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

Abstract

Medical diagnostic imaging is essential for the differential diagnosis of cervical lymphadenopathy. Here we develop an ultrasound radiomics method for accurately differentiating cervical lymph node tuberculosis (LNTB), cervical lymphoma, reactive lymph node hyperplasia, and metastatic lymph nodes especially in the multi-operator, cross-machine, multicenter context. The inter-observer and intra-observer consistency of radiomics parameters from the region of interest were 0.8245 and 0.9228, respectively. The radiomics model showed good and repeatable diagnostic performance for multiple classification diagnosis of cervical lymphadenopathy, especially in LNTB (area under the curve, AUC: 0.673, 0.662, and 0.626) and cervical lymphoma (AUC: 0.623, 0.644, and 0.602) in the whole set, training set, and test set, respectively. However, the diagnostic performance of lymphadenopathy among skilled radiologists was varied (Kappa coefficient: 0.108, *p < 0.001). The diagnostic performance of radiomics is comparable and more reproducible compared with those of skilled radiologists. Our study offers a more comprehensive method for differentiating LNTB, cervical lymphoma, reactive lymph node hyperplasia, and metastatic LN.

Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review

“…All 47 studies are summarized in Supplementary Table 1 [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48] and were divided based on the tumor of origin: breast cancer, head and neck cancer, and cervical cancer. Most of the papers were published between 2019 and 2022 (n = 19 articles), and the analyses were conducted using MATLAB, R software, SPSS, OriginPro, a papillary thyroid carcinoma (PTC), cervical LN metastasis (LNM) prediction system (custom-built, n = 3), MIPAV, and Mazda.…”

“…Feature extraction was mainly performed with PyRadiomics, ITK-SNAP, LifeX, MedCalc, ImageJ, PASW and Prism. The values of the area under the curve (AUC), sensitivity, specificity, and accuracy are summarized in ▶ Table 1 [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48].…”

“…We identified three studies on cervical cancer. They focused on the consequences of manual vs. automatic segmentation (AUC: 0.755) [26], the creation of a classification model to predict the etiology of cervical lymphadenopathies (AUC: 0.673) [27], and the prediction of LN status in patients with early-stage cervical cancer (AUC: 0.770) [28].…”

“…In the last few years, some studies have emerged on the use of US-based radiomics in determining LN status in patients suffering from cervical cancer [26,27,28]. Generally, the results obtained from different and independent studies have shown that the radiomics approach based on the textural features of US images can be considered a promising noninvasive prediction method used to facilitate preoperative identification of LNs in patients with early-stage cervical cancer [30].…”

Development and validation of nomograms by radiomic features on ultrasound imaging for predicting overall survival in patients with primary nodal diffuse large B-cell lymphoma

“…Radiomic based on ultrasound has a good application prospect in the evaluation of curative effect and prognosis of other malignant tumors such as breast and gastrointestinal tumors ( 23 – 25 ). In lymphoma, radiomic shows hope in the differential diagnosis of lymphoma from other Lymph node diseases ( 26 ). To our knowledge, no previous study has associated radiomic signatures based on ultrasound with the outcome of patients with N-DLBCL.…”