Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer

Abstract

Background: Radiomic features retrieved from standard CT-images have shown prognostic power in several tumor sites. In this study, we investigated the prognostic value of pretreatment CT radiomic features to predict overall survival of esophageal cancer patients after chemoradiotherapy.

Material and methods: Two datasets of independent centers were analyzed, consisting of esophageal cancer patients treated with concurrent chemotherapy (Carboplatin/Paclitaxel) and 41.4Gy radiotherapy, followed by surgery if feasible. In total, 1049 radiomic features were calculated from the primary tumor volume. Recursive feature elimination was performed to select the 40 most relevant predictors. Using these 40 features and six clinical variables as input, two random forest (RF) models predicting 3-year overall survival were developed.

Results: In total 165 patients from center 1 and 74 patients from center 2 were used. The radiomics-based RF model yielded an area under the curve (AUC) of 0.69 (95%CI 0.61–0.77), with the top-5 most important features for 3-year survival describing tumor heterogeneity after wavelet filtering. In the validation dataset, the RF model yielded an AUC of 0.61 (95%CI 0.47–0.75). Kaplan Meier plots were significantly different between risk groups in the training dataset (p = .027) and borderline significant in the validation dataset (p = .053). The clinical RF model yielded AUCs of 0.63 (95%CI 0.54–0.71) and 0.62 (95%CI 0.49–0.76) in the training and validation dataset, respectively. Risk groups did not reach a significant correlation with pathological response in the primary tumor.

Conclusions: A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients. The radiomics model had better prognostic power compared to the model using standard clinical variables.

Disclosure statement

Ralph Leijenaar is Chief Technology Officer of OncoRadiomics. Ralph Leijenaar and Philippe Lambin are co-inventors of several radiomics patents.

Additional information

Funding

This Authors acknowledge financial support from ERC advanced grant [ERC-ADG-2015, no 694812 - Hypoximmuno]. This research is also supported by the Dutch Technology Foundation STW [grant no 10696 DuCAT & no P14-19 Radiomics STRaTegy], which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. Authors also acknowledge financial support from the EU 7th Framework Program [ARTFORCE - no 257144, REQUITE - no 601826], SME Phase 2 [EU proposal 673780 – RAIL], EUROSTARS (DART), the European Program H2020-2015-17 [BD2Decide - PHC30-689715 and ImmunoSABR - no 733008], Interreg V-A Euregio Meuse-Rhine (Euradiomics), Kankeronderzoekfonds Limburg from the Health Foundation Limburg and the Dutch Cancer Society.