Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach
Background: Treatment of locally advanced rectal cancer (LARC) involves neoadjuvant chemoradiotherapy (nCRT), followed by total mesorectal excision. Examining the response to treatment is one of the most important factors in the follow-up of patients; therefore, in this study, radiomics patterns der...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2024-12-01
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| Series: | Journal of Medical Signals and Sensors |
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| Online Access: | https://journals.lww.com/10.4103/jmss.jmss_46_23 |
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| author | Seyyed Hossein Mousavie Anijdan Daryush Moslemi Reza Reiazi Hamid Fallah Tafti Ali Akbar Moghadamnia Reza Paydar |
| author_facet | Seyyed Hossein Mousavie Anijdan Daryush Moslemi Reza Reiazi Hamid Fallah Tafti Ali Akbar Moghadamnia Reza Paydar |
| author_sort | Seyyed Hossein Mousavie Anijdan |
| collection | DOAJ |
| description | Background:
Treatment of locally advanced rectal cancer (LARC) involves neoadjuvant chemoradiotherapy (nCRT), followed by total mesorectal excision. Examining the response to treatment is one of the most important factors in the follow-up of patients; therefore, in this study, radiomics patterns derived from pretreatment computed tomography images in rectal cancer and its relationship with treatment response measurement criteria have been investigated.
Methods:
Fifty patients with rectal adenocarcinoma who were candidates for nCRT and surgery were included. The information obtained from the tumor surgical pathology report, including pathological T and N, the degree of tumor differentiation, lymphovascular invasion, and perineural invasion along with radiomics characteristics to each patient, was assessed. Modeling with disturbed forest model was used for radiomics data. For other variables, Shapiro–Wilk, Chi-Square, and Pearson Chi-square tests were used.
Results:
The participants of this study were 50 patients (23 males [46%] and 27 females [54%]). There was no significant difference in the rate of response to neoadjuvant treatment in between age and gender groups. According to the modeling based on combined clinical and radiomics data together, area under the curves for the nonresponders and complete respond group (responder group) was 0.97 and 0.99, respectively.
Conclusion:
Random forests modeling based on combined radiomics and clinical characteristics of the pretreatment tumor images has the ability to predict the response or non-response to neoadjuvant treatment in LARC to an acceptable extent. |
| format | Article |
| id | doaj-art-6c6abea774d5413e8811512b52f825e6 |
| institution | Kabale University |
| issn | 2228-7477 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Signals and Sensors |
| spelling | doaj-art-6c6abea774d5413e8811512b52f825e62025-01-12T10:51:40ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772024-12-011412323210.4103/jmss.jmss_46_23Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning ApproachSeyyed Hossein Mousavie AnijdanDaryush MoslemiReza ReiaziHamid Fallah TaftiAli Akbar MoghadamniaReza PaydarBackground: Treatment of locally advanced rectal cancer (LARC) involves neoadjuvant chemoradiotherapy (nCRT), followed by total mesorectal excision. Examining the response to treatment is one of the most important factors in the follow-up of patients; therefore, in this study, radiomics patterns derived from pretreatment computed tomography images in rectal cancer and its relationship with treatment response measurement criteria have been investigated. Methods: Fifty patients with rectal adenocarcinoma who were candidates for nCRT and surgery were included. The information obtained from the tumor surgical pathology report, including pathological T and N, the degree of tumor differentiation, lymphovascular invasion, and perineural invasion along with radiomics characteristics to each patient, was assessed. Modeling with disturbed forest model was used for radiomics data. For other variables, Shapiro–Wilk, Chi-Square, and Pearson Chi-square tests were used. Results: The participants of this study were 50 patients (23 males [46%] and 27 females [54%]). There was no significant difference in the rate of response to neoadjuvant treatment in between age and gender groups. According to the modeling based on combined clinical and radiomics data together, area under the curves for the nonresponders and complete respond group (responder group) was 0.97 and 0.99, respectively. Conclusion: Random forests modeling based on combined radiomics and clinical characteristics of the pretreatment tumor images has the ability to predict the response or non-response to neoadjuvant treatment in LARC to an acceptable extent.https://journals.lww.com/10.4103/jmss.jmss_46_23computed tomographyneoadjuvant chemoradiationradiomics featurerectal cancer |
| spellingShingle | Seyyed Hossein Mousavie Anijdan Daryush Moslemi Reza Reiazi Hamid Fallah Tafti Ali Akbar Moghadamnia Reza Paydar Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach Journal of Medical Signals and Sensors computed tomography neoadjuvant chemoradiation radiomics feature rectal cancer |
| title | Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach |
| title_full | Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach |
| title_fullStr | Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach |
| title_full_unstemmed | Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach |
| title_short | Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach |
| title_sort | computed tomography scan and clinical based complete response prediction in locally advanced rectal cancer after neoadjuvant chemoradiotherapy a machine learning approach |
| topic | computed tomography neoadjuvant chemoradiation radiomics feature rectal cancer |
| url | https://journals.lww.com/10.4103/jmss.jmss_46_23 |
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