Radiomics combined with clinical features in distinguishing non-calcifying tuberculosis granuloma and lung adenocarcinoma in small pulmonary nodules

Introduction

Tuberculosis (TB) is an infectious disease that is caused by a single source (MacNeil et al., 2020). According to statistics, there are about 10 million new TB patients and 1.5 million deaths each year, more than any other infectious disease (Thwaites & Nahid, 2020). Among them, pulmonary TB is the most common, accounting for about 85% of all tuberculosis cases (Reid et al., 2019). Its pathological manifestation is chronic granulomatous inflammation (Yuan & Sampson, 2018). In 2020, 1,930 million new cancer cases and 10 million deaths were estimated worldwide, with approximately 2.2 million (11.4%) new lung cancer cases and 1.8 million (18%) deaths (Sung et al., 2021). LAC is the most common malignant tumor, its prognosis is much worse than tuberculosis, so early diagnosis and treatment are very important. However, it is difficult to distinguish TBG and LAC in chest images, and even nuclear medicine is nonspecific (Fischer, Lassen & Højgaard, 2011; McWilliams et al., 2013). Because both diseases can be shown as solid nodules or masses on imaging studies and have similar radiological features. The confirmative diagnosis of pulmonary nodules is usually biopsy or surgery (Siegel, Miller & Jemal, 2019). However, this invasive examination may lead to possible tissue damage (Pisano et al., 2020). Besides, unnecessary imaging studies may also delay treatment, or miss the best treatment time window (Huo et al., 2019). Therefore, it is expected in clinical practice that a method can be used to monitor pulmonary nodules noninvasively, and may also provide effective support for the diagnosis and treatment of pulmonary nodules. Radiomics is used to extract features from radiological images and make these features in a quantifiable manner. Its purpose is to better or more consistently discover radiological features, and provide objective features that cannot be provided by standard visual image interpretation for quantitative and qualitative density and morphological characteristics of pulmonary nodules (Bi et al., 2019; Peikert, Bartholmai & Maldonado, 2020). Radiomics can be used for auxiliary diagnosis of pulmonary nodules and prognosis prediction of lung cancer (Mu et al., 2020; Hosny et al., 2018). Importantly, radiomics has been applied to evaluate the molecular and clinical features of lung cancer because of its capacity of detecting atypical features in tumor lesions (Grossmann et al., 2017). In this study, we hypothesized that radiomics analysis could distinguish TBG and LAC in small pulmonary nodules based on imaging and clinical features. To test this idea, we extracted the features of small nodules from lung CT using radiomics technology, obtained the radiological model through statistical analysis, and combined it with clinical features. Our goal is to develop a non-invasive method of distinguishing benign and malignant pulmonary nodules using radiomics models in a combination of clinical features.

Materials & Methods

Radiomics feature extraction and model building

We selected 196 cases as the training dataset (96/100 = TBG/LAC) and 84 cases as the testing dataset (41/43 = TBG/LAC). 851 radiomics features were extracted from each ROI and divided into three main categories: (1) First-order features. (2) Shape characteristics. (3) Texture features, including gray level co-occurrence matrix (GLCM) features, grey-level run-length matrix (GLRLM) features, gray level size zone matrix (GLSZM) features, neighborhood grey tone difference matrix (NGTDM) features, and grey level dependence matrix (GLDM) features. Figure 2 showed how Grey Level Histogram worked. FAE applied uniformization automatically to the feature matrix when preprocessing CT data, where each feature vector subtracted its average value and then divided by its length. Since the dimensional feature space was very high, the similarity of each feature pair was compared. If the Pearson Correlation Coefficient (PCC) of one feature pair was greater than 0.99, one of them from the pair was removed. After this preprocessing procedure, the size of the feature space was reduced, and each feature was independent of another. Kruskal Wallis was utilized to explore the important features corresponding to labels. In the FAE software, Pearson and Kruskal Wallis methods were automatically selected in the FAE software and we applied them to the training dataset. To evaluate the relationship between features and labels, we calculated the F value. Afterward, we ranked the top 14 features according to the corresponding F value. These 14 features were chosen by the FAE software based on the highest F value. Eventually, Random Forest Model with the highest AUC value was chosen automatically by FAE software as a classifier from all existing models including Support Vector Machine (SVM), Latent Dirichlet Allocation (LDA), Autoencoder (AE), Random Forest, Logistic Regression-Lasso, Adaboost, Decision Tree, Gaussian Process, Naive Bayes. To determine the hyperparameters of the model (e.g., The number of features), we applied 10 times cross-validation on the training dataset. Therefore, hyperparameters were set according to the model performance on the validation dataset (Fig. 1).

Results

Feature selection and radiological model construction

Table 2 illustrated the prediction performance of the training dataset and testing dataset. The accuracy of the training data set was 0.781, AUC was 0.834 (95% Confidence Interval = 0.7712–0.887), NPV was 0.782, PPV was 0.779, sensitivity was 0.771, and specificity was 0.790. Accuracy of the testing dataset was 0.726, AUC was 0.751 (95% confidence interval = 0.6382–0.853), NPV was 0.794, PPV was 0.680, sensitivity was 0.829, and specificity was 0.628. Table 3 showed features with the 14 highest AUC values on the testing dataset (Table 3 and Fig. 3). In addition, the ROC curve was shown in Fig. 4 (Training dataset AUC = 0.834; Testing dataset AUC = 0.751).

Table 2:

Clinical statistics in the diagnosis.

	Accuracy	AUC	AUC 95% CIs	NPV	PPV	Sensitivity	Specificity
Training data set	0.7806	0.8344	0.7712–0.8872	0.7822	0.7789	0.7708	0.7900
Test data set	0.7262	0.751	0.6382–0.8531	0.7941	0.68	0.8293	0.6279

DOI: 10.7717/peerj.14127/table-2

Table 3:

The rank of selected features.

Features	Rank
original_firstorder_90Percentile	1
original_firstorder_Energy	2
original_firstorder_Mean	3
wavelet-HHL_firstorder_Median	4
wavelet-HHL_glcm_ClusterProminence	5
wavelet-HHL_glcm_Imc1	6
wavelet-HHL_glcm_Imc2	7
wavelet-HHL_gldm_DependenceEntropy	8
wavelet-HHL_glrlm_RunEntropy	9
wavelet-HHL_glszm_GrayLevelNonUniformityNormalized	10
wavelet-HHL_glszm_SizeZoneNonUniformityNormalized	11
wavelet-HHL_ngtdm_Busyness	12
wavelet-HHL_ngtdm_Strength	13
wavelet-LLH_glcm_MCC	14

DOI: 10.7717/peerj.14127/table-3

Discussion

The article discussed a non-invasive diagnostic method for distinguishing non-calcifying tuberculosis granuloma from lung adenocarcinoma. The results of this study showed that age, gender, and lobulation were important predictors for distinguishing the LAC group and TB group  (Cui et al., 2020). On the one hand, the average age of patients in the TB group was lower than that in the LAC group, which may be explained by the fact that LAC is a malignant tumor, which is common in elderly patients. On the other hand, the number of female patients in the LAC group was more than that in the TB group, whereas the number of male patients in the LAC group was more than that in the TB group. The gender imbalance in the two groups may lead to statistical differences. It could be explained by the fact that females are prone to LAC compared to males, and males are more susceptible to TB compared to females (Marçôa et al., 2018). Radiomics is a process that transforms the subjective evaluation of images into objective quantitative data. Many studies have shown that it can be used as a non-invasive method to predict the benign and malignant effects of pulmonary nodules (Feng et al., 2020b; Xu et al., 2019; Wilson & Devaraj, 2017). These objective data cannot be identified visually but can be determined in a computer-aided manner. The CT appearance ‘lobulated shape’ in this study was statistically different in both groups. This feature can reflect the heterogeneity within pulmonary nodules and help to identify benign and malignant nodules (Jiang et al., 2021). In this study, 196 cases were selected as the training dataset and 84 cases were chosen as the testing dataset. A total of 851 radiomics features were extracted from each ROI randomly and automatically by software, including 18 first-order features, 14 shape features, 24 gray level co-occurrence matrices, 16 gray area size matrices, 16 gray level travel matrices, five domain gray difference matrices, 14 gray level correlation matrices, and 744 wavelet features. The features were sorted according to the corresponding F value, and the first 14 features are selected according to the verification performance. There are three firstorder features, including original _ firstorder _ 90Percentile, original _ firstorder _ Energy and original _ firstorder _ Mean. The first-order features stand for the difference in the distribution of individual prime parameter values, which reflects the difference in the density of lesions. This is the density difference in internal space between lung adenocarcinoma and non-calcified granuloma, which is difficult to identify from the eyes since it is a high-dimensional spatial feature. These features are related to gray matrix parameters. This indicates that the change of gray level in CT images of lung lesions may potentially contribute to the differential diagnosis of lung adenocarcinoma and non-calcified granuloma (Cui et al., 2020). Random forest was used as a classifier in the model because of its highest AUC value among all models. Lung cancer and granuloma were commonly found in the upper lobe in this study. This may be due to changes in lobulation caused by lung cancer infiltration. However, chronic inflammation may also have similar characteristics. This could explain the reason for the relatively low AUC in the results. The AUC of the training dataset and the testing dataset were 0.834 and 0.751, respectively. The AUC of the training dataset is 0.834 compared to 0.751 in the AUC of the testing dataset. The NPV, PPV, sensitivity, and specificity have high similarities when compared to previous studies of its kind (Feng et al., 2020a; Chen et al., 2020). It may not be appropriate to observe lung cancer for a long time without providing treatment, but suspected nodules that grow slowly are not easily identifiable with imaging studies without a sufficient waiting period. In addition, lung cancer and granuloma cannot be accurately distinguished in PET scans as well (Du et al., 2021). Although the gold standard for lung cancer diagnosis is the surgical biopsy, it is considered overtreatment if the nodule is a granuloma. On the contrary, conservative treatment may delay the timely treatment for lung cancer. Overall, it is difficult to distinguish benign and malignant pulmonary nodules merely using a lung CT scan. Physicians have been seeking a non-invasive examination to solve this problem. Radiomics, in combination with clinical features, shows its potential to be used as an effective tool to assist radiologists to distinguish benign and malignant pulmonary nodules. However, we have several limitations in this study. Firstly, it was a retrospective analysis. The sample size was relatively small and selection bias could be a potential issue. More high-quality samples are needed to prove the validity of the study in the future. Secondly, selected patients who had surgeries were more likely to be patients diagnosed with malignant tumors. Future research should maintain a relatively equal number of pathology results in both LAC and TB groups. Thirdly, different CT scans may affect the quality of image parameters. Therefore, thin-layer CT scanning (with a value of 0.625 mm) was adopted, and Radiomics normalization preprocessing was used to improve the quality of the data.

Conclusions

In summary, radiomics combined with clinical features is a possible non-invasive tool to distinguish non-calcifying tuberculosis granuloma and lung adenocarcinoma in small pulmonary nodules. The application of this combination has a great potential to decrease overdiagnosis and overtreatment in the future.

Supplemental Information