Spatial and task attention network for treatment response prediction in locally advanced cervical cancer radiotherapy.

Cervical cancer is a common gynecological tumor treated predominantly with radiotherapy for locally advanced cases. However, despite treatment, almost one-third of patients experience recurrence within 18 months. Accurate prediction of patient response to therapy is critical for selecting optimal treatment plans. Currently, MRI images are manually segmented to identify the tumor region and predict treatment response using image information within the tumor. However, current methods treat segmentation and response prediction as separate tasks and manual segmentation can be expensive. To address these issues, we propose a spatial and task attention network that simultaneously segments the tumor and predicts the response to cervical cancer radiotherapy. Our approach employs a spatial attention module to focus on the tumor region and a task attention module to explore the correlation between tumor segmentation and treatment response prediction, achieving automatic segmentation of the tumor. We retrospectively collected MRI images from 138 patients with locally advanced cervical cancer before radiotherapy and conducted 5-fold cross-validation experiments, demonstrating that our method achieves competitive results. [Display omitted] • We devise a multitask architecture that simultaneously predicts the treatment response and segments the tumor region for locally advanced cervical cancer radiotherapy. • The proposed spatial attention module can allow the network to focus more on the tumor region. The attention module can effectively improve the performance of both tasks by utilizing the inherent correlation of them. • Extensive experiments using retrospective locally advanced cervical cancer data showcase the effectiveness of our proposed method in predicting treatment response and segmenting tumors. [ABSTRACT FROM AUTHOR]