Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

Objective. To develop an artificial intelligence method predicting lymph node metastasis (LNM) for patients with colorectal cancer (CRC). Impact Statement. A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers. Introduction. Preoperative diagnosis of LNM is essential in treatment planning for CRC patients. Existing radiology imaging and genomic tests approaches are either unreliable or too costly. Methods. A total of 1338 patients were recruited, where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort. We developed a Multimodal Multiple Instance Learning (MMIL) model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status. The heatmaps of the obtained MMIL model were generated for model interpretation. Results. The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve (AUCs) of 0.926, 0.878, 0.809, and 0.857 for patients with stage T1, T2, T3, and T4 CRC, on the discovery cohort. On the external cohort, it obtained AUCs of 0.855, 0.832, 0.691, and 0.792, respectively (T1-T4), which indicates its prediction accuracy and potential adaptability among multiple centres. Conclusion. The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers, which is easily accessed in different institutes. We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features

Granular Flow–Obstacle Interaction and Granular Dam Break Using the S-H Model with the TVD-MacCormack Scheme

An accurate second-order spatial and temporal finite-difference scheme is applied to solve the dynamics model of a depth-averaged avalanche. Within the framework of the MacCormack scheme, a total variation diminishing term supplements the corrector step to suppress large oscillations in domains with steep gradients. The greatest strength of the scheme lies in its high computational efficiency while maintaining satisfactory accuracy. The performance of the scheme is tested on a granular flume flow–obstacle interaction scenario and a granular dam breaking scenario. In the former, the flume flow splits into two granular streams when an obstacle is encountered. The opening between the two granular streams widens when the side length of the obstacle increases. In the simulation, shock waves with a fan-shaped configuration are captured, and successive waves in the tail of the avalanche between the two streams are observed. In the latter scenario, the average values and the fluctuations in the flow rate and velocity (at relatively steady state) decrease with the width of the breach. The capture of complex and typical granular-flow phenomena indicates the applicability and effectiveness of combining the TVD-MacCormack Scheme and S-H model to simulate dam breaking and inclined flow–obstacle interaction cases. In this study, the dense granular flow strikes on a rigid obstacle that is described by a wall boundary, rather than a topographic feature with a finite slope. This shows that the TVD-MacCormack scheme has a shock-capturing ability. The results of granular dam break simulations also revealed that the boundary conditions (open or closed) affect the collapse of the granular pile, i.e., the grains evenly breached out under closed boundary conditions, whereas the granules breaching out of the opening were mostly grains adjacent to the boundaries under open boundary conditions