Abstract
PurposeTo develop a nomogram model for predicting local progress-free survival (LPFS) in esophageal squamous cell carcinoma (ESCC) patients treated with concurrent chemo-radiotherapy (CCRT).MethodsWe collected the clinical data of ESCC patients treated with CCRT in our hospital. Eligible patients were randomly divided into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) with COX regression was performed to select optimal radiomic features to calculate Rad-score for predicting LPFS in the training cohort. The univariate and multivariate analyses were performed to identify the predictive clinical factors for developing a nomogram model. The C-index was used to assess the performance of the predictive model and calibration curve was used to evaluate the accuracy.ResultsA total of 221 ESCC patients were included in our study, with 155 patients in training cohort and 66 patients in validation cohort. Seventeen radiomic features were selected by LASSO COX regression analysis to calculate Rad-score for predicting LPFS. The patients with a Rad-score ≥ 0.1411 had high risk of local recurrence, and those with a Rad-score < 0.1411 had low risk of local recurrence. Multivariate analysis showed that N stage, CR status and Rad-score were independent predictive factors for LPFS. A nomogram model was built based on the result of multivariate analysis. The C-index of the nomogram was 0.745 (95% CI 0.7700–0.790) in training cohort and 0.723(95% CI 0.654–0.791) in validation cohort. The 3-year LPFS rate predicted by the nomogram model was highly consistent with the actual 3-year LPFS rate both in the training cohort and the validation cohort.ConclusionWe developed and validated a prediction model based on radiomic features and clinical factors, which can be used to predict LPFS of patients after CCRT. This model is conducive to identifying the patients with ESCC benefited more from CCRT.
Highlights
Esophageal cancer (EC) is the sixth common malignant tumors in China with an estimated 477.9 thousand new cases, accounting for half of the new esophageal cancerLuo et al Radiat Oncol (2021) 16:201 worldwide [1, 2]
We explored the prognostic value of 3D radiomic features from pretreatment computed tomography (CT) images of esophageal cancer patients and developed a model combined radiomic features and clinical information to predict local progress-free survival (LPFS) in patients with esophageal squamous cell carcinomas (ESCC) after concurrent chemo-radiotherapy (CCRT)
Patients were excluded if they met the exclusion criteria as follows: (1) patients received esophagectomy and preoperative or postoperative adjuvant radiotherapy; (2) patients had distant metastatic disease; (3) patients received low-dose (< 50 Gy) palliative radiotherapy; (4) clinicopathological information of the patients was incomplete; (5) patients were diagnosed as esophageal fistula before treatment; (6) poor visualization quality due to image artifacts or the tumor was too small to be recognized on CT images; (7) patients had other primary tumor; (8) patients died within three months after chemoradiotherapy
Summary
Esophageal cancer (EC) is the sixth common malignant tumors in China with an estimated 477.9 thousand new cases, accounting for half of the new esophageal cancerLuo et al Radiat Oncol (2021) 16:201 worldwide [1, 2]. In China, approximately 90% of the patients with esophageal cancer are histologically diagnosed as esophageal squamous cell carcinomas (ESCC) which is different from esophageal adenocarcinoma (EAC) in risk factors and prognosis [3]. Most patients with locally advanced ESCC lost the opportunity for surgery, and concurrent chemo-radiotherapy (CCRT) has been recommended as a standard treatment [4]. More than half of patients treated with standard dose CCRT eventually developed local recurrence or distant metastases and succumbed to this disease [5, 6]. A individual CCRT strategy with escalated radiation dose based on PET-CT would benefit the patients with high tumor burden and risk of recurrence [7, 8]. To facilitate a individual CCRT strategy in an early stage, solid predictive model for local recurrence and prognosis could play an important role
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
