Open Access

Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images

  • Authors:
    • Shinya Kato
    • Norikatsu Miyoshi
    • Shiki Fujino
    • Soichiro Minami
    • Ayumi Nagae
    • Rie Hayashi
    • Yuki Sekido
    • Tsuyoshi Hata
    • Atsushi Hamabe
    • Takayuki Ogino
    • Mitsuyoshi Tei
    • Yoshinori Kagawa
    • Hidekazu Takahashi
    • Mamoru Uemura
    • Hirofumi Yamamoto
    • Yuichiro Doki
    • Hidetoshi Eguchi
  • View Affiliations

  • Published online on: September 20, 2023     https://doi.org/10.3892/ol.2023.14062
  • Article Number: 474
  • Copyright: © Kato et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

In current clinical practice, several treatment methods, including neoadjuvant therapy, are being developed to improve overall survival or local recurrence rates for locally advanced rectal cancer. The response to neoadjuvant therapy is usually evaluated using imaging data collected before and after preoperative treatment or postsurgical pathological diagnosis. However, there is a need to accurately predict the response to preoperative treatment before treatment is administered. The present study used a deep learning network to examine colonoscopy images and construct a model to predict the response of rectal cancer to neoadjuvant chemotherapy. A total of 53 patients who underwent preoperative chemotherapy followed by radical resection for advanced rectal cancer at the Osaka University Hospital between January 2011 and August 2019 were retrospectively analyzed. A convolutional neural network model was constructed using 403 images from 43 patients as the learning set. The diagnostic accuracy of the deep learning model was evaluated using 84 images from 10 patients as the validation set. The model demonstrated a sensitivity, specificity, accuracy, positive predictive value and area under the curve of 77.6% (38/49), 62.9% (22/33), 71.4% (60/84), 74.5% (38/51) and 0.713, respectively, in predicting a poor response to neoadjuvant therapy. Overall, deep learning of colonoscopy images may contribute to an accurate prediction of the response of rectal cancer to neoadjuvant chemotherapy.
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November-2023
Volume 26 Issue 5

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Spandidos Publications style
Kato S, Miyoshi N, Fujino S, Minami S, Nagae A, Hayashi R, Sekido Y, Hata T, Hamabe A, Ogino T, Ogino T, et al: Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images. Oncol Lett 26: 474, 2023
APA
Kato, S., Miyoshi, N., Fujino, S., Minami, S., Nagae, A., Hayashi, R. ... Eguchi, H. (2023). Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images. Oncology Letters, 26, 474. https://doi.org/10.3892/ol.2023.14062
MLA
Kato, S., Miyoshi, N., Fujino, S., Minami, S., Nagae, A., Hayashi, R., Sekido, Y., Hata, T., Hamabe, A., Ogino, T., Tei, M., Kagawa, Y., Takahashi, H., Uemura, M., Yamamoto, H., Doki, Y., Eguchi, H."Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images". Oncology Letters 26.5 (2023): 474.
Chicago
Kato, S., Miyoshi, N., Fujino, S., Minami, S., Nagae, A., Hayashi, R., Sekido, Y., Hata, T., Hamabe, A., Ogino, T., Tei, M., Kagawa, Y., Takahashi, H., Uemura, M., Yamamoto, H., Doki, Y., Eguchi, H."Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images". Oncology Letters 26, no. 5 (2023): 474. https://doi.org/10.3892/ol.2023.14062