Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule

Abstract

ObjectiveTo establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability.Materials and MethodsA total of 213 patients with PC and 213 cases of LAC (matched based on age and gender) were recruited into this retrospective research with their clinical characteristics and radiological features. High-dimensional radiomics features were acquired from each mask delineated by radiologists manually. We adopted the max-relevance and min-redundancy (mRMR) approach to filter the redundant features and retained the relevant features at first. Then, we used the least absolute shrinkage and operator (LASSO) algorithms as an analysis tool to calculate the coefficients of features and remove the low-weight features. After multivariable logistic regression analysis, a radiomics nomogram model was constructed with clinical characteristics, radiological signs, and radiomics score. We calculated the performance assessment parameters, such as sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), in various models. The receiver operating characteristic (ROC) curve analysis and the decision curve analysis (DCA) were drawn to visualize the diagnostic ability and the clinical benefit.ResultsWe extracted 1,130 radiomics features from each CT image. The 24 most significant radiomics features in distinguishing PC and LAC were retained, and the radiomics signature was constructed through a three-step feature selection process. Three factors—maximum diameter, lobulation, and pleural retraction—were still statistically significant in multivariate analysis and incorporated into a combined model with radiomics signature to develop the predictive nomogram, which showed excellent classification ability. The area under curve (AUC) yielded 0.91 (sensitivity, 80%; specificity, 83%; accuracy, 82%; NPV, 80%; PPV, 83%) and 0.89 (sensitivity, 81%; specificity, 83%; accuracy, 82%; NPV, 81%; PPV, 82%) in training and test cohorts, respectively. The net reclassification indexes (NRIs) were greater than zero (p < 0.05). The Delong test showed a significant difference (p < 0.0001) between the AUCs from the clinical model and the nomogram.ConclusionsThe radiomics technology can preoperatively differentiate PC and lung adenocarcinoma. The nomogram-integrated CT findings and radiomics feature can provide more clinical benefits in solitary pulmonary solid nodule diagnosis.

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features

“…After removing poorly reproducible and redundant features, the features were sorted using mRMR, and the top 100 features were ultimately selected for lasso screening. Four features were retained for both intra- and peri- nodular ( 17 ), and Intra-RS and Peri-RS models were established ( Figure 3 ). The G-RS model was built for the combined intra- and nodular features using forward stepwise multiple logical regression.…”

“…( 15 ) constructed an radiomics model to identify adenocarcinoma and granulomatous nodules in the lung with good performance, while Liu et al. ( 16 ) included different categories of benign nodules and showed that the diagnostic performance of the radiomics model was better than that of the Lung-RADS model, and the diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT ( 17 ). constructed a clinical-radiomics model to identify pulmonary cryptococcosis and pulmonary adenocarcinoma, screening four visual radiological features of maximum diameter, size, lobulation and pleural retraction, consistent with our routine CT evaluation, including 24 radiomics features of different categories, with wavelet features as the main part (19/24), quantifying the heterogeneity of lesions of different grades that are not recognized by human eyes.…”

“…Radiomics can extract focus information with high throughput and can be combined with clinical and conventional imaging manifestations and laboratory indicators, which greatly improves the diagnostic efficiency. However, most studies have not focused on the peritumor region (15)(16)(17)(18). Chen et al ( 15) constructed an radiomics model to identify adenocarcinoma and granulomatous nodules in the lung with good performance, while Liu et al ( 16) included different categories of benign nodules and showed that the diagnostic performance of the radiomics model was better than that of the Lung-RADS model, and the diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT (17).…”

“…However, most studies have not focused on the peritumor region (15)(16)(17)(18). Chen et al ( 15) constructed an radiomics model to identify adenocarcinoma and granulomatous nodules in the lung with good performance, while Liu et al ( 16) included different categories of benign nodules and showed that the diagnostic performance of the radiomics model was better than that of the Lung-RADS model, and the diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT (17). constructed a clinical-radiomics model to identify pulmonary cryptococcosis and pulmonary…”

“…It has been widely used in many aspects, such as differentiation of benign and malignant pulmonary nodules ( 15 – 19 ), invasion and metastasis ( 20 , 21 ), histological classification ( 22 ), gene expression ( 23 ), and treatment prognosis ( 24 ). The classification of benign and malignant pulmonary nodules, in particular, have achieved excellence in radiomics, from purely benign-malignant differentiation to differentiation with inflammatory granulomas ( 15 ), tuberculous granulomas ( 19 ), and cryptococcal infections ( 17 ). However, most of the studies have only focused on the interior of the nodules.…”