Abstract
<title>Abstract</title> <p>Background: Pathology reports contain clinically important information for cancer diagnosis, staging, and outcome research, but their unstructured format limits scalable reuse. We evaluated whether DeepSeek can support pathology report understanding across cancer type extraction, AJCC stage prediction, and prognosis classification. Methods: We conducted an independent benchmark on PathRep-Bench style pathology report tasks derived from The Cancer Genome Atlas. The dataset contained 9,523 reports split into 7,618 training, 953 validation, and 952 test reports. DeepSeek V4 Flash and DeepSeek V4 Pro were evaluated using JSON-constrained prompting. Cancer type identification used non-reasoning prompting, whereas AJCC staging and prognosis prompting used reasoning-mode prompting. We also trained local TF-IDF logistic regression prognosis models and tested whether DeepSeek-extracted structured variables improved supervised prognosis modeling. Results: DeepSeek V4 Flash achieved 0.9800 accuracy (95% CI, 0.9695--0.9884) and 0.9778 macro F1 (95% CI, 0.9660--0.9867) for cancer type identification. For AJCC stage prediction, DeepSeek V4 Flash achieved 0.8165 accuracy (95% CI, 0.7845--0.8468) and 0.7841 macro F1 (95% CI, 0.7421--0.8201) on 594 labeled test reports. DeepSeek V4 Pro did not significantly improve paired accuracy over Flash for either task and produced 14 empty final outputs during AJCC staging. DeepSeek prognosis prompting was weak on a 100-report subset, improving from 0.5083 to 0.5647 macro F1 with eight examples. A supervised TF-IDF text-only logistic regression classifier achieved the highest prognosis point estimate on the full test set, with 0.8571 accuracy and 0.8543 macro F1. Adding DeepSeek-extracted structured variables did not produce a statistically significant improvement. Conclusions: DeepSeek is effective for pathology information extraction and provides strong AJCC staging support, but prognosis prediction is better framed as supervised outcome modeling under the evaluated framework. These findings support a hybrid architecture in which DeepSeek performs extraction and staging support, while supervised text-based models handle prognosis prediction.</p>