Abstract
Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.
Highlights
Lung cancer is the second most commonly diagnosed cancer for both men and women, representing around 13–14% of yearly cancer diagnoses for both genders
We present a CT radiomic model with a neural network classifier for differentiating small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC) adenocarcinoma with satisfying classification performance achieving an AUC of 0.93
We improved the model performance by including clinical data such as smoking history, which was relevant because smoking was a major risk factor for SCLC
Summary
Lung cancer is the second most commonly diagnosed cancer for both men and women, representing around 13–14% of yearly cancer diagnoses for both genders. Treatment options and survival largely depend on the type of lung cancer [4,5,6]. The new standard of care for advanced SCLC consists of a combination of carboplatin, etoposide and immunotherapy; chemoradiation, targeted therapies, and immunotherapy are the treatment options available to patients with advanced NSCLC [5, 7]. In the context of personalized medicine for NSCLC, targeted therapies for common driver mutations, and immunotherapy targeting the PD-1 receptor and its ligand PD-L1 have shown promising data for improving treatment and survival [10, 11]. The primary factor in survival for both SCLC and NSCLC is early diagnosis that can be facilitated by an identification of radiologic phenotypes for the primary lung cancer subtypes
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