The impact of maximum cross-sectional area of lesion on predicting the early therapeutic response of multidrug-resistant tuberculosis

Background

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.

Methods

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.

Results

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.

Conclusions

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.