Makoto Sakai b, Toshiaki Matsui a, Endang Nuryadi a,c, Tiara Bunga Mayang Permata a,c, Hiro Sato a, Hidemasa Kawamura b, Masahiko Okamoto b, Takuya Kaminuma b, Kazutoshi Murata b, Naoko Okano a, Yuka Hirota a, Tatsuya Ohno b, Jun-ichi Saitoh d, Atsushi Shibata e, Takashi Nakano a,
Background and purpose: Integrated analysis of existing radiosensitivity data obtained by the goldstandard
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.
Keywords: Clonogenic assays | Radiosensitivity | Deep learning | Convolutional neural networks | Radiation oncology