Differential diagnosis of pulmonary nodular mucinous adenocarcinoma and tuberculoma with dynamic CT: a retrospective study

Yin, Yue-Hui; Qi, Yuangang; Wang, Bing

doi:10.21037/jtd-22-372

Cited by 4 publications

(6 citation statements)

References 27 publications

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“…In the diagnosis of lung cancer and tuberculosis, CT scans, especially the CT dynamic contrast-enhanced scan is an important method. There were many reports on CT for lung adenocarcinoma and pulmonary tuberculoma, but there were few reports about the CT features of PNMA and PTB [13,27,28]. As a result of this study, we determined that the mean CT value of PTB on plain scan was 31.00HU and the CT value of PNMA was 17.00HU, whereas ΔCTV was 25.49HU in the PNMA group and 4HU in the PTB group.…”

Section: Discussionmentioning

confidence: 75%

“…Computed tomography (CT) is widely used for tumor detection, staging and therapeutic response monitoring in clinical practice [12]. There have been few studies concerning the differential diagnosis of PNMA and PTB [13], results revealed that satellite lesions, were more often observed in the PTB group and the mean CT attenuation of PTB shown on the plain scan was signi cantly higher than that of PNMA (35.15 ± 16.00 vs. 24.00 ± 12.67 HU; P < 0.01). However, the enhanced value of PTB on venous scan was signi cantly lower than that of PNMA (13.44 ± 13.40 vs. 22.52 ± 14.00 HU; P = 0.02).…”

Section: Introductionmentioning

confidence: 99%

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Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodule

Zhang

Hao

et al. 2023

Preprint

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Objective: To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for preoperative differentiation of pulmonary nodular mucinous adenocarcinoma (PNMA) from pulmonary tuberculoma (PTB). Method：A total of 124 and 53 patients with PNMA and PTB, respectively，were retrospectively analyzed from January 2017 to November 2022 in The Forth Affiliated Hospital of Hebei Medical University. A total of 1037 radiomic features were extracted from the contrast enhanced computed tomography (CT). Patients were randomly divided into training group and test group at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. The clinical model was developed using logistic regression. Finally, a combined model was established based on clinical factors and radiomics features. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the predictive value of developed models. Results：Combined clinical and radiomics models established by Logistic Regression method had the best performance. The ROC-AUC (also decision curve analysis) of combined model were 0.940 and 0.990 in the training group and test group, respectively, which showed a good predictive performance for differentiation of PNMA from PTB. Briser Score of the combined model were 0.132 and 0.068 in the training group and test group, respectively. Conclusion: The combined model incorporating radiomics features and clinical parameters may have potential value for preoperative differentiation of PNMA from PTB.

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Section: Discussionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodule

Zhang

Hao

et al. 2023

Preprint

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“…CT scanning, especially CT dynamic contrast-enhanced scanning, is a valuable tool in the diagnosis of lung cancer and tuberculosis. The CT features of lung adenocarcinoma and pulmonary tuberculoma have received much attention in the literature, but PNMA and PTB have received fewer reports [ 12 , 29 , 30 ]. As a result of this study, we determined that the mean CT value of PTB on plain scan was 31.00 HU and that the CT value of PNMA was 17.00 HU, whereas the ΔCTV was 25.49 HU in the PNMA group and 4 HU in the PTB group.…”

Section: Discussionmentioning

confidence: 99%

“…There may be overlap in the morphology and CT features between PNMA and PTB. In fact, many cases of PNMA are misdiagnosed as PTB in clinical practice, which leads to missing the best treatment time, leading to a shortened survival time [ 12 , 13 ]. Distinguishing PNMA and PTB before treatment in a timely manner is important.…”

Section: Introductionmentioning

confidence: 99%

“…Computed tomography (CT) is widely used for tumor detection, staging and therapeutic response monitoring in clinical practice [ 14 ]. There have been few studies concerning the differential diagnosis of PNMA and PTB [ 12 ]. The results revealed that satellite lesions were more often observed in the PTB group, and the mean CT attenuation of PTB shown on the plain scan was significantly higher than that of PNMA (35.15 ± 16.00 vs. 24.00 ± 12.67 HU; P < 0.01).…”

Section: Introductionmentioning

confidence: 99%

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Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules

Zhang

Hao²,

et al. 2023

BMC Cancer

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Objective To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for preoperative differentiation of pulmonary nodular mucinous adenocarcinoma (PNMA) from pulmonary tuberculoma (PTB). Method A total of 124 and 53 patients with PNMA and PTB, respectively, were retrospectively analyzed from January 2017 to November 2022 in The Fourth Affiliated Hospital of Hebei Medical University (Ligang et al., A machine learning model based on CT and clinical features to distinguish pulmonary nodular mucinous adenocarcinoma from tuberculoma, 2023). A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into a training group and a test group at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (LR), support vector machine (SVM) and random forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. The clinical model was developed using logistic regression. Finally, a combined model was established based on clinical factors and radiomics features. We externally validated the three models in a group of 68 patients (46 and 22 patients with PNMA and PTB, respectively) from Xing Tai People’s Hospital (30 and 14 patients with PNMA and PTB, respectively) and The First Hospital of Xing Tai (16 and 8 patients with PNMA and PTB, respectively). The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the predictive value of the developed models. Results The combined model established by the logistic regression method had the best performance. The ROC-AUC (also a decision curve analysis) of the combined model was 0.940, 0.990 and 0.960 in the training group, test group and external validation group, respectively, and the combined model showed good predictive performance for the differentiation of PNMA from PTB. The Brier scores of the combined model were 0.132 and 0.068 in the training group and test group, respectively. Conclusion The combined model incorporating radiomics features and clinical parameters may have potential value for the preoperative differentiation of PNMA from PTB.

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Pulmonary tuberculoma-induced cyst formation leading to repeated pneumothorax: a case report

Suwa,

Kawamoto,

Morita

et al. 2024

Journal of Surgical Case Reports

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Most cases of secondary spontaneous pneumothorax in patients with active pulmonary tuberculosis are caused by rupturing of the visceral pleura caused by Mycobacterium tuberculosis. The check-valve airway mechanism in the lungs is generally involved in the formation of pulmonary cysts, which often cause spontaneous pneumothorax. Herein, we describe a rare case of repeated spontaneous pneumothorax suspected to have been caused by pulmonary cyst formation as a result of a tuberculoma. The patient was a man with a family history of pulmonary tuberculosis. Pulmonary cysts were gradually enlarged on the peripheral side of a lung mass in the upper lobe of the patient’s right lung, who experienced two spontaneous pneumothoraxes in the area. Exploratory surgery was performed to diagnose the lung mass and treat the pneumothorax, resulting in a final diagnosis of pulmonary tuberculoma. A check-valve mechanism caused by the pulmonary tuberculoma was suspected based on the patient’s clinical course.

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Differential diagnosis of pulmonary nodular mucinous adenocarcinoma and tuberculoma with dynamic CT: a retrospective study

Cited by 4 publications

References 27 publications

Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodule

Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodule

Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules

Pulmonary tuberculoma-induced cyst formation leading to repeated pneumothorax: a case report

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