Abstract
BackgroundTo 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 methodsWe 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.ResultThe 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.ConclusionThe 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.
Highlights
Lung cancer is a malignant tumor with high morbidity and mortality and is the leading cause of cancer death worldwide [1]
Abbreviations SCLC Small cell lung cancer NSCLC Non-small cell lung cancer peripheral SCLC (PSCLC) Peripheral small cell lung cancer peripheral NSCLC (PNSCLC) Peripheral non-small cell lung cancer 95% CI 95% Confidence interval CT Computed tomography magnetic resonance imaging (MRI) Magnetic resonance imaging PET Positron emission tomography Nomenclature Committee of the Fleischner Society (NCFS) Nomenclature Committee of the Fleischner
According to the different biological characteristics, lung cancer can be divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) using the World Health Ognization (WHO) pathological staging system [3]
Summary
Lung cancer is a malignant tumor with high morbidity and mortality and is the leading cause of cancer death worldwide [1]. In China, there is a similar situation regarding lung cancer, with official statistics showing that of 10 common cancers, lung cancer has the highest morbidity and mortality rates [2]. Depending on the different locations of the lesion, lung cancer can be divided into central type and peripheral type. According to the different biological characteristics, lung cancer can be divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) using the World Health Ognization (WHO) pathological staging system [3]. 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. We used β-coefficients as the basis to establish an image scoring prediction model.
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