Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation
© 2023. The Author(s), under exclusive licence to European Society of Radiology..
OBJECTIVE: This study aimed to build radiomic feature-based machine learning models to predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) for esophageal squamous cell carcinoma (ESCC) patients.
METHODS: A total of 112 ESCC patients who underwent nCRT followed by surgical treatment from January 2008 to December 2018 were recruited. According to pCR status (no visible cancer cells in primary cancer lesion), patients were categorized into primary cancer lesion pCR (ppCR) group (N = 65) and non-ppCR group (N = 47). Patients were also categorized into total pCR (tpCR) group (N = 48) and non-tpCR group (N = 64) according to tpCR status (no visible cancer cells in primary cancer lesion or lymph nodes). Radiomic features of pretreatment CT images were extracted, feature selection was performed, machine learning models were trained to predict ppCR and tpCR, respectively.
RESULTS: A total of 620 radiomic features were extracted. For ppCR prediction models, radiomic model had an area under the curve (AUC) of 0.817 (95% CI: 0.732-0.896) in the testing set; and the combination model that included rad-score and clinical features had a great predicting performance, with an AUC of 0.891 (95% CI: 0.823-0.950) in the testing set. For tpCR prediction models, radiomic model had an AUC of 0.713 (95% CI: 0.613-0.808) in the testing set; and the combination model also had a great predicting performance, with an AUC of 0.814 (95% CI: 0.728-0.881) in the testing set.
CONCLUSION: This study built machine learning models for predicting ppCR and tpCR of ESCC patients with favorable predicting performance respectively, which aided treatment plan optimization.
CLINICAL RELEVANCE STATEMENT: This study significantly improved the predictive value of machine learning models based on radiomic features to accurately predict response to therapy of esophageal squamous cell carcinoma patients after neoadjuvant chemoradiation therapy, providing guidance for further treatment.
KEY POINTS: • Combination model that included rad-score and clinical features had a great predicting performance. • Primary tumor pCR predicting models exhibit better predicting performance compared to corresponding total pCR predicting models.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:33 |
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| Enthalten in: |
European radiology - 33(2023), 12 vom: 13. Dez., Seite 8554-8563 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Jin [VerfasserIn] |
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| Links: |
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| Themen: |
Area under the curve |
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| Anmerkungen: |
Date Completed 27.11.2023 Date Revised 30.11.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s00330-023-09884-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM359407307 |
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| 041 | |a eng | ||
| 100 | 1 | |a Wang, Jin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Revised 30.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s), under exclusive licence to European Society of Radiology. | ||
| 520 | |a OBJECTIVE: This study aimed to build radiomic feature-based machine learning models to predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) for esophageal squamous cell carcinoma (ESCC) patients | ||
| 520 | |a METHODS: A total of 112 ESCC patients who underwent nCRT followed by surgical treatment from January 2008 to December 2018 were recruited. According to pCR status (no visible cancer cells in primary cancer lesion), patients were categorized into primary cancer lesion pCR (ppCR) group (N = 65) and non-ppCR group (N = 47). Patients were also categorized into total pCR (tpCR) group (N = 48) and non-tpCR group (N = 64) according to tpCR status (no visible cancer cells in primary cancer lesion or lymph nodes). Radiomic features of pretreatment CT images were extracted, feature selection was performed, machine learning models were trained to predict ppCR and tpCR, respectively | ||
| 520 | |a RESULTS: A total of 620 radiomic features were extracted. For ppCR prediction models, radiomic model had an area under the curve (AUC) of 0.817 (95% CI: 0.732-0.896) in the testing set; and the combination model that included rad-score and clinical features had a great predicting performance, with an AUC of 0.891 (95% CI: 0.823-0.950) in the testing set. For tpCR prediction models, radiomic model had an AUC of 0.713 (95% CI: 0.613-0.808) in the testing set; and the combination model also had a great predicting performance, with an AUC of 0.814 (95% CI: 0.728-0.881) in the testing set | ||
| 520 | |a CONCLUSION: This study built machine learning models for predicting ppCR and tpCR of ESCC patients with favorable predicting performance respectively, which aided treatment plan optimization | ||
| 520 | |a CLINICAL RELEVANCE STATEMENT: This study significantly improved the predictive value of machine learning models based on radiomic features to accurately predict response to therapy of esophageal squamous cell carcinoma patients after neoadjuvant chemoradiation therapy, providing guidance for further treatment | ||
| 520 | |a KEY POINTS: • Combination model that included rad-score and clinical features had a great predicting performance. • Primary tumor pCR predicting models exhibit better predicting performance compared to corresponding total pCR predicting models | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Area under the curve | |
| 650 | 4 | |a Esophageal squamous cell carcinoma | |
| 650 | 4 | |a Humans | |
| 650 | 4 | |a Lymph nodes | |
| 650 | 4 | |a Neoadjuvant therapy | |
| 700 | 1 | |a Zhu, Xiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Xiaojiang |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Qingren |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Qixun |e verfasserin |4 aut | |
| 700 | 1 | |a Ji, Yongling |e verfasserin |4 aut | |
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