CT texture analysis of mediastinal lymphadenopathy: Combining with US-based elastographic parameter and discrimination between sarcoidosis and lymph node metastasis from small cell lung cancer

Can computed tomography findings and radiomics analysis of mediastinal lymph nodes differentiate between sarcoidosis and lymphoma?

AIMS

To assess the ability of computed tomography (CT) findings and radiomics analysis to differentiate mediastinal lymphadenopathies as sarcoidosis versus lymphoma.

MATERIALS AND METHODS

94 patients with lymphoma and 97 patients with sarcoidosis, who had > 1cm mediastinal lymph node were included. Size, location of lymph nodes, and distribution of the largest lymph nodes in two groups were compared. A total of 636 lymphadenopathies in four different regions were segmented for radiomics. Lesion segmentation was semiautomatically performed with a dedicated commercial software package on chest CT images. 149 patients were grouped as a training cohort, while 42 patients who underwent CT in the oncology hospital were used for external validation. The least absolute shrinkage and selection operator (LASSO) analysis was used to perform feature selection. Using selected features, the classification performance of various data mining methods in separating groups of sarcoidosis and lymphoma was investigated.

RESULTS

Distribution and size of lymphadenopathies were significantly different in sarcoidosis and lymphoma groups (<0.05). Radiomics and data mining methods showed excellent performance in differentiating lymph nodes of sarcoidosis and lymphoma according to both the largest lymphadenopathy and lymphadenopathies in four different mediastinal regions (AUC >0,95).

CONCLUSIONS

Distribution and size of lymphadenopathies can help differential diagnosis in patients with sarcoidosis and lymphoma. CT radiomics analysis can discriminate the lymph nodes of sarcoidosis and lymphoma with great performance regardless of lymph node size and location and it can be used safely in the diagnosis of these diseases, which can sometimes be challenging to distinguish from each other.

Value of needle confocal laser microendoscopy combined with endobronchial ultrasound bronchoscopy in the diagnosis of hilar and mediastinal lymph node lesions

Endobronchial ultrasound bronchoscopy (EBUS) and needle confocal laser endomicroscopy (nCLE) are techniques for screening benign and malignant lesions of the hilar and mediastinal lymph node (HMLN). This study investigated the diagnostic potential of EBUS, nCLE, and combined EBUS and nCLE in HMLN lesions. We recruited 107 patients with HMLN lesions who were examined by EBUS and nCLE. A pathological examination was performed, and the diagnostic potential of EBUS, nCLE, and combined EBUS-nCLE approach was analyzed according to the results. Among the 107 cases of HMLN lesions, 43 cases were benign and 64 cases were malignant on pathological examination, 41 cases were benign and 66 cases were malignant on EBUS examination; 42 cases were benign and 65 cases were malignant on nCLE examination; 43 cases were benign and 64 cases were malignant on combined EBUS-nCLE examination. The combination approach had 93.8% sensitivity, 90.7% specificity, and 0.922 area under the curve, which was higher than those of EBUS (84.4%, 72.1%, and 0.782, respectively) and nCLE diagnosis (90.6%, 83.7%, and 0.872, respectively). The combination approach had a higher positive predictive value (0.908), negative predictive value (0.881), and positive likelihood ratio (10.09) than that of EBUS (0.813, 0.721, and 3.03, respectively) and nCLE (0.892, 0.857, and 5.56, respectively), whereas, the negative likelihood ratio was lower than that for EBUS (0.22) and nCLE (0.11). No serious complications occurred in patients with HMLN lesions. To summarize, the diagnostic efficacy of nCLE was better than EBUS. The EBUS-nCLE combination is a suitable approach for diagnosing HMLN lesions.

The Association Between Lung Cancer and Sarcoidosis: A Literature Review

Lung cancer is responsible for a significant number of cancer-related deaths worldwide. While various factors can lead to lung cancer, such as tobacco smoking, this article focuses on the relationship between sarcoidosis, a multisystem granulomatous disorder, and lung neoplasm. To investigate this association, the authors conducted a literature search using relevant keywords. The analysis of these reports concluded that while Sarcoidosis and lung cancer together is rare, it is possible. The presenting symptoms, age, gender, and diagnostic procedures of each case should be evaluated, and appropriate diagnostic procedures should be carried out to determine the appropriate treatment for each patient. Clinicians need to be aware of the possibility of these two diseases co-occurring, as they can impact the management of the patient's condition, whether it is curative or palliative. It is essential to rule out metastatic cancer in individuals with sarcoidosis-like clinical and radiographic features.

CT texture analysis and node-RADS CT score of mediastinal lymph nodes - diagnostic performance in lung cancer patients

BACKGROUND

Texture analysis derived from computed tomography (CT) can provide clinically relevant imaging biomarkers. Node-RADS is a recently proposed classification to categorize lymph nodes in radiological images. The present study sought to investigate the diagnostic abilities of CT texture analysis and Node-RADS to discriminate benign from malignant mediastinal lymph nodes in patients with lung cancer.

METHODS

Ninety-one patients (n = 32 females, 35%) with a mean age of 64.8 ± 10.8 years were included in this retrospective study. Texture analysis was performed using the free available Mazda software. All lymph nodes were scored accordingly to the Node-RADS classification. All primary tumors and all investigated mediastinal lymph nodes were histopathologically confirmed during clinical workup.

RESULTS

In discrimination analysis, Node-RADS score showed statistically significant differences between N0 and N1-3 (p < 0.001). Multiple texture features were different between benign and malignant lymph nodes: S(1,0)AngScMom, S(1,0)SumEntrp, S(1,0)Entropy, S(0,1)SumAverg. Correlation analysis revealed positive associations between the texture features with Node-RADS score: S(4,0)Entropy (r = 0.72, p < 0.001), S(3,0) Entropy (r = 0.72, p < 0.001), S(2,2)Entropy (r = 0.72, p < 0.001).

CONCLUSIONS

Several texture features and Node-RADS derived from CT were associated with the malignancy of mediastinal lymph nodes and might therefore be helpful for discrimination purposes. Both of the two quantitative assessments could be translated and used in clinical routine.

Contribution of F-18 fluorodeoxyglucose PET/CT and contrast-enhanced thoracic CT texture analyses to the differentiation of benign and malignant mediastinal lymph nodes

BACKGROUND

Texture analysis and machine learning methods are useful in distinguishing between benign and malignant tissues.

PURPOSE

To discriminate benign from malignant or metastatic mediastinal lymph nodes using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and contrast-enhanced computed tomography (CT) texture analyses with machine learning and determine lung cancer subtypes based on the analysis of lymph nodes.

MATERIAL AND METHODS

Suitable texture features were entered into the algorithms. Features that statistically significantly differed between the lymph nodes with small cell lung cancer (SCLC), adenocarcinoma (ADC), and squamous cell carcinoma (SCC) were determined.

RESULTS

The most successful algorithms were decision tree with the sensitivity, specificity, and area under the curve (AUC) values of 89%, 50%, and 0.692, respectively, and naive Bayes (NB) with the sensitivity, specificity, and AUC values of 50%, 81%, and 0.756, respectively, for PET/CT, and NB with the sensitivity, specificity, and AUC values of 10%, 96%, and 0.515, respectively, and logistic regression with the sensitivity, specificity, and AUC values of 21%, 83%, and 0.631, respectively, for CT. In total, 13 features were able to differentiate SCLC and ADC, two features SCLC and SCC, and 33 features ADC and SCC lymph node metastases in PET/CT. One feature differed between SCLC and ADC metastases in CT.

CONCLUSION

Texture analysis is beneficial to discriminate between benign and malignant lymph nodes and differentiate lung cancer subtypes based on the analysis of lymph nodes.

Derivation and validation of a prediction model for patients with lung nodules malignancy regardless of mediastinal/hilar lymphadenopathy

BACKGROUND

Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included.

METHODS

A single-center retrospective study was conducted. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit test was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn.

RESULTS

There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The aera under the curve (AUC) of the validation set was 0.91 (95% confidence interval [CI]: 0.85-0.98). In the validation set with mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI: 0.90-0.99). The goodness-of-fit test was 0.22.

CONCLUSIONS

We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy in clinical practice.

Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography

To investigate the usefulness of texture analysis to discriminate between cervical lymph node (LN) metastasis from cancer of unknown primary (CUP) and cervical LN involvement of malignant lymphoma (ML) on unenhanced computed tomography (CT). Cervical LN metastases in 17 patients with CUP and cervical LN involvement in 17 patients with ML were assessed by ¹⁸F-FDG PET/CT. The texture features were obtained in the total cross-sectional area (CSA) of the targeted LN, following the contour of the largest cervical LN on unenhanced CT. Values for the max standardized uptake value (SUVmax) and the mean SUV value (SUVmean), and 34 texture features were compared using a Mann-Whitney U test. The diagnostic accuracy and area under the curve (AUC) of the combination of the texture features were evaluated by support vector machine (SVM) with nested cross-validation. The SUVmax and SUVmean did not differ significantly between cervical LN metastases from CUP and cervical LN involvement from ML. However, significant differences of 9 texture features of the total CSA were observed (p = 0.001 - 0.05). The best AUC value of 0.851 for the texture feature of the total CSA were obtained from the correlation in the gray-level co-occurrence matrix features. SVM had the best AUC and diagnostic accuracy of 0.930 and 84.8%. Radiomics analysis appears to be useful for differentiating cervical LN metastasis from CUP and cervical LN involvement of ML on unenhanced CT.

Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study

OBJECTIVES

This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy.

METHODS

Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training (N = 49) and test (N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy. The DL models that extracted imaging features on pre- and intra-treatment DWI and ADC maps were trained to predict the local recurrence within a 2-year follow-up. In the test group, each DL model was analyzed for recurrence prediction. Additionally, the Kaplan-Meier and multivariable Cox regression analyses were performed to evaluate the prognostic significance of the DL models and clinical variables.

RESULTS

The highest area under the receiver operating characteristics curve and accuracy for predicting the local recurrence in the DL model were 0.767 and 81.0%, respectively, using intra-treatment DWI (DWI_intra). The log-rank test showed that DWI_intra was significantly associated with PFS (p = 0.013). DWI_intra was an independent prognostic factor for PFS in multivariate analysis (p = 0.023).

CONCLUSION

DL models using DWI_intra may have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. The model-related findings may contribute to determining the therapeutic strategy in the early stage of the treatment.

KEY POINTS

• Deep learning models using intra-treatment diffusion-weighted imaging have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. • The findings from these models may contribute to determining the therapeutic strategy at the early stage of the treatment.