Edwin Raja S, Sutha J, Elamparithi P, Jaya Deepthi K, Lalitha S D
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India.
MethodsX. 2025 Mar 22;14:103276. doi: 10.1016/j.mex.2025.103276. eCollection 2025 Jun.
The task of predicting liver tumors is critical as part of medical image analysis and genomics area since diagnosis and prognosis are important in making correct medical decisions. Silent characteristics of liver tumors and interactions between genomic and imaging features are also the main sources of challenges toward reliable predictions. To overcome these hurdles, this study presents two integrated approaches namely, - Attention-Guided Convolutional Neural Networks (AG-CNNs), and Genomic Feature Analysis Module (GFAM). Spatial and channel attention mechanisms in AG-CNN enable accurate tumor segmentation from CT images while providing detailed morphological profiling. Evaluation with three control databases TCIA, LiTS, and CRLM shows that our model produces more accurate output than relevant literature with an accuracy of 94.5%, a Dice Similarity Coefficient of 91.9%, and an F1-Score of 96.2% for the Dataset 3. More considerably, the proposed methods outperform all the other methods in different datasets in terms of recall, precision, and Specificity by up to 10 percent than all other methods including CELM, CAGS, DM-ML, and so on.•Utilization of Attention-Guided Convolutional Neural Networks (AG-CNN) enhances tumor region focus and segmentation accuracy.•Integration of Genomic Feature Analysis (GFAM) identifies molecular markers for subtype-specific tumor classification.
预测肝脏肿瘤的任务作为医学图像分析和基因组学领域的一部分至关重要，因为诊断和预后对于做出正确的医疗决策很重要。肝脏肿瘤的隐匿特征以及基因组和成像特征之间的相互作用也是可靠预测面临挑战的主要来源。为了克服这些障碍，本研究提出了两种集成方法，即注意力引导卷积神经网络（AG-CNN）和基因组特征分析模块（GFAM）。AG-CNN中的空间和通道注意力机制能够从CT图像中准确分割肿瘤，同时提供详细的形态学轮廓。使用三个对照数据库TCIA、LiTS和CRLM进行评估表明，我们的模型比相关文献产生更准确的输出，对于数据集3，准确率为94.5%，骰子相似系数为91.9%，F1分数为96.2%。更值得注意的是，所提出的方法在召回率、精确率和特异性方面比包括CELM、CAGS、DM-ML等在内的所有其他方法在不同数据集中的表现高出多达10%。•注意力引导卷积神经网络（AG-CNN）的使用增强了肿瘤区域聚焦和分割准确性。•基因组特征分析（GFAM）的整合识别用于亚型特异性肿瘤分类的分子标记。
