The paper presents a novel approach for the automatic detection of neoplastic lesions in lymph nodes (LNs). It leverages the latest advances in machine learning (ML) with the LN Reporting and Data System (LN-RADS) scale. By integrating diverse datasets and network structures, the research investigates the effectiveness of ML algorithms in improving diagnostic accuracy and automation potential. Both Multinominal Logistic Regression (MLR)-integrated and fully connected neuron layers are included in the analysis. The methods were trained using three variants of combinations of histopathological data and LN-RADS scale labels to assess their utility. The findings demonstrate that the LN-RADS scale improves prediction accuracy. MLR integration is shown to achieve higher accuracy, while the fully connected neuron approach excels in AUC performance. All of the above suggests a possibility for significant improvement in the early detection and prognosis of cancer using AI techniques. The study underlines the importance of further exploration into combined datasets and network architectures, which could potentially lead to even greater improvements in the diagnostic process.
本文提出了一种用于淋巴结（LNs）肿瘤性病变自动检测的新方法。该方法结合机器学习（ML）的最新进展与淋巴结报告和数据系统（LN-RADS）量表，通过整合多样化数据集与网络结构，研究了ML算法在提升诊断准确性与自动化潜力方面的有效性。分析中同时纳入了多项逻辑回归（MLR）集成模型与全连接神经元层。研究采用组织病理学数据与LN-RADS量表标签的三种组合变体对方法进行训练，以评估其效用。结果表明，LN-RADS量表能有效提升预测准确度。MLR集成方法展现出更高的准确率，而全连接神经元方法则在AUC性能上表现更优。以上发现均表明，人工智能技术在癌症早期检测与预后评估方面具有显著提升潜力。本研究强调了对组合数据集与网络架构进行深入探索的重要性，这可能为诊断流程带来更大程度的改进。
肿瘤(癌症)患者之家