Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer

2021 ◽  
Author(s):  
Wei-mei Ma ◽  
Jiao Li ◽  
Shuang-gang Chen ◽  
Pei-qiang Cai ◽  
Shen Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Cone Beam ◽  
Imaging Biomarkers ◽  
Training Set ◽  
Test Set ◽  
Ps Ii ◽  
Contrast Enhanced ◽  
Significant Difference

Objective: To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer. Methods: Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test. Results: As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938–0.996; p < 0.001] and 0.896 (95% CI, 0.809–0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636–0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05). Conclusion: CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage. Advances in knowledge: CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

Preoperative prediction of histologic grade in invasive breast cancer by using contrast-enhanced spectral mammography-based radiomics

2021 ◽  
pp. 1-10
Author(s):  
Ning Mao ◽  
Zimei Jiao ◽  
Shaofeng Duan ◽  
Cong Xu ◽  
Haizhu Xie
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Low Energy ◽  
Histologic Grade ◽  
Low Grade ◽  
Training Set ◽  
Predictive Tool ◽  
Combined Model ◽  
Test Set ◽  
Contrast Enhanced

OBJECTIVE: To develop and validate a radiomics model based on contrast-enhanced spectral mammography (CESM), and preoperatively discriminate low-grade (grade I/II) and high-grade (grade III) invasive breast cancer. METHOD: A total of 205 patients with CESM examination and pathologically confirmed invasive breast cancer were retrospectively enrolled. We randomly divided patients into two independent sets namely, training set (164 patients) and test set (41 patients) with a ratio of 8:2. Radiomics features were extracted from the low-energy and subtracted images. The least absolute shrinkage and selection operator (LASSO) logistic regression were established for feature selection, which were then utilized to construct three classification models namely, low energy, subtracted images and their combined model to discriminate high- and low-grade invasive breast cancer. Receiver operator characteristic (ROC) curves were used to confirm performance of three models in training set. The clinical usefulness was evaluated by using decision curve analysis (DCA). An independent test set was used to confirm the discriminatory power of the models. To test robustness of the result, we used 100 times LGOCV (leave group out cross validation) to validate three models. RESULTS: From initial radiomics feature pool, 17 and 11 features were selected for low-energy image and subtracted image, respectively. The combined model using 28 features showed the best performance for preoperatively evaluating the histologic grade of invasive breast cancer, with an area under the curve, AUC = 0.88, and 95%confidence interval [CI] 0.85 to 0.92 in the training set and AUC = 0.80 (95%CI 0.67 to 0.92) in the test set. The mean AUC of LGOCV is 0.82. CONCLUSIONS: CESM-based radiomics model is a non-invasive predictive tool that demonstrates good application prospects in preoperatively predicting histological grade of invasive breast cancer.

scholarly journals Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Lirong Song ◽  
Chunli Li ◽  
Jiandong Yin
Keyword(s):  
Breast Cancer ◽  
Kinetic Parameter ◽  
Prediction Models ◽  
Texture Features ◽  
Imaging Biomarkers ◽  
Her2 Status ◽  
Training Set ◽  
Her2 Positive ◽  
Test Set ◽  
Dce Mri

ObjectiveTo evaluate whether texture features derived from semiquantitative kinetic parameter maps based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can determine human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer.Materials and MethodsThis study included 102 patients with histologically confirmed breast cancer, all of whom underwent preoperative breast DCE-MRI and were enrolled retrospectively. This cohort included 48 HER2-positive cases and 54 HER2-negative cases. Seven semiquantitative kinetic parameter maps were calculated on the lesion area. A total of 55 texture features were extracted from each kinetic parameter map. Patients were randomly divided into training (n = 72) and test (n = 30) sets. The least absolute shrinkage and selection operator (LASSO) was used to select features in the training set, and then, multivariate logistic regression analysis was conducted to establish the prediction models. The classification performance was evaluated by receiver operating characteristic (ROC) analysis.ResultsAmong the seven prediction models, the model with features extracted from the early signal enhancement ratio (ESER) map yielded an area under the ROC curve (AUC) of 0.83 in the training set (sensitivity of 70.59%, specificity of 92.11%, and accuracy of 81.94%), and the highest AUC of 0.83 in the test set (sensitivity of 57.14%, specificity of 100.00%, and accuracy of 80.00%). The model with features extracted from the slope of signal intensity (SIslope) map yielded the highest AUC of 0.92 in the training set (sensitivity of 82.35%, specificity of 97.37%, and accuracy of 90.28%), and an AUC of 0.79 in the test set (sensitivity of 92.86%, specificity of 68.75%, and accuracy of 80.00%).ConclusionsTexture features derived from kinetic parameter maps, calculated based on breast DCE-MRI, have the potential to be used as imaging biomarkers to distinguish HER2-positive and HER2-negative breast cancer.

scholarly journals Exploring the Value of Radiomics Features Based on B-Mode and Contrast-Enhanced Ultrasound in Discriminating the Nature of Thyroid Nodules

2021 ◽  
Vol 11 ◽  
Author(s):  
Shi Yan Guo ◽  
Ping Zhou ◽  
Yan Zhang ◽  
Li Qing Jiang ◽  
Yong Feng Zhao
Keyword(s):  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
New Technologies ◽  
Statistical Significance ◽  
Characteristic Curve ◽  
Logistic Models ◽  
Contrast Enhanced Ultrasound ◽  
Clinical Value ◽  
Contrast Enhanced ◽  
Significant Difference

BackgroundWith the improvement of ultrasound imaging resolution and the application of various new technologies, the detection rate of thyroid nodules has increased greatly in recent years. However, there are still challenges in accurately diagnosing the nature of thyroid nodules. This study aimed to evaluate the clinical application value of the radiomics features extracted from B-mode ultrasound (B-US) images combined with contrast-enhanced ultrasound (CEUS) images in the differentiation of benign and malignant thyroid nodules by comparing the diagnostic performance of four logistic models.MethodsWe retrospectively collected and ultimately included B-US images and CEUS images of 123 nodules from 123 patients, and then extracted the corresponding radiomics features from these images respectively. Meanwhile, a senior radiologist combined the thyroid imaging reporting and data system (TI-RADS) and the enhancement pattern of the ultrasonography to make a graded diagnosis of the malignancy of these nodules. Next, based on these radiomics features and grades, logistic regression was used to help build the models (B-US radiomics model, CEUS radiomics model, B-US+CEUS radiomics model, and TI-RADS+CEUS model). Finally, the study assessed the diagnostic performance of these radiomics features with a comparison of the area under the curve (AUC) of the receiver operating characteristic curve of four logistic models for predicting the benignity or malignancy of thyroid nodules.ResultsThe AUC in the differential diagnosis of the nature of thyroid nodules was 0.791 for the B-US radiomics model, 0.766 for the CEUS radiomics model, 0.861 for the B-US+CEUS radiomics model, and 0.785 for the TI-RADS+CEUS model. Compared to the TI-RADS+CEUS model, there was no statistical significance observed in AUC between the B-US radiomics model, CEUS radiomics model, B-US+CEUS radiomics model, and TI-RADS+CEUS model (P&gt;0.05). However, a significant difference was observed between the single B-US radiomics model or CEUS radiomics model and B-US+CEUS radiomics model (P&lt;0.05).ConclusionIn our study, the B-US radiomics model, CEUS radiomics model, and B-US+CEUS radiomics model demonstrated similar performance with the TI-RADS+CEUS model of senior radiologists in diagnosing the benignity or malignancy of thyroid nodules, while the B-US+CEUS radiomics model showed better diagnostic performance than single B-US radiomics model or CEUS radiomics model. It was proved that B-US radiomics features and CEUS radiomics features are of high clinical value as the combination of the two had better diagnostic performance.

scholarly journals CT-Based Radiomics Helps to Predict Residual Lung Lesions in COVID-19 Patients at Three Months after Discharge

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1814
Author(s):  
Jia Huang ◽  
Feihong Wu ◽  
Leqing Chen ◽  
Jie Yu ◽  
Wengang Sun ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Lung Region ◽  
Training Set ◽  
Test Set ◽  
Psychological Burden ◽  
Lung Lesions ◽  
Model Based ◽  
Entire Lung

Background: In this study, our focus was on pulmonary sequelae of coronavirus disease 2019 (COVID-19). We aimed to develop and validate CT-based radiomic models for predicting the presence of residual lung lesions in COVID-19 survivors at three months after discharge. Methods: We retrospectively enrolled 162 COVID-19 confirmed patients in our hospital (84 patients with residual lung lesions and 78 patients without residual lung lesions, at three months after discharge). The patients were all randomly allocated to a training set (n = 114) or a test set (n = 48). Radiomic features were extracted from chest CT images in different regions (entire lung or lesion) and at different time points (at hospital admission or at discharge) to build different models, sequentially, or in combination, as follows: (1) Lesion_A model (based on the lesion region at admission CT); (2) Lesion_D model (based on the lesion region at discharge CT); (3) Δlesion model (based on the lesion region at admission CT and discharge CT); (4) Lung_A model (based on the lung region at admission CT); (5) Lung_D model (based on the lung region at discharge CT); (6) Δlung model (based on the lung region at admission CT and discharge CT). The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the predictive performances of the radiomic models. Results: Among the six models, the Lesion_D and the Δlesion models achieved better predictive efficacy, with AUCs of 0.907 and 0.927, sensitivity of 0.898 and 0.763, and specificity of 0.855 and 0.964 in the training set, and AUCs of 0.875 and 0.837, sensitivity of 0.920 and 0.680, and specificity of 0.826 and 0.913 in the test set, respectively. Conclusions: The CT-based radiomic models showed good predictive effects on the presence of residual lung lesions in COVID-19 survivors at three months after discharge, which may help doctors to plan follow-up work and to reduce the psychological burden of COVID-19 survivors.

scholarly journals Diagnostic accuracy of contrast-enhanced digital mammography in breast cancer detection in comparison to tomosynthesis, synthetic 2D mammography and tomosynthesis combined with ultrasound in women with dense breast

2020 ◽  
pp. 20201046
Author(s):  
Rashmi Sudhir ◽  
Kamala Sannapareddy ◽  
Alekya Potlapalli ◽  
Pooja Boggaram Krishnamurthy ◽  
Suryakala Buddha ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Digital Mammography ◽  
Characteristic Curve ◽  
Breast Cancer Detection ◽  
Dense Breast ◽  
Breast Clinic ◽  
Data Set ◽  
Iodinated Contrast ◽  
Contrast Enhanced

Objective: To assess the diagnostic efficacy of contrast-enhanced digital mammography (CEDM) in breast cancer detection in comparison to synthetic two-dimensional mammography (s2D MG), digital breast tomosynthesis (DBT) alone and DBT supplemented with ultrasound examination in females with dense breast with histopathology as the gold-standard. Methods: It was a prospective study, where consecutive females presenting to symptomatic breast clinic between April 2019 and June 2020 were evaluated with DBT. Females who were found to have heterogeneously dense (ACR type C) or extremely dense (ACR type D) breast composition detected on s2D MG were further evaluated with high-resolution breast ultrasound and thereafter with CEDM, but before the core biopsy or surgical excision, were included in the study. s2D MG was derived from post-processing reconstruction of DBT data set. Females with pregnancy, renal insufficiency or prior allergic reaction to iodinated contrast agent were excluded from the study. Image interpretation was done by two experienced breast radiologists and both were blinded to histological diagnosis. Results: This study included 166 breast lesions in130 patients with mean age of 45 ± 12 years (age range 24–72 years). There were 87 (52.4%) malignant and 79 (47.6%) benign lesions. The sensitivity of CEDM was 96.5%, significantly higher than synthetic 2D MG (75.6%, p < 0.0001), DBT alone (82.8%, p < 0.0001) and DBT + ultrasound (88.5%, p = 0.0057); specificity of CEDM was 81%, significantly higher than s2D MG (63.3%, p = 0.0002) and comparable to DBT alone (84.4%, p = 0.3586) and DBT + ultrasound (79.7%, p = 0.4135). In receiver operating characteristic curve analysis, the area under the curve was of 0.896 for CEDM, 0.841 for DBT + ultrasound, 0.769 for DBT alone and 0.729 for s2D MG. Conclusion: CEDM is an accurate diagnostic technique for cancer detection in dense breast. CEDM allowed a significantly higher number of breast cancer detection than the s2D MG, DBT alone and DBT supplemented with ultrasonography in females with dense breast. Advances in knowledge: CEDM is a promising novel technology with higher sensitivity and negative predictive value for breast cancer detection in females with dense breast in comparison to DBT alone or DBT supplemented with ultrasound.

Predictive Performance and Variability of the Cardiac Anesthesia Risk Evaluation Score

2004 ◽  
Vol 100 (6) ◽  
pp. 1405-1410 ◽  
Author(s):  
Alexandre Ouattara ◽  
Michaëla Niculescu ◽  
Sarra Ghazouani ◽  
Ario Babolian ◽  
Marc Landi ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Risk Evaluation ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Cardiac Anesthesia ◽  
Goodness Of Fit Test ◽  
Significant Difference ◽  
Receiver Operating

Background The Cardiac Anesthesia Risk Evaluation (CARE) score, a simple Canadian classification for predicting outcome after cardiac surgery, was evaluated in 556 consecutive patients in Paris, France. The authors compared its performance to those of two multifactorial risk indexes (European System for Cardiac Operative Risk Evaluation [EuroSCORE] and Tu score) and tested its variability between groups of physicians (anesthesiologists, surgeons, and cardiologists). Methods Each patient was simultaneously assessed using the three scores by an attending anesthesiologist in the immediate preoperative period. In a blinded study, the CARE score category was also determined by a cardiologist the day before surgery, by a surgeon in the operating room, and by a second anesthesiologist at arrival in intensive care unit. Calibration and discrimination for predicting outcomes were assessed by goodness-of-fit test and area under the receiver operating characteristic curve, respectively. The level of agreement of the CARE scoring between the three physicians was then assessed. Results The calibration analysis revealed no significant difference between expected and observed outcomes for the three classifications. The areas under the receiver operating characteristic curves for mortality were 0.77 with the CARE score, 0.78 with the EuroSCORE, and 0.73 with the Tu score (not significant). The agreement rate of the CARE scoring between two anesthesiologists, between anesthesiologists and surgeons, and between anesthesiologists and cardiologists were 90%, 83%, and 77%, respectively. Conclusions Despite its simplicity, the CARE score predicts mortality and major morbidity as well the EuroSCORE. In addition, it remains devoid of significant variability when used by groups of physicians of different specialties.

Contrast-enhanced ultrasound (CEUS) in assessing early response among patients with invasive breast cancer undergoing neoadjuvant chemotherapy

2016 ◽  
Vol 58 (4) ◽  
pp. 394-402 ◽  
Author(s):  
Ariel Saracco ◽  
Botond K Szabó ◽  
Ervin Tánczos ◽  
Jonas Bergh ◽  
Thomas Hatschek
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Contrast Agent ◽  
Real Time ◽  
Invasive Breast Cancer ◽  
Early Response ◽  
Contrast Enhanced Ultrasound ◽  
Time Harmonic ◽  
Contrast Enhanced ◽  
Significant Difference

Background One of the big challenges in onco-radiology is to find a reliable imaging method that may predict early response during the first cycles of any neoadjuvant chemotherapy. Purpose To evaluate the use of real-time harmonic contrast-enhanced ultrasound (CEUS) in predicting early response in breast cancer tumors under neoadjuvant chemotherapy (NAC) treatment. Material and Methods Nineteen consecutive patients with invasive breast cancer were evaluated with a bolus dose of 2.4 mL contrast agent using CEUS, before and after two cycles of epirubicin and docetaxel. The lognormal function was used for quantitative analysis of kinetic data to evaluate early response. Results There was statistically significant difference in time-to-peak ( tp) between responders and non-responders (two sample t-test, P = 0.027) where tp was significantly longer at the week 5 than at the baseline scan among responders when compared to non-responders. Conclusion In-flow of intravascular contrast agent in tumors is significantly slower in responders at real-time harmonic CEUS, and might be effectively used for the evaluation of early response to chemotherapy in invasive breast cancer. However, further investigations in a larger and more heterogeneous population should be performed to corroborate the reliability of the method.

scholarly journals Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Mou Li ◽  
Ling Yang ◽  
Yufeng Yue ◽  
Jingxu Xu ◽  
Chencui Huang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Combined Model ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
Validation Set

ObjectiveTo investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).MethodsThis was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.ResultsA total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P &lt; 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).ConclusionsThe radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

Contrast-Enhanced Ultrasound for Early Prediction of Response of Breast Cancer to Neoadjuvant Chemotherapy

2018 ◽  
Vol 40 (02) ◽  
pp. 194-204 ◽  
Author(s):  
Youn Lee ◽  
Sung Kim ◽  
Bong Kang ◽  
Yun Kim
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Characteristic Curve ◽  
Mean Transit Time ◽  
Early Time ◽  
Early Prediction ◽  
Contrast Enhanced Ultrasound ◽  
Time Intensity Curve ◽  
Minor Response ◽  
Contrast Enhanced

Abstract Purpose To evaluate the time-intensity curve (TIC) parameters on contrast-enhanced ultrasound (CEUS) for early prediction of the response of breast cancer to neoadjuvant chemotherapy (NAC). Materials and Methods This prospective study included 41 patients with breast cancer. CEUS was performed before and after the first cycle of NAC. TIC parameters were analyzed for different regions of interest (ROIs). ROI 1 targeted the hotspot area of greatest enhancement, ROI 2 delineated the area of hyperenhancement, ROI 3 included the entire tumor on grayscale ultrasound, and ROI 4 encircled the normal parenchyma. The TIC perfusion values for ROI 1, 2, and 3 were divided by the ROI 4 value. Results 11 (26.8 %) of the 41 patients showed a good response (Miller-Payne score 4 or 5) and 30 (73.2 %) showed a minor response (Miller-Payne score 1, 2, or 3). There were significant differences in the wash-out area under the curve, the wash-in and wash-out areas under the curve on ROI 1/4 after the first cycle of NAC, pre-NAC mean transit time local (mTTl) on ROI 2/4, and pre-NAC mTTl on ROI 3/4 between good and minor responders (area under the receiver-operating characteristic curve > 0.70, p < 0.05). Conclusion Some TIC parameters obtained by CEUS may allow prediction of the response of breast cancer to NAC at a very early time point.

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