e16368 Background: Pancreatic ductal adenocarcinoma (PDAC) is predicted to be the second cause of cancer death in the US by 2040. For the minority of patients who are candidates for curative-intent surgical resection, a neoadjuvant approach with chemotherapy or chemotherapy followed by chemoradiotherapy (chemoRT) has become the standard of care. The tumor microenvironment (TME) has been recognized as a key factor in therapeutic resistance and disease progression. This study aims to explore the prognostic value of human-interpretable machine learning image features (HIFs) derived from whole-slide H&E images. Methods: Consecutive PDAC patients who underwent neoadjuvant therapy followed by surgical resection were studied in this single-institution retrospective study. Clinical variables collected include tumor site (head/body/tail), surgery extent, histology, sex, age at diagnosis, grade, treatment response (4-point scale), LVI, PNI, resection margin status, pathological TNM staging, number of lymph nodes removed/positive, neoadjuvant strategy (chemotherapy vs chemoRT), and number of neoadjuvant chemo cycles. These factors were analyzed with a multivariable Cox proportional hazards model for disease-free survival (DFS) after surgery. Additionally, 420 human-interpretable image features (HIFs) derived from a commercial machine learning software were analyzed with a univariable Cox model on a binary outcome of 1-yr DFS, applying a Bonferroni correction for multiple comparisons to identify significant predictors. Results: Increased neoadjuvant chemotherapy cycles (HR = 0.62, p = 0.001, [median: 8, IQR: 6-12]) and receipt of combination neoadjuvant therapy (HR = 0.15, p = 0.020, [29% chemo, 71% +chemoRT]) exhibited a protective effect in the multivariable DFS model. 3 HIFs quantifying immune cells and macrophages within the cancerous tissue and its stroma were associated with an improved probability of 1-year DFS. Conclusions: In addition to clinical factors, HIFs may offer valuable insights into the tumor microenvironment's influence on PDAC progression, paving the way for improved prognostic models and therapeutic strategies. Future work will integrate clinical predictors with HIFs in a combined model.