中心博士生赵麾宇同学的工作——An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data（面向胆囊癌早期诊断的基于多模态医学数据的可解释机器学习框架）的相关成果最近被BMC Cancer期刊接收，该工作得到了国家自然科学基金（T2225023）部分资助。
胆囊癌（GBC）的早期准确诊断被认为是肿瘤学领域面临的主要挑战之一。然而，鲜有研究聚焦于基于多种模态的GBC综合分类。本研究旨在基于影像学和非影像学医学数据，建立GBC的综合诊断框架。
本研究回顾性研究纳入了298例胆囊疾病临床患者。提出了一种新型端到端可解释性GBC诊断框架，以处理包括CT影像、人口统计学数据、肿瘤标志物、凝血功能检测及常规血液检测在内的多种医学模态。为实现影像模态的更好特征提取与融合，还开发了一种新型全局-混合-局部网络（GHL-Net）。采用集成学习策略融合多模态数据并获得最终分类结果。此外，应用两种可解释方法帮助临床医生理解基于模型的决策。模型性能通过准确率、精确率、特异性、敏感性、F1分数、曲线下面积（AUC）及马修斯相关系数（MCC）进行评估。
在二分类和多分类场景中，所提方法在两个数据集上的性能均优于其他对比方法。尤其在二分类场景中，所提方法实现了最高的准确率、敏感性、特异性、精确率、F1分数、ROC-AUC、PR-AUC和MCC，分别为95.24%、93.55%、96.87%、96. 67%、95.08%、0.9591、0.9636和0.9051。基于可解释方法获得的可视化结果也表明中间决策过程具有较高的临床相关性。本研究验证了基于机器学习的框架可有效提升GBC诊断的准确性，并有望在其他癌症诊断场景中产生更显著的影响。
摘要:
- Background: The accurate early-stage diagnosis of gallbladder cancer (GBC) is regarded as one of the major challenges in the field of oncology. However, few studies have focused on the comprehensive classification of GBC based on multiple modalities. This study aims to develop a comprehensive diagnostic framework for GBC based on both imaging and non-imaging medical data.
- Methods: This retrospective study reviewed 298 clinical patients with gallbladder disease or volunteers from two devices. A novel end-to-end interpretable diagnostic framework for GBC is proposed to handle multiple medical modalities, including CT imaging, demographics, tumor markers, coagulation function tests, and routine blood tests. To achieve better feature extraction and fusion of the imaging modality, a novel global-hybrid-local network, namely GHL-Net, has also been developed. The ensemble learning strategy is employed to fuse multi-modality data and obtain the final classification result. In addition, two interpretable methods are applied to help clinicians understand the model-based decisions. Model performance was evaluated through accuracy, precision, specificity, sensitivity, F1-score, and area under the curve (AUC), and matthews correlation coefficient (MCC).
- Results: In both binary and multi-class classification scenarios, the proposed method showed better performance compared to other comparison methods in both datasets. Especially in the binary classification scenario, the proposed method achieved the highest accuracy, sensitivity, specificity, precision, F1-score, ROC-AUC, PR-AUC, and MCC of 95.24%, 93.55%, 96.87%, 96.67%, 95.08%, 0.9591, 0.9636, and 0.9051 respectively. The visualization results obtained based on the interpretable methods also demonstrated a high clinical relevance of the intermediate decision-making processes. Ablation studies then provided an in-depth understanding of our methodology.
- Conclusion: The machine learning-based framework can effectively improve the accuracy of GBC diagnosis and is expected to have a more significant impact in other cancer diagnosis scenarios.