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

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...

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Autores principales: Kato, Shinya, Miyoshi, Norikatsu, Fujino, Shiki, Minami, Soichiro, Nagae, Ayumi, Hayashi, Rie, Sekido, Yuki, Hata, Tsuyoshi, Hamabe, Atsushi, Ogino, Takayuki, Tei, Mitsuyoshi, Kagawa, Yoshinori, Takahashi, Hidekazu, Uemura, Mamoru, Yamamoto, Hirofumi, Doki, Yuichiro, Eguchi, Hidetoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10551859/
https://www.ncbi.nlm.nih.gov/pubmed/37809043
http://dx.doi.org/10.3892/ol.2023.14062
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author Kato, Shinya
Miyoshi, Norikatsu
Fujino, Shiki
Minami, Soichiro
Nagae, Ayumi
Hayashi, Rie
Sekido, Yuki
Hata, Tsuyoshi
Hamabe, Atsushi
Ogino, Takayuki
Tei, Mitsuyoshi
Kagawa, Yoshinori
Takahashi, Hidekazu
Uemura, Mamoru
Yamamoto, Hirofumi
Doki, Yuichiro
Eguchi, Hidetoshi
author_facet Kato, Shinya
Miyoshi, Norikatsu
Fujino, Shiki
Minami, Soichiro
Nagae, Ayumi
Hayashi, Rie
Sekido, Yuki
Hata, Tsuyoshi
Hamabe, Atsushi
Ogino, Takayuki
Tei, Mitsuyoshi
Kagawa, Yoshinori
Takahashi, Hidekazu
Uemura, Mamoru
Yamamoto, Hirofumi
Doki, Yuichiro
Eguchi, Hidetoshi
author_sort Kato, Shinya
collection PubMed
description 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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spelling pubmed-105518592023-10-06 Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images Kato, Shinya Miyoshi, Norikatsu Fujino, Shiki Minami, Soichiro Nagae, Ayumi Hayashi, Rie Sekido, Yuki Hata, Tsuyoshi Hamabe, Atsushi Ogino, Takayuki Tei, Mitsuyoshi Kagawa, Yoshinori Takahashi, Hidekazu Uemura, Mamoru Yamamoto, Hirofumi Doki, Yuichiro Eguchi, Hidetoshi Oncol Lett Articles 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. D.A. Spandidos 2023-09-20 /pmc/articles/PMC10551859/ /pubmed/37809043 http://dx.doi.org/10.3892/ol.2023.14062 Text en Copyright: © Kato et al. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Kato, Shinya
Miyoshi, Norikatsu
Fujino, Shiki
Minami, Soichiro
Nagae, Ayumi
Hayashi, Rie
Sekido, Yuki
Hata, Tsuyoshi
Hamabe, Atsushi
Ogino, Takayuki
Tei, Mitsuyoshi
Kagawa, Yoshinori
Takahashi, Hidekazu
Uemura, Mamoru
Yamamoto, Hirofumi
Doki, Yuichiro
Eguchi, Hidetoshi
Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images
title Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images
title_full Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images
title_fullStr Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images
title_full_unstemmed Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images
title_short Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images
title_sort treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10551859/
https://www.ncbi.nlm.nih.gov/pubmed/37809043
http://dx.doi.org/10.3892/ol.2023.14062
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