Augmented Intelligence and Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of McGill University Health Centre, 5252 Boulevard de Maisonneuve O, Montréal, QC, H4A 3S9, Canada.
Department of Radiology, McGill University Health Centre, 1001 Decarie Blvd, Montreal, QC, H4A 3J1, Canada.
Sci Rep. 2022 Feb 22;12(1):2962. doi: 10.1038/s41598-022-06884-3.
Non-tuberculous mycobacterial (NTM) infection is an emerging infectious entity that often presents as lymphadenitis in the pediatric age group. Current practice involves invasive testing and excisional biopsy to diagnose NTM lymphadenitis. In this study, we performed a retrospective analysis of 249 lymph nodes selected from 143 CT scans of pediatric patients presenting with lymphadenopathy at the Montreal Children's Hospital between 2005 and 2018. A Random Forest classifier was trained on the ten most discriminative features from a set of 1231 radiomic features. The model classifying nodes as pyogenic, NTM, reactive, or proliferative lymphadenopathy achieved an accuracy of 72%, a precision of 68%, and a recall of 70%. Between NTM and all other causes of lymphadenopathy, the model achieved an area under the curve (AUC) of 89%. Between NTM and pyogenic lymphadenitis, the model achieved an AUC of 90%. Between NTM and the reactive and proliferative lymphadenopathy groups, the model achieved an AUC of 93%. These results indicate that radiomics can achieve a high accuracy for classification of NTM lymphadenitis. Such a non-invasive highly accurate diagnostic approach has the potential to reduce the need for invasive procedures in the pediatric population.
非结核分枝杆菌（NTM）感染是一种新出现的传染病实体，常表现为儿科年龄组的淋巴结炎。目前的做法包括侵入性检测和切除活检来诊断 NTM 淋巴结炎。在这项研究中，我们对 2005 年至 2018 年间在蒙特利尔儿童医院就诊的有淋巴结病的儿科患者的 143 次 CT 扫描中选择的 249 个淋巴结进行了回顾性分析。在一组 1231 个放射组学特征中，随机森林分类器使用 10 个最具鉴别力的特征进行训练。该模型将淋巴结分类为化脓性、NTM、反应性或增殖性淋巴结病的准确率为 72%，精度为 68%，召回率为 70%。在 NTM 和所有其他引起淋巴结病的原因之间，该模型的曲线下面积（AUC）为 89%。在 NTM 和化脓性淋巴结炎之间，该模型的 AUC 为 90%。在 NTM 与反应性和增殖性淋巴结病组之间，该模型的 AUC 为 93%。这些结果表明，放射组学可以实现 NTM 淋巴结炎分类的高准确性。这种非侵入性的高度准确的诊断方法有可能减少儿科人群中侵入性手术的需求。
