Development and Validation Study of the Prognostic Impact of Deep Learning-Determined Myxoid Stroma After Neoadjuvant Chemotherapy in Patients with Esophageal Squamous Cell Carcinoma

This study was designed to investigate the prognostic significance of artificial intelligence (AI)-based quantification of myxoid stroma in patients undergoing esophageal squamous cell carcinoma (ESCC) surgery after neoadjuvant chemotherapy (NAC) and to verify its significance in an independent validation cohort from another hospital.

We evaluated two datasets of patients with pathological stage II or III ESCC who underwent surgery after NAC. Cohort 1 consisted of 85 patients who underwent R0 surgery for the primary tumor after NAC. Cohort 2, the validation cohort, consisted of 80 patients who received same treatments in another hospital. AI-based myxoid stroma was evaluated in resected specimens, and its area was categorized by using the receiver operating characteristic curve for overall survival (OS) of cohort 1.

The F1 scores, which are the degree of agreement between the automatically detected myxoid stroma and manual annotations, were 0.83 and 0.79 for cohorts 1 and 2. The myxoid stroma-high group had a significantly poorer prognosis than the myxoid stroma-low group in terms of OS, disease-specific survival (DSS), and recurrence-free survival (RFS) in cohort 1. Comparable results were observed in cohort 2, where OS, DSS, and RFS were significantly affected by myxoid stroma. Multivariate analysis for RFS revealed that AI-determined myxoid stroma-high was one of the independent prognostic factors in cohort 1 (hazard ratio [HR] 1.97, p = 0.037) and cohort 2 (HR 4.45, p < 0.001).

AI-determined myxoid stroma may be a novel and useful prognostic factor for patients with pathological stage II or III ESCC after NAC.