Diagnostic Performance
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2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Kun Sun ◽  
Zhicheng Jiao ◽  
Hong Zhu ◽  
Weimin Chai ◽  
Xu Yan ◽  
...  

Abstract Background This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast lesions. Methods This retrospective study included 542 lesions from February 2018 to November 2018. One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADCall b, mADC0–1000), BE (mD, mD*, mf), SE (mDDC, mα), and DKI (mK, mD) were also calculated for comparison. The area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance. Results RF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC0–1000). There were significant differences in the AUCs between the mean values of all diffusion metrics and radiomics features of AUCs (all P < 0.001) for the differentiation of benign and malignant breast lesions. Of the radiomics features computed, the most important sequence was BE_D (AUC: 0.85), and the most important feature was FO-10 percentile (Feature Importance: 0.04). Conclusions The radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics.


Author(s):  
Isil Basara Akin ◽  
Hakan Abdullah Ozgul ◽  
Canan Altay ◽  
Merih Guray Durak ◽  
Suleyman Ozkan Aksoy ◽  
...  

Abstract Purpose Phyllodes tumors (PTs) are uncommon fibroepithelial breast lesions that are classified as three different forms as benign phyllodes tumor (BPT), borderline phyllodes tumor (BoPT), and malignant phyllodes tumor (MPT). Conventional radiologic methods make only a limited contribution to exact diagnosis, and texture analysis data increase the diagnostic performance. In this study, we aimed to evaluate the contribution of texture analysis of US images (TAUI) of PTs in order to discriminate between BPTs and BoPTs-MPTs. Methods The number of patients was 63 (41 BPTs, 12 BoPTs, and 10 MPTs). Patients were divided into two groups (Group 1-BPT, Group 2-BoPT/MPT). TAUI with LIFEx software was performed retrospectively. An independent machine learning approach, MATLAB R2020a (Math- Works, Natick, Massachusetts) was used with the dataset with p < 0.004. Two machine learning approaches were used to build prediction models for differentiating between Group 1 and Group 2. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance of statistically significant texture data between phyllodes subgroups. Results In TAUI, 10 statistically significant second order texture values were identified as significant factors capable of differentiating among the two groups (p < 0.05). Both of the models of our dataset make a diagnostic contribution to the discrimination between BopTs-MPTs and BPTs. Conclusion In PTs, US is the main diagnostic method. Adding machine learning-based TAUI to conventional US findings can provide optimal diagnosis, thereby helping to choose the correct surgical method. Consequently, decreased local recurrence rates can be achieved.


2021 ◽  
Author(s):  
Ping He ◽  
Lan Zeng ◽  
Liying Miao ◽  
Tianli Wang ◽  
Juxiang Ye ◽  
...  

Abstract Purpose To compare the diagnostic performance of double contrast-enhanced ultrasound (DCEUS) and multi-detector row computed tomography (MDCT) in the gross classification of gastric cancer (GC) preoperatively. Methods 54 patients with GC proved by histology were included in this study. The sensitivity and specificity of DCEUS and MDCT for gross classification were calculated and compared. The area under the curve (AUC) from a receiver operating characteristic curve analysis was used to evaluate the difference of the diagnostic performance between these two methods.Results There were no significant differences between DCEUS and MDCT in terms of AUC values for early gastric cancer (EGC) and Borrmann Ⅰ-Ⅲ (P = 0.248, 0.317, 0.717 and 0.464, respectively). However, the sensitivities of DCEUS for EGC, Borrmann Ⅰ and Borrmann Ⅲ were higher than those of MDCT (75% versus 62%; 100% versus 50%; 90% versus 73%). The specificity of DCEUS for Borrmann Ⅲ was lower than that of MDCT (50% versus 75%). The AUC value of MDCT for Borrmann Ⅳ was significantly higher than that of DCEUS (0.927 versus 0.625; P=0.001). The accuracy and specificity of DCEUS and MDCT for Borrmann Ⅳ were similar, but the sensitivity of MDCT was significantly higher than that of DCEUS (88% versus 25%).Conclusion DCEUS may be considered as a useful complementary imaging modality to MDCT for the evaluation of the gross classification of GC preoperatively.


2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Chenao Zhan ◽  
Dazhong Tang ◽  
Lu Huang ◽  
Yayuan Geng ◽  
Tao Ai ◽  
...  

Background: The clinical manifestations of amyloid cardiomyopathy (AC) are not specific; therefore, AC is often misdiagnosed as hypertrophic cardiomyopathy (HCM) or hypertensive heart disease (HHD). A differential diagnosis of these three conditions is often necessary in the clinical setting. Objectives: To investigate the differential diagnostic performance of radiomic analysis, based on cardiac magnetic resonance (CMR) native T1 mapping images for the left ventricular hypertrophy (LVH) etiologies. Methods: This retrospective, case-control study was conducted on 91 participants (68 males and 23 females; mean age: 48 ± 13 years), including 22 patients with HHD, 27 patients with AC, 28 patients with HCM, and 14 controls in Tongji Hospital (Shanghai, China). All participants underwent 3.0T CMR imaging. Besides, radiomic analyses were performed using T1 mapping images. The cases were divided into training and test datasets using a random seed. Next, the models were constructed with the training dataset and evaluated with the test dataset. Results: A total of 1,033 radiomic features were extracted in this study. Overall, 11, 28, 19, and eight features were selected to construct the basal T1 mapping, mid-chamber T1 mapping, apical T1 mapping, and multi-module conjoint models, respectively. Optimal performance was reported in the mid-chamber and basal T1 mapping models. The area under the curve (AUC), precision, recall, and F1 score were 0.96, 0.84, 0.82, and 0.83 for the mid-chamber T1 mapping model and 0.96, 0.90, 0.89, and 0.88 for the basal T1 mapping model in the independent test dataset, respectively. The lowest diagnostic performance was observed in the apical T1 mapping model. The AUC, precision, recall, and F1 score of the apical T1 mapping model were 0.86, 0.71, 0.70, and 0.70 in the independent test dataset, respectively. Conclusions: The radiomic analysis of T1 mapping could accurately distinguish the three causes of myocardial hypertrophy, including HCM, HHD, and AC. It may be also a suitable alternative to late gadolinium enhancement-CMR.


Author(s):  
Joyce Y. C. Chan ◽  
Baker K. K. Bat ◽  
Adrian Wong ◽  
Tak Kit Chan ◽  
Zhaohua Huo ◽  
...  

AbstractDigital drawing tests have been proposed for cognitive screening over the past decade. However, the diagnostic performance is still to clarify. The objective of this study was to evaluate the diagnostic performance among different types of digital and paper-and-pencil drawing tests in the screening of mild cognitive impairment (MCI) and dementia. Diagnostic studies evaluating digital or paper-and-pencil drawing tests for the screening of MCI or dementia were identified from OVID databases, included Embase, MEDLINE, CINAHL, and PsycINFO. Studies evaluated any type of drawing tests for the screening of MCI or dementia and compared with healthy controls. This study was performed according to PRISMA and the guidelines proposed by the Cochrane Diagnostic Test Accuracy Working Group. A bivariate random-effects model was used to compare the diagnostic performance of these drawing tests and presented with a summary receiver-operating characteristic curve. The primary outcome was the diagnostic performance of clock drawing test (CDT). Other types of drawing tests were the secondary outcomes. A total of 90 studies with 22,567 participants were included. In the screening of MCI, the pooled sensitivity and specificity of the digital CDT was 0.86 (95% CI = 0.75 to 0.92) and 0.92 (95% CI = 0.69 to 0.98), respectively. For the paper-and-pencil CDT, the pooled sensitivity and specificity of brief scoring method was 0.63 (95% CI = 0.49 to 0.75) and 0.77 (95% CI = 0.68 to 0.84), and detailed scoring method was 0.63 (95% CI = 0.56 to 0.71) and 0.72 (95% CI = 0.65 to 0.78). In the screening of dementia, the pooled sensitivity and specificity of the digital CDT was 0.83 (95% CI = 0.72 to 0.90) and 0.87 (95% CI = 0.79 to 0.92). The performances of the digital and paper-and-pencil pentagon drawing tests were comparable in the screening of dementia. The digital CDT demonstrated better diagnostic performance than paper-and-pencil CDT for MCI. Other types of digital drawing tests showed comparable performance with paper-and-pencil formats. Therefore, digital drawing tests can be used as an alternative tool for the screening of MCI and dementia.


2021 ◽  
Vol 11 ◽  
Author(s):  
Shi Yun Sun ◽  
Yingying Ding ◽  
Zhuolin Li ◽  
Lisha Nie ◽  
Chengde Liao ◽  
...  

ObjectivesTo evaluate the value of synthetic magnetic resonance imaging (syMRI), diffusion-weighted imaging (DWI), DCE-MRI, and clinical features in breast imaging–reporting and data system (BI-RADS) 4 lesions, and develop an efficient method to help patients avoid unnecessary biopsy.MethodsA total of 75 patients with breast diseases classified as BI-RADS 4 (45 with malignant lesions and 30 with benign lesions) were prospectively enrolled in this study. T1-weighted imaging (T1WI), T2WI, DWI, and syMRI were performed at 3.0 T. Relaxation time (T1 and T2), apparent diffusion coefficient (ADC), conventional MRI features, and clinical features were assessed. “T” represents the relaxation time value of the region of interest pre-contrast scanning, and “T+” represents the value post-contrast scanning. The rate of change in the T value between pre- and post-contrast scanning was represented by ΔT%.ResultsΔT1%, T2, ADC, age, body mass index (BMI), menopause, irregular margins, and heterogeneous internal enhancement pattern were significantly associated with a breast cancer diagnosis in the multivariable logistic regression analysis. Based on the above parameters, four models were established: model 1 (BI-RADS model, including all conventional MRI features recommended by BI-RADS lexicon), model 2 (relaxation time model, including ΔT1% and T2), model 3 [multi-parameter (mp)MRI model, including ΔT1%, T2, ADC, margin, and internal enhancement pattern], and model 4 (combined image and clinical model, including ΔT1%, T2, ADC, margin, internal enhancement pattern, age, BMI, and menopausal state). Among these, model 4 has the best diagnostic performance, followed by models 3, 2, and 1.ConclusionsThe mpMRI model with DCE-MRI, DWI, and syMRI is a robust tool for evaluating the malignancies in BI-RADS 4 lesions. The clinical features could further improve the diagnostic performance of the model.


Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5185
Author(s):  
Emin Gültekin ◽  
Christoph Wetz ◽  
Jürgen Braun ◽  
Dominik Geisel ◽  
Christian Furth ◽  
...  

Purpose: To evaluate the diagnostic performance of tomoelastography in differentiating pancreatic neuroendocrine tumors (PNETs) from healthy pancreatic tissue and to assess the prediction of tumor aggressiveness by correlating PNET stiffness with PET derived asphericity. Methods: 13 patients with PNET were prospectively compared to 13 age-/sex-matched heathy volunteers (CTR). Multifrequency MR elastography was combined with tomoelastography-postprocessing to provide high-resolution maps of shear wave speed (SWS in m/s). SWS of pancreatic neuroendocrine tumor (PNET-T) were compared with nontumorous pancreatic tissue in patients with PNET (PNET-NT) and heathy pancreatic tissue (CTR). The diagnostic performance of tomoelastography was evaluated by ROC-AUC analysis. PNET-SWS correlations were calculated with Pearson’s r. Results: SWS was higher in PNET-T (2.02 ± 0.61 m/s) compared to PNET-NT (1.31 ± 0.18 m/s, p < 0.01) and CTR (1.26 ± 0.09 m/s, p < 0.01). An SWS-cutoff of 1.46 m/s distinguished PNET-T from PNET-NT (AUC = 0.89; sensitivity = 0.85; specificity = 0.92) and a cutoff of 1.49 m/s differentiated pancreatic tissue of CTR from PNET-T (AUC = 0.96; sensitivity = 0.92; specificity = 1.00). The SWS of PNET-T was positively correlated with PET derived asphericity (r = 0.81; p = 0.01). Conclusions: Tomoelastography provides quantitative imaging markers for the detection of PNET and the prediction of greater tumor aggressiveness by increased stiffness.


2021 ◽  
Vol 11 ◽  
Author(s):  
Shi Yan Guo ◽  
Ping Zhou ◽  
Yan Zhang ◽  
Li Qing Jiang ◽  
Yong Feng Zhao

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.


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