Duggar William N, Vengaloor Thomas Toms, Wang Yibin, Rahman Abdur, Wang Haifeng, Roberts Paul R, Bian Linkan, Gatewood Ronald T, Vijayakumar Srinivasan
Radiation Oncology, University of Mississippi Medical Center, Jackson, USA.
Industrial Systems and Engineering, Mississippi State University, Starkville, USA.
Cureus. 2023 Feb 8;15(2):e34769. doi: 10.7759/cureus.34769. eCollection 2023 Feb.
Background This study aimed to demonstrate both the potential and development progress in the identification of extracapsular nodal extension in head and neck cancer patients prior to surgery. Methodology A deep learning model has been developed utilizing multilayer gradient mapping-guided explainable network architecture involving a volume extractor. In addition, the gradient-weighted class activation mapping approach has been appropriated to generate a heatmap of anatomic regions indicating why the algorithm predicted extension or not. Results The prediction model shows excellent performance on the testing dataset with high values of accuracy, the area under the curve, sensitivity, and specificity of 0.926, 0.945, 0.924, and 0.930, respectively. The heatmap results show potential usefulness for some select patients but indicate the need for further training as the results may be misleading for other patients. Conclusions This work demonstrates continued progress in the identification of extracapsular nodal extension in diagnostic computed tomography prior to surgery. Continued progress stands to see the obvious potential realized where not only can unnecessary multimodality therapy be avoided but necessary therapy can be guided on a patient-specific level with information that currently is not available until postoperative pathology is complete.
背景 本研究旨在展示在头颈癌患者术前识别包膜外淋巴结转移的潜力和进展情况。方法 利用包含体积提取器的多层梯度映射引导的可解释网络架构开发了一种深度学习模型。此外，采用梯度加权类激活映射方法生成解剖区域热图，以说明算法为何预测有转移或无转移。结果 该预测模型在测试数据集上表现出色，准确率、曲线下面积、灵敏度和特异度分别高达0.926、0.945、0.924和0.930。热图结果显示对部分特定患者有潜在用处，但也表明需要进一步训练，因为结果可能会误导其他患者。结论 这项工作表明在术前诊断性计算机断层扫描中识别包膜外淋巴结转移方面取得了持续进展。持续进展有望实现明显的潜力，不仅可以避免不必要的多模态治疗，还可以根据患者的具体情况，利用目前直到术后病理完成才可得的信息指导必要的治疗。
