Diagnostic Test Comparison of Tuberculous Lesions Found in Lymph Nodes From Slaughtered Cattle

Morey-Matamalas, A.; Vidal, Enríc; Val, Bernat Pérez de; Marco, Alberto; Martínez, Jorge

doi:10.1016/j.jcpa.2017.10.131

Cited by 1 publication

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“…Solitary tuberculous nodules are often misdiagnosed as lung cancer, in ammatory nodules and other lesions because they are relatively rare and lack common tuberculosis features [19] . Tuberculous nodules are essentially granulomatous lesions, consisting mainly of brous tissue and its surrounding caseous necrosis, surrounded by granulation tissue [20] . Due to the proliferation of granulation tissue and the binding of the bronchial wall, there is less marginal in ltration.…”

Section: Discussionmentioning

confidence: 99%

The Value of CT Radiomics Nomogram Analysis of Parenchyma Surrounding Pulmonary Nodules in the Differentiation of Inflammatory Nodules from Tuberculous Nodules

Fan

Zhang

et al. 2021

Preprint

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Background: To investigate the value of CT Radiomics nomogram based on pulmonary nodules and surrounding parenchyma in differentiating non-tuberculous inflammatory pulmonary nodules from tuberculous nodules.Methods: A retrospective analysis was performed on 273 patients with pulmonarynodules confirmed by surgery and pathology in the Second Affiliated Hospital of Army Military Medical University from January 2015 to March 2021, including 164 cases of non-tuberculous inflammatory nodules and 109 cases of tuberculous nodules.Pulmonary nodule (ROI1), 3mm parenchymal band around nodule (ROI2) and nodules with external expansion (ROI3) were segmented andradiomic features were extracted by A.K. software. The stability of the features was analyzed by ICC, and the features were divided into the training set and the verification set by random stratified sampling according to the ratio of 7:3. The radiomics label was constructed by using the maximum relevance and minimum redundancy method (mRMR) and the least absolute shrinkage and selection operator method (LASSO) after two dimensional reduction.Finally, multiple Logistic regression analysis was conducted to select the optimal model among the three models, and the classification model including the radiomicfeatures and clinical risk factors was established.The identification effectiveness of the model was evaluated in terms of average area under curve (AUC), accuracy, sensitivity, and specificity, and was independently validated in a validation cohort.Results: There was significant difference in the type of pulmonary nodules between the two groups (p< 0.05). Nodules with external expansion model has the best diagnostic performance, and combined with clinical features (nodular type) to construct a classification model.The AUC of both the training cohort and the validation cohort was 0.93. Decision curve analysis showed that the radiomics nomogram had good value in classification of the two categories of nodules.Conclusions: The CT radiomics nomogram based on nodule plus surrounding substance has good performance in judging non-tuberculous inflammatory nodules and tuberculous nodules, which can provide reference for clinical diagnosis and treatment, and has good clinical application value and development prospect.

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Section: Discussionmentioning

confidence: 99%