Application values of 99mTc-methoxyisobutylisonitrile imaging for differentiating benign and malignant thymic masses.
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ABSTRACT:
SUBMITTER: Lu C
PROVIDER: S-EPMC5530039 | biostudies-other | 2017 Aug
REPOSITORIES: biostudies-other
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Project description:The aim of the current study was to investigate the importance of strain elastography (SE) in the differential diagnosis of benign and malignant soft tissue masses. SE was adopted to examine 61 patients with superficial masses, classify their elastic scores and assess their strain ratios (SRs) between the masses and the surrounding structures. Significantly increased SR values and elastic scores were observed in the malignant masses compared with the benign masses (5.42±3.47 vs. 1.80±2.10, P<0.001; 3.13±0.34 vs. 2.03±0.99, P<0.001). Area under receiver operating characteristic curve values of the SRs and elastic scores were 0.87 (P<0.001) and 0.805 (P=0.001), respectively. With an SR of >2.295 as the optimal threshold value, the sensitivity, specificity and positive and negative predictive values for diagnosing a malignant mass were 93.8, 80.5, 65.2 and 97.1%, respectively; whilst using an elastic score of ≥3 as the optimal threshold value, the sensitivity, specificity and positive and negative predictive values for diagnosing a malignant mass were 100, 51.6, 51.6 and 100%, respectively. SR values and elastic scores were significantly different between the malignant and benign soft tissue masses. Therefore, SE may be used to effectively differentiate between malignant and benign soft tissue masses.
| S-EPMC5530143 | biostudies-other
Project description:ObjectivesThe use of magnetic resonance (MR) imaging in differentiation between benign and malignant adnexal masses in children and adolescents might be of great value in the diagnostic workup of sonographically indeterminate masses, since preserving fertility is of particular importance in this population. This systematic review evaluates the diagnostic value of MR imaging in children with an ovarian mass.MethodsThe review was made according to the PRISMA Statement. PubMed and EMBASE were systematically searched for studies on the use of MR imaging in differential diagnosis of ovarian masses in both adult women and children from 2008 to 2018.ResultsSixteen paediatric and 18 adult studies were included. In the included studies, MR imaging has shown good diagnostic performance in differentiating between benign and malignant ovarian masses. MR imaging techniques including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging seem to further improve the diagnostic performance.ConclusionThe addition of DWI with apparent diffusion coefficient (ADC) values measured in enhancing components of solid lesions and DCE imaging may further increase the good diagnostic performance of MR imaging in the pre-operative differentiation between benign and malignant ovarian masses by increasing specificity. Prospective age-specific studies are needed to confirm the high diagnostic performance of MR imaging in children and adolescents with a sonographically indeterminate ovarian mass.Key points• MR imaging, based on several morphological features, is of good diagnostic performance in differentiating between benign and malignant ovarian masses. Sensitivity and specificity varied between 84.8 to 100% and 20.0 to 98.4%, respectively. • MR imaging techniques like diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging seem to improve the diagnostic performance. • Specific studies in children and adolescents with ovarian masses are required to confirm the suggested increased diagnostic performance of DWI and DCE in this population.
| S-EPMC6957553 | biostudies-literature
Project description:BackgroundThe aim of this study was to determine whether the indicators obtained from intravoxel incoherent motion (IVIM) imaging can improve the characterization of benign and malignant breast masses compared with conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI).Patients and methodsThis study included 23 benign and 31 malignant breast masses of 48 patients. Main indicators were initial enhancement ratio (IER), time-signal intensity curve (TIC), apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f). The discriminative abilities of the different models were compared by means of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) analysis.ResultsD had the highest AUC (0.980), sensitivity (93.55%), specificity (100%), and diagnostic accuracy (96.36%). Both D and TIC could provide the independent predicted features for malignant breast masses. The combination of D and TIC had an AUC of up to 0.990.ConclusionD of IVIM can effectively complement existing conventional DCE-MRI and DW-MRI in differentiating malignant from benign breast masses. IVIM combined with DCE-MRI is a robust means of evaluating breast masses.
| S-EPMC5040879 | biostudies-literature
Project description:BackgroundMany diseases are accompanied by portal vein thrombosis (PVT), and its nature is closely related to its prognosis and treatment. It is important to evaluate magnetic resonance imaging (MRI) parameters, including susceptibility-weighted imaging (SWI) and qualitative diffusion-weighted imaging (DWI), in the differentiation between benign and malignant PVT.MethodsIn this retrospective study, we collected clinical imaging data from 140 patients with PVTs characterized as benign or malignant based on enhanced MRI between January 2011 and April 2016 and retrospectively analyzed PVTs using SWI and DWI. There were 37 benign and 103 malignant PVTs. Image review was performed by 2 radiologists blinded to clinical information. The signal intensity (SI) of PVTs was recorded on SWI. The apparent diffusion coefficient (ADC) and the ratio of signal intensity (SIR) on SWI (SIRSWI) and ADC (SIRADC) between the PVTs and the spinal cord were calculated. Finally, we generated receiver operating characteristic (ROC) curves to evaluate the efficacy of SIRSWI and SIRADC for distinguishing benign and malignant PVTs.ResultsOn SWI and DWI, 100.0% (36/36) and 80.5% (29/36) of benign PVTs were hypointense, respectively. For malignant PVTs on SWI and DWI, 99.0% (103/104) and 89.4% (93/104) were hyperintense, respectively. The SIRSWI values of benign and malignant PVTs were 0.58±0.13 and 0.88±0.06, respectively, representing a significant difference (P<0.001). The SIRADC values of benign and malignant PVTs were 0.72±0.32 and 0.62±0.17, respectively, representing a significant difference (P=0.034). The area under the ROC curve (AUROC) for SIRSWI [0.990; 95% confidence interval (CI): 0.971-1.000] was significantly higher than that for SIRADC (0.619; 95% CI: 0.500-0.737; P<0.001). The SIRSWI had a sensitivity of 100.0% and a specificity of 97.3% with a cutoff value of 0.749, while the SIRADC had a sensitivity of 45.9% and specificity of 83.3% with a cutoff value of 0.791.ConclusionsThe diagnostic performance of SWI is superior to that of DWI in the differentiation of benign and malignant PVTs.
| S-EPMC10102778 | biostudies-literature
Project description:Objective: The objective of this study was to prospectively assess the extent to which magnetic resonance imaging (MRI) can differentiate malignant from benign lesions of the testis. Materials and Methods: All included patients underwent multiparametric testicular MRI, including diffusion-weighted imaging (DWI) and subtraction dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). Subsequently, all patients underwent a histopathological examination via orchiectomy or testicular biopsy/partial resection. The Kolmogorov-Smirnov test, t-test, Mann-Whitney U test, Fisher's exact test, and logistic regression were applied for statistical analysis. Results: We included 48 male patients (median age 37.5 years [range 18-69]) with testicular tumors. The median tumor size on MRI was 2.0 cm for malignant tumors and 1.1 cm for benign tumors (p < 0.05). A statistically significant difference was observed for the type (type 0-III curve, p < 0.05) and pattern of enhancement (homogeneous, heterogeneous, or rim-like, p < 0.01) between malignant and benign tumors. The minimum apparent diffusion coefficient (ADC) value was 0.9 for benign tumors and 0.7 for malignant tumors (each ×103 mm2/s, p < 0.05), while the mean ADC was 0.05. The mean ADC value was significantly lower for malignant tumors; the mean ADC value was 1.1 for benign tumors and 0.9 for malignant tumors (each ×103 mm2/s, p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of multiparametric MRI for differentiating malignant from benign testicular lesions were 94.3%, 76.9%, 91.7%, and 83.3%, respectively. The surgical procedures performed included orchiectomy (n = 33; 71.7%) and partial testicular resection (n = 11; 23.9%). Histopathology (HP) revealed malignancy in 35 patients (72.9%), including 26 with seminomas and 9 with non-seminomatous germ cell tumors (NSGCTs). The HP was benign in 13 (27.1%) patients, including 5 with Leydig cell tumors. Conclusions: Malignant and benign tumors differ in MRI characteristics in terms of the type and pattern of enhancement and the extent of diffusion restriction, indicating that MRI can be an important imaging modality for the accurate diagnosis of testicular lesions.
| S-EPMC11313008 | biostudies-literature
Project description:Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in comparison with human ophthalmologists. We included 4954 photographs from 928 patients in this retrospective cross-sectional study. Images were classified into three categories: malignant lesion, benign lesion, and no lesion. Two pre-trained convolutional neural network (CNN) models, DenseNet-161 and EfficientNetV2-M architectures, were fine-tuned to classify images into three or two (malignant versus benign) categories. For a ternary classification, the mean diagnostic accuracies of the CNNs were 82.1% and 83.0% using DenseNet-161 and EfficientNetV2-M, respectively, which were inferior to those of the nine clinicians (87.0-89.5%). For the binary classification, the mean accuracies were 87.5% and 92.5% using DenseNet-161 and EfficientNetV2-M models, which was similar to that of the clinicians (85.8-90.0%). The mean AUC of the two CNN models was 0.908 and 0.950, respectively. Gradient-weighted class activation map successfully highlighted the eyelid tumors on clinical photographs. Deep learning models showed a promising performance in discriminating malignant versus benign eyelid lesions on clinical photographs, reaching the level of human observers.
| S-EPMC10011394 | biostudies-literature
Project description:BackgroundTuberculosis pleural effusion (TPE) and malignant pleural effusion (MPE) are very common clinical complications. Considering the totally different prognosis and clinical treatment of TPE and MPE, the accurate and non-invasive diagnosis are very critical for patients with pleural effusion to initiate efficient management and treatment. However, effective clinical biomarkers were rarely explored to distinguish benign from MPE. The purpose of this study is to identify potential miRNAs which can probably be used to differentiate malignant pleural effusion from TPE.ResultsA total of 23 significantly differentially expressed miRNAs were identified in MPE, with 18 up-expressed and 5 down-expressed. And the target genes of the miRNAs mainly involved in the biology process of nervous system, cancer, immune system and metabolic process etc. Three high confident target genes, AGO4, FGF9 and LEF1 can be regulated by miR-195-5p, miR-182-5p and miR-34a-5p respectively. And these genes participate in the canonical pathway of regulation of the Epithelial-Mesenchymal and the biological functions of apoptosis, growth of tumor and cell proliferation of tumor cell lines. Further, RT-PCR validation results based on 64 collected individuals showed that the expression levels of the three miRNAs were 2-5 times higher in MPE samples, which were consistent with the microarray results. In addition, ROC curve analysis demonstrated that the combination of the three miRNAs can achieve higher AUC of 0.93 (p-value< 0.0001) to differentiate MPE from TPE.ConclusionsThe identified miR-195-5p, miR-182-5p and miR-34a-5p can become potential diagnostic biomarkers for MPE with further evidences.
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Project description:Differentiating between pancreatic ductal adenocarcinoma (PDAC) and cholangiocarcinoma (CCA) is crucial for the appropriate course of treatment, especially with advancements in the role of neoadjuvant chemotherapies for PDAC, compared to CCA. Furthermore, benign pancreaticobiliary diseases can mimic malignant disease, and indeterminate lesions may require repeated investigations to achieve a diagnosis. As bile flows in close proximity to these lesions, we aimed to establish a bile-based microRNA (miRNA) signature to discriminate between malignant and benign pancreaticobiliary diseases. We performed miRNA discovery by global profiling of 800 miRNAs using the NanoString nCounter platform in prospectively collected bile samples from malignant (n = 43) and benign (n = 14) pancreaticobiliary disease. Differentially expressed miRNAs were validated by RT-qPCR and further assessed in an independent validation cohort of bile from malignant (n = 37) and benign (n = 38) pancreaticobiliary disease. MiR-148a-3p was identified as a discriminatory marker that effectively distinguished malignant from benign pancreaticobiliary disease in the discovery cohort (AUC = 0.797 [95% CI 0.68-0.92]), the validation cohort (AUC = 0.772 [95% CI 0.66-0.88]), and in the combined cohorts (AUC = 0.752 [95% CI 0.67-0.84]). We also established a two-miRNA signature (miR-125b-5p and miR-194-5p) that distinguished PDAC from CCA (validation: AUC = 0.815 [95% CI 0.67-0.96]; and combined cohorts: AUC = 0.814 [95% CI 0.70-0.93]). Our research stands as the largest, multicentric, global profiling study of miRNAs in the bile from patients with pancreaticobiliary disease. We demonstrated their potential as clinically useful diagnostic tools for the detection and differentiation of malignant pancreaticobiliary disease. These bile miRNA biomarkers could be developed to complement current approaches for diagnosing pancreaticobiliary cancers.
| S-EPMC10693033 | biostudies-literature
Project description:ObjectivesTo perform a meta-analysis assessing the ability of shear wave elastography (SWE) to identify malignant breast masses.MethodsPubMed, the Cochrane Library, and the ISI Web of Knowledge were searched for studies evaluating the accuracy of SWE for identifying malignant breast masses. The diagnostic accuracy of SWE was evaluated according to sensitivity, specificity, and hierarchical summary receiver operating characteristic (HSROC) curves. An analysis was also performed according to the SWE mode used: supersonic shear imaging (SSI) and the acoustic radiation force impulse (ARFI) technique. The clinical utility of SWE for identifying malignant breast masses was evaluated using analysis of Fagan plot.ResultsA total of 9 studies, including 1888 women and 2000 breast masses, were analyzed. Summary sensitivities and specificities were 0.91 (95% confidence interval [CI], 0.88-0.94) and 0.82 (95% CI, 0.75-0.87) by SSI and 0.89 (95% CI, 0.81-0.94) and 0.91 (95% CI, 0.84-0.95) by ARFI, respectively. The HSROCs for SSI and ARFI were 0.92 (95% CI, 0.90-0.94) and 0.96 (95% CI, 0.93-0.97), respectively. SSI and ARFI were both very informative, with probabilities of 83% and 91%, respectively, for correctly differentiating between benign and malignant breast masses following a "positive" measurement (over the threshold value) and probabilities of disease as low as 10% and 11%, respectively, following a "negative" measurement (below the threshold value) when the pre-test probability was 50%.ConclusionsSWE could be used as a good identification tool for the classification of breast masses.
| S-EPMC3799848 | biostudies-literature
Project description:Discrimination between benign and malignant adnexal masses is essential for optimal treatment planning, but still remains challenging in a routine clinical setting. In this retrospective study, we aimed to compare albumin as a single parameter to calculate models by analyzing laboratory parameters of 1552 patients with an adnexal mass (epithelial ovarian cancer (EOC): n= 294; borderline tumor of the ovary (BTO): n = 66; benign adnexal mass: n = 1192) undergoing surgery. Models comprising classical laboratory parameters show better accuracies (AUCs 0.92-0.93; 95% CI 0.90-0.95) compared to the use of single markers, and could easily be implemented in clinical practice by containing only readily available markers. This has been incorporated into a nomogram.
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