Background: Esophagectomy is recommended after endoscopic resection of an early esophageal cancer when pejorative histoprognostic criteria indicate a high risk of lymph node involvement. Our aim was to analyze the clinical outcomes of a non-surgical, organ preserving management in this clinical setting. Patients and Methods: This retrospective study was performed in two tertiary centers from 2015 to 2020. Patients were included if they had histologically complete resection of an early esophageal cancer, with poor differentiation, lymphovascular invasion or deep submucosal invasion. Endoscopic resection was followed by chemoradiotherapy or follow-up in case of surgical contraindications or patient refusal. Outcome measures were disease-free survival (DFS), overall survival (OS), cancer specific survival (CSS) and toxicity of chemoradiotherapy. Results: Forty-one patients (36 with squamous cell carcinoma and 5 with adenocarcinomas) were included. The estimated high risk of lymph node involvement was based on poor differentiation (10/41; 24%), lympho-vascular invasion (11/41; 27%), muscularis mucosa invasion or deep sub-mucosal invasion (38/41; 93%). Thirteen patients (13/41; 32%) were closely monitored, and 28 (28/41; 68%) were treated by chemoradiotherapy or radiotherapy alone. In the close follow-up group, DFS, OS and CSS were 92%, 92% and 100%, respectively vs. 75%, 79% and 96%, respectively in the chemoradiotherapy group at the end of the follow-up. Serious adverse events related to chemoradiotherapy occurred in 10% of the patients. There were no treatment-related deaths. Conclusions: Our study shows that close follow-up may be an alternative to systematic esophagectomy after endoscopic resection of early esophageal cancer with a predicted high risk of lymph node involvement.

Radiomics is a discipline that involves studying medical images through their digital data. Using “artificial intelligence” algorithms, radiomics utilizes quantitative and high-throughput analysis of an image’s textural richness to obtain relevant information for clinicians, from diagnosis assistance to therapeutic guidance. Exploitation of these data could allow for a more detailed characterization of each phenotype, for each patient, making radiomics a new biomarker of interest, highly promising in the era of precision medicine. Moreover, radiomics is non-invasive, cost-effective, and easily reproducible in time. In the field of oncology, it performs an analysis of the entire tumor, which is impossible with a single biopsy but is essential for understanding the tumor’s heterogeneity and is known to be closely related to prognosis. However, current results are sometimes less accurate than expected and often require the addition of non-radiomics data to create a performing model. To highlight the strengths and weaknesses of this new technology, we take the example of hepatocellular carcinoma and show how radiomics could facilitate its diagnosis in difficult cases, predict certain histological features, and estimate treatment response, whether medical or surgical.

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