Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

Quantitative vascular feature-based multimodality prediction model for multi-origin malignant cervical lymphadenopathy

Background

The precise prediction of multi-origin malignant cervical lymphadenopathy is limited by the low inter-reader reproducibility of imaging interpretation, and a quantitative method to improve this aspect is lacking. This study aimed to develop and validate an artificial intelligence framework integrating quantitative vascular features for assessing cervical lymphadenopathy and explore its utility among radiologists.

Methods

For this retrospective study, a total of 21,298 ultrasound images of 10,649 cervical lymph nodes (LNs) from 10,386 patients and 2366 images of 1183 LNs from 1151 patients at the Sun Yat-sen University Cancer Center between January 2011 and July 2022 were used for model development and internal testing, respectively. For external model testing, we used 776 images of 388 LNs from 360 patients at the Chongqing University Cancer Hospital between January and December 2022. Quantitative features used to characterize the vascular distribution and degree of richness were fused with morphological and semantic features on B-mode and color Doppler ultrasound images to develop a dual-modality, multi-feature, fusion lymph node network (DMFLNN). Subsequently, the performance of DMFLNN was compared with that of six radiologists, and its auxiliary value was assessed in test cohorts.

Findings

DMFLNN achieved an area under the receiver operating characteristic curve (AUC) of 0.937 for the internal test cohort and 0.875 for the external test cohort. Using the internal test cohort with assistance from DMFLNN, the average AUC improved from 0.814 to 0.836 for senior radiologists (P = 0.00018), and from 0.778 to 0.847 for junior radiologists (P < 0.0001). Additionally, the average inter-radiologist agreement improved from fair to moderate (improvement in kappa: from 0.590 to 0.696 for senior radiologists; from 0.571 to 0.750 for junior radiologists). Similar trends were observed for the external test cohort. Moreover, the radiologists' average false-positive rate decreased by 3.8% and 9.8% for the internal and external test cohorts, respectively.

Interpretation

DMFLNN could improve radiologists' performance and potentially reduce unnecessary biopsies of cervical lymphadenopathy. However, further testing is warranted before its wide adoption in clinical practice.

Funding

The National Natural Science Foundation of China (82171955; 62371476; 82441027); the China Department of Science and Technology (2023YFE0204300); and the R&D project of Pazhou Lab (HuangPu) (2023K0606).

Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVES

Accurate classification of lymphadenopathy is essential for determining the pathological nature of lymph nodes (LNs), which plays a crucial role in treatment selection. The biopsy method is invasive and carries the risk of sampling failure, while the utilization of non-invasive approaches such as ultrasound can minimize the probability of iatrogenic injury and infection. With the advancement of artificial intelligence (AI) and machine learning, the diagnostic efficiency of LNs is further enhanced. This study evaluates the performance of ultrasound-based AI applications in the classification of benign and malignant LNs.

METHODS

The literature research was conducted using the PubMed, EMBASE, and Cochrane Library databases as of June 2024. The quality of the included studies was evaluated using the QUADAS-2 tool. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated to assess the diagnostic efficacy of ultrasound-based AI in classifying benign and malignant LNs. Subgroup analyses were also conducted to identify potential sources of heterogeneity.

RESULTS

A total of 1,355 studies were identified and reviewed. Among these studies, 19 studies met the inclusion criteria, and 2,354 cases were included in the analysis. The pooled sensitivity, specificity, and DOR of ultrasound-based machine learning in classifying benign and malignant LNs were 0.836 (95% CI [0.805, 0.863]), 0.850 (95% CI [0.805, 0.886]), and 33.331 (95% CI [22.873, 48.57]), respectively, indicating no publication bias (p = 0.12). Subgroup analyses may suggest that the location of lymph nodes, validation methods, and type of primary tumor are the sources of heterogeneity.

CONCLUSION

AI can accurately differentiate benign from malignant LNs. Given the widespread use of ultrasonography in diagnosing malignant LNs in cancer patients, there is significant potential for integrating AI-based decision support systems into clinical practice to enhance the diagnostic accuracy.

Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review

This systematic review aims to evaluate the role of ultrasound (US) radiomics in assessing lymphadenopathy in patients with cancer and the ability of radiomics to predict metastatic lymph node involvement. A systematic literature search was performed in the PubMed (MEDLINE), Cochrane Central Register of Controlled Trials (CENTRAL), and EMBASE (Ovid) databases up to June 13, 2023. 42 articles were included in which the lymph node mass was assessed with a US exam, and the analysis was performed using radiomics methods. From the survey of the selected articles, experimental evidence suggests that radiomics features extracted from US images can be a useful tool for predicting and characterizing lymphadenopathy in patients with breast, head and neck, and cervical cancer. This noninvasive and effective method allows the extraction of important information beyond mere morphological characteristics, extracting features that may be related to lymph node involvement. Future studies are needed to investigate the role of US-radiomics in other types of cancers, such as melanoma.

Quantitative and qualitative analysis of contrast-enhanced ultrasound for differentiating benign and malignant superficial enlarged lymph nodes

Background

In many clinical situations, it is critical to exclude or identify abnormally lymph nodes (LNs). The nature of superficial abnormally LNs is closely related to the stage, treatment, and prognosis of the disease. Ultrasound (US) is an important method for examining superficial LNs due to its cheap and safe characteristics. However, it is still difficult to determine the nature of some LNs with overlapping benign and malignant features in images. Contrast-enhanced ultrasound (CEUS) can be used to evaluate the microperfusion status of tissues in real time, and it can improve diagnostic accuracy to a certain extent. Therefore, in this study, we will analyze the correlation between CEUS quantitative parameters and benign and malignant superficial abnormally LNs, to evaluate the efficacy and value of CEUS in distinguishing benign and malignant superficial LNs.

Methods

This study retrospectively analyzed 120 patients of abnormal LNs who underwent US and CEUS at the China-Japan Union Hospital of Jilin University from December 2020 to August 2023. All 120 cases of abnormal LNs underwent US-guided coarse needle biopsy, and accurate pathological results were obtained, along with complete US and CEUS images. According to the pathological results, LNs were divided into benign and malignant groups, and the qualitative and quantitative parameters of US and CEUS between the two groups were analyzed. The cutoff value is determined by the receiver operating characteristic (ROC) curve of the subjects, and sensitivity, specificity, and accuracy are applied to evaluate the ability of the cutoff value to distinguish between the two groups.

Results

There were a total of 120 LNs, including 36 in the benign group and 84 in the malignant group. The results showed that malignant LNs were usually characterized by the disappearance of lymphatic hilum, round ness index (L/T) <2, irregular morphology, and the manifestation of uneven perfusion (P<0.05). The differences in the quantitative parameters peak enhancement (PE), rise time (RT), time to peak (TTP), wash-in rate (WIR), and wash-out rate (WOR) were statistically significant (P<0.05). The result showed that RT and TTP in the malignant LNs were higher than those in the benign LNs, while the PE, WIR, and WOR were lower. A comparison of the ∆ values showed that the differences in ∆PE, ∆WIR, and ∆fall time (FT) were statistically significant (P<0.05), Among them, the ∆PE and ∆WIR of malignant LNs were higher than those of benign LNs, while the ∆FT was lower than that of benign LNs.

Conclusions

Quantitative analysis of CEUS features is valuable in the diagnosis of benign and malignant LNs, and US combined with CEUS helps to improve the accuracy of identifying the nature of LNs.

Using T2-weighted magnetic resonance imaging-derived radiomics to classify cervical lymphadenopathy in children

BACKGROUND

Cervical lymphadenopathy is common in children and has diverse causes varying from benign to malignant, their similar manifestations making differential diagnosis difficult.

OBJECTIVE

This study aimed to investigate whether radiomic models using conventional magnetic resonance imaging (MRI) could classify pediatric cervical lymphadenopathy.

METHODS

A total of 419 cervical lymph nodes from 146 patients, and encompassing four common etiologies (Kikuchi disease, reactive hyperplasia, suppurative lymphadenitis and malignancy), were randomly divided into training and testing sets in a ratio of 7:3. For each lymph node, 1,218 features were extracted from T2-weighted images. Then, the least absolute shrinkage and selection operator (LASSO) models were used to select the most relevant ones. Two models were built using a support vector machine classifier, one was to classify benign and malignant lymph nodes and the other further distinguished four different diseases. The performance was assessed by receiver operating characteristic curves and decision curve analysis.

RESULTS

By LASSO, 20 features were selected to construct a model to distinguish benign and malignant lymph nodes, which achieved an area under the curve (AUC) of 0.89 and 0.80 in the training and testing sets, respectively. Sixteen features were selected to construct a model to distinguish four different cervical lymphadenopathies. For each etiology, Kikuchi disease, reactive hyperplasia, suppurative lymphadenitis, and malignancy, an AUC of 0.97, 0.91, 0.88, and 0.87 was achieved in the training set, and an AUC of 0.96, 0.80, 0.82, and 0.82 was achieved in the testing set, respectively.

CONCLUSION

MRI-derived radiomic analysis provides a promising non-invasive approach for distinguishing causes of cervical lymphadenopathy in children.

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

Classification of superficial suspected lymph nodes: non-invasive radiomic model based on multiphase contrast-enhanced ultrasound for therapeutic options of lymphadenopathy

Background

Accurate determination of the types of lymphadenopathy is of great importance in disease diagnosis and treatment and is usually confirmed by pathological findings. Radiomics is a non-invasive tool that can extract quantitative information from medical images. Our study was designed to develop a non-invasive radiomic approach based on multiphase contrast-enhanced ultrasound (CEUS) images for the classification of different types of lymphadenopathy.

Methods

A total of 426 patients with superficial suspected lymph nodes (LNs) from three centres were grouped into a training cohort (n=190), an internal testing cohort (n=127), and an external testing cohort (n=109). The radiomic features were extracted from the prevascular phase, vascular phase, and postvascular phase of the CEUS images. Model 1 (the conventional feature model), model 2 (the multiphase radiomics model), and model 3 (the combined feature model) were established for lymphadenopathy classification. The area under the curve (AUC) and confusion matrix were used to evaluate the performance of the three models. The usefulness of the models was assessed in different threshold probabilities by decision curve analysis.

Results

There were 139 patients (32.6%) with benign LNs, 110 patients (25.8%) with lymphoma, and 177 patients (41.5%) with metastatic LNs in our population. Finally, twenty features were selected to construct the radiomics models for these three types of lymphadenopathy. Model 2 integrating multiphase images of the CEUS yielded the AUCs of 0.838, 0.739, and 0.733 in the training cohort, internal testing cohort, and external testing cohort, respectively. After the combination of conventional features and radiomic features, the AUCs of model 3 improved to 0.943, 0.823 and 0.785 in the training cohort, internal testing cohort, and external testing cohort. Besides, model 3 had an accuracy of 81.05%, sensitivity of 80%, and specificity of 90.43% in the training cohort. Model performance was further confirmed in the internal testing cohort and external testing cohort.

Conclusions

We constructed a combined feature model using a series of CEUS images for the classification of the lymphadenopathies. For patients with superficial suspected LNs, this model can help clinicians make a decision on the LN type noninvasively and choose appropriate treatments.

Development and validation of nomograms by radiomic features on ultrasound imaging for predicting overall survival in patients with primary nodal diffuse large B-cell lymphoma

Objectives

To develop and validate a nomogram to predict the overall survival (OS) of patients with primary nodal diffuse large B-cell lymphoma(N-DLBCL) based on radiomic features and clinical features.

Materials and methods

A retrospective analysis was performed on 145 patients confirmed with N-DLBCL and they were randomly assigned to training set(n=78), internal validation set(n=33), external validation set(n=34). First, a clinical model (model 1) was established according to clinical features and ultrasound (US) results. Then, based on the radiomics features extracted from conventional ultrasound images, a radiomic signature was constructed (model 2), and the radiomics score (Rad-Score) was calculated. Finally, a comprehensive model was established (model 3) combined with Rad-score and clinical features. Receiver operating characteristic (ROC) curves were employed to evaluate the performance of model 1, model 2 and model 3. Based on model 3, we plotted a nomogram. Calibration curves were used to test the effectiveness of the nomogram, and decision curve analysis (DCA) was used to asset the nomogram in clinical use.

Results

According to multivariate analysis, 3 clinical features and Rad-score were finally selected to construct the model 3, which showed better predictive value for OS in patients with N-DLBCL than mode 1 and model 2 in training (AUC,0. 891 vs. 0.779 vs.0.756), internal validation (AUC, 0.868 vs. 0.713, vs.0.756) and external validation (AUC, 914 vs. 0.866, vs.0.789) sets. Decision curve analysis demonstrated that the nomogram based on model 3 was more clinically useful than the other two models.

Conclusion

The developed nomogram is a useful tool for precisely analyzing the prognosis of N-DLBCL patients, which could help clinicians in making personalized survival predictions and assessing individualized clinical options.