Intravoxel incoherent motion (IVIM) modeling offers the parameters f, D and D* as biomarkers for different lesion types and cancer stages from diffusion MR signals. Challenges with the available ...optimization algorithms in fitting the model to the signals motive new studies for improved parameter estimations. In this study, one thousand value sets of f, D, D* for human breast are assembled and used to generate five thousand diffusion MR signals considering noise-free and noisy situations exhibiting signal-to-noise ratios (SNR) of 20, 40, 60 and 80. The estimates of f, D, D* are obtained using Levenberg-Marquardt (LM), trust-region (TR) and particle swarm (PS) algorithms. On average, the algorithms provide the highest fitting performance for the noise-free signals (R2adj=1.000) and great fitting performances on the noisy signals with SNR>20 (R2adj>0.988). TR algorithm performs slightly better for SNR=20 (R2adj=0.947). TR and PS algorithms achieve the highest parameter estimation performance for all the parameters while LM algorithm reveals the highest performance for f and D only on the noise-free signals (r=1.00). For the noisy signals, performances increase while SNR increases. All algorithms accomplish poor performances for D* (r=0.01-0.20) while TR and PS algorithms perform same for f (r=0.48-0.97) and D (r=0.85-0.99) but remarkably better than LM algorithm for f (r=0.08-0.97) and D (r=0.53-0.99). Overall, TR and PS algorithms demonstrate better but indistinguishable performances. Without requiring any user-given initial value, PS algorithm may facilitate improved estimation of IVIM parameters of the human breast tissue. Further studies are needed to determine its benefit in clinical practice.
Purpose
Extraprostatic extension (EPE) is an unfavorable prognostic factor and the grade of EPE is also shown to be correlated with the prognosis of prostate cancer. The current study assessed the ...value of prostate magnetic resonance imaging (MRI) in measuring the radial distance (RD) of EPE and the role of T2 WI signs in predicting the grade of EPE.
Materials and methods
A total of 110 patients who underwent prostate MRI before radical prostatectomy are enrolled in this retrospective study. Eighty-four patients have organ confined disease and the remaining twenty-six patients have EPE all verified by histopathology. Prostate MRI examinations were conducted with 3T MRI scanner and phased array coil with the following sequences: T2 WI, T1 WI, DCE, DWI with ADC mapping, and high
b
-value at
b
= 1500 s/mm
2
. The likelihood of EPE with 5-point Likert scale was assigned, several MRI features were extracted for each dominant tumor identified by using T2 WI. Tumors with Likert scales 4–5 were evaluated further to obtain MRI-based RD. The relationship between pathological and MRI-determined RD was tested. Univariate and multivariate logistic regression models were developed to detect the grade of pathological EPE. The inputs were among the 2 clinical parameters and 4 MRI features.
Results
There is a moderate correlation between pathological RD and MRI-determined RD (
ρ
= 0.45,
P
< 0.01). In univariate and multivariate models, MRI features and clinical parameters possess varying significance levels (univariate models;
P
= 0.048–0.788, multivariate models;
P
= 0.173–0.769). Multivariate models perform better than the univariate models by offering fair to good performances (AUC = 0.69–0.85). The multivariate model that employs the MRI features offers better performance than the model employs clinical parameters (AUC = 0.81 versus 0.69).
Conclusion
Co-existence of T2 WI signs provide higher diagnostic value even than clinical parameters in predicting the grade of EPE. Combined use of clinical parameters and MRI features deliver slightly superior performance than MRI features alone.
Studies examining prediction of complete response (CR) in locally advanced rectum cancer (LARC) from pre/post chemoradiotherapy (CRT) magnetic resonance imaging (MRI) are performed mostly with ...segmentations of the tumor, whereas only in two studies segmentation included tumor and mesorectum. Additionally, pelvic extramesorectal region, which is included in the clinical target volume (CTV) of radiotherapy, may contain information. Therefore, we aimed to compare predictive rates of radiomics analysis with features extracted from segmentations of tumor, tumor+mesorectum, and CTV.
Ninety-three LARC patients who underwent CRT in our institution between 2012 and 2019 were retrospectively scanned. Patients were divided into CR and non-CR groups. Tumor, tumor+mesorectum and CTV were segmented on T2 preCRT MRI images. Extracted features were compared for best area under the curve (AUC) of CR prediction with 15 machine-learning models.
CR was observed in 25 patients (26.8%), of whom 13 had pathological, and 12 had clinical complete response. For tumor, tumor+mesorectum and CTV segmentations, the best AUC were 0.84, 0.81, 0.77 in the training set and 0.85, 0.83 and 0.72 in the test set, respectively; sensitivity and specificity for the test set were 76%, 90%, 76% and 71%, 67% and 62%, respectively.
Although the highest AUC result is obtained from the tumor segmentation, the highest accuracy and sensitivity are detected with tumor+mesorectum segmentation and these findings align with previous studies, suggesting that the mesorectum contains valuable insights for CR. The lowest result is obtained with CTV segmentation. More studies with mesorectum and pelvic nodal regions included in segmentation are needed.
To assess the value of joint evaluation of diffusion tensor imaging (DTI) measures by using logistic regression modelling to detect high GS risk group prostate tumors.
Fifty tumors imaged using DTI ...on a 3 T MRI device were analyzed. Regions of interests focusing on the center of tumor foci and noncancerous tissue on the maps of mean diffusivity (MD) and fractional anisotropy (FA) were used to extract the minimum, the maximum and the mean measures. Measure ratio was computed by dividing tumor measure by noncancerous tissue measure. Logistic regression models were fitted for all possible pair combinations of the measures using 5-fold cross validation.
Systematic differences are present for all MD measures and also for all FA measures in distinguishing the high risk tumors GS ≥ 7(4 + 3) from the low risk tumors GS ≤ 7(3 + 4) (P < 0.05). Smaller value for MD measures and larger value for FA measures indicate the high risk. The models enrolling the measures achieve good fits and good classification performances (R2adj = 0.55–0.60, AUC = 0.88–0.91), however the models using the measure ratios perform better (R2adj = 0.59–0.75, AUC = 0.88–0.95). The model that employs the ratios of minimum MD and maximum FA accomplishes the highest sensitivity, specificity and accuracy (Se = 77.8%, Sp = 96.9% and Acc = 90.0%).
Joint evaluation of MD and FA diffusion tensor imaging measures is valuable to detect high GS risk group peripheral zone prostate tumors. However, use of the ratios of the measures improves the accuracy of the detections substantially. Logistic regression modelling provides a favorable solution for the joint evaluations easily adoptable in clinical practice.
The aim of this article is to facilitate the estimation of the distributed diffusion coefficient (DDC) of breast tissue in quantitative diffusion-weighted imaging using artificial neural networks. ...Diffusion signals of breast lesions and of healthy glandular tissue of contralateral breasts from 174 women measured on diffusion-weighted images captured with a 3T MR scanner using small region of interests were used. Traditional DDC estimates were obtained by a stretched exponential model and nonlinear least-squares fitting applied to the diffusion signals. Various multilayer perceptrons having one hidden layer but with different number of neurons were developed. Diffusion signals normalized and DDCs estimated traditionally were the input vectors and the target outputs of the neural networks, respectively, that were randomly divided into training, validation and test datasets. Supervised leanings were performed with the training and the validation datasets using a backpropagation algorithm followed by tests with the test dataset. DDC estimation by least-squares fitting takes 38 ms, on average. Strong positive correlations are observable between the DDC estimates by least-squares fitting and by the neural networks (overall
r
= 0.962–0.999). However, a network having seventeen neurons in its hidden layer provides the strongest correlation (
r
= 1.000). Once the learning of the network is accomplished, the network computes a DDC estimate only in 73 μs without requiring any initial value or any boundary constraint. Multilayer perceptrons facilitate the estimation of the distributed diffusion coefficient of breast tissue in diffusion-weighted imaging by offering less computational complexity and reduced computation time compared to nonlinear least-squares fitting.
Purpose
To investigate the accuracy of diffusion coefficients and diffusion coefficient ratios of breast lesions and of glandular breast tissue from mono‐ and stretched‐exponential models for ...quantitative diagnosis in diffusion‐weighted magnetic resonance imaging (MRI).
Materials and Methods
We analyzed pathologically confirmed 170 lesions (85 benign and 85 malignant) imaged using a 3.0T MR scanner. Small regions of interest (ROIs) focusing on the highest signal intensity for lesions and also for glandular tissue of contralateral breast were obtained. Apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were estimated by performing nonlinear fittings using mono‐ and stretched‐exponential models, respectively. Coefficient ratios were calculated by dividing the lesion coefficient by the glandular tissue coefficient.
Results
A stretched exponential model provides significantly better fits then the monoexponential model (P < 0.001): 65% of the better fits for glandular tissue and 71% of the better fits for lesion. High correlation was found in diffusion coefficients (0.99–0.81 and coefficient ratios (0.94) between the models. The highest diagnostic accuracy was found by the DDC ratio (area under the curve AUC = 0.93) when compared with lesion DDC, ADC ratio, and lesion ADC (AUC = 0.91, 0.90, 0.90) but with no statistically significant difference (P > 0.05). At optimal thresholds, the DDC ratio achieves 93% sensitivity, 80% specificity, and 87% overall diagnostic accuracy, while ADC ratio leads to 89% sensitivity, 78% specificity, and 83% overall diagnostic accuracy.
Conclusion
The stretched exponential model fits better with signal intensity measurements from both lesion and glandular tissue ROIs. Although the DDC ratio estimated by using the model shows a higher diagnostic accuracy than the ADC ratio, lesion DDC, and ADC, it is not statistically significant. J. Magn. Reson. Imaging 2016;44:1633–1641.
Denim fabrics form today's mostly utilized fabric type. As is the case with the other textile products, there are many factors affecting the properties and the performance of the denim products. ...Within the scope of this study, we have evaluated the effect of the density changes in the use of the dual-core threads - used in an ever-increasing fashion in the textile industry - in weft have on the fabric properties. We have analyzed the extent to which the weight, size, elasticity, tensile strength, and cost properties of the denim fabrics woven with the dual-core weft thread in various densities are affected by the changes in the number of dual-core weft threads per unit length. In conclusion, we have come to such striking remarks as that the construction has a much more impact on the fabric width and thus on the unit weight than the elasticity ratio, and that density changes in the elastane-containing threads cause serious differences on the fabric's color values.
Abstract In contrast to ST-elevation myocardial infarction (STEMI) treatment, there is no clear definition for when and which patient to discharge. Our study’s main goal was to test the hypothesis ...that an early discharge strategy (within 48–56 hours) in patients with successful primary percutaneous coronary intervention (PPCI) is as safe as in patients who stay longer. The Early Discharge after Primary Percutaneous Coronary Intervention (EDAP PCI) trial was designed in a prospective, randomized, multicenter fashion and registered with http://clinicaltrials.gov (NCT01860079). Out of 900 STEMI patients, the study randomized 769 eligible patients to the early or the standard discharge group. The study’s primary outcomes were all-cause mortality and readmission at 30 days. We considered assessment of functional status and health-related quality of life to be secondary outcomes. The early discharge group had significantly shorter length of hospital stay compared to the standard discharge group (45.99 ± 9.12 hours vs. 114.87 ± 63.53 hours; p < .0001). Neither all-cause mortality nor re-admissions were different between the two study groups ( p = .684 and p = .061, respectively). Quality-of-life measures were not statistically different between the two study groups. Our study reveals that discharge within 48–56 hours following successful PPCI is feasible, safe, and does not increase the 30-day readmission rate. Moreover, the patients perceived health status at 30 days didn't differ with early discharge.
The prediction of pathological responses for locally advanced rectal cancer using magnetic resonance imaging (MRI) after neoadjuvant chemoradiotherapy (CRT) is a challenging task for radiologists, as ...residual tumor cells can be mistaken for fibrosis. Texture analysis of MR images has been proposed to understand the underlying pathology.
This study aimed to assess the responses of lesions to CRT in patients with locally advanced rectal cancer using the first-order textural features of MRI T2-weighted imaging (T2-WI) and apparent diffusion coefficient (ADC) maps.
Forty-four patients with locally advanced rectal cancer (median age: 57 years) who underwent MRI before and after CRT were enrolled in this retrospective study. The first-order textural parameters of tumors on T2-WI and ADC maps were extracted. The textural features of lesions in pathologic complete responders were compared to partial responders using Student's t- or Mann-Whitney U tests. A comparison of textural features before and after CRT for each group was performed using the Wilcoxon rank sum test. Receiver operating characteristic curves were calculated to detect the diagnostic performance of the ADC.
Of the 44 patients evaluated, 22 (50%) were placed in a partial response group and 50% were placed in a complete response group. The ADC changes of the complete responders were statistically more significant than those of the partial responders (P = 0.002). Pathologic total response was predicted with an ADC cut-off of 1310 x 10
mm
/s, with a sensitivity of 72%, a specificity of 77%, and an accuracy of 78.1% after neoadjuvant CRT. The skewness of the T2-WI before and after neoadjuvant CRT showed a significant difference in the complete response group compared to the partial response group (P = 0.001 for complete responders vs. P = 0.482 for partial responders). Also, relative T2-WI signal intensity in the complete response group was statistically lower than that of the partial response group after neoadjuvant CRT (P = 0.006).
As a result of the conversion of tumor cells to fibrosis, the skewness of the T2-WI before and after neoadjuvant CRT was statistically different in the complete response group compared to the partial response group, and the complete response group showed statistically lower relative T2-WI signal intensity than the partial response group after neoadjuvant CRT. Additionally, the ADC cut-off value of 1310 × 10
mm
/s could be used as a marker for a complete response along with absolute ADC value changes within this dataset.
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