Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model
Objectives: Predicting the response to chemotherapy can lead to the optimization of neoadjuvant chemotherapy (NAC). The present study aimed to develop a non-invasive prediction model of therapeutic response to NAC for rectal cancer (RC). Methods: A dataset of the prechemotherapy computed tomography...
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| Format: | Article |
| Language: | English |
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The Japan Society of Coloproctology
2025-04-01
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| Series: | Journal of the Anus, Rectum and Colon |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/jarc/9/2/9_2024-085/_pdf/-char/en |
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| _version_ | 1849311121625841664 |
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| author | Shunsuke Kubota Taiichi Wakiya Hajime Morohashi Takuya Miura Taishu Kanda Masashi Matsuzaka Yoshihiro Sasaki Yoshiyuki Sakamoto Kenichi Hakamada |
| author_facet | Shunsuke Kubota Taiichi Wakiya Hajime Morohashi Takuya Miura Taishu Kanda Masashi Matsuzaka Yoshihiro Sasaki Yoshiyuki Sakamoto Kenichi Hakamada |
| author_sort | Shunsuke Kubota |
| collection | DOAJ |
| description | Objectives: Predicting the response to chemotherapy can lead to the optimization of neoadjuvant chemotherapy (NAC). The present study aimed to develop a non-invasive prediction model of therapeutic response to NAC for rectal cancer (RC).
Methods: A dataset of the prechemotherapy computed tomography (CT) images of 57 patients from multiple institutions who underwent rectal surgery after three courses of S-1 and oxaliplatin (SOX) NAC for RC was collected. The therapeutic response to NAC was pathologically confirmed. It was then predicted whether they were pathologic responders or non-responders. Cases were divided into training, validation and test datasets. A CT patch-based predictive model was developed using a residual convolutional neural network and the predictive performance was evaluated. Binary logistic regression analysis of prechemotherapy clinical factors showed that none of the independent variables were significantly associated with the non-responders.
Results: Among the 49 patients in the training and validation datasets, there were 21 (42.9%) and 28 (57.1%) responders and non-responders, respectively. A total of 3,857 patches were extracted from the 49 patients. In the validation dataset, the average sensitivity, specificity and accuracy was 97.3, 95.7 and 96.8%, respectively. Furthermore, the area under the receiver operating characteristic curve (AUC) was 0.994 (95% CI, 0.991-0.997; P<0.001). In the test dataset, which included 750 patches from 8 patients, the predictive model demonstrated high specificity (89.9%) and the AUC was 0.846 (95% CI, 0.817-0.875; P<0.001).
Conclusions: The non-invasive deep learning model using prechemotherapy CT images exhibited high predictive performance in predicting the pathological therapeutic response to SOX NAC. |
| format | Article |
| id | doaj-art-1c494419f08145b0875205afd4ff0952 |
| institution | Kabale University |
| issn | 2432-3853 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | The Japan Society of Coloproctology |
| record_format | Article |
| series | Journal of the Anus, Rectum and Colon |
| spelling | doaj-art-1c494419f08145b0875205afd4ff09522025-08-20T03:53:32ZengThe Japan Society of ColoproctologyJournal of the Anus, Rectum and Colon2432-38532025-04-019220221210.23922/jarc.2024-0852024-085Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning ModelShunsuke Kubota0Taiichi Wakiya1Hajime Morohashi2Takuya Miura3Taishu Kanda4Masashi Matsuzaka5Yoshihiro Sasaki6Yoshiyuki Sakamoto7Kenichi Hakamada8Department of Gastroenterological Surgery, Hirosaki University Graduate School of MedicineDepartment of Gastroenterological Surgery, Hirosaki University Graduate School of MedicineDepartment of Gastroenterological Surgery, Hirosaki University Graduate School of MedicineDepartment of Gastroenterological Surgery, Hirosaki University Graduate School of MedicineDepartment of Gastroenterological Surgery, Hirosaki University Graduate School of MedicineDepartment of Medical Informatics, Hirosaki University HospitalDepartment of Medical Informatics, Hirosaki University HospitalDepartment of Gastroenterological Surgery, Hirosaki University Graduate School of MedicineDepartment of Gastroenterological Surgery, Hirosaki University Graduate School of MedicineObjectives: Predicting the response to chemotherapy can lead to the optimization of neoadjuvant chemotherapy (NAC). The present study aimed to develop a non-invasive prediction model of therapeutic response to NAC for rectal cancer (RC). Methods: A dataset of the prechemotherapy computed tomography (CT) images of 57 patients from multiple institutions who underwent rectal surgery after three courses of S-1 and oxaliplatin (SOX) NAC for RC was collected. The therapeutic response to NAC was pathologically confirmed. It was then predicted whether they were pathologic responders or non-responders. Cases were divided into training, validation and test datasets. A CT patch-based predictive model was developed using a residual convolutional neural network and the predictive performance was evaluated. Binary logistic regression analysis of prechemotherapy clinical factors showed that none of the independent variables were significantly associated with the non-responders. Results: Among the 49 patients in the training and validation datasets, there were 21 (42.9%) and 28 (57.1%) responders and non-responders, respectively. A total of 3,857 patches were extracted from the 49 patients. In the validation dataset, the average sensitivity, specificity and accuracy was 97.3, 95.7 and 96.8%, respectively. Furthermore, the area under the receiver operating characteristic curve (AUC) was 0.994 (95% CI, 0.991-0.997; P<0.001). In the test dataset, which included 750 patches from 8 patients, the predictive model demonstrated high specificity (89.9%) and the AUC was 0.846 (95% CI, 0.817-0.875; P<0.001). Conclusions: The non-invasive deep learning model using prechemotherapy CT images exhibited high predictive performance in predicting the pathological therapeutic response to SOX NAC.https://www.jstage.jst.go.jp/article/jarc/9/2/9_2024-085/_pdf/-char/encomputational predictionctmachine learningdeep learningnacrc |
| spellingShingle | Shunsuke Kubota Taiichi Wakiya Hajime Morohashi Takuya Miura Taishu Kanda Masashi Matsuzaka Yoshihiro Sasaki Yoshiyuki Sakamoto Kenichi Hakamada Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model Journal of the Anus, Rectum and Colon computational prediction ct machine learning deep learning nac rc |
| title | Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model |
| title_full | Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model |
| title_fullStr | Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model |
| title_full_unstemmed | Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model |
| title_short | Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model |
| title_sort | prediction of the therapeutic response to neoadjuvant chemotherapy for rectal cancer using a deep learning model |
| topic | computational prediction ct machine learning deep learning nac rc |
| url | https://www.jstage.jst.go.jp/article/jarc/9/2/9_2024-085/_pdf/-char/en |
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