International journal of surgery (London, England) · 2025
Development and validation of a deep learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis a multicenter study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Zhang Xu, Dong Zhijian, Li Hongming, Cheng Yijing, Tang Wei, Ni Tu, Zhang Ying, Ai Qinqin, Yang Gaoyi
Аннотация
BACKGROUND: To develop and validate an ensemble machine learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis (LNTB).
MATERIALS AND METHODS: This multicenter study retrospectively included 234 cervical LNTB patients from one center, randomly divided into training (70%) and internal validation (30%) cohorts. Radiomic features were extracted from ultrasound images, and an L1-based method was used for feature selection. A predictive model combining ensemble machine learning and AdaBoost algorithms was developed to predict drug resistance. Model performance was assessed using independent external test sets (Test A and Test B) from two other centers, with metrics including AUC, accuracy, precision, recall, F1 score, and decision curve analysis.
RESULTS: Of the 851 radiometric features extracted, 161 were selected for the model. The model achieved AUCs of 0.998 (95% CI: 0.996-0.999), 0.798 (95% CI: 0.692-0.904), 0.846 (95% CI: 0.700-0.992), and 0.831 (95% CI: 0.688-0.974) in training, internal validation, and external test sets A and B, respectively. The decision curve analysis showed a substantial net benefit across a threshold probability range of 0.38 to 0.57.
CONCLUSION: The LNTB resistance prediction model developed demonstrated high diagnostic efficacy in both internal and external validation. Radiomics, through the application of ensemble machine learning algorithms, provides new insights into drug resistance mechanisms and offers potential strategies for more effective patient treatment.
MATERIALS AND METHODS: This multicenter study retrospectively included 234 cervical LNTB patients from one center, randomly divided into training (70%) and internal validation (30%) cohorts. Radiomic features were extracted from ultrasound images, and an L1-based method was used for feature selection. A predictive model combining ensemble machine learning and AdaBoost algorithms was developed to predict drug resistance. Model performance was assessed using independent external test sets (Test A and Test B) from two other centers, with metrics including AUC, accuracy, precision, recall, F1 score, and decision curve analysis.
RESULTS: Of the 851 radiometric features extracted, 161 were selected for the model. The model achieved AUCs of 0.998 (95% CI: 0.996-0.999), 0.798 (95% CI: 0.692-0.904), 0.846 (95% CI: 0.700-0.992), and 0.831 (95% CI: 0.688-0.974) in training, internal validation, and external test sets A and B, respectively. The decision curve analysis showed a substantial net benefit across a threshold probability range of 0.38 to 0.57.
CONCLUSION: The LNTB resistance prediction model developed demonstrated high diagnostic efficacy in both internal and external validation. Radiomics, through the application of ensemble machine learning algorithms, provides new insights into drug resistance mechanisms and offers potential strategies for more effective patient treatment.