The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma

The most common cause of cancer death around the world is the lung and bronchus according to the 2017 cancer statistics [1‐3]. Patients with lung cancer usually have a bad prognosis because most of them are diagnosed at an advanced stage (III or IV) with no discriminating symptoms as compared to early stage [4]. In clinical practice, accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging. The detection of SPN is increasing with years worldwide, mainly because of the wide use of low-dose chest computed tomography (CT) screening. In the Early Lung Cancer Action Project performed by Henschke et al. [5], the detection rate of SPN was as high as 23%, which increased to 39.5% in patients received lung operation [6]. For indeterminate solid and ground-glass nodules, they should be followed with CT at least 2 and 3 years, respectively, according to the international guidelines for the management of SPN [7, 8]. Therefore, accurate diagnosis of SPN using advanced tool will reduce health costs and extensive CT examinations with no additional benefits. Also, clinicians need an non-invasive imaging tool to determine whether a patient needs surgery or long-term follow-up.

Recently, by high throughput extracting quantitative imaging features from standard-of-care medical images, radiomics provides us a promising and non-invasive tool in cancer research [9, 10]. The radiomic features mined by sophisticated bioinformatics tools might involve the process of diagnosis, prognosis and prediction [11]. Radiomic signature constructed by significant features has been applied for precision diagnosis and treatment of cancer, which will promote the development of precision medicine. Currently, radiomics has been used to decode tumor phenotypes, histological subtypes and pathological response of lung cancer [12‐14].

Therefore, the aim of this study was develop and validate a radiomic nomogram for the individual preoperative prediction of lung adenocarcinoma from benign SPN, which would improve the decision-making of SPN in clinical practice.

We trained and tested a radiomic nomogram based on the radiomic signature and the anatomical CT features for individualized preoperative prediction of lung adenocarcinoma, which showed good discriminative power and calibration. This study indicates that CT-derived radiomic features supplement the CT findings reported by radiologists in the prediction process. Note that, this study provides a non-invasive and effective prediction tool to determine those patients with a high probability of lung adenocarcinoma.

Early diagnosis of cancer is associated with prolonged survival [18], for instance, the 5-year overall survival of breast cancer was 74.8% between 1975 and 1977; between 2003 and 2009, the number has significantly increased to 90.3% [19]. This increase is mainly due to earlier detection because of the extensive application of mammography for cancer screening [19]. Currently, small pulmonary nodules are still a common and challenging clinical problem. The classification performance of CT is limited, especially in small nodules (≤10 mm in diameter). More accurate and reliable non-invasive diagnostic tool is urgently needed for precise treatment. Early diagnosis of malignant pulmonary nodules is crucial for the improvement of patient’s long-term overall survival.

To date, radiologists diagnose lung cancer by largely depending on qualitative features of CT images, such as nodule diameter, evidence of spiculation, upper lobe location, and pleural indentation [20]. Low-dose CT screening for pulmonary nodules may reduce mortality, however, it also has the risk of overdiagnosis due to detect indolent tumors [5]. Some radiologists contended serial examinations for all serendipitous SPN on CT to render an timely lung operation for cure [7], which may be too aggressive. Excessive detection of SPN might has potential adverse implications on current medical system and clinical practice, such as low utilization of limited resources, raised health care costs, increased radiation and risk for morbidity and mortality of patients [7]. CT-guided percutaneous biopsy has commonly used to obtain tumor histological results due to the characteristics of peripheral location of most pulmonary nodules. However, in actual clinical practice, progressively smaller nodules often result in reduced sensitivity for percutaneous biopsy [21, 22] and other factors also influence the accuracy of biopsy including nodule morphology and length of needle path [20]. In addition, percutaneous biopsy has several limitations, such as invasive nature and high risk for complications [23]. Therefore, non-invasive imaging-based biomarkers are needed to provide additional diagnosis information.

Recently, the increased training of medical image analysis and tools has driven additional studies investigating the radiomics of lung cancer. Radiomic signatures may help to mining bioinformatics behind lung cancer on medical image, for instance, tumor staging [24], gene expression patterns [25], treatment response [26, 27], and patient survival [28, 29]. Current determination of whether radiomic features can improve the prediction of pulmonary nodules as being malignant as opposed to conventional visual assessment on CT is a hot topic [30, 31], but most studies have examined nodules smaller than 30 mm in diameter. In this study, 210 SPN less than 10 mm with surgery-proven malignancy or benign status were included for radiomic analysis. All radiomic features were extracted from a same CT scanner, with same imaging parameters and reconstruction slice thicknesses. As Wu et al. indicated, without control of the variability of factors such as imaging scanners, scanning parameters, the performance of radiomic features could be depressed [32]. An increased number of radiomic features has the potential ability to quantify intra-tumoral heterogeneity. However, most of high-dimensional features are redundant, which will cause poor classification performance. We aimed to select the radiomic features that most associated with lung adenocarcinoma. Only five useful features were selected from 385 features by LASSO algorithm. Unlike previous studies, this study describes some important CT findings that contribute to the differential diagnosis of lung adenocarcinoma. After multivariate analysis, internal composition and margins were two independent clinical features of lung adenocarcinoma. Those nodules with GGN, lobulation and/or signs of speculation had a higher risk for malignancy, which was consistent with the radiologists’ experience. The conventional CT signature attained a accuracy of 0.735 and 0.651 in the training and validation dataset, respectively. We hypothesized that radiomic features could further improve the diagnostic accuracy of a CT signature. Our study demonstrated the predictive performance of conventional CT features was improved by adding radiomic features, attaining accuracy of 0.871 and 0.841 in the training and validation datasets, respectively.

A number of risk models have been developed, of varying complexity for identifying risk of incident lung cancer among patients with visible lung nodules [33‐38]. The models were based on significant patient and nodule characteristics. The accuracy and clinical utility of predictive models depends on the case mix of the population in which it was derived and the prevalence of malignancy in that population. The risk prediction models should be externally validated before they are used in a different clinical setting and population. The four validated models were the Mayo Clinic [33], Veterans Administration [34], Herder [39] and Brock [38]. The studies have shown AUC of 0.89 for Mayo Clinic model, 0.74 for Veterans Administration, 0.92 for Herder and 0.90 for Brock. Our radiomic model achieved similar performance, with an AUC of 0.857. Compared with previous models, our model didn’t consider patient data, but included radiomic features extracted from CT images that could reflect intratumoral heterogeneity. However, our model lacks external validation. We hope to explore the added value of radiomics to the existing risk prediction models.

In summary, this study showed the potential of radiomic features extracted from unenhanced CT images for predicting lung cancer before surgery. Radiomic features showed the added value to the conventional CT features in differentiating lung adenocarcinoma from benign SPN. This study provides doctors a radiomic nomogram as a non-invasive tool for individualized prediction of lung cancer preoperatively. However, before applying in real-world setting, more studies are needed to validate the performance of the radiomic nomogram.