Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols

Cai, Hongming; Liu, Lizhi; Peng, Yanxia; Wu, Yaopan; Li, Li

doi:10.1186/1471-2407-14-366

Cited by 41 publications

(27 citation statements)

References 47 publications

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“…The authors achieved sensitivity and specificity of 85% and 89% respectively. They also tested the same set of features on a validation dataset consisting of 93 patients and achieved sensitivity and specificity of 69% and 91% respectively 98 .…”

Section: Radiomics Applicationsmentioning

confidence: 99%

Radiomics: a new application from established techniques

Parekh

Jacobs

2016

Expert Review of Precision Medicine and Drug Development

293

230

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The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of “big data”. Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.

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Section: Radiomics Applicationsmentioning

confidence: 99%

Radiomics: a new application from established techniques

Parekh

Jacobs

2016

Expert Review of Precision Medicine and Drug Development

293

230

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“…These include studies of gliomas 7, 22 prostate cancer 23, 24 renal cell carcinomas 25 and breast cancer 26 . The most common approach in these studies was to analyze textures in the apparent diffusion coefficient (ADC) maps.…”

Section: Introductionmentioning

confidence: 99%

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters

Brynolfsson

Nilsson

Torheim

et al. 2017

Sci Rep

139

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In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

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“…Diffusion-weighted MRI (DW-MRI) is an advanced MRI technique, which emerged in the mid-1980s and allows the mapping of in-vivo water diffusion processes in a non-invasive manner; it can delineate the microscopic anatomy of a target tissue or organ [ 5 ]. It has a higher sensitivity and specificity in detecting suspicious breast disease at a minimum size of 1 cm than regular MRI [ 5 ]. It can provide digital biomarker measurements of tissue properties that are highly relevant to the assessment of tumor progression or responses [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…It has been reported to produce higher detection rates than conventional methods [ 6 , 8 ] and can be easily adopted as an adjunct to standard clinical imaging protocols [ 9 , 10 ]. However, the reported sensitivity and specificity of DW-MRI among previous reports is varied, possibly owing to the variability of MRI equipment and scanning parameters, such as definition of the b value (the strength of the magnetic diffusion gradient) and region of interest (ROI) [ 5 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Differential diagnosis of benign and malignant breast masses using diffusion-weighted magnetic resonance imaging

et al. 2015

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BackgroundDiffusion-weighted magnetic resonance imaging (DW-MRI) is different from conventional diagnostic methods and has the potential to delineate the microscopic anatomy of a target tissue or organ. The purpose of our study was to evaluate the value of DW-MRI in the diagnosis of benign and malignant breast masses, which would help the clinical surgeon to decide the scope and pattern of operation.MethodsA total of 52 female patients with palpable solid breast masses received breast MRI scans using routine sequences, dynamic contrast-enhanced imaging, and diffusion-weighted echo-planar imaging at b values of 400, 600, and 800 s/mm2, respectively. Two regions of interest (ROIs) were plotted, with a smaller ROI for the highest signal and a larger ROI for the overall lesion. Apparent diffusion coefficient (ADC) values were calculated at three different b values for all detectable lesions and from two different ROIs. The sensitivity, specificity, positive predictive value, and positive likelihood ratio of DW-MRI were determined for comparison with histological results.ResultsA total of 49 (49/52, 94.2%) lesions were detected using DW-MRI, including 20 benign lesions (two lesions detected in the same patient) and 29 malignant lesions. Benign lesion had a higher mean ADC value than their malignant counterparts, regardless of b value. According to the receiver operating characteristic (ROC) curve, the smaller-range ROI was more effective in differentiation between benign and malignant lesions. The area under the ROC curve was the largest at a b value of 800 s/mm2. With a threshold ADC value at 1.23 × 10−3 mm2/s, DW-MRI achieved a sensitivity of 82.8%, specificity of 90.0%, positive predictive value of 92.3%, and positive likelihood ratio of 8.3 for differentiating benign and malignant lesions.ConclusionsDW-MRI is an accurate diagnostic tool for differentiation between benign and malignant breast lesions, with an optimal b value of 800 s/mm2. A smaller-range ROI focusing on the highest signal has a better differential value.

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Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols

Cited by 41 publications

References 47 publications

Radiomics: a new application from established techniques

Radiomics: a new application from established techniques

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters

Differential diagnosis of benign and malignant breast masses using diffusion-weighted magnetic resonance imaging

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