Precisely segmenting the organs at risk (OARs) in computed tomography (CT) plays an important role in radiotherapy’s treatment planning, aiding in the protection of critical tissues during irradiation. Renowned deep convolutional neural networks (DCNNs) and prevailing transformer-based architectures are widely utilized to accomplish the segmentation task, showcasing advantages in capturing local and contextual characteristics. Graph convolutional networks (GCNs) are another specialized model designed for processing the nongrid dataset, e.g., citation relationship. The DCNNs and GCNs are considered as two distinct models applicable to the grid and nongrid datasets, respectively. Motivated by the recently developed dynamic-channel GCN (DCGCN) that attempts to leverage the graph structure to enhance the feature extracted by the DCNNs, this paper proposes a novel architecture termed adaptive sparse GCN (ASGCN) to mitigate the inherent limitations in DCGCN from the aspect of node’s representation and adjacency matrix’s construction. For the node’s representation, the global average pooling used in the DCGCN is replaced by the learning mechanism to accommodate the segmentation task. For the adjacency matrix, an adaptive regularization strategy is leveraged to penalize the coefficient in the adjacency matrix, resulting in a sparse one that can better exploit the relationships between nodes. Rigorous experiments on multiple OARs’ segmentation tasks of the head and neck demonstrate that the proposed ASGCN can effectively improve the segmentation accuracy. Comparison between the proposed method and other prevalent architectures further confirms the superiority of the ASGCN.

In colorectal cancer (CRC), accurately predicting lymph node metastasis (LNM) contributes to developing appropriate treatment plans and serves as the key to long‐term survival of patients. In the clinical settings, preoperative LNM diagnosis in CRC predominantly depends on computed tomography (CT). Nevertheless, lymph nodes are small in size and difficult to identify on 3D CT scans, and CT‐based diagnosis of metastatic lymph nodes is prone to a significant misdiagnosis rate and lacks consistency across clinicians. Currently, there is no automatic system available for LNM prediction in CRC via 3D CT scans. In addition, existing deep learning‐ (DL‐) based lymph node detection models present low detection accuracy and high false‐positive rates, and most existing DL‐based lymph node metastasis prediction models mainly use tumor area characteristics but fail to adequately utilize lymph node information, thus not yielding satisfactory results. To tackle these issues, we propose an intelligent diagnosis system for this challenging task, mainly including a lymph node detection (LND) model and a lymph node metastasis prediction (LNMP) model. In detail, the LND model utilizes an encoder‐decoder network to detect lymph nodes, and the LNMP model employs an innovative attention‐based multiple instance learning (MIL) network. An instance‐level self‐attention feature enhancement module is designed to extract and augment lymph node features as a bag of instances. Furthermore, a bag‐level MIL prediction module is employed to extract instance features and create a bag representation for the ultimate LNM prediction. As far as we know, the proposed intelligent system represents the pioneering method for addressing this complex clinical challenge. In experiments, our proposed intelligent system achieves the AUC of 75.4% and the accuracy of 73.9%, showcasing a significant enhancement compared to physicians specialising in CRC and highlighting its strong clinical applicability. The accessible code can be found at https://github.com/SCU-MI/IS-LNM .

Computed tomography (CT) scanning is a fast and painless procedure that can capture clear imaging information beneath the abdomen and is widely used to help diagnose and monitor disease progress. The pelvic lymph node is a key indicator of colorectal cancer metastasis. In the traditional process, an experienced radiologist must read all the CT scanning images slice by slice to track the lymph nodes for future diagnosis. However, this process is time-consuming, exhausting, and subjective due to the complex pelvic structure, numerous blood vessels, and small lymph nodes. Therefore, automated methods are desirable to make this process easier. Currently, the available open-source CTLNDataset only contains large lymph nodes. Consequently, a new data set called PLNDataset, which is dedicated to lymph nodes within the pelvis, is constructed to solve this issue. A two-level annotation calibration method is proposed to guarantee the quality and correctness of pelvic lymph node annotation. Moreover, a novel system composed of a keyframe localization network and a lymph node detection network is proposed to detect pelvic lymph nodes in CT scanning images. The proposed method makes full use of two kinds of prior knowledge: spatial prior knowledge for keyframe localization and anchor prior knowledge for lymph node detection. A series of experiments are carried out to evaluate the proposed method, including ablation experiments, comparing other state-of-the-art methods, and visualization of results. The experimental results demonstrate that our proposed method outperforms other methods on PLNDataset and CTLNDataset. This system is expected to be applied in future clinical practice.

Radiotherapy is an indispensable part of adjuvant therapy for cancer that improves local control, overall survival, and the opportunity for good quality of life. Organ delineation and dose plan design are the key steps in the treatment. Organ delineation controls the area of radiotherapy and dose planning controls its intensity. However, both tasks are time-consuming, exhausting, and subjective, and automated methods are desirable. Although automated methods have been studied, the previous studies either focus on organ segmentation or dose prediction, without considering them from a holistic perspective. In this paper, we treat organ segmentation and dose prediction as similar tasks, and propose an error correction framework to improve their performance based on the same mechanism. The proposed error correction framework consists of a prediction network and a calibration network. The biggest difference between our framework and previous studies is that the state-of-the-art networks can be used as a prediction network or calibration network, and then the performance can be improved by the error correction mechanism. To evaluate the framework, we conducted a series of experiments on dose prediction and organ segmentation. These experimental results show that the framework is superior to other state-of-the-art methods in both tasks.