A combination of ultrasound and contrast-enhanced ultrasound improves diagnostic accuracy for the differentiation of cervical tuberculous lymphadenitis from primary lymphoma

Liu, Naxiang; Chen, Yijie; Wang, Yaoqin; Huang, Weiqin; Zhan, Lili; Du, Zhongshi; Zhong, Zhaoming; Wu, Zhougui; Shen, Youhong; Deng, Xiaohong; Ni, Shixiong; Tang, Lina

doi:10.3233/ch-231876

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…However, health professionals often encounter challenging cases with overlapping imaging features in differentiating metastasis from lymphoma only with conventional B-mode US and even with Doppler US 14 . It has been reported that a combination of ultrasound and contrast-enhanced ultrasound has good diagnostic value in distinguishing between cervical lymphadenitis and primary lymphoma 15 . However, these methods still depend on the subjectivity of the radiologist’s judgment in ultrasound interpretation.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

Wang,

Yang,

Yang

et al. 2024

Sci Rep

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To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

Wang,

Yang,

Yang

et al. 2024

Sci Rep

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Clinical applications of superb microvascular imaging and virtual touch imaging quantification in pediatric mesenteric lymphadenitis diagnosis: A promising pathway to enhanced precision

Zhu,

Zhou,

et al. 2024

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BACKGROUND: Mesenteric lymphadenitis (ML) demonstrates a distinctive inclination for the pediatric and adolescent demographic and the diagnosis of ML in young children poses a substantial challenge. OBJECTIVE: This prospective study aimed to assess the diagnostic efficacy of Superb Microvascular Imaging (SMI) and Virtual Touch Tissue Imaging quantification (VTIQ) in distinguishing pediatric mesenteric lymphadentitis. METHODS: We examined 82 mesentric lymph node (MLN) in pediatric patients with mesenteric lymphadentitis and 50 MLN in a healthy group. SMI was utilized to evaluate vascularity within the MLN, while MLN stiffness, quantified as shear wave velocity (SWV) in meters per second (m/s), was assessed using VTIQ. We compared the diagnostic performance of greyscale Ultrasound, US combined with SMI, US combined with VTIQ, and US combined with both SMI and VTIQ. RESULTS: SMI revealed a significant distinction between mesenteric lymphadentitis and normal MLN (p < 0.001). MLN affected by mesenteric lymphadentis exhibited increased vascularity (marked vascularity: 13/82, 15.85%) compared to normal MLN (marked vascularity: 1/50, 2.00%). Statistically significant differences were observed in SWV values beween mesenteric lymphadentitis and normal MLN (all p-values <0.001). The mean and minimum SWV values for MLN with mesenteric lymphadentitis were 1.66±0.77 m/s and 1.51±0.53 m/s, respectively. Control group SWV values were approximately three times higher than those in the mesenteric lymphadenitis group. The highest area under the curve values were achieved with the combination of all three modalities (0.837, 95% confidence interval: 0.763– 0.896), followed by US + VTIQ (0.795, 0.716– 0.860), US + SMI (0.753, 0.670– 0.824) and US alone (0.642, 0.554– 0.724). CONCLUSION: SMI and VTIQ offer a promising noninvasive adjunct to grayscale ultrasound for identifying mesenteric lymphadentitis in pediatric patients.

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A combination of ultrasound and contrast-enhanced ultrasound improves diagnostic accuracy for the differentiation of cervical tuberculous lymphadenitis from primary lymphoma

Cited by 2 publications

References 27 publications

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

Clinical applications of superb microvascular imaging and virtual touch imaging quantification in pediatric mesenteric lymphadenitis diagnosis: A promising pathway to enhanced precision

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