Introduction
Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancer that is the most common cause of cancer-related mortality worldwide, with an estimated 1.4 million deaths each year [
1]. Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most common subtypes of NSCLC [
2]. Different pathological subtypes have distinct phenotypic and biological characteristics, which are directly related to the clinical treatment and outcome [
3‐
5]. With advances in targeted therapies, molecularly targeted agents that inhibit epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) can significantly improve the efficacy and reduce the toxicity of NSCLC, as almost all these gene mutations are found in ADC [
6,
7]. Therefore, accurately predicting the histological subtypes is essential for determining better therapeutic strategies in NSCLC.
An invasive biopsy for histological confirmation is commonly used in clinical practice [
8]. However, with the development of various detection technologies in recent years, high-precision noninvasive detection has been paid more attention and recognized by clinicians; moreover, biopsy is contraindicated for patients with severe cardiopulmonary insufficiency, such as severe pulmonary arterial hypertension, or uncorrectable coagulopathy, or unable to cooperate with the operation [
9,
10]. In addition, when the pathological tissue obtained from the first puncture is few and fails to meet the needs for an accurate diagnosis, it is more difficult to biopsy again [
11]. Thus, it is clinically important and necessary to explore a reliable, noninvasive, and practical method for the pre-therapy prediction of the histologic subtypes for treatment decision making and prognosis estimation in NSCLC patients.
Radiomics based on conventional medical images has been used to quantitatively assess tumor heterogeneity in more detail than visual analysis by analyzing the distribution and relationship of pixel or voxel gray levels in the lesion area [
12,
13].
18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)–based radiomics have been shown to have potential in differentiating ADC from SCC [
14,
15]. Further studies have revealed that the discrimination performance could be further improved by combining with clinical features, like sex and smoking history (area under curve (AUC) = 0.859), which are higher than that of radiomic alone [
16,
17]. However, only PET radiomic parameters were extracted and analyzed in the above studies. There is no single feature that can adequately describe the pathological phenotype of lesions due to the tumor heterogeneity [
18].
Hence, the aim of this study was to develop and validate a prediction model, integrating the clinical characteristics, tumor marker levels [
19], and radiomic features extracted from both the PET and CT images from the same volume of interest (VOI), for differentiating SCC from ADC in NSCLC and to provide a visually quantitative nomogram in clinical practice.
Discussion
In this study, we successfully constructed and validated a Combined Model containing clinical factors, tumor markers, and radiomic features extracted from both the PET and CT images, which held an excellent performance in noninvasively stratifying NSCLC patients according to their pathological subtypes. In addition, we developed a visually quantitative nomogram for conveniently using this prediction model in clinical practice.
Of the clinical factors selected in the Combined Model, sex differences among NSCLC patients have been widely reported, with that SCC affecting more males than females [
28]. The lesions are generally bigger in SCC patients than in ADC patients [
29]. Tumor markers in serum are beneficial for the diagnosis and prognosis of NSCLC [
30]. The serum levels of SCCA and CYFRA21-1 are highly sensitive in NSCLC and significantly higher in SCC than in ADC [
31]. The results of this study are consistent with the conclusions of the above reports.
Different pathological subtypes lead to various clinical strategies and prognoses for NSCLC patients [
5,
32]. The PET/CT-based radiomic is a relatively new quantitative imaging technique for the noninvasive assessment of tumors [
33]. Ha S, et al. found that PET radiomic features were significantly different between ADC and SCC with 0.90 linear separability, but the study population was only 30 people [
34]. Koyasu S, et al. also showed that PET radiomics was indeed useful in NSCLC subtypes with an AUC of 0.843 [
15]. However, the radiomic approaches in the above studies were not be validated in another independent dataset. In this study, both the PET and CT radiomic approaches were applied and validated to have a good performance in the classification of NSCLC subtypes (AUCs (PET-Rad Model and CT-Rad Model) = 0.835 and 0.784, respectively). The above results indicated that the relationship between medical images and tumor molecular phenotypes can be established by radiomics, and then the diagnostic information of tumors can be obtained noninvasively through medical images for some patients who are not eligible for biopsy.
In addition, since radiomic extracts information from the tumor, an appropriate tumor segmentation algorithm is important for measuring tumor image parameters [
35]. Ideally, the chosen segmentation method is both accurate and robust. Bashir et al. had compared various segmentation algorithms (freehand, 40% of maximum intensity threshold, and fuzzy locally adaptive Bayesian algorithms) in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from NSCLC
18F-FDG PET/CT images [
21]. They found that the models generated by all three segmentation algorithms were of at least equivalent utility. Moreover, segmentation with 40% of maximum threshold leads to the best reproducibility of image biomarkers when used by different observers. In this study, the agreements of the radiomic feature extraction using semiautomatic threshold-based methods were excellent (all ICCs > 0.85,
p < 0.05). The high ability to reproduce and validate radiomic studies is vital to generating sufficient and convincing scientific evidence for translating potential applications into clinical practice [
33,
36].
This study also explored whether the prediction performance based on radiomics could be further improved by combining with clinical factors and tumor marker levels. The Combined Model established in the present study not only significantly improved the prediction efficiency for subtype compared to these factors alone in both the training and validation sets (AUCs = 0.932 (training set), 0.901 (validation set), respectively) but also had higher performance than previous researches [
14‐
17]. This discrepancy may be related to the complete and standard preoperative baseline data and postoperative pathological reports from a single center, as well as the appropriate algorithm [
37]. The results of this study confirm the hypothesis and indicate that the heterogeneity of tumors can be evaluated more comprehensively by combining with multiscale characteristics of tumors, consistent with the report [
38].
In addition, we generated an integrated nomogram on the basis of the Combined Model for facilitating its use in clinical practice. Clinical factors such as patient’s sex and age are recorded routinely at hospital admission. Moreover, we strongly recommend that serum tumor marker levels should be evaluated in patients who are highly suspected of having NSCLC or initially diagnosed with NSCLC, especially SCCA, CYFRA21-1. Both physicians and patients could perform a preoperative individualized prediction of the risk of ADC with this easy-to-use scoring tool, which can provide a noninvasive and accurate approach for patients who are unwilling or unable to undergo biopsy to develop more reasonable and effective treatment plans, especially the need of targeted therapy [
39]. The DCA showed that if the threshold probability of a patient or doctor is > 10%, using this nomogram to predict the subtype adds more benefit than either the treat-all-patients as SCC or the treat-all-patients as ADC, which is more valuable for the current trend toward personalized medicine [
40].
Although the results were encouraging, the present study had several limitations. Firstly, the sample selection was biased in this single-center retrospective study, and a new multicenter prospective study is still necessary to be designed for the further evaluation and verification of the generalization ability of the models. Secondly, some NSCLC patients, especially ADC patients, were excluded from the radiomic analysis due to the faint 18F-FDG uptake or small tumor size to ensure the quality of images and textural data. Small lesions are easier to be discovered in the early stage with the increasing use of imaging screening for lung cancer. Thus, a more sensitive tool that adaptively detects small tumors will be an important direction for future work. Finally, the patients with non-primary lung lesions were also excluded due to the purpose of this study. Noticeably that both primary and metastatic pulmonary nodules are very important for patients and clinical settings in the cancer center. The prediction model that widely used for lung lesions will be continually explored in future studies.
In conclusion, an integrated nomogram was constructed and validated in our study, which could provide a relatively accurate, convenient, and noninvasive method for the individualized discrimination between ADC and SCC in NSCLC patients, assisting in clinical decision making for precision treatment.
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