Radiomic Evaluations of the Diagnostic Performance of DM, DBT, DCE MRI, DWI, and Their Combination for the Diagnosisof Breast Cancer.
Ontology highlight
ABSTRACT: Objectives
This study aims to evaluate digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) MRI, individually and combined, for the values in the diagnosis of breast cancer, and propose a visualized clinical-radiomics nomogram for potential clinical uses.Methods
A total of 120 patients were enrolled between September 2017 and July 2018, all underwent preoperative DM, DBT, DCE, and DWI scans. Radiomics features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) regression. A radiomics nomogram was constructed integrating the radiomics signature and important clinical predictors, and assessed with the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).Results
The radiomics signature derived from DBT plus DM generated a lower area under the ROC curve (AUC) and sensitivity, but a higher specificity compared with that from DCE plus DWI. The nomogram integrating the combined radiomics signature, age, and menstruation status achieved the best diagnostic performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.975 vs. 0.964 vs. 0.782) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.983 vs. 0.978 vs. 0.680) cohorts. DCA confirmed the potential clinical usefulness of the nomogram.Conclusions
The DBT plus DM provided a lower AUC and sensitivity, but a higher specificity than DCE plus DWI for detecting breast cancer. The proposed clinical-radiomics nomogram has diagnostic advantages over each modality, and can be considered as an efficient tool for breast cancer screening.
SUBMITTER: Niu S
PROVIDER: S-EPMC8461299 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: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.
| S-EPMC8554332 | biostudies-literature
Project description:The purpose of this study was to investigate the role of features derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and to incorporated clinical information to predict the molecular subtypes of breast cancer. In particular, 60 breast cancers with the following four molecular subtypes were analyzed: luminal A, luminal B, human epidermal growth factor receptor-2 (HER2)-over-expressing and basal-like. The breast region was segmented and the suspicious tumor was depicted on sequentially scanned MR images from each case. In total, 90 features were obtained, including 88 imaging features related to morphology and texture as well as dynamic features from tumor and background parenchymal enhancement (BPE) and 2 clinical information-based parameters, namely, age and menopausal status. An evolutionary algorithm was used to select an optimal subset of features for classification. Using these features, we trained a multi-class logistic regression classifier that calculated the area under the receiver operating characteristic curve (AUC). The results of a prediction model using 24 selected features showed high overall classification performance, with an AUC value of 0.869. The predictive model discriminated among the luminal A, luminal B, HER2 and basal-like subtypes, with AUC values of 0.867, 0.786, 0.888 and 0.923, respectively. An additional independent dataset with 36 patients was utilized to validate the results. A similar classification analysis of the validation dataset showed an AUC of 0.872 using 15 image features, 10 of which were identical to those from the first cohort. We identified clinical information and 3D imaging features from DCE-MRI as candidate biomarkers for discriminating among four molecular subtypes of breast cancer.
| S-EPMC5293281 | biostudies-literature
Project description:BackgroundDiffusion weighted imaging (DWI) with intravoxel incoherent motion (IVIM) modelling can inform on tissue perfusion without exogenous contrast administration. Dynamic-contrast-enhanced (DCE) MRI can also characterise tissue perfusion, but requires a bolus injection of a Gadolinium-based contrast agent. This study compares the use of DCE-MRI and IVIM-DWI methods in assessing response to anti-angiogenic treatment in patients with colorectal liver metastases in a cohort with confirmed treatment response.MethodsThis prospective imaging study enrolled 25 participants with colorectal liver metastases to receive Regorafenib treatment. A target metastasis > 2 cm in each patient was imaged before and at 15 days after treatment on a 1.5T MR scanner using slice-matched IVIM-DWI and DCE-MRI protocols. MRI data were motion-corrected and tumour volumes of interest drawn on b=900 s/mm2 diffusion-weighted images were transferred to DCE-MRI data for further analysis. The median value of four IVIM-DWI parameters [diffusion coefficient D (10-3 mm2/s), perfusion fraction f (ml/ml), pseudodiffusion coefficient D* (10-3 mm2/s), and their product fD* (mm2/s)] and three DCE-MRI parameters [volume transfer constant Ktrans (min-1), enhancement fraction EF (%), and their product KEF (min-1)] were recorded at each visit, before and after treatment. Changes in pre- and post-treatment measurements of all MR parameters were assessed using Wilcoxon signed-rank tests (P<0.05 was considered significant). DCE-MRI and IVIM-DWI parameter correlations were evaluated with Spearman rank tests. Functional MR parameters were also compared against Response Evaluation Criteria In Solid Tumours v.1.1 (RECIST) evaluations.ResultsSignificant treatment-induced reductions of DCE-MRI parameters across the cohort were observed for EF (91.2 to 50.8%, P<0.001), KEF (0.095 to 0.045 min-1, P<0.001) and Ktrans (0.109 to 0.078 min-1, P=0.002). For IVIM-DWI, only D (a non-perfusion parameter) increased significantly post treatment (0.83 to 0.97 × 10-3 mm2/s, P<0.001), while perfusion-related parameters showed no change. No strong correlations were found between DCE-MRI and IVIM-DWI parameters. A moderate correlation was found, after treatment, between Ktrans and D* (r=0.60; P=0.002) and fD* (r=0.67; P<0.001). When compared to RECIST v.1.1 evaluations, KEF and D correctly identified most clinical responders, whilst non-responders were incorrectly identified.ConclusionIVIM-DWI perfusion-related parameters showed limited sensitivity to the anti-angiogenic effects of Regorafenib treatment in colorectal liver metastases and showed low correlation with DCE-MRI parameters, despite profound and significant post-treatment reductions in DCE-MRI measurements.Trial registrationNCT03010722 clinicaltrials.gov; registration date 6th January 2015.
| S-EPMC8684660 | biostudies-literature
Project description:PurposeWe investigated potentially promising imaging findings and their combinations in the evaluation of cognitive decline.Materials and methodsThis retrospective study included 138 patients with subjective cognitive impairments, who underwent brain MRI. We classified the same group of patients into Alzheimer's disease (AD) and non-AD groups, based on the neuropsychiatric evaluation. We analyzed imaging findings, including white matter hyperintensity (WMH) and cerebral microbleeds (CMBs), using the Kruskal-Wallis test for group comparison, and receiver operating characteristic (ROC) curve analysis for assessing the diagnostic performance of imaging findings.ResultsCMBs in the lobar or deep locations demonstrated higher prevalence in the patients with AD compared to those in the non-AD group. The presence of lobar CMBs combined with periventricular WMH (area under the ROC curve [AUC] = 0.702 [95% confidence interval: 0.599-0.806], p < 0.001) showed the highest performance in differentiation of AD from non-AD group.ConclusionCombinations of imaging findings can serve as useful additive diagnostic tools in the assessment of cognitive decline.
| S-EPMC10864162 | biostudies-literature
Project description:BackgroundMagnetic resonance imaging (MRI) has become important in the diagnostic work-up of neurodegenerative diseases. icobrain dm, a CE-labeled and FDA-cleared automated brain volumetry software, has shown potential in differentiating cognitively healthy controls (HC) from Alzheimer's disease (AD) dementia (ADD) patients in selected research cohorts.ObjectiveThis study examines the diagnostic value of icobrain dm for AD in routine clinical practice, including a comparison to the widely used FreeSurfer software, and investigates if combined brain volumes contribute to establish an AD diagnosis.MethodsThe study population included HC (n = 90), subjective cognitive decline (SCD, n = 93), mild cognitive impairment (MCI, n = 357), and ADD (n = 280) patients. Through automated volumetric analyses of global, cortical, and subcortical brain structures on clinical brain MRI T1w (n = 820) images from a retrospective, multi-center study (REMEMBER), icobrain dm's (v.4.4.0) ability to differentiate disease stages via ROC analysis was compared to FreeSurfer (v.6.0). Stepwise backward regression models were constructed to investigate if combined brain volumes can differentiate between AD stages.Resultsicobrain dm outperformed FreeSurfer in processing time (15-30 min versus 9-32 h), robustness (0 versus 67 failures), and diagnostic performance for whole brain, hippocampal volumes, and lateral ventricles between HC and ADD patients. Stepwise backward regression showed improved diagnostic accuracy for pairwise group differentiations, with highest performance obtained for distinguishing HC from ADD (AUC = 0.914; Specificity 83.0%; Sensitivity 86.3%).ConclusionAutomated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that combined brain volumes improve diagnostic accuracy, using real-world imaging data from a clinical setting.
| S-EPMC8543261 | biostudies-literature
Project description:To evaluate diagnostic accuracy of qualitative analysis and interobserver agreement of single ultrafast-DCE, DWI or 18F-FDG-PET and the combination of modalities for the detection of unknown primary tumor (UPT) in patients presenting with cervical lymph node metastasis from squamous cell carcinoma (SCC). Between 2014-2019, patients with histologically proven cervical lymph node metastasis of UPT SCC were prospectively included and underwent DWI, ultrafast-DCE, and 18F-FDG-PET/CT. Qualitative assessment was performed by two observers per modality. Interobserver agreement was calculated using the proportion specific agreement. Diagnostic accuracy of combined use of DWI, ultrafast-DCE and 18F-FDG-PET/CT was assessed. Twenty-nine patients were included (20 males. [68%], median age 60 years). Nine (31%) primary tumors remained occult. Ultrafast-DCE added reader confidence for suspicious locations (one additional true positive (5%), 2 decisive true malignant (10%). The per-location analysis showed highest specific positive agreement for ultrafast-DCE (77.6%). The per-location rating showed highest sensitivity (95%, 95%CI = 75.1-99.9, YI = 0.814) when either one of all modalities was scored positive, and 97.4% (95%CI = 93.5-99.3, YI = 0.774) specificity when co-detected on all. The per-patient analysis showed highest sensitivity (100%) for 18F-FDG-PET/CT (YI = 0.222) and either DWI or PET (YI = 0.111). Despite highest trends, no significant differences were found. The per-patient analysis showed highest specific positive agreement when co-detected on all modalities (55.6%, 95%CI = 21.2-86.3, YI = 0.456). Ultrafast-DCE showed potential to improve detection of unknown primary tumors in addition to DWI and 18F-FDG-PET/CT in patients with cervical squamous cell carcinoma lymph node metastasis. The combined use of ultrafast-DCE, DWI and 18F-FDG-PET/CT yielded highest sensitivity.
| S-EPMC7600235 | biostudies-literature
Project description:PurposeSclerosing adenosis (SA) is a benign lesion that could mimic breast carcinoma and be evaluated as malignancy by Breast Imaging-Reporting and Data System (BI-RADS) analysis. We aimed to construct and validate the performance of radiomic model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) compared to BI-RADS analysis to identify SA.MethodsSixty-seven patients with invasive ductal carcinoma (IDC) and 58 patients with SA were included in this retrospective study from two institutions. The 125 patients were divided into a training cohort (n= 88) from institution I and a validation cohort from institution II (n=37). Dynamic contrast-enhanced sequences including one pre-contrast and five dynamic post-contrast series were obtained for all cases with different 3T scanners. Single-phase enhancement, multi-phase enhancement, and dynamic radiomic features were extracted from DCE-MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation was performed to build the radscore of each single-phase enhancement and the final model combined multi-phase and dynamic radiomic features. The diagnostic performance of radiomics was evaluated by receiver operating characteristic (ROC) analysis and compared to the performance of BI-RADS analysis. The classification performance was tested using external validation.ResultsIn the training cohort, the AUCs of BI-RADS analysis were 0.71 (95%CI [0.60, 0.80]), 0.78 (95%CI [0.67, 0.86]), and 0.80 (95%CI [0.70, 0.88]), respectively. In single-phase analysis, the second enhanced phase radiomic signature achieved the highest AUC of 0.88 (95%CI [0.79, 0.94]) in distinguishing SA from IDC. Nine multi-phase radiomic features and two dynamic radiomic features showed the best predictive ability for final model building. The final model improved the AUC to 0.92 (95%CI [0.84, 0.97]), and showed statistically significant differences with BI-RADS analysis (p<0.05 for all). In the validation cohort, the AUC of the final model was 0.90 (95%CI [0.75, 0.97]), which was higher than all BI-RADS analyses and showed statistically significant differences with one of the BI-RADS analysis observers (p = 0.03).ConclusionsRadiomics based on DCE-MRI could show better diagnostic performance compared to BI-RADS analysis in differentiating SA from IDC, which may contribute to clinical diagnosis and treatment.
| S-EPMC9133496 | biostudies-literature
Project description:ObjectivesTo assess the multiparametric MRI (mpMRI) appearances of normal peripheral zone (PZ) across age groups in a biopsy-naïve population, where prostate cancer (PCa) was subsequently excluded, and propose a scoring system for background PZ changes.MethodsThis retrospective study included 175 consecutive biopsy-naïve patients (40-74 years) referred with a suspicion of PCa, but with subsequent negative investigations. Patients were grouped by age into categories ≤ 54, 55-59, 60-64, and ≥ 65 years. MpMRI sequences (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI]/apparent diffusion coefficient [ADC], and dynamic contrast-enhanced imaging [DCE]) were independently evaluated by two uro-radiologists on a proposed 4-point grading scale for background change on each sequence, wherein score 1 mirrored PIRADS-1 change and score 4 represented diffuse background change. Peripheral zone T2WI signal intensity and ADC values were also analyzed for trends relating to age.ResultsThere was a negative correlation between age and assigned background PZ scores for each mpMRI sequence: T2WI: r = - 0.52, DWI: r = - 0.49, DCE: r = - 0.45, p < 0.001. Patients aged ≤ 54 years had mean scores of 3.0 (T2WI), 2.7 (DWI), and 3.1 (DCE), whilst patients ≥ 65 years had significantly lower mean scores of 1.7, 1.4, and 1.9, respectively. There was moderate inter-reader agreement for all scores (range κ = 0.43-0.58). Statistically significant positive correlations were found for age versus normalized T2WI signal intensity (r = 0.2, p = 0.009) and age versus ADC values (r = 0.33, p = 0.001).ConclusionThe normal PZ in younger patients (≤ 54 years) demonstrates significantly lower T2WI signal intensity, lower ADC values, and diffuse enhancement on DCE, which may hinder diagnostic interpretation in these patients. The proposed standardized PZ background scoring system may help convey the potential for diagnostic uncertainty to clinicians.Key points• Significant, positive correlations were found between increasing age and higher normalized T2-weighted signal intensity and mean ADC values of the prostatic peripheral zone. • Younger men exhibit lower T2-weighted imaging signal intensity, lower ADC values, and diffuse enhancement on dynamic contrast-enhanced imaging, which may hinder MRI interpretation. • A scoring system is proposed which aims towards a standardized assessment of the normal background PZ. This may help convey the potential for diagnostic uncertainty to clinicians.
| S-EPMC8213603 | biostudies-literature
Project description:DCE MRI is an established component of multi-parametric MRI of the prostate. The sequence highlights the vascularization of cancerous lesions, allowing readers to corroborate suspicious findings on T2W and DW MRI and to note subtle lesions not visible on the other sequences. In this article, we review the technical aspects, methods of evaluation, limitations, and future perspectives of DCE MRI.
| S-EPMC6462146 | biostudies-literature
Project description:ObjectiveTo study the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status in patients with breast cancer.Materials and methodsA total of 176 axillary lymph nodes of patients with breast cancer, consisting of 87 metastatic axillary lymph nodes (ALNM) and 89 negative axillary lymph nodes proven by surgery, were retrospectively reviewed from the database of our cancer center. For each selected axillary lymph node, 106 radiomic features based on preoperative pharmacokinetic modeling dynamic contrast enhanced magnetic resonance imaging (PK-DCE-MRI) and 5 conventional image features were obtained. The least absolute shrinkage and selection operator (LASSO) regression was used to select useful radiomic features. Logistic regression was used to develop diagnostic models for ALNM. Delong test was used to compare the diagnostic performance of different models.ResultsThe 106 radiomic features were reduced to 4 ALNM diagnosis-related features by LASSO. Four diagnostic models including conventional model, pharmacokinetic model, radiomic model, and a combined model (integrating the Rad-score in the radiomic model with the conventional image features) were developed and validated. Delong test showed that the combined model had the best diagnostic performance: area under the curve (AUC), 0.972 (95% CI [0.947-0.997]) in the training cohort and 0.979 (95% CI [0.952-1]) in the validation cohort. The diagnostic performance of the combined model and the radiomic model were better than that of pharmacokinetic model and conventional model (P<0.05).ConclusionRadiomic features extracted from PK-DCE-MRI images of axillary lymph nodes showed promising application for diagnosis of ALNM in patients with breast cancer.
| S-EPMC7920570 | biostudies-literature