Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules

Development and Validation of a Nomogram Based on Multimodality Ultrasonography Images for Differentiating Malignant from Benign American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) 3-5 Thyroid Nodules

OBJECTIVE

The aim of the work described here was to develop and validate a predictive nomogram based on combined image features of gray-scale ultrasonography (US), elastosonography (ES) and contrast-enhanced US (CEUS) to differentiate malignant from benign American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) 3-5 thyroid nodules.

METHODS

Among 2767 thyroid nodules scanned by CEUS in Xijing Hospital between April 2014 and November 2018, 669 nodules classified as ACR TI-RADS 3-5 were included, with confirmed diagnosis and ES examination. Four hundred fifty-five nodules were set as a training cohort and 214 as a validation cohort. Images were categorized as gray-scale US ACR TI-RADS 3, TI-RADS 4 and TI-RADS 5; ES patterns of ES-1 and ES-2; and CEUS patterns of either heterogeneous hypo-enhancement, concentric hypo-enhancement, homogeneous hyper-/iso-enhancement, no perfusion, hypo-enhancement with sharp margin, island-like enhancement or ring-like enhancement. On the basis of multivariate logistic regression analysis, a predictive nomogram model was developed and validated by receiver operating characteristic curve analysis.

RESULTS

In the training cohort, ACR TI-RADS 4 and 5, ES-2, heterogeneous hypo-enhancement, concentric hypo-enhancement and homogeneous hyper-/iso-enhancement were selected as predictors of malignancy by univariate logistic regression analysis. A predictive nomogram (combining indices of ACR TI-RADS, ES and CEUS) indicated excellent predictive ability for differentiating malignant from benign lesions in the training cohort: area under the receiver operating characteristic curve (AUC) = 0.93, 95% confidence interval (CI): 0.90-0.95. The prediction nomogram model was determined to have a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.84, 0.88, 0.91 and 0.81. In the validation cohort, the AUC of the prediction nomogram model was significantly higher than those of the single modalities (p < 0.005) . The AUCs of the validation cohort were 0.93 (95% CI: 0.89-0.96) and 0.93 (95% CI: 0.89-0.97), respectively, for senior and junior radiologists. The prediction nomogram model has a sensitivity, specificity, PPV and NPV of 0.86, 0.87, 0.87 and 0.86.

CONCLUSION

A predictive nomogram model combining ACR TI-RADS, ES and CEUS exhibited potential clinical utility in differentiating malignant from benign ACR TI-RADS 3-5 thyroid nodules.

Dual-modal radiomics nomogram based on contrast-enhanced ultrasound to improve differential diagnostic accuracy and reduce unnecessary biopsy rate in ACR TI-RADS 4-5 thyroid nodules

BACKGROUND

American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS, TR) 4 and 5 thyroid nodules (TNs) demonstrate much more complicated and overlapping risk characteristics than TR1-3 and have a rather wide range of malignancy possibilities (> 5%), which may cause overdiagnosis or misdiagnosis. This study was designed to establish and validate a dual-modal ultrasound (US) radiomics nomogram integrating B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) imaging to improve differential diagnostic accuracy and reduce unnecessary fine needle aspiration biopsy (FNAB) rates in TR 4-5 TNs.

METHODS

A retrospective dataset of 312 pathologically confirmed TR4-5 TNs from 269 patients was collected for our study. Data were randomly divided into a training dataset of 219 TNs and a validation dataset of 93 TNs. Radiomics characteristics were derived from the BMUS and CEUS images. After feature reduction, the BMUS and CEUS radiomics scores (Rad-score) were built. A multivariate logistic regression analysis was conducted incorporating both Rad-scores and clinical/US data, and a radiomics nomogram was subsequently developed. The performance of the radiomics nomogram was evaluated using calibration, discrimination, and clinical usefulness, and the unnecessary FNAB rate was also calculated.

RESULTS

BMUS Rad-score, CEUS Rad-score, age, shape, margin, and enhancement direction were significant independent predictors associated with malignant TR4-5 TNs. The radiomics nomogram involving the six variables exhibited excellent calibration and discrimination in the training and validation cohorts, with an AUC of 0.873 (95% CI, 0.821-0.925) and 0.851 (95% CI, 0.764-0.938), respectively. The marked improvements in the net reclassification index and integrated discriminatory improvement suggested that the BMUS and CEUS Rad-scores could be valuable indicators for distinguishing benign from malignant TR4-5 TNs. Decision curve analysis demonstrated that our developed radiomics nomogram was an instrumental tool for clinical decision-making. Using the radiomics nomogram, the unnecessary FNAB rate decreased from 35.3 to 14.5% in the training cohort and from 41.5 to 17.7% in the validation cohorts compared with ACR TI-RADS.

CONCLUSION

The dual-modal US radiomics nomogram revealed superior discrimination accuracy and considerably decreased unnecessary FNAB rates in benign and malignant TR4-5 TNs. It could guide further examination or treatment options.

Development and validation of a nomogram model based on pretreatment ultrasound and contrast-enhanced ultrasound to predict the efficacy of neoadjuvant chemotherapy in patients with borderline resectable or locally advanced pancreatic cancer

OBJECTIVES

To develop a nomogram using pretreatment ultrasound (US) and contrast-enhanced ultrasound (CEUS) to predict the clinical response of neoadjuvant chemotherapy (NAC) in patients with borderline resectable pancreatic cancer (BRPC) or locally advanced pancreatic cancer (LAPC).

METHODS

A total of 111 patients with pancreatic ductal adenocarcinoma (PDAC) treated with NAC between October 2017 and February 2022 were retrospectively enrolled. The patients were randomly divided (7:3) into training and validation cohorts. The pretreatment US and CEUS features were reviewed. Univariate and multivariate logistic regression analyses were used to determine the independent predictors of clinical response in the training cohort. Then a prediction nomogram model based on the independent predictors was constructed. The area under the curve (AUC), calibration plot, C-index and decision curve analysis (DCA) were used to assess the nomogram's performance, calibration, discrimination and clinical benefit.

RESULTS

The multivariate logistic regression analysis showed that the taller-than-wide shape in the longitudinal plane (odds ratio [OR]:0.20, p = 0.01), time from injection of contrast agent to peak enhancement (OR:3.64; p = 0.05) and Peaktumor/ Peaknormal (OR:1.51; p = 0.03) were independent predictors of clinical response to NAC. The predictive nomogram developed based on the above imaging features showed AUCs were 0.852 and 0.854 in the primary and validation cohorts, respectively. Good calibration was achieved in the training datasets, with C-index of 0.852. DCA verified the clinical usefulness of the nomogram.

CONCLUSIONS

The nomogram based on pretreatment US and CEUS can effectively predict the clinical response of NAC in patients with BRPC and LAPC; it may help guide personalized treatment.

Clinical value of grayscale ultrasound combined with real-time shear wave elastography nomogram in risk prediction of thyroid cancer

OBJECTIVES

This study constructed a nomogram based on grayscale ultrasound features and real-time shear wave elastography (SWE) parameters to predict thyroid cancer.

METHODS

Clinical data of 217 thyroid nodules of 201 patients who underwent grayscale ultrasound, real-time SWE, and thyroid function laboratory examination in Ma'anshan People's Hospital from January 2019 to December 2020 were retrospectively analyzed. The subjects were divided into a benign nodule group (106 nodules) and a malignant nodule group (111 nodules). The differences in grayscale ultrasound features, quantitative parameters of real-time SWE, and laboratory results of thyroid function between benign and malignant thyroid nodules were analyzed. We used a chi-square test for categorical variables and a t-test for continuous variables. Then, the independent risk factors for thyroid cancer were analyzed using multivariate logistic regression. Based on the independent risk factors, a nomogram for predicting thyroid cancer risk was constructed using the RMS package of the R software.

RESULTS

Multivariate logistic regression showed that the grayscale ultrasound features of thyroid nodules were the shape, margin, echogenicity, and echogenic foci of the nodules,the maximum Young's modulus (SWE-max) of thyroid nodules, and the ratio of thyroid nodule and peripheral gland (SWE-ratio) measured by real-time SWE were independent risk factors for thyroid cancer (all p < 0.05), and the other variables had no statistical difference (p > 0.05). Based on the shape (OR = 5.160, 95% CI: 2.252-11.825), the margin (OR = 9.647, 95% CI: 2.048-45.443), the echogenicity (OR = 6.512, 95% CI: 1.729-24.524), the echogenic foci (OR = 2.049, 95% CI: 1.118-3.756), and the maximum Young's modulus (SWE-max) (OR = 1.296, 95% CI: 1.140-1.473), the SWE-ratio (OR = 2.001, 95% CI: 1.403-2.854) of the thyroid nodule to peripheral gland was used to establish the related nomogram prediction model. The bootstrap self-sampling method was used to verify the model. The consistency index (C-index) was 0.979, ROC curve was used to analyze the nomogram scores of all patients, and the AUC of nomogram prediction of thyroid cancer was 0.976, indicating that the nomogram model had high accuracy in the risk prediction of thyroid cancer.

CONCLUSIONS

The nomogram model of grayscale ultrasound features combined with SWE parameters can accurately predict thyroid cancer.

Research to develop a diagnostic ultrasound nomogram to predict benign or malignant lymph nodes in HIV-infected patients

BACKGROUND

This study aimed to establish an effective ultrasound diagnostic nomogram for benign or malignant lymph nodes in HIV-infected patients.

METHODS

The nomogram is based on a retrospective study of 131 HIV-infected patients who underwent ultrasound assess at the Shanghai Public Health Clinical Center from December 2017 to July 2022. The nomogram's predictive accuracy and discriminative ability were determined by concordance index (C-index) and calibration curve analysis. A nomogram combining the lymph node US characteristics were generated based on the multivariate logistic regression results.

RESULTS

Predictors contained in the ultrasound diagnostic nomogram included age (OR 1.044 95%CI: 1.014-1.074 P = 0.004), number of enlarged lymph node regions (OR 5.445 95%CI: 1.139-26.029 P = 0.034), and color Doppler flow imaging (CDFI) grades (OR 9.614 95%CI: 1.889-48.930 P = 0.006). The model displayed good discrimination with a C (ROC) of 0.775 and good calibration.

CONCLUSIONS

The proposed nomogram may result in more-accurate diagnostic predictions for benign or malignant lymph nodes in patients with HIV infection.

Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma

Background

The typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC).

Patients and Methods

A total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist’s score, and combination of ultrasomics features and radiologist’s score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC).

Results

A total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist’s score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist’s score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist’s score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist’s score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001).

Conclusions

Machine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist’s score improves the diagnostic performance in differentiating FNH and aHCC.

Nomogram Based on Ultrasonography and Clinical Features for Predicting Malignancy in Soft Tissue Tumors

Purpose

The objective of this study was to establish a predictive nomogram based on ultrasound (US) and clinical features for patients with soft tissue tumors (STTs).

Patients and Methods

A total of 260 patients with STTs were enrolled in this retrospective study and were divided into a training cohort (n=200, including 110 malignant and 90 benign masses) and a validation cohort (n=60, including 30 malignant and 30 benign masses). Multivariate analysis was performed by binary logistic regression analysis to determine the significant factors predictive of malignancy. A simple nomogram was established based on these independent risk factors including US and clinical features. The predictive accuracy and discriminative ability of the nomogram were measured by the calibration curve and the concordance index (C-index).

Results

The nomogram, comprising US features (maximum diameter, margin and vascular density) and clinical features (sex, age, and duration of disease), showed a favorable performance for predicting malignancy, with a sensitivity of 88.2% and a specificity of 78.7%. The calibration curve for malignancy probability in the training cohort showed good agreement between the nomogram predictions and actual observations. The C-indexes of the training cohort and validation cohort for predicting malignancy were 0.89 (95% CI: 0.85–0.94) and 0.83 (95% CI: 0.73–0.94), respectively.

Conclusion

The nomogram based on US and clinical features could be a simple, intuitive and reliable tool to individually predict malignancy in patients with STTs.

Do Molecular Profiles of Primary Versus Metastatic Radioiodine Refractory Differentiated Thyroid Cancer Differ?

Management of metastatic radioiodine refractory differentiated thyroid cancer (DTC) can be a therapeutic challenge. Generally, little is known about the paired molecular profile of the primary tumor and the metastases and whether they harbor the same genetic abnormalities. The present study compared the molecular profile of paired tumor specimens (primary tumor/metastatic sites) from patients with radioiodine refractory DTC in order to gain insight into a possible basis for resistance to radioiodine. Twelve patients with radioiodine refractory metastases were studied; median age at diagnosis of 61 years (range, 25-82). Nine patients had papillary TC (PTC), one had follicular TC (FTC), and two had Hürthle cell TC (HTC). Distant metastases were present in the lungs (n = 10), bones (n = 4), and liver (n = 1). The molecular profiling of paired tumors was performed with a panel of 592 genes for Next Generation Sequencing, RNA-sequencing, and immunohistochemistry. Digital microfluidic PCR was used to investigate TERT promoter mutations. The genetic landscape of all paired sites comprised BRAF, NRAS, HRAS, TP53, ATM, MUTYH, POLE, and NTRK genes, including BRAF and NTRK fusions. BRAF V600E was the most common point mutation in the paired specimens (5/12). TERT promoter mutation C228T was detected in one case. PD-L1 expression at metastatic sites was highly positive (95%) for one patient with HTC. All specimens were stable for microsatellite instability testing, and the tumor mutation burden was low to intermediate. Therefore, the molecular profile of DTC primary and metastatic lesions can show heterogeneity, which may help explain some altered responses to therapeutic intervention.

Contrast-enhanced ultrasound: a valuable modality for extracapsular extension assessment in papillary thyroid cancer

OBJECTIVES

To evaluate the diagnostic accuracy of preoperative contrast-enhanced ultrasound (CEUS) to detect extracapsular extension (ECE) and identify the relationship between ECE and nodule enhancement patterns in patients with papillary thyroid cancer (PTC).

METHODS

Patients with suspected thyroid cancer underwent ultrasound (US) and CEUS examinations. The US and CEUS features of the PTC nodules and thyroid capsule were recorded and classified individually. The accuracy of US and CEUS in detecting ECE was compared individually, and its relationship with various tumour enhancement patterns was analysed. The presence or absence of ECE and cervical lymph node metastasis (LNM) was confirmed pathologically.

RESULTS

The final dataset included 119 patients with 124 PTC nodules. Seventy-two (60.5%) of these patients with PTC had no ECE (including 38 patients with single capsule invasion), while the remaining 52 had ECE. A significant difference was found in nodules with non-capsule invasion, single capsule invasion, and ECE between the cervical LNM and non-LNM groups (p < 0.01). Receiver operating characteristic curve analysis demonstrated that area under the curve (AUC) of ECE for cervical LNM was higher than that of capsule invasion (71.9% vs. 49.6%). Moreover, the CEUS images acquired to detect ECE showed higher AUC values than those of US images (79.4% vs. 65.8%) (p = 0.02). Among the PTC nodules with differential enhancement, hyper-enhanced nodules had the highest incidence of capsule invasion (41.9%), while hypo-enhanced ones had a higher incidence of ECE (47.4%).

CONCLUSIONS

Compared with conventional US, CEUS is a more valuable and non-invasive imaging modality to detect ECE.

KEY POINTS

• Single capsular invasion was a poor predictor of cervical lymph node metastasis, while extracapsular extension assessments were clinically significant for predicting cervical lymph node metastasis. • CEUS is better than conventional US in detecting extracapsular extension in papillary thyroid carcinoma (AUC: 79.4% vs. 65.8%) (p = 0.02). • Among the thyroid papillary carcinoma nodules with differential enhancement, hyper-enhanced nodules had the highest incidence of single capsule invasion (41.9%), while hypo-enhanced ones had a higher incidence of ECE (47.4%).

A nomogram to predict survival of patients with intermediate-stage hepatocellular carcinoma after transarterial chemoembolization combined with microwave ablation

OBJECTIVES

To develop a prognostic nomogram based on the albumin-bilirubin (ALBI) grade for prediction of the long-term survival of patients with intermediate-stage hepatocellular carcinoma (HCC) after transarterial chemoembolization combined with microwave ablation (TACE-MWA).

METHODS

We retrospectively studied 546 consecutive patients with intermediate-stage HCC according to the Barcelona Clinic Liver Cancer guidelines who underwent TACE-MWA between January 2000 and December 2016. Overall survival (OS) and progression-free survival (PFS) were analyzed. The predictive value of the ALBI grade was investigated. The prognostic nomogram was constructed using the independent predictors assessed by the multivariate Cox proportional hazards model.

RESULTS

After a median follow-up of 35.0 months (range, 4.0-221.0 months), 380 patients had died. The median OS was 35.0 months (95% confidence interval (CI), 30.84-39.16 months), and the median PFS was 6.5 months (95% CI, 6.13-6.87 months). The ALBI grade was validated as an independent predictor of OS (p < 0.001). Multivariate analyses showed that Eastern Cooperative Oncology Group performance status score more than 0, presence of liver cirrhosis, a-fetoprotein level above 400 ng/mL, tumor size greater than 5 cm, tumor number more than 3, advanced ALBI grade, and treatment sessions of TACE or MWA fewer than 3 were independently associated with overall mortality. The prognostic nomogram incorporating these eight predictors achieved good calibration and discriminatory abilities with a concordance index of 0.770 (95% CI, 0.746-0.795).

CONCLUSIONS

The prognostic nomogram based on the ALBI grade resulted in reliable efficacy for prediction of individualized OS in patients with intermediate-stage HCC after TACE-MWA.

KEY POINTS

• TACE-MWA was associated with a median overall survival of 35.0 months for patients with intermediate-stage HCC. • A prognostic nomogram was built to predict individualized survival of patients with intermediate-stage HCC after TACE-MWA. • The prognostic nomogram incorporating eight predictors achieved good calibration and discriminatory abilities with a concordance index of 0.770.

Prediction of suspicious thyroid nodule using artificial neural network based on radiofrequency ultrasound and conventional ultrasound: A preliminary study

This study explored the use of backscattered radiofrequency ultrasound signals combined with artificial neural network (ANN) technology to differentiate benign and malignant thyroid nodules, in comparison with conventional ultrasound techniques. The proposed method uses the gray level co-occurrence matrix algorithm and principal component analysis to identify principal characteristics for use as inputs in the ANN. The dataset consisted of 131 ultrasound images, of which 59 were benign and 72 were malignant, as determined by subsequent surgeries. The nodules were divided randomly into training, validation, and testing groups. Receiver operating characteristic curves (ROC) were drawn to compare the diagnostic efficiency of the ANN when applied to radiofrequency and conventional ultrasound images. The sensitivity, specificity, and accuracy of the ANN in predicting malignancy from the radiofrequency ultrasound images were 100, 91.5, and 96.2%, respectively; from conventional ultrasound, the corresponding values were 94.4, 93.2, and 93.9%, respectively. The area under the receiver operating characteristic curve (AUC) was also higher for radiofrequency than conventional ultrasound (AUC = 0.945 vs. 0.917, 95% confidence interval = 0.901-0.998 vs. 0.854-0.979, using a P-value of 0.26). We then classified each nodule into new risk categories according to the output of each sample generated by the proposed method. The malignancy risks in the proposed Categories 3, 4, and 5 were 0, 18.8, and 94.5%, respectively, compared with 0, 55.1, and 88.2% using the American College of Radiology's Thyroid Imaging Reporting and Data System. Thus, this preliminary study initially indicated that the proposed method of using radiofrequency ultrasound and the ANN was more accurate at predicting malignancy and stratifying thyroid nodules than conventional ultrasound methods, thus offering significant potential to reduce the number of unnecessary thyroid biopsies.