A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning
BackgroundPreoperative chemoradiotherapy (CRT) is a standard treatment for locally advanced rectal cancer (LARC). However, individual responses to preoperative CRT vary from patient to patient. The aim of this study is to develop a scoring system for the response of preoperative CRT in LARC using bl...
Guardado en:
| Autores principales: | , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Frontiers Media S.A.
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/dd11b78edeab4395b8ea448deec6d66c |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:dd11b78edeab4395b8ea448deec6d66c |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:dd11b78edeab4395b8ea448deec6d66c2021-12-01T11:18:46ZA Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning2234-943X10.3389/fonc.2021.790894https://doaj.org/article/dd11b78edeab4395b8ea448deec6d66c2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.790894/fullhttps://doaj.org/toc/2234-943XBackgroundPreoperative chemoradiotherapy (CRT) is a standard treatment for locally advanced rectal cancer (LARC). However, individual responses to preoperative CRT vary from patient to patient. The aim of this study is to develop a scoring system for the response of preoperative CRT in LARC using blood features derived from machine learning.MethodsPatients who underwent total mesorectal excision after preoperative CRT were included in this study. The performance of machine learning models using blood features before CRT (pre-CRT) and from 1 to 2 weeks after CRT (early-CRT) was evaluated. Based on the best model, important features were selected. The scoring system was developed from the selected model and features. The performance of the new scoring system was compared with those of systemic inflammatory indicators: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and the prognostic nutritional index.ResultsThe models using early-CRT blood features had better performances than those using pre-CRT blood features. Based on the ridge regression model, which showed the best performance among the machine learning models (AUROC 0.6322 and AUPRC 0.5965), a novel scoring system for the response of preoperative CRT, named Response Prediction Score (RPS), was developed. The RPS system showed higher predictive power (AUROC 0.6747) than single blood features and systemic inflammatory indicators and stratified the tumor regression grade and overall downstaging clearly.ConclusionWe discovered that we can more accurately predict CRT response by using early-treatment blood data. With larger data, we can develop a more accurate and reliable indicator that can be used in real daily practices. In the future, we urge the collection of early-treatment blood data and pre-treatment blood data.Jaesik KimJaesik KimJaesik KimKyung-Ah SohnKyung-Ah SohnJung-Hak KwakMin Jung KimMin Jung KimSeung-Bum RyooSeung-Yong JeongSeung-Yong JeongKyu Joo ParkHyun-Cheol KangEui Kyu ChieEui Kyu ChieSang-Hyuk JungSang-Hyuk JungSang-Hyuk JungDokyoon KimDokyoon KimJi Won ParkJi Won ParkJi Won ParkJi Won ParkFrontiers Media S.A.articlemachine learningpreoperative chemoradiotherapyrectal cancerpathologic responseearly-treatment blood featurespredictionNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
machine learning preoperative chemoradiotherapy rectal cancer pathologic response early-treatment blood features prediction Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
| spellingShingle |
machine learning preoperative chemoradiotherapy rectal cancer pathologic response early-treatment blood features prediction Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Jaesik Kim Jaesik Kim Jaesik Kim Kyung-Ah Sohn Kyung-Ah Sohn Jung-Hak Kwak Min Jung Kim Min Jung Kim Seung-Bum Ryoo Seung-Yong Jeong Seung-Yong Jeong Kyu Joo Park Hyun-Cheol Kang Eui Kyu Chie Eui Kyu Chie Sang-Hyuk Jung Sang-Hyuk Jung Sang-Hyuk Jung Dokyoon Kim Dokyoon Kim Ji Won Park Ji Won Park Ji Won Park Ji Won Park A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning |
| description |
BackgroundPreoperative chemoradiotherapy (CRT) is a standard treatment for locally advanced rectal cancer (LARC). However, individual responses to preoperative CRT vary from patient to patient. The aim of this study is to develop a scoring system for the response of preoperative CRT in LARC using blood features derived from machine learning.MethodsPatients who underwent total mesorectal excision after preoperative CRT were included in this study. The performance of machine learning models using blood features before CRT (pre-CRT) and from 1 to 2 weeks after CRT (early-CRT) was evaluated. Based on the best model, important features were selected. The scoring system was developed from the selected model and features. The performance of the new scoring system was compared with those of systemic inflammatory indicators: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and the prognostic nutritional index.ResultsThe models using early-CRT blood features had better performances than those using pre-CRT blood features. Based on the ridge regression model, which showed the best performance among the machine learning models (AUROC 0.6322 and AUPRC 0.5965), a novel scoring system for the response of preoperative CRT, named Response Prediction Score (RPS), was developed. The RPS system showed higher predictive power (AUROC 0.6747) than single blood features and systemic inflammatory indicators and stratified the tumor regression grade and overall downstaging clearly.ConclusionWe discovered that we can more accurately predict CRT response by using early-treatment blood data. With larger data, we can develop a more accurate and reliable indicator that can be used in real daily practices. In the future, we urge the collection of early-treatment blood data and pre-treatment blood data. |
| format |
article |
| author |
Jaesik Kim Jaesik Kim Jaesik Kim Kyung-Ah Sohn Kyung-Ah Sohn Jung-Hak Kwak Min Jung Kim Min Jung Kim Seung-Bum Ryoo Seung-Yong Jeong Seung-Yong Jeong Kyu Joo Park Hyun-Cheol Kang Eui Kyu Chie Eui Kyu Chie Sang-Hyuk Jung Sang-Hyuk Jung Sang-Hyuk Jung Dokyoon Kim Dokyoon Kim Ji Won Park Ji Won Park Ji Won Park Ji Won Park |
| author_facet |
Jaesik Kim Jaesik Kim Jaesik Kim Kyung-Ah Sohn Kyung-Ah Sohn Jung-Hak Kwak Min Jung Kim Min Jung Kim Seung-Bum Ryoo Seung-Yong Jeong Seung-Yong Jeong Kyu Joo Park Hyun-Cheol Kang Eui Kyu Chie Eui Kyu Chie Sang-Hyuk Jung Sang-Hyuk Jung Sang-Hyuk Jung Dokyoon Kim Dokyoon Kim Ji Won Park Ji Won Park Ji Won Park Ji Won Park |
| author_sort |
Jaesik Kim |
| title |
A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning |
| title_short |
A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning |
| title_full |
A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning |
| title_fullStr |
A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning |
| title_full_unstemmed |
A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning |
| title_sort |
novel scoring system for response of preoperative chemoradiotherapy in locally advanced rectal cancer using early-treatment blood features derived from machine learning |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/dd11b78edeab4395b8ea448deec6d66c |
| work_keys_str_mv |
AT jaesikkim anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jaesikkim anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jaesikkim anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT kyungahsohn anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT kyungahsohn anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT junghakkwak anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT minjungkim anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT minjungkim anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT seungbumryoo anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT seungyongjeong anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT seungyongjeong anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT kyujoopark anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT hyuncheolkang anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT euikyuchie anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT euikyuchie anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT sanghyukjung anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT sanghyukjung anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT sanghyukjung anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT dokyoonkim anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT dokyoonkim anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jiwonpark anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jiwonpark anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jiwonpark anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jiwonpark anovelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jaesikkim novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jaesikkim novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jaesikkim novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT kyungahsohn novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT kyungahsohn novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT junghakkwak novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT minjungkim novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT minjungkim novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT seungbumryoo novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT seungyongjeong novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT seungyongjeong novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT kyujoopark novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT hyuncheolkang novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT euikyuchie novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT euikyuchie novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT sanghyukjung novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT sanghyukjung novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT sanghyukjung novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT dokyoonkim novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT dokyoonkim novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jiwonpark novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jiwonpark novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jiwonpark novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning AT jiwonpark novelscoringsystemforresponseofpreoperativechemoradiotherapyinlocallyadvancedrectalcancerusingearlytreatmentbloodfeaturesderivedfrommachinelearning |
| _version_ |
1718405264319709184 |