Zhang Fuzhen, Zhang Yu, Yang Zilong, Liu Ruichao, Li Shanshan, Pang Yu, Li Liang
Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, PR China; Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China.
Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN 47405, USA.
J Infect Public Health. 2025 Feb;18(2):102628. doi: 10.1016/j.jiph.2024.102628. Epub 2024 Dec 20.
Early evaluation of culture conversion after 6-month treatment of multidrug-resistant tuberculosis (MDR-TB) is vital for outcome prediction. This study aims to merge the maximum lesion cross-sectional area observed via computed tomography (CT) imaging during treatment to predict therapeutic response.
We retrospectively involved MDR-TB patients who completed 6 months of treatment from two hospitals. Patients were categorized into culture conversation and no culture conversation groups based on sputum culture results. The data from the two hospitals were used as internal training and external testing cohorts, respectively. Logistic regression and random forest models were developed using the maximum lesion cross-sectional area and most important predictive features. The model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score.
In the model without the maximum lesion cross-sectional area to predict culture conversion for MDR-TB after 6 months of treatment, logistic regression and random forest models achieved AUC values of 0.796 and 0.958, sensitivities of 0.725 and 0.993, and F1 scores of 0.803 and 0.957 in the training cohort, respectively. In the testing cohort, logistic regression and random forest models achieved AUC values of 0.889 and 0.855, respectively. Evaluating the maximum lesion cross-sectional area at baseline, 2 months, and 6 months, logistic regression and random forest models in the training cohort yielded AUC values of 0.819 and 0.998, sensitivities of 0.674 and 1.000, and F1 scores of 0.772 and 0.986. In the testing cohort, the AUC values were 0.869 and 0.920, sensitivities were 0.933 and 1.000, and F1 scores were 0.848 and 0.841, respectively.
The integration of maximum lesion cross-sectional area during treatment can improve the prediction of early treatment response in MDR-TB. When applied in a clinical setting, the random forest model is more suitable for guiding appropriate treatment plans quickly.
对耐多药结核病（MDR-TB）进行6个月治疗后早期评估培养转化情况对于预测治疗结果至关重要。本研究旨在整合治疗期间通过计算机断层扫描（CT）成像观察到的最大病灶横截面积，以预测治疗反应。
我们回顾性纳入了来自两家医院完成6个月治疗的耐多药结核病患者。根据痰培养结果将患者分为培养转化组和未培养转化组。两家医院的数据分别用作内部训练队列和外部测试队列。使用最大病灶横截面积和最重要的预测特征建立逻辑回归和随机森林模型。使用曲线下面积（AUC）、准确性、敏感性、特异性和F1分数评估模型性能。
在没有最大病灶横截面积的模型中，用于预测耐多药结核病6个月治疗后的培养转化情况，逻辑回归和随机森林模型在训练队列中的AUC值分别为0.796和0.958，敏感性分别为0.725和0.993，F1分数分别为0.803和0.957。在测试队列中，逻辑回归和随机森林模型的AUC值分别为0.889和0.855。评估基线、2个月和6个月时的最大病灶横截面积，训练队列中的逻辑回归和随机森林模型的AUC值分别为0.819和0.998，敏感性分别为0.674和1.000，F1分数分别为0.772和0.986。在测试队列中，AUC值分别为0.869和0.920，敏感性分别为0.933和1.000，F1分数分别为0.848和0.841。
整合治疗期间的最大病灶横截面积可改善耐多药结核病早期治疗反应的预测。在临床环境中应用时，随机森林模型更适合快速指导适当的治疗方案。
