Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study

Enhancing prediction of cervical lymph node metastasis in papillary thyroid carcinoma through nodule-oriented quantification: combined S-Detect and ultrasound elastography

Background

Papillary thyroid carcinoma (PTC) frequently metastasizes to cervical lymph nodes (LNs), with metastasis rates of 20-90%, significantly impacting patient prognosis. Although ultrasound (US) is the primary preoperative assessment tool, its accuracy (Acc) in detecting LN metastasis (LNM) remains insufficient, with conventional US detecting only 50% of confirmed cases. This study aimed to improve the prediction of cervical LNM in PTC by combining quantitative nodule orientation parameters with multi-modal US techniques.

Methods

Data were retrospectively collected from 117 patients (141 nodules: 85 non-metastasis and 56 metastasis) who underwent PTC resection and cervical LN dissection from September 2023 to May 2024. All patients underwent US, US elastography (UE), and S-Detect examinations before surgery. For each nodule, the angle between the nodule's maximum diameter and the skin was measured. Logistic regression analysis assessed the correlation between each variable and cervical LNM, identified significant predictive factors, and a predictive model presented as a nomogram was constructed.

Results

Univariate analysis showed significant differences between non-metastasis and metastasis groups in orientation quantification [-9.3° (-35.2°, 17.2°) vs. 13.9° (-1.6°, 54.0°), P<0.001], age (P=0.002), maximum nodule diameter (P=0.017), boundary (P=0.021), microcalcifications on S-Detect (P=0.014), microcalcifications (P=0.036), and ECI scores (P=0.043). Multivariate analysis identified seven independent predictors for cervical LNM, with S-Detect-detected microcalcifications showing the highest odds ratio (OR) [OR =4.159; 95% confidence interval (CI): 1.545-11.199]. The combined predictive model incorporating conventional US, UE, S-Detect, and orientation quantification demonstrated superior diagnostic performance [area under the curve (AUC) =0.861; 95% CI: 0.803-0.919] compared to individual models (P<0.001), achieving sensitivity (Sen) of 0.911 and specificity (Spe) of 0.659. The nomogram showed good calibration with no significant deviation (χ2=3.271; P=0.926).

Conclusions

S-Detect accurately identifies the direction of the maximum diameter of thyroid nodules, and quantification of the longitudinal section orientation can be used as an independent predictor for LNM in PTC.

Prediction of peripheral lymph node metastasis (LNM) in thyroid cancer using delta radiomics derived from enhanced CT combined with multiple machine learning algorithms

OBJECTIVES

This study aimed to develop a model for predicting peripheral lymph node metastasis (LNM) in thyroid cancer patients by combining enhanced CT radiomic features with machine learning algorithms. It increased the clinical utility and interpretability of the predictions through SHAP (SHapley Additive exPlanation) values and nomograms for model explanation and visualization.

METHODS

Clinical and enhanced CT image data from 375 patients with thyroid cancer confirmed by postoperative pathology at Xiangyang No. 1 People's Hospital were collected from January 2015 to July 2023. Among them, there were 88 patients in the LNM group and 287 patients in the non-LNM group. The delta radiomic features of the tumours were extracted. Various machine learning algorithms (such as SVM, GBM, RF, XGBoost, KNN, and LightGBM) were trained on the clinical and radiomic feature data sets and used to construct a reliable prediction model. During model training, cross-validation was used to evaluate model performance, and the optimal model was selected. In addition, SHAP values were used to interpret the prediction results of the optimal model, analyse the contribution of each feature to the prediction results, and further develop a nomogram to visually display the prediction results.

RESULTS

Univariate analysis confirmed that sex, Hashimoto's disease, tumour adjacency to the thyroid capsule, pathological subtype, Delta Radscore, and Radscore 1 are risk factors for peripheral lymph node metastasis in thyroid cancer patients. The machine learning model based on enhanced CT radiomics performed well in predicting peripheral lymph node metastasis in thyroid cancer patients. In the test set, the optimal model, SVM, achieved high AUC (0.879), sensitivity (0.849), and specificity (0.769) values. Through SHAP value analysis, the importance and contribution of tumour adjacency to the thyroid capsule, pathological subtype, Delta Radscore, and Radscore 1 in the prediction were clarified, providing a more detailed and intuitive basis for clinical decision-making. The nomogram illustrated the model prediction process, facilitating understanding and application by clinicians.

CONCLUSIONS

This study successfully constructed a model for predicting peripheral lymph node metastasis in thyroid cancer patients on the basis of enhanced CT radiomics combined with machine learning and improved the interpretability and clinical utility of the model through SHAP values and nomograms. The model not only improves the accuracy of predictions but also provides a more scientific and intuitive basis for clinical decision-making, with potential clinical application value.

Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma

BACKGROUND

Cervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastasis. Accurate preoperative CLNM diagnosis is of more importance in patients with papillary thyroid cancer (PTC). However, there is currently no unified methods to objectively predict CLNM risk from ultrasonography in PTC patients.This study aimed to develop a deep learning (DL) model to help clinicians more accurately determine the existence of CLNM risk in patients with PTC and then assist them with treatment decisions.

METHODS

Ultrasound dynamic videos of 388 patients with 717 thyroid nodules were retrospectively collected from Zhejiang Cancer Hospital between January 2020 and June 2022. Five deep learning (DL) models were investigated to examine its efficacy for predicting CLNM risks and their performances were also compared with those predicted using two-dimensional ultrasound static images.

RESULTS

In the testing dataset (n = 78), the DenseNet121 model trained on ultrasound dynamic videos outperformed the other four DL models as well as the DL model trained using the two-dimensional (2D) static images across all metrics. Specifically, using DenseNet121, the comparison between the 3D model and 2D model for all metrics are shown as below: AUROC: 0.903 versus 0.828, sensitivity: 0.877 versus 0.871, specificity: 0.865 versus 0.659.

CONCLUSIONS

This study demonstrated that the DenseNet121 model has the greatest potential in distinguishing CLNM from non-CLNM in patients with PTC. Dynamic videos also offered more information about the disease states which have proven to be more efficient and robust in identifying CLNM compared to statis images.

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).

High-risk habitat radiomics model based on ultrasound images for predicting lateral neck lymph node metastasis in differentiated thyroid cancer

BACKGROUND

This study aims to evaluate the predictive usefulness of a habitat radiomics model based on ultrasound images for anticipating lateral neck lymph node metastasis (LLNM) in differentiated thyroid cancer (DTC), and for pinpointing high-risk habitat regions and significant radiomics traits.

METHODS

A group of 214 patients diagnosed with differentiated thyroid carcinoma (DTC) between August 2021 and August 2023 were included, consisting of 107 patients with confirmed postoperative lateral lymph node metastasis (LLNM) and 107 patients without metastasis or lateral cervical lymph node involvement. An additional cohort of 43 patients was recruited to serve as an independent external testing group for this study. Patients were randomly divided into training and internal testing group at an 8:2 ratio. Region of interest (ROI) was manually outlined, and habitat analysis subregions were defined using the K-means method. The ideal number of subregions (n = 5) was determined using the Calinski-Harabasz score, leading to the creation of a habitat radiomics model with 5 subregions and the identification of the high-risk habitat model. Area under the curve (AUC) values were calculated for all models to assess their validity, and predictive model nomograms were created by integrating clinical features. The internal and external testing dataset is employed to assess the predictive performance and stability of the model.

RESULTS

In internal testing group, Habitat 3 was identified as the high-risk habitat model in the study, showing the best diagnostic efficacy among all models (AUC(CRM) vs. AUC(Habitat 3) vs. AUC(CRM + Habitat 3) = 0.84(95%CI:0.71-0.97) vs. 0.90(95%CI:0.80-1.00) vs. 0.79(95%CI:0.65-0.93)). Moreover, integrating the Habitat 3 model with clinical features and constructing nomograms enhanced the predictive capability of the combined model (AUC = 0.95(95%CI:0.88-1.00)). In this study, an independent external testing cohort was utilized to assess the model's accuracy, yielding an AUC of 0.88 (95%CI: 0.78-0.98).

CONCLUSION

The integration of the High-Risk Habitats (Habitat 3) radiomics model with clinical characteristics demonstrated a high predictive accuracy in identifying LLNM. This model has the potential to offer valuable guidance to surgeons in deciding the necessity of LLNM dissection for DTC.

CLINICAL TRIAL NUMBER

Not applicable.

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.

Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer

PURPOSE

A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study.

METHODS

A total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis.

RESULTS

The area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis.

CONCLUSION

This model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.

Nomogram for predicting the risk of central lymph node metastasis in papillary thyroid microcarcinoma: a combination of sonographic findings and clinical factors

Background

A considerable controversy over performing thyroidectomy and central lymph node dissection in patients with papillary thyroid microcarcinoma (PTMC) remained. However, accurate prediction of central lymph node metastasis (CLNM) is crucial for surgical extent and proper management. The aim of this study was to develop and validate a practical nomogram for predicting CLNM in patients with PTMC.

Methods

A total of 1,029 patients with PTMC who underwent thyroidectomy and central lymph node dissection at Tangdu Hospital (the Second Affiliated Hospital of Air Force Medical University) and Xijing Hospital (the First Affiliated Hospital of Air Force Medical University) were selected. Seven hundred and nine patients were assigned to the training set and 320 patients to the validation set. Data encompassing demographic characteristics, ultrasonography results, and biochemical indicators were obtained. Stepwise backward selection and multiple logistic regression were used to screen the variables and establish the nomogram. Concordance index (C-index), receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were employed to evaluate the nomogram's distinguishability, accuracy, and clinical utility.

Results

Young age, multifocality, bigger tumor, presence of microcalcification, aspect ratio (height divided by width) ≥1, loss of fatty hilum, high free thyroxine (FT4), and lower anti-thyroid peroxidase antibody (TPOAb) were significantly associated with CLNM. The nomogram showed strong predictive capacity, with a C-index and accuracy of 0.784 and 0.713 in the training set and 0.779 and 0.703 in the external validation set, respectively. DCA indicated that the nomogram demonstrated strong clinical applicability.

Conclusions

We established a reliable, cost-effective, reproducible, and noninvasive nomogram for predicting CLNM in patients with PTMC. This tool could be a valuable guidance for deciding on management in PTMC.

Combining radiomics with thyroid imaging reporting and data system to predict lateral cervical lymph node metastases in medullary thyroid cancer

BACKGROUND

Medullary Thyroid Carcinoma (MTC) is a rare type of thyroid cancer. Accurate prediction of lateral cervical lymph node metastases (LCLNM) in MTC patients can help guide surgical decisions and ensure that patients receive the most appropriate and effective surgery. To our knowledge, no studies have been published that use radiomics analysis to forecast LCLNM in MTC patients. The purpose of this study is to develop a radiomics combined with thyroid imaging reporting and data system (TI-RADS) model that can use preoperative thyroid ultrasound images to noninvasively predict the LCLNM status of MTC.

METHODS

We retrospectively included 218 MTC patients who were confirmed from postoperative pathology as LCLNM negative (n=111) and positive (n=107). Ultrasound features were selected using the Student's t-test, while radiomics features are first extracted from preoperative thyroid ultrasound images, and then a two-step feature selection approach was used to select features. These features are then used to establish three regularized logistic regression models, namely the TI-RADS model (TM), the radiomics model (RM), and the radiomics-TI-RADS model (RTM), in 5-fold cross-validation to determine the likelihood of the LCLNM. The Delong's test and decision curve analysis (DCA) were used to evaluate and compare the performance of the models.

RESULTS

The ultrasound features of margin and TI-RADS level, and a total of 12 selected radiomics features, were significantly different between the LCLNM negative and positive groups (p<0.05). The TM, RM, and RTM yielded an averaged AUC of 0.68±0.05, 0.78±0.06, and 0.82±0.05 in the 5-fold cross-validation dataset, respectively. RM and RTM are statistically better than TM (p<0.05 and p<0.001) according to Delong test. DCA demonstrates that RTM brings more benefit than TM and RM.

CONCLUSIONS

We have developed a joint radiomics-based model for noninvasive prediction of the LCLNM in MTC patients solely using preoperative thyroid ultrasound imaging. It has the potential to be used as a complementary tool to help guide treatment decisions for this rare form of thyroid cancer.

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models

OBJECTIVE

The accurate clinical diagnosis of cervical lymph node metastasis plays an important role in the treatment of differentiated thyroid cancer (DTC). This study aimed to explore and summarize a more objective approach to detect cervical malignant lymph node metastasis of DTC via radiomics models.

METHODS

PubMed, Web of Science, MEDLINE, EMBASE, and Cochrane databases were searched for all eligible studies. Articles using radiomics models based on ultrasound, computed tomography, or magnetic resonance imaging to assess cervical lymph node metastasis preoperatively were included. Characteristics and diagnostic accuracy measures were extracted. Bias and applicability judgments were evaluated by the revised QUADAS-2 tool. The estimates were pooled using a random-effects model. Additionally, the leave-one-out method was conducted to assess the heterogeneity.

RESULTS

Twenty-nine radiomics studies with 6160 validation set patients were included in the qualitative analysis, and 11 studies with 3863 validation set patients were included in the meta-analysis. Four of them had an external independent validation set. The studies were heterogeneous, and a significant risk of bias was found in 29 studies. Meta-analysis showed that the pooled sensitivity and specificity for preoperative prediction of lymph node metastasis via US-based radiomics were 0.81 (95% CI, 0.73-0.86) and 0.87 (95% CI, 0.83-0.91), respectively.

CONCLUSIONS

Although radiomics-based models for cervical lymphatic metastasis in DTC have been demonstrated to have moderate diagnostic capabilities, broader data, standardized radiomics features, robust feature selection, and model exploitation are still needed in the future.

ADVANCES IN KNOWLEDGE

The radiomics models showed great potential in detecting malignant lymph nodes in thyroid cancer.

Clinical features and imaging examination assessment of cervical lymph nodes for thyroid carcinoma

BACKGROUNDS

The purpose of this study is to investigate the relationship between clinical characteristics and cervical lymph node metastasis (LNM) in patients with thyroid carcinoma, as well as estimate the preoperative diagnosis values of ultrasound (US) and contrast enhanced computed tomography (CECT) examinations on the neck for detection of cervical LNM in thyroid carcinoma.

METHODS

A retrospective analysis of 3 026 patients with surgically proven thyroid carcinoma was conducted. Patients' clinical characteristics, including gender, age, tumor size, bilateral lesions, multifocality, adenomatous nodules, Hashimoto's thyroiditis (HT), and extrathyroidal extension, were collected to explore their association with cervical LNM in thyroid carcinoma. Preoperative assessments for central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM) were conducted through US and CECT. The diagnostic value of US, CECT and US combined with CECT for detection of LNM located in various cervical compartments was estimated based on the pathological results.

RESULTS

The risk of cervical LNM was higher in thyroid cancer patients who were male, age < 55 years old, tumor size > 10 mm, bilateral lesions, and extrathyroidal extension, while multifocality, adenomatous nodules and HT had no significant effect on LNM. US, CECT and US combined with CECT all had a higher sensitivity to LLNM (93.1%, 57.8%, 95.4%) than to CLNM (32.3%, 29.0%, 43.4%). US and CECT had a high specificity to both CLNM and LLNM (94.3-97.8%).

CONCLUSION

Preoperative clinical characteristics and imaging examinations on patients with thyroid carcinoma are crucial to the evaluation of cervical lymph nodes and conducive to individualizing surgical treatments by clinicians. US combined with CECT are superior to single US or CECT alone in detection of CLNM and LLNM.

Radiomics features from whole thyroid gland tissue for prediction of cervical lymph node metastasis in the patients with papillary thyroid carcinoma

OBJECTIVE

We aimed to develop a clinical-radiomics nomogram that could predict the cervical lymph node metastasis (CLNM) of patients with papillary thyroid carcinoma (PTC) using clinical characteristics as well as radiomics features of dual energy computed tomography (DECT).

METHOD

Patients from our hospital with suspected PTC who underwent DECT for preoperative assessment between January 2021 and February 2022 were retrospectively recruited. Clinical characteristics were obtained from the medical record system. Clinical characteristics and rad-scores were examined by univariate and multivariate logistic regression. All features were incorporated into the LASSO regression model, with penalty parameter tuning performed using tenfold cross-validation, to screen risk factors for CLNM. An easily accessible radiomics nomogram was constructed. Receiver Operating Characteristic (ROC) curve together with Area Under the Curve (AUC) analysis was conducted to evaluate the discrimination performance of the model. Calibration curves were employed to assess the calibration performance of the clinical-radiomics nomogram, followed by goodness-of-fit testing. Decision curve analysis (DCA) was performed to determine the clinical utility of the established models by estimating net benefits at varying threshold probabilities for training and testing groups.

RESULTS

A total of 461 patients were retrospectively recruited. The rates of CLNM were 49.3% (70 /142) in the training cohort and 53.3% (32/60) in the testing cohort. Out of the 960 extracted radiomics features, 192 were significantly different in positive and negative groups (p < 0.05). On the basis of the training cohort, 12 stable features with nonzero coefficients were selected using LASSO regression. LASSO regression identified 7 risk factors for CLNM, including male gender, maximum tumor size > 10 mm, multifocality, CT-reported central CLN status, US-reported central CLN status, rad-score, and TGAb. A nomogram was developed using these factors to predict the risk of CLNM. The AUC values in each cohort were 0.850 and 0.797, respectively. The calibration curve together with the Hosmer-Lemeshow test for the nomogram indicated good agreement between predicted and pathological CLN statuses in the training and testing cohorts. Results of DCA proved that the nomogram offers a superior net benefit for predicting CLNM compared to the "treat all or none" strategy across the majority of risk thresholds.

CONCLUSION

A nomogram comprising the clinical characteristics as well as radiomics features of DECT and US was constructed for the prediction of CLNM for patients with PTC, which in determining whether lateral compartment neck dissection is warranted.

Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: A systematic review and meta-analysis

PURPOSE

To evaluate the diagnostic performance of radiomics in lymph node metastasis (LNM) prediction in patients with papillary thyroid carcinoma (PTC) through a systematic review and meta-analysis.

METHOD

A literature search of PubMed, EMBASE, and Web of Science was conducted to find relevant studies published until February 18th, 2023. Studies that reported the accuracy of radiomics in different imaging modalities for LNM prediction in PTC patients were selected. The methodological quality of included studies was evaluated by radiomics quality score (RQS) and quality assessment of diagnostic accuracy studies (QUADAS-2) tools. General characteristics and radiomics accuracy were extracted. Overall sensitivity, specificity, and area under the curve (AUC) were calculated for diagnostic accuracy evaluation. Spearman correlation coefficient and subgroup analysis were performed for heterogeneity exploration.

RESULTS

In total, 25 studies were included, of which 22 studies provided adequate data for meta-analysis. We conducted two types of meta-analysis: one focused solely on radiomics features models and the other combined radiomics and non-radiomics features models in the analysis. The pooled sensitivity, specificity, and AUC of radiomics and combined models were 0.75 [0.68, 0.80] vs. 0.77 [0.74, 0.80], 0.77 [0.74, 0.81] vs. 0.83 [0.78, 0.87] and 0.80 [0.73, 0.85] vs 0.82 [0.75, 0.88], respectively. The analysis showed a high heterogeneity level among the included studies. There was no threshold effect. The subgroup analysis demonstrated that utilizing ultrasonography, 2D segmentation, central and lateral LNM detection, automatic segmentation, and PyRadiomics software could slightly improve diagnostic accuracy.

CONCLUSIONS

Our meta-analysis shows that the radiomics has the potential for pre-operative LNM prediction in PTC patients. Although methodological quality is sufficient but we still need more prospective studies with larger sample sizes from different centers.

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis

OBJECTIVE

This retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC).

METHODS

In total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers-adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared.

RESULTS

The Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases.

CONCLUSION

The ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions.

Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

OBJECTIVES

To build a combined model based on the ultrasound radiomic and morphological features, and evaluate its diagnostic performance for preoperative prediction of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC).

METHOD

A total of 295 eligible patients, who underwent preoperative ultrasound scan and were pathologically diagnosed with unifocal PTC were included at our hospital from October 2019 to July 2022. According to ultrasound scanners, patients were divided into the training set (115 with CLNM; 97 without CLNM) and validation set (45 with CLNM; 38 without CLNM). Ultrasound radiomic, morphological, and combined models were constructed using multivariate logistic regression. The diagnostic performance was assessed by the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, sensitivity, and specificity.

RESULTS

A combined model was built based on the morphology, boundary, length diameter, and radiomic score. The AUC was 0.960 (95% CI, 0.924-0.982) and 0.966 (95% CI, 0.901-0.993) in the training and validation set, respectively. Calibration curves showed good consistency between prediction and observation, and DCA demonstrated the clinical benefit of the combined model.

CONCLUSION

Based on ultrasound radiomic and morphological features, the combined model showed a good performance in predicting CLNM of patients with PTC preoperatively.

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models

OBJECTIVES

Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer.

METHODS

An in-house deep learning-based model called "ClymphNet" was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively.

RESULTS

Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts.

CONCLUSION

A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.

Machine Learning-Based Shear Wave Elastography Elastic Index (SWEEI) in Predicting Cervical Lymph Node Metastasis of Papillary Thyroid Microcarcinoma: A Comparative Analysis of Five Practical Prediction Models

Purpose

Although many factors determine the prognosis of papillary thyroid carcinoma (PTC), cervical lymph node metastasis (CLNM) is one of the most terrible factors. In view of this, this study aimed to build a CLNM prediction model for papillary thyroid microcarcinoma (PTMC) with the help of machine learning algorithm.

Methods

We retrospectively analyzed 387 PTMC patients hospitalized in the Department of Medical Oncology, Enshi Tujia and Miao Autonomous Prefecture Central Hospital from January 1, 2015, to January 31, 2022. Based on supervised learning algorithms, namely random forest classifier (RFC), artificial neural network(ANN), support vector machine(SVM), decision tree(DT), and extreme gradient boosting gradient(XGboost) algorithm, the LNM prediction model was constructed, and the prediction efficiency of ML-based model was evaluated via receiver operating characteristic curve(ROC) and decision curve analysis(DCA).

Results

Finally, a total of 24 baseline variables were included in the supervised learning algorithm. According to the iterative analysis results, the pulsatility index(PI), resistance index(RI), peak systolic blood flow velocity(PSBV), systolic acceleration time(SAT), and shear wave elastography elastic index(SWEEI), such as average value(Emean), maximum value(Emax), and minimum value(Emix) were candidate predictors. Among the five supervised learning models, RFC had the strongest prediction efficiency with area under curve(AUC) of 0.889 (95% CI: 0.838–0.940) and 0.878 (95% CI: 0.821–0.935) in the training set and testing set, respectively. While ANN, DT, SVM and XGboost had prediction efficiency between 0.767 (95% CI: 0.716–0.818) and 0.854 (95% CI: 0.803–0.905) in the training set, and ranged from 0.762 (95% CI: 0.705–0.819) to 0.861 (95% CI: 0.804–0.918) in the testing set.

Conclusion

We have successfully constructed an ML-based prediction model, which can accurately classify the LNM risk of patients with PTMC. In particular, the RFC model can help tailor clinical decisions of treatment and surveillance.