Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery
Background and purposeUnnecessary surgery can be avoided, and more appropriate treatment plans can be developed for patients if the efficacy of neoadjuvant immunochemotherapy for esophageal cancer (EC) can be predicted before surgery. The purpose of this study was to evaluate the ability of machine...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-05-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1131883/full |
| _version_ | 1827949574520569856 |
|---|---|
| author | Kaiyuan Li Yuetong Li Zhulin Wang Chunyao Huang Shaowu Sun Xu Liu Wenbo Fan Guoqing Zhang Xiangnan Li |
| author_facet | Kaiyuan Li Yuetong Li Zhulin Wang Chunyao Huang Shaowu Sun Xu Liu Wenbo Fan Guoqing Zhang Xiangnan Li |
| author_sort | Kaiyuan Li |
| collection | DOAJ |
| description | Background and purposeUnnecessary surgery can be avoided, and more appropriate treatment plans can be developed for patients if the efficacy of neoadjuvant immunochemotherapy for esophageal cancer (EC) can be predicted before surgery. The purpose of this study was to evaluate the ability of machine learning models based on delta features of immunochemotherapy CT images to predict the efficacy of neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma (ESCC) compared with machine learning models based solely on postimmunochemotherapy CT images.Materials and methodsA total of 95 patients were enrolled in our study and randomly divided into a training group (n = 66) and test group (n = 29). We extracted preimmunochemotherapy radiomics features from preimmunochemotherapy enhanced CT images in the preimmunochemotherapy group (pregroup) and postimmunochemotherapy radiomics features from postimmunochemotherapy enhanced CT images in the postimmunochemotherapy group (postgroup). We then subtracted the preimmunochemotherapy features from the postimmunochemotherapy features and obtained a series of new radiomics features that were included in the delta group. The reduction and screening of radiomics features were carried out by using the Mann-Whitney U test and LASSO regression. Five pairwise machine learning models were established, the performance of which was evaluated by receiver operating characteristic (ROC) curve and decision curve analyses.ResultsThe radiomics signature of the postgroup was composed of 6 radiomics features; that of the delta-group was composed of 8 radiomics features. The area under the ROC curve (AUC) of the machine learning model with the best efficacy was 0.824 (0.706-0.917) in the postgroup and 0.848 (0.765-0.917) in the delta group. The decision curve showed that our machine learning models had good predictive performance. The delta group performed better than the postgroup for each corresponding machine learning model.ConclusionWe established machine learning models that have good predictive efficacy and can provide certain reference values for clinical treatment decision-making. Our machine learning models based on delta imaging features performed better than those based on single time-stage postimmunochemotherapy imaging features. |
| first_indexed | 2024-04-09T13:12:10Z |
| format | Article |
| id | doaj.art-3c2fe09131784f358764f7cf0b5dad79 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T13:12:10Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-3c2fe09131784f358764f7cf0b5dad792023-05-12T06:08:30ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-05-011310.3389/fonc.2023.11318831131883Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgeryKaiyuan Li0Yuetong Li1Zhulin Wang2Chunyao Huang3Shaowu Sun4Xu Liu5Wenbo Fan6Guoqing Zhang7Xiangnan Li8Department of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaClinical Medical College, Henan University, Henan, Kaifeng, ChinaDepartment of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaBackground and purposeUnnecessary surgery can be avoided, and more appropriate treatment plans can be developed for patients if the efficacy of neoadjuvant immunochemotherapy for esophageal cancer (EC) can be predicted before surgery. The purpose of this study was to evaluate the ability of machine learning models based on delta features of immunochemotherapy CT images to predict the efficacy of neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma (ESCC) compared with machine learning models based solely on postimmunochemotherapy CT images.Materials and methodsA total of 95 patients were enrolled in our study and randomly divided into a training group (n = 66) and test group (n = 29). We extracted preimmunochemotherapy radiomics features from preimmunochemotherapy enhanced CT images in the preimmunochemotherapy group (pregroup) and postimmunochemotherapy radiomics features from postimmunochemotherapy enhanced CT images in the postimmunochemotherapy group (postgroup). We then subtracted the preimmunochemotherapy features from the postimmunochemotherapy features and obtained a series of new radiomics features that were included in the delta group. The reduction and screening of radiomics features were carried out by using the Mann-Whitney U test and LASSO regression. Five pairwise machine learning models were established, the performance of which was evaluated by receiver operating characteristic (ROC) curve and decision curve analyses.ResultsThe radiomics signature of the postgroup was composed of 6 radiomics features; that of the delta-group was composed of 8 radiomics features. The area under the ROC curve (AUC) of the machine learning model with the best efficacy was 0.824 (0.706-0.917) in the postgroup and 0.848 (0.765-0.917) in the delta group. The decision curve showed that our machine learning models had good predictive performance. The delta group performed better than the postgroup for each corresponding machine learning model.ConclusionWe established machine learning models that have good predictive efficacy and can provide certain reference values for clinical treatment decision-making. Our machine learning models based on delta imaging features performed better than those based on single time-stage postimmunochemotherapy imaging features.https://www.frontiersin.org/articles/10.3389/fonc.2023.1131883/fullesophageal cancerdelta radiomicsneoadjuvant immunochemotherapypathological complete responsemachine learning |
| spellingShingle | Kaiyuan Li Yuetong Li Zhulin Wang Chunyao Huang Shaowu Sun Xu Liu Wenbo Fan Guoqing Zhang Xiangnan Li Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery Frontiers in Oncology esophageal cancer delta radiomics neoadjuvant immunochemotherapy pathological complete response machine learning |
| title | Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery |
| title_full | Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery |
| title_fullStr | Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery |
| title_full_unstemmed | Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery |
| title_short | Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery |
| title_sort | delta radiomics based on ct predicts pathologic complete response in escc treated with neoadjuvant immunochemotherapy and surgery |
| topic | esophageal cancer delta radiomics neoadjuvant immunochemotherapy pathological complete response machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1131883/full |
| work_keys_str_mv | AT kaiyuanli deltaradiomicsbasedonctpredictspathologiccompleteresponseinescctreatedwithneoadjuvantimmunochemotherapyandsurgery AT yuetongli deltaradiomicsbasedonctpredictspathologiccompleteresponseinescctreatedwithneoadjuvantimmunochemotherapyandsurgery AT zhulinwang deltaradiomicsbasedonctpredictspathologiccompleteresponseinescctreatedwithneoadjuvantimmunochemotherapyandsurgery AT chunyaohuang deltaradiomicsbasedonctpredictspathologiccompleteresponseinescctreatedwithneoadjuvantimmunochemotherapyandsurgery AT shaowusun deltaradiomicsbasedonctpredictspathologiccompleteresponseinescctreatedwithneoadjuvantimmunochemotherapyandsurgery AT xuliu deltaradiomicsbasedonctpredictspathologiccompleteresponseinescctreatedwithneoadjuvantimmunochemotherapyandsurgery AT wenbofan deltaradiomicsbasedonctpredictspathologiccompleteresponseinescctreatedwithneoadjuvantimmunochemotherapyandsurgery AT guoqingzhang deltaradiomicsbasedonctpredictspathologiccompleteresponseinescctreatedwithneoadjuvantimmunochemotherapyandsurgery AT xiangnanli deltaradiomicsbasedonctpredictspathologiccompleteresponseinescctreatedwithneoadjuvantimmunochemotherapyandsurgery |