Establishment and validation of a convenient and efficient screening tool for active pulmonary tuberculosis in lung...

Coexistent pulmonary tuberculosis and lung cancer (PTB-LC) is a rare type of disease with frequent under- and/or mis-diagnosis. Establishment of a reliable screening model for PTB-LC holds considerable medical and economic significance.

We aimed to develop an efficient and convenient tool to identify high-risk individuals for tuberculosis (TB) infection among LC patients based on commonly available parameters in clinical practice.

This study consisted of a primary retrospective patient cohort for model construction and verification, and a prospective patient cohort for prospective validation.

Patients with active PTB-LC and LC diagnosed in Beijing Chest Hospital from 2018 to 2022 were collected and 1:1 matched according to time of admission and were classified into a training set (n = 281) and testing set (n = 121). Baseline information, clinicopathological features, imaging manifestations, and blood testing results were collected and analyzed. Five machine learning methods, including logistic regression (LR), random forest (RF), support vector machine (SVM), decision tree (DT), and neural network (NN), were employed to develop a screening model for PTB-LC.

Through multivariable analysis, gender, pleural effusion, cavitation, monocyte count (MONO), and plasma adenosine deaminase (ADA) levels were identified as independent predictors of PTB-LC and included in model construction. LR, RF, SVM, DT, and NN were used to construct the screening or pre-diagnosis models. The RF demonstrated the best performance with an area under the curve of 0.966 in the training set, 0.817 in the testing set, and 0.805 in the prospective dataset. The accuracy, precision, recall, and F1 score of the RF model of the training set were 0.88, 0.87, 0.89, and 0.88, respectively, and these indicators of the testing set were 0.71, 0.75, 0.72, and 0.74, respectively, which were superior to those of other methods. The prospective cohort further validated the good performance of the screening model. We also established a nomogram with gender, pleural effusion, cavitation, MONO, and serum ADA in assessing high-risk patients of developing TB infection. Further TB-related diagnostic tests were recommended for these high-risk patients.

The RF screening model constructed with gender, pleural effusion, cavitation, MONO, and ADA may help identify high-risk patients of PTB-LC from LC alone cases.