Top

European Journal of Medical Research

Published in:

Open Access 01-12-2024 | Esophageal Cancer | Research

Prediction of malignant esophageal fistula in esophageal cancer using a radiomics-clinical nomogram

Authors: Chao Zhu, Wenju Sun, Cunhai Chen, Qingtao Qiu, Shuai Wang, Yang Song, Xuezhen Ma

Published in: European Journal of Medical Research | Issue 1/2024

Abstract

Background

Malignant esophageal fistula (MEF), which occurs in 5% to 15% of esophageal cancer (EC) patients, has a poor prognosis. Accurate identification of esophageal cancer patients at high risk of MEF is challenging. The goal of this study was to build and validate a model to predict the occurrence of esophageal fistula in EC patients.

Methods

This study retrospectively enrolled 122 esophageal cancer patients treated by chemotherapy or chemoradiotherapy (53 with fistula, 69 without), and all patients were randomly assigned to a training (n = 86) and a validation (n = 36) cohort. Radiomic features were extracted from pre-treatment CTs, clinically predictors were identified by logistic regression analysis. Lasso regression model was used for feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the clinical nomogram, radiomics-clinical nomogram and radiomics prediction model. The models were validated and compared by discrimination, calibration, reclassification, and clinical benefit.

Results

The radiomic signature consisting of ten selected features, was significantly associated with esophageal fistula (P = 0.001). Radiomics-clinical nomogram was created by two predictors including radiomics signature and stenosis, which was identified by logistic regression analysis. The model showed good discrimination with an AUC = 0.782 (95% CI 0.684–0.8796) in the training set and 0.867 (95% CI 0.7461–0.987) in the validation set, with an AIC = 101.1, and good calibration. When compared to the clinical prediction model, the radiomics-clinical nomogram improved NRI by 0.236 (95% CI 0.153, 0.614) and IDI by 0.125 (95% CI 0.040, 0.210), P = 0.004.

Conclusion

We developed and validated the first radiomics-clinical nomogram for malignant esophageal fistula, which could assist clinicians in identifying patients at high risk of MEF.

Kosugi SI, Ichikawa H, Kanda T, et al. Clinicopathological characteristics and prognosis of patients with esophageal carcinoma invading adjacent structures found during esophagectomy. J Surg Oncol. 2012;105(8):767–72. https://doi.org/10.1002/jso.22092.CrossRefPubMed

Uno K, Koike T, Takahashi S, et al. Management of aorto-esophageal fistula secondary after thoracic endovascular aortic repair: a review of literature. Clin J Gastroenterol. 2017;10(5):393–402. https://doi.org/10.1007/s12328-017-0762-z.CrossRefPubMed

Shamji FM, Inculet R. Management of malignant tracheoesophageal fistula. Thorac Cardiovasc Surg. 2018;28(3):393–402. https://doi.org/10.1016/j.thorsurg.2018.04.007.CrossRef

Tsushima T, Mizusawa J, Sudo K, et al. Risk factors for esophageal fistula associated with chemoradiotherapy for locally advanced unresectable esophageal cancer. Medicine. 2016;95(20): e3699. https://doi.org/10.1097/MD.0000000000003699.CrossRefPubMedPubMedCentral

Sun X, Han S, Gu F, et al. A retrospective comparison of taxane and fluorouracil-based chemoradiotherapy in patients with inoperable esophageal squamous cell carcinoma. J Cancer. 2016;7(9):1066–73. https://doi.org/10.7150/jca.13547.CrossRefPubMedPubMedCentral

Zhou Z, Zhen C, Bai W, et al. Salvage radiotherapy in patients with local recurrent esophageal cancer after radical radiochemotherapy. Radiat Oncol. 2015;10(1):54. https://doi.org/10.1186/s13014-015-0358-z.CrossRefPubMedPubMedCentral

Kawakami T, Tsushima T, Hayashi K, et al. Risk factors for esophageal fistula in esophageal squamous cell carcinoma invading adjacent organs (T4b) treated with definitive chemoradiotherapy. Ann Oncol. 2016. https://doi.org/10.1186/s12885-018-4486-3.CrossRefPubMed

Chen B, Deng M, Yang C, et al. High incidence of esophageal fistula on patients with clinical T4b esophageal squamous cell carcinoma who received chemoradiotherapy: a retrospective analysis. Radiother Oncol. 2021;158:191–9. https://doi.org/10.1016/j.radonc.2021.02.031.CrossRefPubMed

Tsushima T, Mizusawa J, Sudo K, et al. risk factors for esophageal fistula associated with chemoradiotherapy for locally advanced unresectable esophageal cancer: a supplementary analysis of JCOG0303. Medicine. 2016;95(20): e3699. https://doi.org/10.1097/MD.0000000000003699.CrossRefPubMedPubMedCentral

10.

Sato Y, Takayama T, Sagawa T, et al. A phase I/II study of nedaplatin and 5-fluorouracil with concurrent radiotherapy in patients with esophageal cancer. Cancer Chemother Pharmacol. 2006;58(5):570–6. https://doi.org/10.1007/s00280-006-0193-x.CrossRefPubMed

11.

Xu Y, Wang L, He B, et al. Development and validation of a risk prediction model for radiotherapy-related esophageal fistula in esophageal cancer. Radiat Oncol. 2019;14(1):181. https://doi.org/10.1186/s13014-019-1385-y.CrossRefPubMedPubMedCentral

12.

Shi YJ, Liu C, Wei YY, et al. Quantitative CT analysis to predict esophageal fistula in patients with advanced esophageal cancer treated by chemotherapy or chemoradiotherapy. Cancer Imaging. 2022;22(1):62. https://doi.org/10.1186/s40644-022-00490-2.CrossRefPubMedPubMedCentral

13.

Xiao N, Pei Z, Lu W, et al. Editorial: Application of radiomics in understanding tumor biological behaviors and treatment response. Front Oncol. 2023;13:1257447. https://doi.org/10.3389/fonc.2023.1257447.CrossRefPubMedPubMedCentral

14.

Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol. 2020;196(10):879–87. https://doi.org/10.1007/s00066-020-01625-9.CrossRefPubMed

15.

Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62. https://doi.org/10.1038/nrclinonc.2017.141.CrossRefPubMed

16.

Iwashita K, Kubota H, Nishioka R, et al. Prognostic value of radiomics analysis of skeletal muscle after radical irradiation of esophageal cancer. Anticancer Res. 2023;43(4):1749–60. https://doi.org/10.21873/anticanres.16328.CrossRefPubMed

17.

Yang Z, Gong J, Li J, et al. The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis. Int J Surg. 2023;109(8):2451–66. https://doi.org/10.1097/JS9.0000000000000441.CrossRefPubMedPubMedCentral

18.

Peng H, Yang Q, Xue T, et al. Computed tomography-based radiomics analysis to predict lymphovascular invasion in esophageal squamous cell carcinoma. Br J Radiol. 2022;95(1130):20210918. https://doi.org/10.1259/bjr.20210918.CrossRefPubMed

19.

Zheng SJ, Zheng CP, Zhai TT, et al. Development and validation of a new staging system for esophageal squamous cell carcinoma patients based on combined pathological TNM, radiomics, and proteomics. Ann Surg Oncol. 2023;30(4):2227–41. https://doi.org/10.1245/s10434-022-13026-6].CrossRefPubMed

20.

Daiko H, Kato K. Updates in the 8th edition of the TNM staging system for esophagus and esophagogastric junction cancer. Jpn J Clin Oncol. 2020;50(8):847–51. https://doi.org/10.1093/jjco/hyaa082.CrossRefPubMed

21.

Zhu C, Wang S, You Y, et al. Risk factors for esophageal fistula in esophageal cancer patients treated with radiotherapy: a systematic review and meta-analysis. Oncol Res Treat. 2020;43(1–2):34–40. https://doi.org/10.1159/000503754.CrossRefPubMed

22.

Hu B, Jia F, Zhou H, et al. Risk factors associated with esophageal fistula after radiotherapy for esophageal squamous cell carcinoma. J Cancer. 2020;11(12):3693–700. https://doi.org/10.7150/jca.39033.CrossRefPubMedPubMedCentral

23.

Han X, Zhao YS, Fang Y, et al. Placement of transnasal drainage catheter and covered esophageal stent for the treatment of perforated esophageal carcinoma with mediastinal abscess. J Surg Oncol. 2016;114(6):725–30. https://doi.org/10.1002/jso.24384.CrossRefPubMed

24.

Zhang Y, Li Z, Zhang W, et al. Risk factors for esophageal fistula in patients with locally advanced esophageal carcinoma receiving chemoradiotherapy. Onco Targets Ther. 2018;11:2311–7. https://doi.org/10.2147/OTT.S161803.CrossRefPubMedPubMedCentral

25.

Sugawara K, Fukuda T, Kishimoto Y, et al. The impact of pretreatment esophageal stenosis on survival of esophageal cancer patients. Ann Surg Oncol. 2023;30(5):2703–12. https://doi.org/10.1245/s10434-022-12945-8.CrossRefPubMed

26.

Pao TH, Chen YY, Chang WL, et al. Esophageal fistula after definitive concurrent chemotherapy and intensity modulated radiotherapy for esophageal squamous cell carcinoma. PLoS ONE. 2021;16(5): e0251811. https://doi.org/10.1371/journal.pone.0251811.CrossRefPubMedPubMedCentral

27.

Guan X, Liu C, Zhou T, Ma Z, Zhang Y. Survival and prognostic factors of patients with esophageal fistula in advanced esophageal squamous cell carcinoma. Biosci Rep. 2020;40(1):BSR20193379. https://doi.org/10.1042/BSR20193379.CrossRefPubMedPubMedCentral

28.

Bohanes P, Yang D, Chhibar RS, et al. Influence of sex on the survival of patients with esophageal cancer. J Clin Oncol. 2012;30(18):2265–72. https://doi.org/10.1200/JCO.2011.38.8751.CrossRefPubMedPubMedCentral

29.

Al-Tarrah K, Moiemen N, Lord JM. The influence of sex steroid hormones on the response to trauma and burn injury. Burns Trauma. 2017;14(5):29. https://doi.org/10.1186/s41038-017-0093-9.CrossRef

30.

Wang J, Zhang B, Meng J, et al. Analysis of risk factors for post-operative complications and prognostic predictors of disease recurrence following definitive treatment of patients with esophageal cancer from two medical centers in Northwest China. Exp Ther Med. 2017;14(3):2584–94. https://doi.org/10.3892/etm.2017.4835.CrossRefPubMedPubMedCentral

31.

Avanzo M, Stancanello J, el Naqa I. Beyond imaging: the promise of radiomics. Physica Med. 2017;38:122–39. https://doi.org/10.1016/j.ejmp.2017.05.071.CrossRef

32.

Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, Fabro AT, de Azevedo-Marques PM. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Programs Biomed. 2018;159:23–30. https://doi.org/10.1016/j.cmpb.2018.02.015.CrossRefPubMed

33.

Hawkins S, Wang H, Liu Y, et al. Predicting malignant nodules from screening CT scans. J Thorac Oncol. 2016;11(12):2120–8. https://doi.org/10.1016/j.jtho.2016.07.002.CrossRefPubMedPubMedCentral

34.

Qiu Q, Duan J, Deng H, et al. Development and validation of a radiomics nomogram model for predicting postoperative recurrence in patients with esophageal squamous cell cancer who achieved pCR after neoadjuvant chemoradiotherapy followed by surgery. Front Oncol. 2020;11(10):1398. https://doi.org/10.3389/fonc.2020.01398.CrossRef

35.

Moussa AM, Ziv E. Radiogenomics in interventional oncology. Curr Oncol Rep. 2021;23:1–10. https://doi.org/10.1007/s11912-020-00994-9.CrossRef

36.

Bodalal Z, Trebeschi S, Nguyen-Kim TDL, et al. Radiogenomics: bridging imaging and genomics. Abdom Radiol. 2019;44(6):1960–84. https://doi.org/10.1007/s00261-019-02028-w.CrossRef

37.

Umeoka S, Koyama T, Togashi K, et al. Esophageal cancer: evaluation with triple-phase dynamic CT—initial experience. Radiology. 2006;239(3):777–83. https://doi.org/10.1148/radiol.2393050222.CrossRefPubMed

Title: Prediction of malignant esophageal fistula in esophageal cancer using a radiomics-clinical nomogram
Authors: Chao Zhu
Wenju Sun
Cunhai Chen
Qingtao Qiu
Shuai Wang
Yang Song
Xuezhen Ma
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Esophageal Cancer
Esophageal Cancer
Cytostatic Therapy
Published in: European Journal of Medical Research / Issue 1/2024
Electronic ISSN: 2047-783X
DOI: https://doi.org/10.1186/s40001-024-01746-2

At a glance: The STEP trials

Springer Medicine

Prediction of malignant esophageal fistula in esophageal cancer using a radiomics-clinical nomogram

Abstract

Background

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2024

Transcriptomic analysis identifies diagnostic genes in polycystic ovary syndrome and periodontitis

Role of fenofibrate in multiple sclerosis

Identification and validation of an individualized metabolic prognostic signature for predicting the biochemical recurrence of prostate cancer based on the immune microenvironment

Possible uses of Hunter–Schreger bands of dental enamel for automated personal identification

Promotion of hair growth by a conditioned medium from human umbilical cord mesenchymal stem cells cultivated in a 3D scaffold of gelatin sponge

Prone positioning does not improve outcomes of intubated patients with pneumocystis pneumonia and moderate–severe acute respiratory distress syndrome: a single-center, retrospective, observational, cohort study