To investigate the utility of the DeepSeek large language model (LLM) in the structured generation of ultrasound reports and the automatic assessment of T-staging for gastric and rectal cancer.

A total of 121 ultrasound examination reports for gastric and rectal cancer, collected from Sichuan Cancer Hospital between January 2023 and December 2024, were included in this study. A structured template for gastric and rectal cancer ultrasound reports was developed by a team of senior sonographers. The DeepSeek R1 and V3 models were employed to extract structured information and assess T-staging. The performance of structured report generation was evaluated using recall, precision, and F1 score, while T-staging performance was assessed based on accuracy. Three physicians were invited to compare the reports generated by DeepSeek with the original reports to evaluate review efficiency and clinical usability.

Regarding structured information extraction, both DeepSeek R1 and V3 models achieved recall, precision, and F1 scores exceeding 0.9, with no statistically significant differences between the two (P＞0.05). In T-staging assessment, the DeepSeek R1 model (utilizing reasoning mode) achieved the highest accuracy of 76.86%, which was significantly superior to the 59.50% achieved by the DeepSeek V3 model (χ²=8.51, P＜0.05). Compared to the average time required to review original reports [(60.96±6.11) s/report], the review time for structured reports generated by DeepSeek R1 [(18.12±4.52) s/report] (t=60.38; P＜0.001) and DeepSeek V3 [(17.15±2.60) s/report] (t=71.98; P＜0.001) was significantly shortened. The 5-point Likert scale evaluation showed that the score for the original reports was 3 (3, 3), while the scores for the DeepSeek R1 and V3 reports were 1 (1, 2) (Z=-9.72; P＜0.001) and 1 (1, 2) (Z=-9.95; P＜0.001), respectively, indicating a statistically significant difference.

The DeepSeek large language models, particularly the R1 version, can effectively extract structured information from gastric and rectal cancer ultrasound reports and demonstrates high accuracy in T-staging assessment. The generated reports contribute to improved review efficiency and possess the potential to assist in clinical decision-making.