A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients
Similar clinical features make the differential diagnosis difficult, particularly between lung cancer and pulmonary tuberculosis (TB), without pathological evidence for patients with concomitant TB infection. Our study aimed to build a nomogram to predict malignant pulmonary lesions applicable to c...
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2024-03-01
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| Series: | Monaldi Archives for Chest Disease |
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| Online Access: | https://www.monaldi-archives.org/macd/article/view/2847 |
| _version_ | 1797263112986951680 |
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| author | Zhi Xia Xueyao Rong Qiong Chen Min Fang Jian Xiao |
| author_facet | Zhi Xia Xueyao Rong Qiong Chen Min Fang Jian Xiao |
| author_sort | Zhi Xia |
| collection | DOAJ |
| description |
Similar clinical features make the differential diagnosis difficult, particularly between lung cancer and pulmonary tuberculosis (TB), without pathological evidence for patients with concomitant TB infection. Our study aimed to build a nomogram to predict malignant pulmonary lesions applicable to clinical practice. We retrospectively analyzed clinical characteristics, imaging features, and laboratory indicators of TB infection patients diagnosed with lung cancer or active pulmonary TB at Xiangya Hospital of Central South University. A total of 158 cases from January 1, 2018 to May 30, 2019 were included in the training cohort. Predictive factors for lung cancer were screened by a multiple-stepwise logistic regression analysis. A nomogram model was established, and the discrimination, stability, and prediction performance of the model were analyzed. A total of 79 cases from June 1, 2019, to December 30, 2019, were used as the validation cohort to verify the predictive value of the model. Eight predictor variables, including age, pleural effusion, mediastinal lymph node, the number of positive tumor markers, the T cell spot test for TB, pulmonary lesion morphology, location, and distribution, were selected to construct the model. The corrected C-statistics and the Brier scores were 0.854 and 0.130 in the training cohort, and 0.823 and 0.163 in the validation cohort. Calibration plots showed good performance, and decision curve analysis indicated a high net benefit. In conclusion, the nomogram model provides an effective method to calculate the probability of lung cancer in TB infection patients, and it has excellent discrimination, stability, and prediction performance in detecting a malignant diagnosis of undiagnosed pulmonary lesions.
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| first_indexed | 2024-04-25T00:07:50Z |
| format | Article |
| id | doaj.art-3f7e3829d68b42129c7a2f0ac6f5d8dd |
| institution | Directory Open Access Journal |
| issn | 1122-0643 2532-5264 |
| language | English |
| last_indexed | 2024-04-25T00:07:50Z |
| publishDate | 2024-03-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Monaldi Archives for Chest Disease |
| spelling | doaj.art-3f7e3829d68b42129c7a2f0ac6f5d8dd2024-03-13T20:18:56ZengPAGEPress PublicationsMonaldi Archives for Chest Disease1122-06432532-52642024-03-0110.4081/monaldi.2024.2847A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patientsZhi Xia0Xueyao Rong1Qiong Chen2Min Fang3Jian Xiao4Department of Oncology, Hunan Provincial People's Hospital, Changsha; Key Laboratory of Small Molecule Targeted Drug Research and Creation in Hunan Province, Changsha; Hunan Provincial Clinical Medical Research Center for Hepatobiliary Pancreatic Tumors, ChangshaDepartment of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, ChangshaDepartment of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, ChangshaHunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, the “Double-First Class” Application Characteristic Discipline of Hunan Province (Pharmaceutical Science), Changsha Medical University; School of Pharmacy, Changsha Medical UniversityDepartment of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha Similar clinical features make the differential diagnosis difficult, particularly between lung cancer and pulmonary tuberculosis (TB), without pathological evidence for patients with concomitant TB infection. Our study aimed to build a nomogram to predict malignant pulmonary lesions applicable to clinical practice. We retrospectively analyzed clinical characteristics, imaging features, and laboratory indicators of TB infection patients diagnosed with lung cancer or active pulmonary TB at Xiangya Hospital of Central South University. A total of 158 cases from January 1, 2018 to May 30, 2019 were included in the training cohort. Predictive factors for lung cancer were screened by a multiple-stepwise logistic regression analysis. A nomogram model was established, and the discrimination, stability, and prediction performance of the model were analyzed. A total of 79 cases from June 1, 2019, to December 30, 2019, were used as the validation cohort to verify the predictive value of the model. Eight predictor variables, including age, pleural effusion, mediastinal lymph node, the number of positive tumor markers, the T cell spot test for TB, pulmonary lesion morphology, location, and distribution, were selected to construct the model. The corrected C-statistics and the Brier scores were 0.854 and 0.130 in the training cohort, and 0.823 and 0.163 in the validation cohort. Calibration plots showed good performance, and decision curve analysis indicated a high net benefit. In conclusion, the nomogram model provides an effective method to calculate the probability of lung cancer in TB infection patients, and it has excellent discrimination, stability, and prediction performance in detecting a malignant diagnosis of undiagnosed pulmonary lesions. https://www.monaldi-archives.org/macd/article/view/2847Nomogramlung cancertuberculosis infectionT-SPOT.TB |
| spellingShingle | Zhi Xia Xueyao Rong Qiong Chen Min Fang Jian Xiao A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients Monaldi Archives for Chest Disease Nomogram lung cancer tuberculosis infection T-SPOT.TB |
| title | A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients |
| title_full | A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients |
| title_fullStr | A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients |
| title_full_unstemmed | A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients |
| title_short | A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients |
| title_sort | nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients |
| topic | Nomogram lung cancer tuberculosis infection T-SPOT.TB |
| url | https://www.monaldi-archives.org/macd/article/view/2847 |
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