Prognostic impact of tumor cell nuclear size assessed by artificial intelligence in esophageal squamous cell carcinoma

Tumor cell nuclear size (NS) indicates malignant potential in breast cancer; however, its clinical significance in esophageal squamous cell carcinoma (ESCC) is unknown. Artificial intelligence (AI) can quantitatively evaluate histopathological findings. The aim was to measure NS in ESCC using AI and elucidate its clinical significance. We investigated the relationship between NS assessed by AI and prognosis in 138 patients with ESCC who underwent curative esophagectomy. Hematoxylin and eosin-stained slides from the deepest tumor sections were digitized. Using HALO-AI DenseNet v2, we created a deep learning classifier that identified tumor cells with an NS area >20 μm². Median NS was 40.14 μm², which was used to divide patients into NS-high and NS-low groups (n = 69 per group). Five-year overall survival (OS) and relapse-free survival rates were significantly lower in the NS-high group (43.2% and 39.6%) than in the NS-low group (67.7% and 49.6%). Multivariate analysis showed that greater tumor depth and NS-high status (hazard ratio [HR]: 1.79; p = 0.032) were independent risk factors for OS. In 77 cases with neoadjuvant chemotherapy, increased tumor depth and NS-high status (HR: 1.99; p = 0.048) were independent prognostic factors for unfavorable OS. Compared to the NS-low group, the NS-high group had significantly higher anisokaryosis, higher Ki-67 expression as calculated by AI analysis of immunostaining, and higher NS heterogeneity as examined by equidividing the tumors into square tiles. In conclusion, NS assessed by AI is a simple and useful prognostic factor for ESCC.