Abstract
BACKGROUND: Co-existent pulmonary tuberculosis and lung cancer (PTB-LC) represent a unique disease entity often characterized by missed or delayed diagnosis. This study aimed to investigate the clinical and radiological features of patients diagnosed with PTB-LC. METHODS: Patients diagnosed with active PTB-LC (APTB-LC), inactive PTB-LC (IAPTB), and LC alone without PTB between 2010 and 2022 at our institute were retrospectively collected and 1:1:1 matched based on gender, age, and time of admission. Symptoms and clinicopathological features were compared among the three groups of patients. Logistic regression was employed for risk factor analysis. RESULTS: Compared to LC or IAPTB-LC, patients with APTB-LC exhibited higher proportions of weight loss (p < 0.001) and fever (p < 0.001) at the time of diagnosis. Additionally, radiological features such as nodules (p = 0.007), tree-in-bud (p < 0.001), cavitation (p < 0.001), and calcification (p < 0.001) were significantly more prevalent in APTB-LC patients compared to the other groups. Patients with APTB-LC were more susceptible to lymph node involvement (p < 0.001) and distant metastasis (p = 0.006) compared to those with IAPTB-LC or LC alone. Additionally, in comparison to LC alone, patients with IAPTB-LC exhibited more complex symptoms, imaging features, and lymph node metastases. Logistic regression results indicated that factors such as BMI, fever, patchy shadow, cavitation, neck or supraclavicular lymph node metastasis, and liver injury favor the diagnosis of APTB-LC over LC alone. The pre-diagnostic model exhibited robust performance, achieving area under the curve (AUC) values of 0.864 in the training set and 0.830 in the test set. CONCLUSION: Our results indicate that PTB-LC is a distinct disease characterized by complex clinicopathological features and a more aggressive nature. Based on our findings, we recommend conducting TB-related tests for LC patients who exhibit relevant risk factors or are identified as high-risk cases according to the pre-diagnostic model.