scholarly journals Study on the Value of DCE-MRI in Differentiating Glossitis and Tongue Cancer and the Intratumour Heterogeneity

2021 ◽  
Vol Volume 13 ◽  
pp. 6925-6934
Author(s):  
Fenghai Liu ◽  
Meng Zhao ◽  
Shan Lu ◽  
Liqing Kang
Keyword(s):  
Tongue Cancer ◽  
Dce Mri ◽  
Intratumour Heterogeneity

Dynamic contrast-enhanced magnetic resonance imaging in correlation with tongue cancer stages

2020 ◽  
pp. 028418512097518
Author(s):  
Chih-Feng Chen ◽  
Shin-Lei Peng ◽  
Chen-Chang Lee ◽  
Chun-Chung Lui ◽  
Hsuan-Ying Huang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Logistic Regression ◽  
Magnetic Resonance ◽  
Tongue Cancer ◽  
Imaging Biomarkers ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Tumor Stages

Background Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in tumor stage as it can be used to measure tissue perfusion and permeability of tumors. Purpose To investigate the relationships between both quantitative and semi-quantitative variables obtained from DCE-MRI and tongue cancer stages. Material and Methods Mean values of Ktrans, enhancement ratio (ER), wash-in slope (slope), and the 95th percentile (95%) values of the distribution for Ktrans, ER, and slope values (Ktrans (95%), ER (95%), and slope (95%), respectively) were calculated for 53 patients with tongue cancers (American Joint Committee on Cancer 8th Edition stage group: 10 in stages I and II, 14 in stage III, 21 in stage IVa, and eight in stage IVb as determined by histopathologic assessment). The relationship between tumor staging and each of the six DCE-MRI parameters was assessed separately using ordinal logistic regression. Results The logistic regression analysis revealed that both mean and 95th percentile values of Ktrans were significantly and positively correlated with tongue cancer stage ( P < 0.01). More aggressive tumor stages had larger kinetic parameter. Moreover, the semi-quantitative parameters, such as ER (95%) and slope (95%), may be more significant predictors for evaluating tongue cancer stages than the mean ER and mean slope. Conclusion Both quantitative and semi-quantitative imaging biomarkers are useful for evaluating the stages of tongue cancer, and the indices obtained from DCE-MRI were positively correlated with the tumor stages. These parameters have the potential to non-invasively evaluate the stages of tongue cancer in the clinical setting.

Assoziationen zwischen der DCE-MRI und der Gefäßdichte bei Plattenepithelkarzinomen der Kopf-Hals Region.

2020 ◽  
Author(s):  
H Meyer ◽  
G Hamerla ◽  
L Leifels ◽  
A Höhn ◽  
A Surov
Keyword(s):  
Dce Mri

ANALISIS KASUS KANKER LIDAH DALAM KONTEKS ASUHAN KEPERAWATAN DENGAN PENDEKATAN COMFORT THEORY MODEL

2018 ◽  
Vol 12 (02) ◽  
Author(s):  
Dewi Nurviana Suharto
Keyword(s):  
Nursing Care ◽  
Tongue Cancer ◽  
Theory Model ◽  
Breathing Patterns ◽  
Patients With Cancer ◽  
Starting Point ◽  
Squamous Cell Carcinoma Tongue ◽  
Very High ◽  
Carcinoma Tongue ◽  
Comfort Theory

ABSTRACT The prevalence of patients with cancer increase every year. Tongue cancer is a type of malignancy of the tongue, and almost 95% is squamous cell carcinoma. Tongue cancer is a cancer with high progression with bad prognosis so that the mortality rate is very high and often causes discomfort. Comfort is the starting point of various healing that will be achieved by the client. Improvements in client conditions will not be achieved if the need of comfort is not fulfilled. In nursing care the problems that arise in tongue cancer are chronic pain, nutrient imbalance: less than body needs, and ineffective breathing patterns. Analysis of residency practice processes shows that comfort theory can be applied to patients with malignancy cases in nursing care, as it can identify patients' holistic discomfort from the physical, psychospiritual, sociocultural and environmental aspects.Keyword : Comfort Theory, Tongue Cancer

Adherence to a Dysphagia Exercise Program by Oropharyngeal Cancer Survivors

2019 ◽  
Author(s):  
Stephanie Knollhoff ◽  
Jeff Searle
Keyword(s):  
Oropharyngeal Cancer ◽  
Tongue Cancer ◽  
Exercise Program ◽  
Exercise Protocol ◽  
Sense Of Effort ◽  
Oral Pain ◽  
History Of ◽  
Post Radiation ◽  
Therapy Completion ◽  
Swallowing Exercises

Abstract Introduction: Adherence to a swallowing exercise protocol and a common compliance barrier, oral pain, was evaluated and described. Methods: A four-week dysphagia exercise program was completed by 12 individuals with a history of base of tongue cancer who were experiencing latent dysphagia. Adherence to a dysphagia exercise program was quantified. Focused outcome measures on oral pain related to dysphagia exercises and exercise related sense of effort were also included. Results: Moderate to strong adherence was reported by 75% of participants. Overall, 78.9% of exercise sessions were completed. Individuals reported little to no pain associated with dysphagia exercises throughout protocol participation. Conclusions: Routine reminders and establishment of a tracking method supported adherence with a dysphagia exercise protocol. Oral pain and sense of effort associated with completing oral and dysphagia exercises were not demonstrated to be barriers to participation in a dysphagia exercise program in people who are several years post radiation therapy completion. Keywords: dysphagia, oropharyngeal cancer, latent dysphagia, swallowing exercises

scholarly journals Perfusion in hand arthritis on dynamic contrast-enhanced computed tomography: a randomized prospective study using MRI as a standard of reference

2020 ◽  
Vol 50 (1) ◽  
pp. 59-68
Author(s):  
Sevtap Tugce Ulas ◽  
Kay Geert Hermann ◽  
Marcus R. Makowski ◽  
Robert Biesen ◽  
Fabian Proft ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Mean Transit Time ◽  
Region Of Interest ◽  
T Test ◽  
Dose Length Product ◽  
Suitable Alternative ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced

Abstract Objective To evaluate the performance of dynamic contrast-enhanced CT (DCE-CT) in detecting and quantitatively assessing perfusion parameters in patients with arthritis of the hand compared with dynamic contrast-enhanced MRI (DCE-MRI) as a standard of reference. Materials and methods In this IRB-approved randomized prospective single-centre study, 36 consecutive patients with suspected rheumatoid arthritis underwent DCE-CT (320-row, tube voltage 80 kVp, tube current 8.25 mAs) and DCE-MRI (1.5 T) of the hand. Perfusion maps were calculated separately for mean transit time (MTT), time to peak (TTP), relative blood volume (rBV), and relative blood flow (rBF) using four different decomposition techniques. Region of interest (ROI) analysis was performed in metacarpophalangeal joints II–V and in the wrist. Pairs of perfusion parameters in DCE-CT and DCE-MRI were compared using a two-tailed t test for paired samples and interpreted for effect size (Cohen’s d). According to the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) scoring results, differentiation of synovitis-positive and synovitis-negative joints with both modalities was assessed with the independent t test. Results The two modalities yielded similar perfusion parameters. Identified differences had small effects (d 0.01–0.4). DCE-CT additionally differentiates inflamed and noninflamed joints based on rBF and rBV but tends to underestimate these parameters in severe inflammation. The total dose-length product (DLP) was 48 mGy*cm with an estimated effective dose of 0.038 mSv. Conclusion DCE-CT is a promising imaging technique in arthritis. In patients with a contraindication to MRI or when MRI is not available, DCE-CT is a suitable alternative to detect and assess arthritis.

scholarly journals Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

2021 ◽  
Vol 11 (4) ◽  
pp. 1880
Author(s):  
Roberta Fusco ◽  
Adele Piccirillo ◽  
Mario Sansone ◽  
Vincenza Granata ◽  
Paolo Vallone ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Feature Selection ◽  
Resonance Imaging ◽  
Dynamic Features ◽  
Surgical Specimen ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Malignant Lesions

Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were analyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions.

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