Diagnostic accuracy of computed tomography imaging for the detection of differences between peripheral small cell lung cancer and peripheral non-small cell lung cancer
| Autor: | Weidong Hu, Xiaoyan Shen, Xiaoxuan Wei, Yanchen Ren, Yiyuan Cao |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
Lung Neoplasms Peripheral non-small cell lung cancer Pleural Neoplasms CT features 030218 nuclear medicine & medical imaging 03 medical and health sciences Sensitivity 0302 clinical medicine Surgical oncology Carcinoma Non-Small-Cell Lung Humans Medicine Peripheral small cell lung cancer Lung cancer Pathological Retrospective Studies business.industry Area under the curve Retrospective cohort study Hematology General Medicine medicine.disease Small Cell Lung Carcinoma Confidence interval Peripheral Logistic Models Oncology 030220 oncology & carcinogenesis Multivariate Analysis Specificity Original Article Surgery Radiology Tomography X-Ray Computed business |
| Zdroj: | International Journal of Clinical Oncology |
| ISSN: | 1437-7772 1341-9625 |
| DOI: | 10.1007/s10147-017-1131-0 |
| Popis: | Background To evaluate the computed tomography features of peripheral small cell lung cancer and non-small cell lung cancer and to establish a predictive model to conveniently distinguish between them. Materials and methods We retrospectively reviewed the computed tomography features of 51 patients with peripheral small cell lung cancer and 207 patients with peripheral non-small cell lung cancer after pathological diagnosis. Thirteen computed tomography morphologic findings were included and analyzed statistically. Meaningful features were analyzed by logistic regression for multivariate analysis. We then used β-coefficients as the basis to establish an image scoring prediction model. Result The meaningful morphologic features for distinguishing between peripheral small cell lung cancer and other tumor types are multinodular shape and lymphadenectasis, with scores of 12 and 11, respectively. The scores ranged from −51 to 23, and the most reasonable cut-off was −24. The available area under the curve was 0.834 (95% confidence interval [CI] 0.783–0.877). Sensitivity and specificity were 86.3% (95% CI 0.737–0.943) and 69.6% (95% CI 0.628–0.758), respectively. Conclusion The image scoring predictive model that we constructed provides a simple and economical noninvasive method for distinguishing between peripheral small cell lung cancer and peripheral non-small cell lung cancer. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink