Quantitative CT analysis to predict esophageal fistula in patients with advanced esophageal cancer treated by chemotherapy or chemoradiotherapy
Abstract Background: Esophageal fistula is one of the most serious complications of chemotherapy or chemoradiotherapy (CRT) for advanced esophageal cancer. This study aimed to evaluate the performance of quantitative computed tomography (CT) analysis and to establish a practical imaging model for predicting esophageal fistula in esophageal cancer patients administered chemotherapy or chemoradiotherapy. Methods: This study retrospectively enrolled 204 esophageal cancer patients (54 patients with fistula, 150 patients without fistula) and all patients were allocated to the test and validation cohorts according to the time of inclusion in a 1:1 ratio. Ulcer depth, tumor thickness and length, and minimum and maximum enhanced values for esophageal cancer were measured in pretreatment CT imaging. Logistic regression analysis was used to evaluate the associations of CT quantitative measurements with esophageal fistula. Receiver operating characteristic curve (ROC) analysis was also used. Results: Logistic regression analysis showed that independent predictors of esophageal fistula included tumor thickness [odds ratio (OR)=1.167; p = 0.037], the ratio of ulcer depth to adjacent tumor thickness (OR=164.947; p < 0.001), and the ratio of minimum to maximum enhanced CT value (OR=0.006; p = 0.039) in the test cohort at baseline CT imaging. These predictors were used to establish a predictive model for predicting esophageal fistula, with areas under the receiver operating characteristic curves (AUCs) of 0.946 and 0.841 in the test and validation groups, respectively. Conclusions: Quantitative pretreatment CT analysis has excellent performance for predicting fistula formation in esophageal cancer patients who treated by chemotherapy or chemoradiotherapy..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
ResearchSquare.com - (2022) vom: 14. März Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sun, Ying-Shi [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.21203/rs.3.rs-742527/v2 |
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| funding: |
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| PPN (Katalog-ID): |
XRA035309334 |
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| 520 | |a Abstract Background: Esophageal fistula is one of the most serious complications of chemotherapy or chemoradiotherapy (CRT) for advanced esophageal cancer. This study aimed to evaluate the performance of quantitative computed tomography (CT) analysis and to establish a practical imaging model for predicting esophageal fistula in esophageal cancer patients administered chemotherapy or chemoradiotherapy. Methods: This study retrospectively enrolled 204 esophageal cancer patients (54 patients with fistula, 150 patients without fistula) and all patients were allocated to the test and validation cohorts according to the time of inclusion in a 1:1 ratio. Ulcer depth, tumor thickness and length, and minimum and maximum enhanced values for esophageal cancer were measured in pretreatment CT imaging. Logistic regression analysis was used to evaluate the associations of CT quantitative measurements with esophageal fistula. Receiver operating characteristic curve (ROC) analysis was also used. Results: Logistic regression analysis showed that independent predictors of esophageal fistula included tumor thickness [odds ratio (OR)=1.167; p = 0.037], the ratio of ulcer depth to adjacent tumor thickness (OR=164.947; p < 0.001), and the ratio of minimum to maximum enhanced CT value (OR=0.006; p = 0.039) in the test cohort at baseline CT imaging. These predictors were used to establish a predictive model for predicting esophageal fistula, with areas under the receiver operating characteristic curves (AUCs) of 0.946 and 0.841 in the test and validation groups, respectively. Conclusions: Quantitative pretreatment CT analysis has excellent performance for predicting fistula formation in esophageal cancer patients who treated by chemotherapy or chemoradiotherapy. | ||
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| 700 | 1 | |a Liu, Chang |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Yi-Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xiao-Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Zhi-Hao |e verfasserin |4 aut | |
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