Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node.

BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical resections, we used artificial intelligence to build a model to identify T1 colorectal tumors at risk for metastasis to lymph node and validated the model in a separate set of patients. METHODS: We collected data from 3134 patients with T1 CRC treated at 6 hospitals in Japan from April 1997 through September 2017 (training cohort). We developed a machine-learning artificial neural network (ANN) using data on patients' age and sex, as well as tumor size, location, morphology, lymphatic and vascular invasion, and histologic grade. We then conducted the external validation on the ANN model using independent 939 patients at another hospital during the same period (validation cohort). We calculated areas under the receiver operator characteristics curves (AUCs) for the ability of the model and US guidelines to identify patients with lymph node metastases. RESULTS: Lymph node metastases were found in 319 (10.2%) of 3134 patients in the training cohort and 79 (8.4%) of /939 patients in the validation cohort. In the validation cohort, the ANN model identified patients with lymph node metastases with an AUC of 0.83, whereas the guidelines identified patients with lymph node metastases with an AUC of 0.73 (P < .001). When the analysis was limited to patients with initial endoscopic resection (n = 517), the ANN model identified patients with lymph node metastases with an AUC of 0.84 and the guidelines identified these patients with an AUC of 0.77 (P = .005). CONCLUSIONS: The ANN model outperformed guidelines in identifying patients with T1 CRCs who had lymph node metastases. This model might be used to determine which patients require additional surgery after endoscopic resection of T1 CRCs. UMIN Clinical Trials Registry no: UMIN000038609.

A visual SLAM-based bronchoscope tracking scheme for bronchoscopic navigation.

PURPOSE: Due to the complex anatomical structure of bronchi and the resembling inner surfaces of airway lumina, bronchoscopic examinations require additional 3D navigational information to assist the physicians. A bronchoscopic navigation system provides the position of the endoscope in CT images with augmented anatomical information. To overcome the shortcomings of previous navigation systems, we propose using a technique known as visual simultaneous localization and mapping (SLAM) to improve bronchoscope tracking in navigation systems. METHODS: We propose an improved version of the visual SLAM algorithm and use it to estimate nt-specific bronchoscopic video as input. We improve the tracking procedure by adding more narrow criteria in feature matching to avoid mismatches. For validation, we collected several trials of bronchoscopic videos with a bronchoscope camera by exploring synthetic rubber bronchus phantoms. We simulated breath by adding periodic force to deform the phantom. We compared the camera positions from visual SLAM with the manually created ground truth of the camera pose. The number of successfully tracked frames was also compared between the original SLAM and the proposed method. RESULTS: We successfully tracked 29,559 frames at a speed of 80 ms per frame. This corresponds to 78.1% of all acquired frames. The average root mean square error for our technique was 3.02 mm, while that for the original was 3.61 mm. CONCLUSION: We present a novel methodology using visual SLAM for bronchoscope tracking. Our experimental results showed that it is feasible to use visual SLAM for the estimation of the bronchoscope camera pose during bronchoscopic navigation. Our proposed method tracked more frames and showed higher accuracy than the original technique did. Future work will include combining the tracking results with virtual bronchoscopy and validation with in vivo cases.