Abstract: Prediction of pelvic lymph node metastasis (PLNM) based on quantitative preoperative magnetic resonance imaging features of prostate cancer primary tumor is an important reference for treatment planning. However, current prediction methods inadequately capture the heterogeneity information within the primary tumor, resulting in insufficient correlation between extracted quantitative image features and PLNM prediction. In response to the aforementioned issues, an attention guided multi-task learning network with tumor segmentation as an auxiliary task is proposed for PLNM prediction. Firstly, within the tumor segmentation network, a multi-branch anisotropic large kernel attention module is introduced, where a larger receptive field is obtained through different branches and anisotropic large convolutional kernels, effectively capturing both local and global tumor information. Subsequently, within the PLNM prediction network, a multi-scale feature interaction fusion attention module is introduced to hierarchically fuse and select features from multiple scales. Experimental results on a dataset of 320 cases demonstrate that the proposed method achieves superior AUPRC (85.44%±2.04%) and AUROC (91.86%±2.18%) values compared to state-of-the-art methods and multi-task methods.

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