Published online Jul 15, 2026. doi: 10.4251/wjgo.v18.i7.120437
Revised: April 10, 2026
Accepted: April 24, 2026
Published online: July 15, 2026
Processing time: 129 Days and 18.5 Hours
Considering that colorectal cancer (CRC) has high global incidence and mortality, accurate tumor staging before surgery is essential for individualized treatment formulation. Limited by insufficient capacities of traditional imaging modalities for assessing tumor invasion depth and lymph node metastasis, the exploration of more objective and accurate auxiliary diagnostic tools is critical.
To develop and validate an ultrasound (US) image-based deep learning (DL) model for automated, high-precision preoperative tumor node metastasis (TNM) staging prediction in CRC patients, this research intended to address subjectivity and poor repeatability issues in traditional US diagnoses and presents an objective clinical decision-making model.
The artificial intelligence diagnostic model was developed and validated. 680 CRC patients retrospectively enrolled from The Third People’s Hospital of Henan Province (January 2019 to June 2022) constituted the development cohort, with their US images analyzed. A temporally independent test set, consisting of another 170 CRC cases (January 2023 to December 2024), was used for final evaluation. Postoperative pathological TNM staging served as the gold standard. A dual-path context-aware fusion network was designed to separately extract local tumor features and global contextual information from surrounding tissues, integrating them through a cross-attention mechanism to simulate clinical diagnostic thinking. Model performance for T (T1-T4) and N (N0-N2) staging was evaluated on the independent test set and compared with three sonographers of different seniority. Accuracy, area under the curve (AUC), and decision curve analysis (DCA) served as the primary outcome measures.
On the independent test set, the DL model demonstrated excellent diagnostic performance, with an overall AUC of 0.907 [95% confidence intervals (CI): 0.862-0.954] for the T-stage prediction and 0.912 (95%CI: 0.867-0.956) for the N-stage prediction. Accuracy was significantly higher than that of the physician group for both T (89.8% vs 81.9%) and N (94.9% vs 83.9%) staging, as were AUC values (all P < 0.05). DCA indicated that the model provided clear net clinical benefits across a wide range of threshold probabilities for both T and N staging.
In this study, an US-based dual-path DL model was successfully developed and validated, demonstrating superior diagnostic performance to sonographers in preoperative T and N staging of CRC on a temporally independent test set. The model shows promise as an objective and reliable auxiliary diagnostic tool to improve staging accuracy and consistency, although multicenter prospective validation is warranted.
Core Tip: This study proposes an ultrasound-based deep learning model for preoperative tumor node metastasis staging of colorectal cancer (CRC). The model demonstrates strong diagnostic performance for T and N staging and provides clear clinical net benefits, providing an objective artificial intelligence-based tool for accurate preoperative staging of CRC.