Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status.

Purpose: The detection of human epidermal growth factor receptor 2 (HER2) expression status is essential to determining the chemotherapy regimen for breast cancer patients and to improving their prognosis. We developed a deep learning radiomics (DLR) model combining time-frequency domain features of ultrasound (US) video of breast lesions with clinical parameters for predicting HER2 expression status. Patients and Methods: Data for this research was obtained from 807 breast cancer patients who visited from February 2019 to July 2020. Ultimately, 445 patients were included in the study. Pre-operative breast ultrasound examination videos were collected and split into a training set and a test set. Building a training set of DLR models combining time-frequency domain features and clinical features of ultrasound video of breast lesions based on the training set data to predict HER2 expression status. Test the performance of the model using test set data. The final models integrated with different classifiers are compared, and the best performing model is finally selected. Results: The best diagnostic performance in predicting HER2 expression status is provided by an Extreme Gradient Boosting (XGBoost)-based time-frequency domain feature classifier combined with a logistic regression (LR)-based clinical parameter classifier of clinical parameters combined DLR, particularly with a high specificity of 0.917. The area under the receiver operating characteristic curve (AUC) for the test cohort was 0.810. Conclusion: Our study provides a non-invasive imaging biomarker to predict HER2 expression status in breast cancer patients.

Application of preoperative ultrasound features combined with clinical factors in predicting HER2-positive subtype (non-luminal) breast cancer.

BACKGROUND: Human epidermal growth factor receptor2+ subtype breast cancer has a high degree of malignancy and a poor prognosis. The aim of this study is to develop a prediction model for the human epidermal growth factor receptor2+ subtype (non-luminal) of breast cancer based on the clinical and ultrasound features related with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor2. METHODS: We collected clinical data and reviewed preoperative ultrasound images of enrolled breast cancers from September 2017 to August 2020. We divided the data into in three groups as follows. Group I: estrogen receptor ± , Group II: progesterone receptor ± and Group III: human epidermal growth factor receptor2 ± . Univariate and multivariate logistic regression analyses were used to analyze the clinical and ultrasound features related with biomarkers among these groups. A model to predict human epidermal growth factor receptor2+ subtype was then developed based on the results of multivariate regression analyses, and the efficacy was evaluated using the area under receiver operating characteristic curve, accuracy, sensitivity, specificity. RESULTS: The human epidermal growth factor receptor2+ subtype accounted for 138 cases (11.8%) in the training set and 51 cases (10.1%) in the test set. In the multivariate regression analysis, age ≤ 50 years was an independent predictor of progesterone receptor + (p = 0.007), and posterior enhancement was a negative predictor of progesterone receptor + (p = 0.013) in Group II; palpable axillary lymph node, round, irregular shape and calcifications were independent predictors of the positivity for human epidermal growth factor receptor-2 in Group III (p = 0.001, p = 0.007, p = 0.010, p < 0.001, respectively). In Group I, shape was the only factor related to estrogen receptor status in the univariate analysis (p < 0.05). The area under receiver operating characteristic curve, accuracy, sensitivity, specificity of the model to predict human epidermal growth factor receptor2+ subtype breast cancer was 0.697, 60.14%, 72.46%, 58.49% and 0.725, 72.06%, 64.71%, 72.89% in the training and test sets, respectively. CONCLUSIONS: Our study established a model to predict the human epidermal growth factor receptor2-positive subtype with moderate performance. And the results demonstrated that clinical and ultrasound features were significantly associated with biomarkers.

An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma.

Purpose: This study aimed to establish and validate an ultrasound radiomics nomogram for the preoperative prediction of central lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). Patients and Methods: The prediction model was developed in 609 patients with clinicopathologically confirmed unifocal PTC who received ultrasonography between Jan 2018 and June 2018. Radiomic features were extracted after the ultrasonography of PTC. Lasso regression model was used for data dimensionality reduction, feature selection, and radiomics signature building. The predicting model was established based on the multivariable logistic regression analysis in which the radiomics signature, ultrasonography-reported LN status, and independent clinicopathologic risk factors were incorporated, and finally a radiomics nomogram was established. The performance of the nomogram was assessed with respect to the discrimination and consistence. An independent validation was performed in 326 consecutive patients from July 2018 to Sep 2018. Results: The radiomics signature consisted of 23 selected features and was significantly associated with LN status in both primary and validation cohorts. The independent predictors in the radiomics nomogram included the radiomics signature, age, TG level, TPOAB level, and ultrasonography-reported LN status. The model showed good discrimination and consistence in both cohorts: C-index of 0.816 (95% CI, 0.808-0.824) in the primary cohort and 0.858 (95% CI, 0.849-0.867) in the validation cohort. The area under receiver operating curve was 0.858. In the validation cohort, the accuracy, sensitivity, specificity and AUC of this model were 0.812, 0.816, 0.810, and 0.858 (95% CI, 0.785-0.930), respectively. Decision curve analysis indicated the radiomics nomogram was clinically useful. Conclusion: This study presents a convenient, clinically useful ultrasound radiomics nomogram that can be used for the pre-operative individualized prediction of central LN metastasis in patients with PTC.

The Role of Contrast-Enhanced Ultrasound in the Diagnosis and Pathologic Response Prediction in Breast Cancer: A Meta-analysis and Systematic Review.

PURPOSE: To determine the overall performance of contrast-enhanced ultrasound (CEUS) in differentiating between benign and malignant breast lesions and in predicting the pathologic response to neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC). MATERIALS AND METHODS: Articles published up to April 2019 were systematically searched in Medline, Web of Science, and China National Knowledge Infrastructure. The sensitivities and specificities across studies, the calculations of positive and negative likelihood ratios (LR+ and LR-), diagnostic odds ratio (OR), and constructed summary receiver operating characteristic curves were determined. Methodologic quality was assessed using the QUADAS (Quality Assessment of Diagnostic Accuracy Studies) tool. Subgroup analyses and metaregression were performed on prespecified study-level characteristics. RESULTS: Fifty-one studies involving 4875 patients with 5246 breast lesions and 10 studies involving 462 patients with BC receiving NAC were included. Methodologic quality was relatively high, and no publication bias was detected. The overall sensitivity, specificity, diagnostic OR, LR+, and LR- for CEUS were 0.88 (95% confidence interval [CI], 0.86-0.89), 0.82 (95% CI, 0.80-0.83), 30.55 (95% CI, 21.40-43.62), 4.29 (95% CI, 3.51-5.25), and 0.16 (95% CI, 0.13-0.21), respectively, showing statistical heterogeneity. Multivariable metaregression analysis showed contrast mode to be the most significant source of heterogeneity. The overall sensitivity, specificity, LR+, LR, and diagnostic OR of CEUS imaging in predicting the overall pathologic response to NAC in patients with BC were 0.89 (95% CI, 0.83-0.93), 0.83 (95% CI, 0.78-0.88), 4.49 (95% CI, 3.04-6.62), 0.16 (95% CI, 0.10-0.24,), and 32.21 (95% CI, 16.74-62.01), respectively, showing mild heterogeneity. CONCLUSION: Our data confirmed the excellent performance of breast CEUS in differentiating between benign and malignant breast lesions as well as pathologic response prediction in patients with BC receiving NAC.

Development and Validation of a Simple-to-Use Nomogram for Predicting the Upgrade of Atypical Ductal Hyperplasia on Core Needle Biopsy in Ultrasound-Detected Breast Lesions.

BACKGROUND: The rate of carcinoma upgrade for atypical ductal hyperplasia (ADH) diagnosed on core needle biopsy (CNB) is variable on open excision. The purpose of the present study was to develop and validate a simple-to-use nomogram for predicting the upgrade of ADH diagnosed with ultrasound (US)-guided core needle biopsy in patients with US-detected breast lesions. METHODS: Two retrospective sets, the training set (n = 401) and the validation set (n = 186), from Fudan University Shanghai Cancer Center between January 2014 and December 2019 were retrospectively analyzed. Clinicopathological and US features were selected using univariate and multivariable logistic regression, and the significant features were incorporated to build a nomogram model. Model discrimination and calibration were assessed in the training set and validation set. RESULTS: Of the 587 ADH biopsies, 67.7% (training set: 267/401, 66.6%; validation set: 128/186, 68.8%) were upgraded to cancers. In the multivariable analysis, the risk factors were age [odds ratio (OR) 2.739, 95% confidence interval (CI): 1.525-5.672], mass palpation (OR 3.008, 95% CI: 1.624-5.672), calcifications on US (OR 4.752, 95% CI: 2.569-9.276), ADH extent (OR 3.150, 95% CI: 1.951-5.155), and suspected malignancy (OR 4.162, CI: 2.289-7.980). The model showed good discrimination, with an area under curve (AUC) of 0.783 (95% CI: 0.736-0.831), and good calibration (p = 0.543). The application of the nomogram in the validation set still had good discrimination (AUC = 0.753, 95% CI: 0.666-0.841) and calibration (p = 0.565). Instead of surgical excision of all ADHs, if those categorized with the model to be at low risk for upgrade were surveillanced and the remainder were excised, then 63.7% (37/58) of surgeries of benign lesions could have been avoided and 78.1% (100/128) malignant lesions could be treated in time. CONCLUSIONS: This study developed a simple-to-use nomogram by incorporating clinicopathological and US features with the overarching goal of predicting the probability of upgrade in women with ADH. The nomogram could be expected to decrease unnecessary surgery by nearly two-third and to identify most of the malignant lesions, helping guide clinical decision making with regard to surveillance versus surgical excision of ADH lesions.

Sonographic Features of Triple-Negative Breast Carcinomas Are Correlated With mRNA-lncRNA Signatures and Risk of Tumor Recurrence.

BACKGROUND: To determine a correlation between mRNA and lncRNA signatures, sonographic features, and risk of recurrence in triple-negative breast cancers (TNBC). METHODS: We retrospectively reviewed the data from 114 TNBC patients having undergone transcriptome analysis. The risk of tumor recurrence was determined based on the correlation between transcriptome profiles and recurrence-free survival. Ultrasound (US) features were described according to the Breast Imaging Reporting and Data System. Multivariate logistic regression analysis determined the correlation between US features and risk of recurrence. The predictive value of sonographic features in determining tumor recurrence was analyzed using receiver operating characteristic curves. RESULTS: Three mRNAs (CHRDL1, FCGR1A, and RSAD2) and two lncRNAs (HIF1A-AS2 and AK124454) were correlated with recurrence-free survival in patients with TNBC. Among the three mRNAs, two were upregulated (FCGR1A and RSAD2) and one was downregulated (CHRDL1) in TNBCs. LncRNAs HIF1A-AS2 and AK124454 were upregulated in TNBCs. Based on these signatures, an integrated mRNA-lncRNA model was established using Cox regression analysis to determine the risk of tumor recurrence. Benign-like sonographic features, such as regular shape, circumscribed margin, posterior acoustic enhancement, and no calcifications, were associated with HIF1A-AS2 expression and high risk of tumor recurrence (P<0.05). Malignant-like features, such as irregular shape, uncircumscribed margin, no posterior acoustic enhancement, and calcifications, were correlated with CHRDL1 expression and low risk of tumor recurrence (P<0.05). CONCLUSIONS: Sonographic features and mRNA-lncRNA signatures in TNBCs represent the risk of tumor recurrence. Taken together, US may be a promising technique in determining the prognosis of patients with TNBC.

Does Lesion Size Affect the Value of Shear Wave Elastography for Differentiating Between Benign and Malignant Thyroid Nodules?

OBJECTIVES: We aimed to investigate the diagnostic performance of shear wave elastography (SWE) combined with conventional ultrasonography (US) for differentiating between benign and malignant thyroid nodules of different sizes. METHODS: A total of 445 thyroid nodules from 445 patients were divided into 3 groups based on diameter (group 1, ≤ 10 mm; group 2, 10-20 mm; and group 3, > 20 mm). The mean elasticity index of the whole lesion was automatically calculated, and the threshold for differentiation between benign and malignant nodules was constructed by a receiver operating characteristic curve analysis. Diagnostic performances of conventional US and SWE were compared by using pathologic results as reference standards. RESULTS: The mean elasticity was significantly higher in malignant versus benign nodules for all size groups. The differences in mean elasticity in the size groups were not statistically significant for malignant or benign nodules. The specificity of US combined with SWE for group 1 was significantly higher than that for groups 2 and 3 (77.8% versus 62.9% and 53.3%; P < .05), and compared with group 1, the sensitivity was significantly higher for groups 2 and 3 (92.4% and 94.3% versus 80.7%; P < .05). When SWE was added, the specificity increased and the sensitivity and diagnostic accuracy decreased for group 1, and the sensitivity increased and the specificity decreased for groups 2 and 3; however, the differences were not significant. CONCLUSIONS: Combined with SWE, US yielded higher specificity for nodules of 10 mm and smaller and higher sensitivity for nodules larger than 10 mm.

Study on breast cancer animal model of tumor-micro vessel variation before and after the chemotherapy by contrast enhanced ultrasound quantitative analysis.

Aim to discuss whether the contrast enhanced ultrasound (CEUS) can effectively monitor the efficacy on neoadjuvant chemotherapy of breast cancer or not by analyzing the indicators on chemotherapy CEUS and breast cancer tumor biology, especially tumor microcirculation indicator on animal mode. Human breast cancer cell lines MCF-7 are planted under the skins of nude mice. By simulating clinical neoadjuvant chemotherapy regimen periodically inject CMF (cyclophosphamide, methotrexate and fluorouracil) into the experimental group, and normal saline into the control group. Then detect the data from CEUS and record the parameters: maximum intensity (IMAX), rise time (RT), time to peak (TTP) and mean transit time (mTT). Execute animal after CEUS, obtain tumor biological indicator and record parameters: micro vessel density (MVD), vascular endothelial growth factor receptors 1/2/3/4 (VEGFR-1/2/3/4) and tumor cells. In the aspect of tumor biological indicator, the experimental group after the first drug delivery: inter- and intra-group comparisons of VEGFR-1/4drop significantly. The experimental group after the second drug delivery: inter- and intra-group comparisons of MVD, VEGFR-1/3/4drop significantly. In the aspect of parameters on tumor CEUS, the experimental group after the first drug delivery: inter- and intra-group comparisons of IMAX drop significantly. The experimental group after the second drug delivery: inter- and intra-group comparisons of IMAX decrease steeply; while inter-and intra-group comparisons of TTP rise significantly. There are great changes about the intra-group comparisons of the number of tumor cells before and after the experiment. In the process of chemotherapy, it maintains the consistency of the changes of CEUS parameters IMAX and TTP, tumor microcirculation indicators MVD and VEGFR-1/3/4 and tumor cells. So CEUS has a potential to make an early prediction on the efficacy of neoadjuvant chemotherapy.

Is Ultrasonography More Sensitive Than Computed Tomography for Identifying Calcifications in Thyroid Nodules?

OBJECTIVES: The primary objective of this study was to evaluate the difference and agreement between ultrasonography (US) and computed tomography (CT) for identifying calcifications in thyroid nodules. METHODS: Data from the medical records of 20,248 patients were reviewed for preoperative diagnostic investigations and postoperative pathologic diagnoses. In total, 5247 records were selected for analysis based on the presence of calcifications reported in any of the following 3 modes: US, CT, and pathologic analysis. All 5247 patients underwent US examinations, whereas 3827 underwent cervical CT examinations. All patients had a postoperative pathologic diagnosis serving as a reference. The value of US for identification of calcifications and prediction of malignancy was analyzed on the basis of the entire cohort of 5247 records, whereas that of CT was based on 3827 records. The agreement between US and CT was analyzed on the basis of the 3827 common records. RESULTS: Of the 5247 patients who underwent US, 4855 (92.5%) were found to have calcifications, whereas of the 3827 patients who underwent CT, 2040 (53.3%) were found to have calcifications (P < .0005). Among the 404 cases with calcifications reported by pathologic analysis, the agreement rate between US and pathologic findings was significantly higher than that between CT and pathologic findings (87.9% versus 81.9%, respectively; P = .018). CONCLUSIONS: US is more sensitive and accurate than CT for detecting calcifications in thyroid nodules. Hence, US is recommended as the preferred imaging modality for calcification detection in thyroid nodules.