Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia.
Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
J Cancer Res Clin Oncol. 2023 Dec;149(20):18039-18064. doi: 10.1007/s00432-023-05464-w. Epub 2023 Nov 20.
An automated computerized approach can aid radiologists in the early diagnosis of breast cancer. In this study, a novel method is proposed for classifying breast tumors into benign and malignant, based on the ultrasound images through a Graph Neural Network (GNN) model utilizing clinically significant features.
Ten informative features are extracted from the region of interest (ROI), based on the radiologists' diagnosis markers. The significance of the features is evaluated using density plot and T test statistical analysis method. A feature table is generated where each row represents individual image, considered as node, and the edges between the nodes are denoted by calculating the Spearman correlation coefficient. A graph dataset is generated and fed into the GNN model. The model is configured through ablation study and Bayesian optimization. The optimized model is then evaluated with different correlation thresholds for getting the highest performance with a shallow graph. The performance consistency is validated with k-fold cross validation. The impact of utilizing ROIs and handcrafted features for breast tumor classification is evaluated by comparing the model's performance with Histogram of Oriented Gradients (HOG) descriptor features from the entire ultrasound image. Lastly, a clustering-based analysis is performed to generate a new filtered graph, considering weak and strong relationships of the nodes, based on the similarities.
The results indicate that with a threshold value of 0.95, the GNN model achieves the highest test accuracy of 99.48%, precision and recall of 100%, and F1 score of 99.28%, reducing the number of edges by 85.5%. The GNN model's performance is 86.91%, considering no threshold value for the graph generated from HOG descriptor features. Different threshold values for the Spearman's correlation score are experimented with and the performance is compared. No significant differences are observed between the previous graph and the filtered graph.
The proposed approach might aid the radiologists in effective diagnosing and learning tumor pattern of breast cancer.
自动化计算机方法可以帮助放射科医生早期诊断乳腺癌。在这项研究中，提出了一种基于图形神经网络（GNN）模型的新方法，该方法利用临床有意义的特征对乳腺肿瘤进行良性和恶性分类。
从感兴趣区域（ROI）中提取 10 个信息特征，基于放射科医生的诊断标记。使用密度图和 T 检验统计分析方法评估特征的显著性。生成一个特征表，其中每一行代表一个单独的图像，视为节点，节点之间的边由计算 Spearman 相关系数来表示。生成一个图数据集，并将其输入到 GNN 模型中。通过消融研究和贝叶斯优化来配置模型。然后，使用不同的相关阈值来评估优化后的模型，以获得浅图的最高性能。通过 k 折交叉验证验证性能一致性。通过将模型的性能与整个超声图像的方向梯度直方图（HOG）描述符特征进行比较，评估利用 ROI 和手工制作特征进行乳腺肿瘤分类的影响。最后，根据相似性，基于节点的弱和强关系，进行基于聚类的分析，生成一个新的过滤图。
结果表明，在阈值为 0.95 的情况下，GNN 模型的测试准确率最高为 99.48%，精度和召回率均为 100%，F1 评分为 99.28%，减少了 85.5%的边数。考虑到生成的 HOG 描述符特征的图没有阈值，GNN 模型的性能为 86.91%。对 Spearman 相关得分的不同阈值进行实验，并比较性能。未观察到前一个图和过滤图之间有显著差异。
该方法可能有助于放射科医生有效诊断和学习乳腺癌肿瘤模式。
