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

Diagnostic performance of Node-RADS score for mesorectal lymph node metastasis in rectal cancer

PURPOSE

To explore the diagnostic performance of the Node-RADS scoring system on preoperative assessment of mesorectal lymph node metastasis (LNM) status in rectal cancer, in comparison with the ESGAR category and size of lymph node (LN).

METHODS

Preoperative clinical and MRI data of 154 rectal adenocarcinoma patients treated with radical resection surgery were retrospectively analyzed. The differences in the clinical, pathological and imaging characteristics between the pN- and pN + groups were surveyed. The correlations of Node-RADS score and ESGAR category to pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological LNM were compared among individual approaches. A nomogram combined the imaging and clinical features was also established and evaluated.

RESULTS

Significant differences in CEA, tumor maximum diameter, tumor location, LN short-axis diameter, Node-RADS score and ESGAR category were found between the pN- and pN + groups. Node-RADS correlated significantly with pN stage, LNM number, and LNR (r = 0.665, 0.685, and 0.675, p < 0.001). Node-RADS had the highest AUC (0.862) for predicting pN + status, surpassing ESGAR (AUC = 0.797, p = 0.040) and LN size (AUC = 0.762, p = 0.015). The nomogram had the best diagnostic performance (AUC = 0.901), significantly outperforming Node-RADS alone (p = 0.037).

CONCLUSIONS

The Node-RADS scoring system is comparable to the ESGAR category and surpasses short-axis diameter in preoperatively predicting LNM in rectal cancer. Integrating imaging and clinical features will lead to an enhancement in diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.

Endobronchial Ultrasound-Based Support Vector Machine Model for Differentiating between Benign and Malignant Mediastinal and Hilar Lymph Nodes

INTRODUCTION

The aim of the study was to establish an ultrasonographic radiomics machine learning model based on endobronchial ultrasound (EBUS) to assist in diagnosing benign and malignant mediastinal and hilar lymph nodes (LNs).

METHODS

The clinical and ultrasonographic image data of 197 patients were retrospectively analyzed. The radiomics features extracted by EBUS-based radiomics were analyzed by the least absolute shrinkage and selection operator. Then, we used a support vector machine (SVM) algorithm to establish an EBUS-based radiomics model. A total of 205 lesions were randomly divided into training (n = 143) and validation (n = 62) groups. The diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 13 stable radiomics features with non-zero coefficients were selected. The SVM model exhibited promising performance in both groups. In the training group, the SVM model achieved an ROC area under the curve (AUC) of 0.892 (95% CI: 0.885-0.899), with an accuracy of 85.3%, sensitivity of 93.2%, and specificity of 79.8%. In the validation group, the SVM model had an ROC AUC of 0.906 (95% CI: 0.890-0.923), an accuracy of 74.2%, a sensitivity of 70.3%, and a specificity of 74.1%.

CONCLUSION

The EBUS-based radiomics model can be used to differentiate mediastinal and hilar benign and malignant LNs. The SVM model demonstrated excellent potential as a diagnostic tool in clinical practice.

Diagnostic accuracy and reliability of CT-based Node-RADS for colon cancer

OBJECTIVE

The Node-RADS classification was recently published as a classification system to better characterize lymph nodes in oncological imaging. The present analysis investigated the diagnostic benefit of the Node-RADS classification of staging computed tomography (CT) images to categorize and stage lymph nodes in patients with colon cancer.

MATERIALS AND METHODS

All patients were surgically resected and the lymph nodes were histopathological analyzed. All investigated lymph nodes were scored in accordance to the Node-RADS classification by two experienced radiologists. Interreader variability was assessed with Cohen's kappa analysis, discrimination analysis was performed with Mann-Whitney-U test and diagnostic accuracy was assessed with receiver-operating characteristics (ROC) curve analysis.

RESULTS

Overall, 108 patients (n = 49 females, 45.3%) with a mean age of 70.08 ± 14.34 years were included. In discrimination analysis, the total Node-RADS score showed statistically significant differences between N- and N + stage (for reader 1: mean 1.89 ± 1.09 score for N- versus 2.93 ± 1.62 score for N+, for reader 2: 1.33 ± 0.48 score for N- versus 3.65 ± 0.94 score for N+, p = 0.001, respectively). ROC curve analysis for lymph node discrimination showed an area under the curve of 0.68. A threshold value of 2 resulted in a sensitivity of 0.62 and a specificity of 0.71.

CONCLUSION

Node-RADS score derived from staging CT shows only limited diagnostic accuracy to correctly predict nodal positivity in colon cancer. The interreader variability seems to be high and should question the clinical translation for this tumour entity.

Performance of MRI for standardized lymph nodes assessment in breast cancer: are we ready for Node-RADS?

OBJECTIVES

The Node-RADS score was recently introduced to offer a standardized assessment of lymph node invasion (LNI). We tested its diagnostic performance in accurately predicting LNI in breast cancer (BC) patients with magnetic resonance imaging. The study also explores the consistency of the score across three readers.

MATERIALS AND METHODS

A retrospective study was conducted on BC patients who underwent preoperative breast contrast-enhanced magnetic resonance imaging and lymph node dissection between January 2020 and January 2023. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated for different Node-RADS cut-off values. Pathologic results were considered the gold standard. The overall diagnostic performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC). A logistic regression analysis was performed. Cohen's Kappa analysis was used for inter-reader agreement.

RESULTS

The final population includes 192 patients and a total of 1134 lymph nodes analyzed (372 metastatic and 762 benign). Increasing the Node-RADS cut-off values, specificity and PPV rose from 71.4% to 100% and 76.7% to 100%, respectively, for Reader 1, 69.4% to 100% and 74.6% to 100% for Reader 2, and from 64.3% to 100% and 72% to 100% for Reader 3. Node-RADS > 2 could be considered the best cut-off value due to its balanced performance. Node-RADS exhibited a similar AUC for the three readers (0.97, 0.93, and 0.93). An excellent inter-reader agreement was found (Kappa values between 0.71 and 0.83).

CONCLUSIONS

The Node-RADS score demonstrated moderate-to-high overall accuracy in identifying LNI in patients with BC, suggesting that the scoring system can aid in the identification of suspicious lymph nodes and facilitate appropriate treatment decisions.

CLINICAL RELEVANCE STATEMENT

Node-RADS > 2 can be considered the best cut-off for discriminating malignant nodes, suggesting that the scoring system can effectively help identify suspicious lymph nodes by staging the disease and providing a global standardized language for clear communication.

KEY POINTS

Axillary lymphadenopathies in breast cancer are crucial for determining the disease stage. Node-RADS was introduced to provide a standardized evaluation of breast cancer lymph nodes. RADS > 2 can be considered the best cut-off for discriminating malignant nodes.

Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT

BACKGROUND

To compare the diagnostic performance of the Node-RADS scoring system and lymph node (LN) size in preoperative LN assessment for rectal cancer (RC), and to investigate whether the selection of size as the primary criterion whereas morphology as the secondary criterion for LNs can be considered the preferred method for clinical assessment.

METHODS

Preoperative CT data of 146 RC patients treated with radical resection surgery were retrospectively analyzed. The Node-RADS score and short-axis diameter of size-prioritized LNs and the morphology-prioritized LNs were obtained. The correlations of Node-RADS score to the pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological lymph node metastasis were compared between Node-RADS score and short-axis diameter. A nomogram combined the Node-RADS score and clinical features was also evaluated.

RESULTS

Node-RADS score showed significant correlation with pN stage, LNM number and LNR (Node-RADS of size-prioritized LN: r = 0.600, 0.592, and 0.606; Node-RADS of morphology-prioritized LN: r = 0.547, 0.538, and 0.527; Node-RADSmax: r = 0.612, 0.604, and 0.610; all p < 0.001). For size-prioritized LN, Node-RADS achieved an AUC of 0.826, significantly superior to short-axis diameter (0.826 vs. 0.743, p = 0.009). For morphology-prioritized LN, Node-RADS exhibited an AUC of 0.758, slightly better than short-axis diameter (0.758 vs. 0.718, p = 0.098). The Node-RADS score of size-prioritized LN was significantly better than that of morphology-prioritized LN (0.826 vs. 0.758, p = 0.038). The nomogram achieved the best diagnostic performance (AUC = 0.861) than all the other assessment methods (p < 0.05).

CONCLUSIONS

The Node-RADS scoring system outperforms the short-axis diameter in predicting lymph node metastasis in RC. Size-prioritized LN demonstrates superior predictive efficacy compared to morphology-prioritized LN. The nomogram combined the Node-RADS score of size-prioritized LN with clinical features exhibits the best diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.

CT texture analysis of vertebrobasilar artery calcification to identify culprit plaques

Objectives

The aim of this study was to extract radiomic features from vertebrobasilar artery calcification (VBAC) on head computed tomography (CT) images and investigate its diagnostic performance to identify culprit lesions responsible for acute cerebral infarctions.

Methods

Patients with intracranial atherosclerotic disease who underwent vessel wall MRI (VW-MRI) and head CT examinations from a single center were retrospectively assessed for VBAC visual and textural analyses. Each calcified plaque was classified by the likelihood of having caused an acute cerebral infarction identified on VW-MRI as culprit or non-culprit. A predefined set of texture features extracted from VBAC segmentation was assessed using the minimum redundancy and maximum relevance method. Five key features were selected to integrate as a radiomic model using logistic regression by the Aikaike Information Criteria. The diagnostic value of the radiomic model was calculated for discriminating culprit lesions over VBAC visual assessments.

Results

A total of 1,218 radiomic features were extracted from 39 culprit and 50 non-culprit plaques, respectively. In the VBAC visual assessment, culprit plaques demonstrated more observed presence of multiple calcifications, spotty calcification, and intimal predominant calcification than non-culprit lesions (all p < 0.05). In the VBAC texture analysis, 55 (4.5%) of all extracted features were significantly different between culprit and non-culprit plaques (all p < 0.05). The radiomic model incorporating 5 selected features outperformed multiple calcifications [AUC = 0.81 with 95% confidence interval (CI) of 0.72, 0.90 vs. AUC = 0.61 with 95% CI of 0.49, 0.73; p = 0.001], intimal predominant calcification (AUC = 0.67 with 95% CI of 0.58, 0.76; p = 0.04) and spotty calcification (AUC = 0.62 with 95% CI of 0.52, 0.72; p = 0.005) in the identification of culprit lesions.

Conclusion

Culprit plaques in the vertebrobasilar artery exhibit distinct calcification radiomic features compared to non-culprit plaques. CT texture analysis of VBAC has potential value in identifying lesions responsible for acute cerebral infarctions, which may be helpful for stroke risk stratification in clinical practice.

Diagnostic performance of Node Reporting and Data System (Node-RADS) for regional lymph node staging of gastric cancer by CT

OBJECTIVES

Diagnostic performance of imaging for regional lymph node assessment in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. At the same time, there is an increasing demand for structured reporting using Reporting and Data Systems (RADS) to standardize oncological imaging. We aimed at investigating the diagnostic performance of Node-RADS compared to the use of various individual criteria for assessing regional lymph nodes in gastric cancer using histopathology as reference.

METHODS

In this retrospective single-center study, consecutive 91 patients (median age, 66 years, range 33-91 years, 54 men) with CT scans and histologically proven gastric adenocarcinoma were assessed using Node-RADS assigning scores from 1 to 5 for the likelihood of regional lymph node metastases. Additionally, different Node-RADS criteria as well as subcategories of altered border contour (lobulated, spiculated, indistinct) were assessed individually. Sensitivity, specificity, and Youden's index were calculated for Node-RADS scores, and all criteria investigated. Interreader agreement was calculated using Cohen's kappa.

RESULTS

Among all criteria, best performance was found for Node-RADS scores ≥ 3 and ≥ 4 with a sensitivity/specificity/Youden's index of 56.8%/90.7%/0.48 and 48.6%/98.1%/0.47, respectively, both with substantial interreader agreement (κ = 0.73 and 0.67, p < 0.01). Among individual criteria, the best performance was found for short-axis diameter of 10 mm with sensitivity/specificity/Youden's index of 56.8%/87.0%/0.44 (κ = 0.65, p < 0.01).

CONCLUSION

This study shows that structured reporting of combined size and configuration criteria of regional lymph nodes in gastric cancer slightly improves overall diagnostic performance compared to individual criteria including short-axis diameter alone. The results show an increase in specificity and unchanged sensitivity.

CLINICAL RELEVANCE STATEMENT

The results of this study suggest that Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer.

KEY POINTS

• Assessment of lymph nodes in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. • Node-RADS in gastric cancer improves overall diagnostic performance compared to individual criteria including short-axis diameter. • Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer.

Diagnostic Performance of Node Reporting and Data System Magnetic Resonance Imaging Score in Detecting Metastatic Cervical Lymph Nodes of Nasopharyngeal Carcinoma

Background

The Node Reporting and Data System (Node-RADS) is a recently proposed classification system for the categorization of lymph nodes in radiological images. This study was conducted to retrospectively evaluate the diagnostic accuracy of the Node-RADS score for metastatic cervical lymph nodes on magnetic resonance imaging (MRI) of patients with nasopharyngeal carcinoma (NPC).

Methods

We retrospectively analyzed cervical lymph nodes of NPC cases. Two radiologists independently evaluated each lymph node on the MRI scans using Node-RADS. Interobserver agreement between 2 radiologists for Node-RADS score assessment was evaluated by linear weighted kappa statistics. The correlation between metastasis and the Node-RADS score of each lymph node was analyzed using multivariate regression analysis. To investigate the diagnostic performance of the Node-RADS score, we further conducted receiver operating characteristic curve analysis. Correspondently, the sensitivity, specificity, positive predictive value, and negative predictive value of each different cutoff (>1, >2, >3, and >4) were computed.

Results

In all, 119 patients with NPC were assessed, including 203 cervical lymph nodes consisting of 140 (69%) of 203 metastatic and 63 (31%) of 203 benign. The kappa agreement between the 2 readers for the Node-RADS score was 0.863 (95% CI = 0.830-0.897, P < .001). Node-RADS score on MRI scan was shown to be an independent predictive factor of lymph node metastasis after multivariate regression analysis (odds ratio [OR] = 6.745, 95% CI = 3.964-11.474, P < .001). Node-RADS achieved an area under the curve (AUC) of 0.950 (95% CI = 0.921-0.979) in diagnosing metastatic lymph nodes. When Node-RADS >2 was identified as the best cutoff based on balanced values, the sensitivity and positive predictive value were 0.92 and 0.94, respectively.

Conclusions

Our study suggests that the Node-RADS score has high accuracy in predicting NPC cervical lymph node metastasis. Nevertheless, this conclusion requires confirmation in a larger cohort of patients with NPC.

Revolutionizing lymph node metastasis imaging: the role of drug delivery systems and future perspectives

The deployment of imaging examinations has evolved into a robust approach for the diagnosis of lymph node metastasis (LNM). The advancement of technology, coupled with the introduction of innovative imaging drugs, has led to the incorporation of an increasingly diverse array of imaging techniques into clinical practice. Nonetheless, conventional methods of administering imaging agents persist in presenting certain drawbacks and side effects. The employment of controlled drug delivery systems (DDSs) as a conduit for transporting imaging agents offers a promising solution to ameliorate these limitations intrinsic to metastatic lymph node (LN) imaging, thereby augmenting diagnostic precision. Within the scope of this review, we elucidate the historical context of LN imaging and encapsulate the frequently employed DDSs in conjunction with a variety of imaging techniques, specifically for metastatic LN imaging. Moreover, we engage in a discourse on the conceptualization and practical application of fusing diagnosis and treatment by employing DDSs. Finally, we venture into prospective applications of DDSs in the realm of LNM imaging and share our perspective on the potential trajectory of DDS development.

Performance of node reporting and data system (node-RADS): a preliminary study in cervical cancer

BACKGROUND

Node Reporting and Data System (Node-RADS) was proposed and can be applied to lymph nodes (LNs) across all anatomical sites. This study aimed to investigate the diagnostic performance of Node-RADS in cervical cancer patients.

METHODS

A total of 81 cervical cancer patients treated with radical hysterectomy and LN dissection were retrospectively enrolled. Node-RADS evaluations were performed by two radiologists on preoperative MRI scans for all patients, both at the LN level and patient level. Chi-square and Fisher's exact tests were employed to evaluate the distribution differences in size and configuration between patients with and without LN metastasis (LNM) in various regions. The receiver operating characteristic (ROC) and the area under the curve (AUC) were used to explore the diagnostic performance of the Node-RADS score for LNM.

RESULTS

The rates of LNM in the para-aortic, common iliac, internal iliac, external iliac, and inguinal regions were 7.4%, 9.3%, 19.8%, 21.0%, and 2.5%, respectively. At the patient level, as the NODE-RADS score increased, the rate of LNM also increased, with rates of 26.1%, 29.2%, 42.9%, 80.0%, and 90.9% for Node-RADS scores 1, 2, 3, 4, and 5, respectively. At the patient level, the AUCs for Node-RADS scores > 1, >2, > 3, and > 4 were 0.632, 0.752, 0.763, and 0.726, respectively. Both at the patient level and LN level, a Node-RADS score > 3 could be considered the optimal cut-off value with the best AUC and accuracy.

CONCLUSIONS

Node-RADS is effective in predicting LNM for scores 4 to 5. However, the proportions of LNM were more than 25% at the patient level for scores 1 and 2, which does not align with the expected very low and low probability of LNM for these scores.

Characterization of hypermetabolic lymph nodes after SARS-CoV-2 vaccination using PET-CT derived node-RADS, in patients with melanoma

This study aimed to evaluate the diagnostic accuracy of Node Reporting and Data System (Node-RADS) in discriminating between normal, reactive, and metastatic axillary LNs in patients with melanoma who underwent SARS-CoV-2 vaccination. Patients with proven melanoma who underwent a 2-[18F]-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]-FDG PET/CT) between February and April 2021 were included in this retrospective study. Primary melanoma site, vaccination status, injection site, and 2-[18F]-FDG PET/CT were used to classify axillary LNs into normal, inflammatory, and metastatic (combined classification). An adapted Node-RADS classification (A-Node-RADS) was generated based on LN anatomical characteristics on low-dose CT images and compared to the combined classification. 108 patients were included in the study (54 vaccinated). HALNs were detected in 42 patients (32.8%), of whom 97.6% were vaccinated. 172 LNs were classified as normal, 30 as inflammatory, and 14 as metastatic using the combined classification. 152, 22, 29, 12, and 1 LNs were classified A-Node-RADS 1, 2, 3, 4, and 5, respectively. Hence, 174, 29, and 13 LNs were deemed benign, equivocal, and metastatic. The concordance between the classifications was very good (Cohen's k: 0.91, CI 0.86-0.95; p-value < 0.0001). A-Node-RADS can assist the classification of axillary LNs in melanoma patients who underwent 2-[18F]-FDG PET/CT and SARS-CoV-2 vaccination.

Endobronchial ultrasound sonographic characteristics of mediastinal lymph node in evaluation of lung cancer

Endobronchial ultrasound (EBUS) and contrast enhanced computed tomography (CECT) are essential components of lung cancer evaluation. Features of mediastinal lymph nodes on EBUS and CECT can help in predicting metastatic disease. Clinical, radiological and EBUS data of patients with clinico-radiological suspicion of lung cancer and have undergone EBUS with no trans-bronchial needle aspiration (TBNA) or nonyielding EBUS-TBNA were retrospectively collected from medical records. EBUS features of lymph nodes for metastatic disease [Size >1cm, round shape, heterogeneous echo-texture, indistinct margin, coagulation necrosis(CN), absence of central hilar structures (CHS) and grade II-III vascularity] were noted. CECT findings were noted from CECT films and reports to analyse and compare with EBUS findings. Scoring criteria of EBUS sonographic characteristics from previous studies for discriminating benign and malignant lymph-nodes were also assessed for possible prediction. Thirty-one (31) patients [male=18 (58.1%), female=13 (41.9); age (mean±SD) =52.9±15.7 years] with CECT findings suggesting lung cancer were studied. EBUS showed mediastinal lymphadenopathy at 82 lymph node station in 29 patients. Size>1cm, round shape, heterogeneous echo-texture, distinct margin, CN, absence of CHS and grade II-III vascularity at 33 (40%), 28 (34%), 31 (38%), 55 (67%), 3 (4%), 77 (94%) and 6 (7.3%) lymph nodes, respectively. Malignant or benign status assigned to lymph nodes using different scoring criteria were highly discordant. Compared to EBUS, CECT revealed abnormal mediastinal lymph nodes (LN) in significantly less number of patients [21( 67.7%) vs 29 (93.5%), p=0.01] involving less number of lymph node stations (LNS) [(37 vs 82, p<0.001]. Lymphadenopathy frequency at different LNS on EBUS and CECT showed a weak positive but significant correlation (r=0.356; p=0.0426). EBUS characteristics and related scores have limited accuracy in differentiating benign and malignant nodes. CECT underestimates lymph-adenopathy in comparison to EBUS. Larger prospective study of EBUS features with cyto/histo-pathology correlation may elicit its clinical significance and help to create a better and composite scoring criteria.