Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis

Abstract

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.

Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions

“…For example, radiomics-based models were able to differentiate malignant and benign early-stage lung nodules in computed tomography 7 . However, radiomics has also been shown to be powerful in cardiovascular imaging 8 , 9 , chronic diseases such as lung fibrosis 10 , 11 , urologic diseases such as kidney stones 12 , and infectious diseases such as COVID-19 13 , 14 or mycobacterial lymphadenitis 15 .…”

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

“…b. Machine learning: Machine learning (ML) was reported in 46 studies to analyse, classify, diagnose, manage, monitor, and predict different health conditions or diseases (e.g., frailty, back pain, ischemic stroke, cancer, COVID-19, tuberculosis, diabetes, mortality, hypertension, mental health conditions, bacterial vaginosis, and heart disease) ( 21 , 22 , 25 – 27 , 30 , 33 , 37 , 39 , 41 , 43 , 46 , 47 , 50 , 52 , 54 , 56 , 58 , 60 , 63 , 65 , 67 , 70 , 71 , 73 , 76 , 77 , 81 , 82 , 84 , 87 , 92 , 96 , 101 , 103 , 105 , 108 , 110 ). This approach was also used to create patient re-admission files, pre-authorization in health insurance, and for finding missed cases of disease; these all form a significant part in delivering medical care services ( 30 , 76 , 111 ).…”

Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

“…Coroller et al used lymph node radiomic features for predicting the pathological response to neoadjuvant chemoradiation ( 29 ). Another study developed a radiomic model to distinguish non-tuberculous mycobacteria from other causes of lymphadenopathy based on CT images from one hospital and obtained an AUC of 89% ( 30 ). Considering that this study contains four categories of lymph nodes from six medical centers, it is more complicated and difficult than a binary classification study and much closer to clinical practice, but the classification accuracy is not compromised.…”