Histogram analysis of diffusion kurtosis imaging of nasopharyngeal carcinoma: Correlation between quantitative parameters and clinical stage

Purpose

To evaluate the correlation between histogram parameters derived from diffusion-kurtosis (DK) imaging and the clinical stage of nasopharyngeal carcinoma (NPC).

Results

High T-stage (T3/4) NPC showed significantly higher K_app-mean (P = 0.018), K_app-median (P = 0.029) and K_app-90th (P = 0.003) than low T-stage (T1/2) NPC. High N-stage NPC (N2/3) showed significantly lower Dapp-mean (P = 0.002), Dapp-median (P = 0.002) and Dapp-10th (P < 0.001) than low N-stage NPC (N0/1). High AJCC-stage NPC (III/IV) showed significantly lower D_app-10th (P = 0.038) than low AJCC-stage NPC (I/II). ROC analyses indicated that Kapp-90th was optimal for predicting high T-stage (AUC, 0.759; sensitivity, 0.842; specificity, 0.607), while Dapp-10th was best for predicting high N- and AJCC-stage (N-stage, AUC, 0.841; sensitivity, 0.875; specificity, 0.807; AJCC-stage, AUC, 0.671; sensitivity, 0.800; specificity, 0.588).

Materials and Methods

DK imaging data of forty-seven consecutive NPC patients were retrospectively analyzed. Apparent diffusion for Gaussian distribution (D_app) and apparent kurtosis coefficient (K_app) were generated using diffusion-kurtosis model. Histogram parameters, including mean, median, 10th, 90th percentiles, skewness and kurtosis of D_app and K_app were calculated. Patients were divided into low and high T, N and clinical stage based on American Joint Committee on Cancer (AJCC) staging system. Differences of histogram parameters between low and high T, N and AJCC stages were compared using t test. Multiple receiver operating characteristic (ROC) curves were used to determine and compare the value of significant parameters in predicting high T, N and AJCC stage, respectively.

Conclusions

DK imaging-derived parameters correlated well with clinical stage of NPC, therefore could serve as an adjunctive imaging technique for evaluating NPC.

Lepidic Predominant Pulmonary Lesions (LPL): CT-based Distinction From More Invasive Adenocarcinomas Using 3D Volumetric Density and First-order CT Texture Analysis

RATIONALE AND OBJECTIVES

This study aimed to differentiate pathologically defined lepidic predominant lesions (LPL) from more invasive adenocarcinomas (INV) using three-dimensional (3D) volumetric density and first-order texture histogram analysis of surgically excised stage 1 lung adenocarcinomas.

MATERIALS AND METHODS

This retrospective study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Sixty-four cases of pathologically proven stage 1 lung adenocarcinoma surgically resected between September 2006 and October 2015, including LPL (n = 43) and INV (n = 21), were evaluated using high-resolution computed tomography. Quantitative measurements included nodule volume, percent solid volume (% solid), and first-order texture histogram analysis including skewness, kurtosis, entropy, and mean nodule attenuation within each histogram quartile. Binomial logistic regression models were used to identify the best set of parameters distinguishing LPL from INV.

RESULTS

Univariate analysis of 3D volumetric density and histogram features was statistically significant between LPL and INV groups (P < .05). Accuracy of a binomial logistic model to discriminate LPL from INV based on size and % solid was 85.9%. With optimized probability cutoff, the model achieves 81% sensitivity, 76.7% specificity, and area under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.821-0.973). An additional model based on size and mean nodule attenuation of the third quartile (Hu_Q3) of the histogram achieved similar accuracy of 81.3% and area under the receiver operating characteristic curve of 0.877 (95% confidence interval, 0.790-0.964).

CONCLUSIONS

Both 3D volumetric density and first-order texture analysis of stage 1 lung adenocarcinoma allow differentiation of LPL from more invasive adenocarcinoma with overall accuracy of 85.9%-81.3%, based on multivariate analyses of either size and % solid or size and Hu_Q3, respectively.

Histogram analysis of apparent diffusion coefficient for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy

Background Apparent diffusion coefficient (ADC) histogram analysis has been widely used in determining tumor prognosis. Purpose To investigate the dynamic changes of ADC histogram parameters during concurrent chemo-radiotherapy (CCRT) in patients with advanced cervical cancers. Material and Methods This prospective study enrolled 32 patients with advanced cervical cancers undergoing CCRT who received diffusion-weighted (DW) magnetic resonance imaging (MRI) before CCRT, at the end of the second and fourth week during CCRT and one month after CCRT completion. The ADC histogram for the entire tumor volume was generated, and a series of histogram parameters was obtained. Dynamic changes of those parameters in cervical cancers were investigated as early biomarkers for treatment response. Results All histogram parameters except AUC_low showed significant changes during CCRT (all P < 0.05). There were three variable trends involving different parameters. The mode, 5th, 10th, and 25th percentiles showed similar early increase rates (33.33%, 33.99%, 34.12%, and 30.49%, respectively) at the end of the second week of CCRT. The pre-CCRT 5th and 25th percentiles of the complete response (CR) group were significantly lower than those of the partial response (PR) group. Conclusion A series of ADC histogram parameters of cervical cancers changed significantly at the early stage of CCRT, indicating their potential in monitoring early tumor response to therapy.

Potential Information Loss Due to Categorization of Minimum Inhibitory Concentration Frequency Distributions

A bacterial isolate's susceptibility to antimicrobial is expressed as the lowest drug concentration inhibiting its visible growth, termed minimum inhibitory concentration (MIC). The susceptibilities of isolates from a host population at a particular time vary, with isolates with specific MICs present at different frequencies. Currently, for either clinical or monitoring purposes, an isolate is most often categorized as Susceptible, Intermediate, or Resistant to the antimicrobial by comparing its MIC to a breakpoint value. Such data categorizations are known in statistics to cause information loss compared to analyzing the underlying frequency distributions. The U.S. National Antimicrobial Resistance Monitoring System (NARMS) includes foodborne bacteria at the food animal processing and retail product points. The breakpoints used to interpret the MIC values for foodborne bacteria are those relevant to clinical treatments by the antimicrobials in humans in whom the isolates were to cause infection. However, conceptually different objectives arise when inference is sought concerning changes in susceptibility/resistance across isolates of a bacterial species in host populations among different sampling points or times. For the NARMS 1996-2013 data for animal processing and retail, we determined the fraction of comparisons of susceptibility/resistance to 44 antimicrobial drugs of twelve classes of a bacterial species in a given animal host or product population where there was a significant change in the MIC frequency distributions between consecutive years or the two sampling points, while the categorization-based analyses concluded no change. The categorization-based analyses missed significant changes in 54% of the year-to-year comparisons and in 71% of the slaughter-to-retail within-year comparisons. Hence, analyses using the breakpoint-based categorizations of the MIC data may miss significant developments in the resistance distributions between the sampling points or times. Methods considering the MIC frequency distributions in their entirety may be superior for epidemiological analyses of resistance dynamics in populations.

Textural pattern classification for oral squamous cell carcinoma

Despite being an area of cancer with highest worldwide incidence, oral cancer yet remains to be widely researched. Studies on computer-aided analysis of pathological slides of oral cancer contribute a lot to the diagnosis and treatment of the disease. Some researches in this direction have been carried out on oral submucous fibrosis. In this work an approach for analysing abnormality based on textural features present in squamous cell carcinoma histological slides have been considered. Histogram and grey-level co-occurrence matrix approaches for extraction of textural features from biopsy images with normal and malignant cells are used here. Further, we have used linear support vector machine classifier for automated diagnosis of the oral cancer, which gives 100% accuracy.

Whole-lesion histogram analysis of the apparent diffusion coefficient: Evaluation of the correlation with subtypes of mucinous breast carcinoma

PURPOSE

To evaluate the utility of the whole-lesion histogram apparent diffusion coefficient (ADC) for characterizing the heterogeneity of mucinous breast carcinoma (MBC) and to determine which ADC metrics may help to best differentiate subtypes of MBC.

MATERIALS AND METHODS

This retrospective study involved 52 MBC patients, including 37 pure MBC (PMBC) and 15 mixed MBC (MMBC). The PMBC patients were subtyped into PMBC-A (20 cases) and PMBC-B (17 cases) groups. All patients underwent preoperative diffusion-weighted imaging (DWI) at 1.5T and the whole-lesion ADC assessments were generated. Histogram-derived ADC parameters were compared between PMBC vs. MMBC and PMBC-A vs. PMBC-B, and receiver operating characteristic (ROC) curve analysis was used to determine optimal histogram parameters for differentiating these groups.

RESULTS

The PMBC group exhibited significantly higher ADC values for the mean (P = 0.004), 25^th (P = 0.004), 50^th (P = 0.004), 75^th (P = 0.006), and 90^th percentiles (P = 0.013) and skewness (P = 0.021) than did the MMBC group. The 25^th percentile of ADC values achieved the highest area under the curve (AUC) (0.792), with a cutoff value of 1.345 × 10^-3 mm² /s, in distinguishing PMBC and MMBC. The PMBC-A group showed significantly higher ADC values for the mean (P = 0.049), 25^th (P = 0.015), and 50^th (P = 0.026) percentiles and skewness (P = 0.004) than did the PMBC-B group. The 25^th percentile of the ADC cutoff value (1.476 × 10^-3 mm² /s) demonstrated the best AUC (0.837) among the ADC values for distinguishing PMBC-A and PMBC-B.

CONCLUSION

Whole-lesion ADC histogram analysis enables comprehensive evaluation of an MBC in its entirety and differentiating subtypes of MBC. Thus, it may be a helpful and supportive tool for conventional MRI.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:391-400.

Crop Classification in Satellite Images through Probabilistic Segmentation Based on Multiple Sources

Classification methods based on Gaussian Markov Measure Field Models and other probabilistic approaches have to face the problem of construction of the likelihood. Typically, in these methods, the likelihood is computed from 1D or 3D histograms. However, when the number of information sources grows, as in the case of satellite images, the histogram construction becomes more difficult due to the high dimensionality of the feature space. In this work, we propose a generalization of Gaussian Markov Measure Field Models and provide a probabilistic segmentation scheme, which fuses multiple information sources for image segmentation. In particular, we apply the general model to classify types of crops in satellite images. The proposed method allows us to combine several feature spaces. For this purpose, the method requires prior information for building a 3D histogram for each considered feature space. Based on previous histograms, we can compute the likelihood of each site of the image to belong to a class. The computed likelihoods are the main input of the proposed algorithm and are combined in the proposed model using a contrast criteria. Different feature spaces are analyzed, among them are 6 spectral bands from LANDSAT 5 TM, 3 principal components from PCA on 6 spectral bands and 3 principal components from PCA applied on 10 vegetation indices. The proposed algorithm was applied to a real image and obtained excellent results in comparison to different classification algorithms used in crop classification.

Comparison between types I and II epithelial ovarian cancer using histogram analysis of monoexponential, biexponential, and stretched-exponential diffusion models

PURPOSE

To evaluate the utility of histogram analysis of monoexponential, biexponential, and stretched-exponential models to a dualistic model of epithelial ovarian cancer (EOC).

MATERIALS AND METHODS

Fifty-two patients with histopathologically proven EOC underwent preoperative magnetic resonance imaging (MRI) (including diffusion-weighted imaging [DWI] with 11 b-values) using a 3.0T system and were divided into two groups: types I and II. Apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α) histograms were obtained based on solid components of the entire tumor. The following metrics of each histogram were compared between two types: 1) mean; 2) median; 3) 10th percentile and 90th percentile. Conventional MRI morphological features were also recorded.

RESULTS

Significant morphological features for predicting EOC type were maximum diameter (P = 0.007), texture of lesion (P = 0.001), and peritoneal implants (P = 0.001). For ADC, D, f, DDC, and α, all metrics were significantly lower in type II than type I (P < 0.05). Mean, median, 10th, and 90th percentile of D* were not significantly different (P = 0.336, 0.154, 0.779, and 0.203, respectively). Most histogram metrics of ADC, D, and DDC had significantly higher area under the receiver operating characteristic curve values than those of f and α (P < 0.05) CONCLUSION: It is feasible to grade EOC by morphological features and three models with histogram analysis. ADC, D, and DDC have better performance than f and α; f and α may provide additional information.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1797-1809.

Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps

PURPOSE

To investigate the efficacy of histogram analysis of the entire tumor volume in apparent diffusion coefficient (ADC) maps for differentiating between histological grades in gastric cancer.

MATERIALS AND METHODS

Seventy-eight patients with gastric cancer were enrolled in a retrospective 3.0T magnetic resonance imaging (MRI) study. ADC maps were obtained at two different b values (0 and 1000 sec/mm² ) for each patient. Tumors were delineated on each slice of the ADC maps, and a histogram for the entire tumor volume was subsequently generated. A series of histogram parameters (eg, skew and kurtosis) were calculated and correlated with the histological grade of the surgical specimen. The diagnostic performance of each parameter for distinguishing poorly from moderately well-differentiated gastric cancers was assessed by using the area under the receiver operating characteristic curve (AUC).

RESULTS

There were significant differences in the 5^th , 10^th , 25^th , and 50^th percentiles, skew, and kurtosis between poorly and well-differentiated gastric cancers (P < 0.05). There were correlations between the degrees of differentiation and histogram parameters, including the 10^th percentile, skew, kurtosis, and max frequency; the correlation coefficients were 0.273, -0.361, -0.339, and -0.370, respectively. Among all the histogram parameters, the max frequency had the largest AUC value, which was 0.675.

CONCLUSION

Histogram analysis of the ADC maps on the basis of the entire tumor volume can be useful in differentiating between histological grades for gastric cancer.

LEVEL OF EVIDENCE

4 J. Magn. Reson. Imaging 2017;45:440-449.

Clinical application of a new method that segments the region of interest into multiple layers for RF amplitude histogram analysis in the cirrhotic liver

PURPOSE

We used texture analysis in conjunction with an alternative method of analyzing the amplitude histogram using a radiofrequency (RF) signal to differentiate ultrasonograms of normal and cirrhotic livers. This method segments the region of interest (ROI) into multiple layers (sub-ROIs). In each sub-ROI of a homogeneous medium, the histogram of enveloped-amplitude of RF backscattered echoes resembles a Rayleigh distribution. Theoretically, the values of the signal-to-noise ratio (SNR), skewness, and kurtosis for Rayleigh statistics are constant and independent of the mean scattering intensity, which is contributed by such undesirable effects as tissue attenuation, beam diffraction, and incident waveforms. These values, which averaged overall sub-ROI, should provide an unbiased estimator.

METHODS

We studied 36 normal livers and 28 cirrhotic livers, all confirmed by clinical findings including laboratory and pathology data; the SNR, skewness, and kurtosis values of the disease groups were compared. At the same time, these values were estimated using the conventional method, which did not segment the ROI into multiple sub-ROIs. The unpaired t-test was used to determine statistical significance.

RESULTS

With the new method, all values obtained from cirrhotic livers differed significantly from those obtained from normal livers, and the standard deviation of these values was smaller than those obtained using the conventional method.

CONCLUSIONS

These results suggest that the new method can be used to diagnose the cirrhotic liver objectively.

Value of MR histogram analyses for prediction of microvascular invasion of hepatocellular carcinoma

The objective is to explore the value of preoperative magnetic resonance (MR) histogram analyses in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Fifty-one patients with histologically confirmed HCC who underwent diffusion-weighted and contrast-enhanced MR imaging were included. Histogram analyses were performed and mean, variance, skewness, kurtosis, 1th, 10th, 50th, 90th, and 99th percentiles were derived. Quantitative histogram parameters were compared between HCCs with and without MVI. Receiver operating characteristics (ROC) analyses were generated to compare the diagnostic performance of tumor size, histogram analyses of apparent diffusion coefficient (ADC) maps, and MR enhancement.The mean, 1th, 10th, and 50th percentiles of ADC maps, and the mean, variance. 1th, 10th, 50th, 90th, and 99th percentiles of the portal venous phase (PVP) images were significantly different between the groups with and without MVI (P <0.05), with area under the ROC curves (AUCs) of 0.66 to 0.74 for ADC and 0.76 to 0.88 for PVP. The largest AUC of PVP (1th percentile) showed significantly higher accuracy compared with that of arterial phase (AP) or tumor size (P <0.001).MR histogram analyses-in particular for 1th percentile for PVP images-held promise for prediction of MVI of HCC.

Differentiation between malignant and benign thyroid nodules and stratification of papillary thyroid cancer with aggressive histological features: Whole-lesion diffusion-weighted imaging histogram analysis

PURPOSE

To explore the usefulness of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) derived from reduced field-of-view (r-FOV) diffusion-weighted imaging (DWI) in differentiating malignant and benign thyroid nodules and stratifying papillary thyroid cancer (PTC) with aggressive histological features.

MATERIALS AND METHODS

This Institutional Review Board-approved, retrospective study included 93 patients with 101 pathologically proven thyroid nodules. All patients underwent preoperative r-FOV DWI at 3T. The whole-lesion ADC assessments were performed for each patient. Histogram-derived ADC parameters between different subgroups (pathologic type, extrathyroidal extension, lymph node metastasis) were compared. Receiver operating characteristic curve analysis was used to determine optimal histogram parameters in differentiating benign and malignant nodules and predicting aggressiveness of PTC.

RESULTS

Mean ADC, median ADC, 5^th percentile ADC, 25^th percentile ADC, 75^th percentile ADC, 95^th percentile ADC (all P < 0.001), and kurtosis (P = 0.001) were significantly lower in malignant thyroid nodules, and mean ADC achieved the highest AUC (0.919) with a cutoff value of 1842.78 × 10^-6 mm² /s in differentiating malignant and benign nodules. Compared to the PTCs without extrathyroidal extension, PTCs with extrathyroidal extension showed significantly lower median ADC, 5^th percentile ADC, and 25^th percentile ADC. The 5^th percentile ADC achieved the highest AUC (0.757) with cutoff value of 911.5 × 10^-6 mm² /s for differentiating between PTCs with and without extrathyroidal extension.

CONCLUSION

Whole-lesion ADC histogram analysis might help to differentiate malignant nodules from benign ones and show the PTCs with extrathyroidal extension. J. Magn. Reson. Imaging 2016;44:1546-1555.

The Objective Use of Pulse Oximetry to Predict Respiratory Support Transition in Preterm Infants: An Observational Pilot Study

BACKGROUND

Preterm infants often require some form of respiratory support with supplemental oxygen and are monitored by continuous pulse oximetry (SpO2 ). The study objective was to determine whether the histogram distribution of SpO2 over a 24-h period will predict readiness for weaning respiratory support in preterm infants. We hypothesize that infants with ≥15% of time spent with SpO2 <86% before transitioning from CPAP or high-flow nasal cannula (HFNC) to low-flow nasal cannula, oxyhood, or room air are more likely to fail transitioning.

METHODS

The SpO2 histograms were downloaded daily for 31 infants, 24-32 weeks gestational age, before transition from CPAP or HFNC to low-flow nasal cannula, oxyhood, or room air. The SpO2 histogram downloads were continued for each infant for 1 week after transition or when the infant reached 36 weeks postmenstrual age or when SpO2 monitoring was discontinued. Failure was defined as an increase in respiratory support within 72 h of transitioning. We compared the percentage of time for each SpO2 quintile for the 24-h periods before and immediately following CPAP/HFNC transitioning between groups.

RESULTS

Twenty-four subjects transitioned successfully, but 7 subjects failed. Two of 7 subjects (28.6%) who failed transition experienced SpO2 <86% ≥15% of the time pretransition compared with none in the success group (P = .045). The failure group experienced SpO2 <86% 10.7 ± 11.9% of time pre-wean compared with 3.3 ± 4.7% of time in the success group (P = .02). In contrast, infants who were successfully weaned tended to experience a greater percentage of time with SpO2 >95% compared with the failure group, both pre-wean (54.3 ± 36.1% vs. 33 ± 27.7%, P = .16) and post-wean (52 ± 35.4% vs. 27.4 ± 27.7%, P = .10).

CONCLUSIONS

These data suggest that pulse oximetry histograms may be useful in assessing CPAP/HFNC support transition readiness.

Diffusion-weighted imaging: Apparent diffusion coefficient histogram analysis for detecting pathologic complete response to chemoradiotherapy in locally advanced rectal cancer

PURPOSE

To investigate the usefulness of apparent diffusion coefficient (ADC) values derived from histogram analysis of the whole rectal cancer as a quantitative parameter to evaluate pathologic complete response (pCR) on preoperative magnetic resonance imaging (MRI).

MATERIALS AND METHODS

We enrolled a total of 86 consecutive patients who had undergone surgery for rectal cancer after neoadjuvant chemoradiotherapy (CRT) at our institution between July 2012 and November 2014. Two radiologists who were blinded to the final pathological results reviewed post-CRT MRI to evaluate tumor stage. Quantitative image analysis was performed using T2 -weighted and diffusion-weighted images independently by two radiologists using dedicated software that performed histogram analysis to assess the distribution of ADC in the whole tumor.

RESULTS

After surgery, 16 patients were confirmed to have achieved pCR (18.6%). All parameters from pre- and post-CRT ADC histogram showed good or excellent agreement between two readers. The minimum, 10th, 25th, 50th, and 75th percentile and mean ADC from post-CRT ADC histogram were significantly higher in the pCR group than in the non-pCR group for both readers. The 25th percentile value from ADC histogram in post-CRT MRI had the best diagnostic performance for detecting pCR, with an area under the receiver operating characteristic curve of 0.796.

CONCLUSION

Low percentile values derived from the ADC histogram analysis of rectal cancer on MRI after CRT showed a significant difference between pCR and non-pCR groups, demonstrating the utility of the ADC value as a quantitative and objective marker to evaluate complete pathologic response to preoperative CRT in rectal cancer. J. Magn. Reson. Imaging 2016;44:212-220.

Evaluation of a Mathematical Model for Digital Image Enhancement

Objective : The purpose of this study is to compare the detected number of holes on a stepwedge on images resulting from the application of the 5th degree polynomial model compared to the images resulting from the application of linear enhancement. Material and Methods : A 10-step aluminum step wedge with holes randomly drilled on each step was exposed with three different kVp and five exposure times per kVp on a Schick33^® sensor. The images were enhanced by brightness/contrast adjustment, histogram equalization and with the 5th degree polynomial model and compared to the original non-enhanced images by six observers in two separate readings. Results : There was no significant difference between the readers and between the first and second reading. There was a significant three-factor interaction among Method, Exposure time, and kVp in detecting holes. The overall pattern was: “Poly” results in the highest counts, “Original” in the lowest counts, with “B/C” and “Equalized” intermediate. Conclusion : The 5th degree polynomial model showed more holes when compared to the other modalities.

Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response

BACKGROUND

The value of diffusion-weighted imaging (DWI) for reliable differentiation between pathologic complete response (pCR) and residual tumor is still unclear. Recently, a few studies reported that histogram analysis can be helpful to monitor the therapeutic response in various cancer research.

PURPOSE

To investigate whether post-chemoradiotherapy (CRT) apparent diffusion coefficient (ADC) histogram analysis can be helpful to predict a pCR in locally advanced rectal cancer (LARC).

MATERIAL AND METHODS

Fifty patients who underwent preoperative CRT followed by surgery were enrolled in this retrospective study, non-pCR (n = 41) and pCR (n = 9), respectively. ADC histogram analysis encompassing the whole tumor was performed on two post-CRT ADC600 and ADC1000 (b factors 0, 600 vs. 0, 1000 s/mm(2)) maps. Mean, minimum, maximum, SD, mode, 10th, 25th, 50th, 75th, 90th percentile ADCs, skewness, and kurtosis were derived. Diagnostic performance for predicting pCR was evaluated and compared.

RESULTS

On both maps, 10th and 25th ADCs showed better diagnostic performance than that using mean ADC. Tenth percentile ADCs revealed the best diagnostic performance on both ADC600 (AZ 0.841, sensitivity 100%, specificity 70.7%) and ADC1000 (AZ 0.821, sensitivity 77.8%, specificity 87.8%) maps. In comparison between 10th percentile and mean ADC, the specificity was significantly improved on both ADC600 (70.7% vs. 53.7%; P = 0.031) and ADC1000 (87.8% vs. 73.2%; P = 0.039) maps.

CONCLUSION

Post-CRT ADC histogram analysis is helpful for predicting pCR in LARC, especially, in improving the specificity, compared with mean ADC.

Use of basis images for detection and classification of celiac disease

Celiac disease commonly occurs in approximately 1% of populations, but it can be difficult to diagnose. The standard method to diagnose celiac disease includes analysis of endoscopy images of the small intestinal mucosa to detect presence of villous atrophy, which can be subtle. We have devised a means to improve the image-based detection of villous atrophy and other abnormality in videocapsule endoscopy by means of incorporating basis images. Basis images were extracted from a series of 200 consecutive image frames acquired over 100 seconds at the level of the duodenal bulb in 13 celiac patients and in 13 controls. They were converted from color to 256 grayscale levels (gsl; 0 = black, 255 = white). Eight basis images were used for analysis. A histogram was constructed for each basis image, and the mean and standard deviation of the histogram values were tabulated. The significance of the difference in histogram mean level for celiacs versus controls was determined. Then the histogram mean was plotted versus the standard deviation, separately for all eight basis images, and also averaged for all bases combined. The mean histogram level for celiacs was 127.59+6.05 gsl versus 129.25+5.53 gsl for controls (p< 0.05). Thus celiac basis images tended to be darker and also more variable as compared with controls. For nonlinear classification, using the average of combined basis images, the sensitivity was 84.6% while the specificity was 92.3%. Using the single most important basis image for nonlinear classification, the sensitivity was 84.6% while the specificity was 76.9%. Construction of basis images can be useful to condense videocapsule image series into salient information, for detection of differences in grayscale level mean and variation in celiac versus control image series, and for classification of celiac versus control videoclips with nonlinear discriminant functions.

Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: a preliminary correlation study with histological grade

BACKGROUND

Until now, several investigators have explored the value of diffusion-weighted magnetic resonance imaging (DWI) for the preoperative tumor grading of endometrial cancer. However, the diagnostic value of DWI with quantitative analysis of apparent diffusion coefficient (ADC) has been controversial.

PURPOSE

To explore the role of histogram analysis of ADC maps based on entire tumor volume in determining the grade of endometrial cancer.

MATERIAL AND METHODS

This study was IRB-approved with waiver of informed consent. Thirty-three patients with endometrial cancer underwent DWI (b = 0, 600, 1000 s/mm(2)), and corresponding ADC maps were acquired. Regions of interest (ROIs) were drawn on all slices of the ADC map in which the tumor was visualized including areas of necrosis to derive volume-based histographic ADC data. Histogram parameters (5th-95th percentiles, mean, standard deviation, skewness, kurtosis) were correlated with histological grade using one-way ANOVA with Tukey-Kramer test for post hoc comparisons, and were compared between high (grade 3) and low (grades 1/2) grade using Student t-test. ROC curve analysis was performed to determine the optimum threshold value for each parameter, and their corresponding sensitivity and specificity.

RESULTS

The standard deviation, quartile, 75th, 90th, and 95th percentiles of ADC showed significant differences between grades (P ≤ 0.03 for all) and between high and low grades (P ≤ 0.024 for all). There were no significant correlations between tumor grade and other parameters. ROC curve analysis yielded sensitivities and specificities of 75% and 96%, 62.5% and 92%, 100% and 52%, 100% and 72%, and 100% and 88%, using standard deviation, quartile, 75th, 90th, and 95th percentiles for determining high grade with corresponding areas under the curve (AUCs) of 0.787, 0.792, 0.765, 0.880, and 0.925, respectively.

CONCLUSION

Histogram analysis of ADC maps based on entire tumor volume can be useful for predicting the histological grade of endometrial cancer. The 90th and 95th percentiles of ADC were the most promising parameters for differentiating high from low grade.

Histogram analysis of hepatobiliary phase MR imaging as a quantitative value for liver cirrhosis: preliminary observations

PURPOSE

To investigate whether histogram analysis of the hepatobiliary phase on gadoxetate enhanced-MRI could be used as a quantitative index for determination of liver cirrhosis.

MATERIALS AND METHODS

A total of 63 patients [26 in a normal liver function (NLF) group and 37 in a cirrhotic group] underwent gadoxetate-enhanced MRI, and hepatobiliary phase images were obtained at 20 minutes after contrast injection. The signal intensity of the hepatic parenchyma was measured at four different regions of interest (ROI) of the liver, avoiding vessels and bile ducts. Standard deviation (SD), coefficient of variation (CV), and corrected CV were calculated on the histograms at the ROIs. The distributions of CVs calculated from the ROI histogram were examined and statistical analysis was carried out.

RESULTS

The CV value was 0.041±0.009 (mean CV±SD) in the NLF group, while that of cirrhotic group was 0.071±0.020. There were statistically significant differences in the CVs and corrected CV values between the NLF and cirrhotic groups (p<0.001). The most accurate cut-off value among CVs for distinguishing normal from cirrhotic group was 0.052 (sensitivity 83.8% and specificity 88.5%). There was no statistically significant differences in SD between NLF and cirrhotic groups (p=0.307).

CONCLUSION

The CV of histograms of the hepatobiliary phase on gadoxetate-enhanced MRI may be useful as a quantitative value for determining the presence of liver cirrhosis.

Reproducibility of histogram and texture parameters derived from intravoxel incoherent motion diffusion-weighted MRI of FN13762 rat breast Carcinomas

AIM

To determine the reproducibility of histogram and texture parameters derived from intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) of FN13762 rat breast carcinomas.

MATERIALS AND METHODS

IVIM diffusion-weighted MRI was performed twice, nine days after tumor implantation in 11 rats. At each session, histogram and texture parameters of entire tumors were extracted from apparent diffusion coefficient (ADC), true-diffusion coefficient (Dt), pseudo-diffusion coefficient (Dp), and perfusion fraction (Pf) maps. Intraobserver and interscan measurement reproducibilities were evaluated using intraclass correlation coefficients (ICC).

RESULTS

Mean, entropy, 5th, 10th, 25th percentiles from ADC and Dt maps revealed good intra-observer and interscan agreements [lower limits of 95% confidence interval (CI) for ICC≥0.75]. However, all parameters from Dp and Pf maps gave relatively poor intra-observer and interscan agreements (lower limits of 95% CI for ICC<0.75).

CONCLUSION

Histogram and texture parameters derived from ADC and Dt maps were more reproducible than those from Dp and Pf maps.

Comparison of 18F-FET PET and perfusion-weighted MR imaging: a PET/MR imaging hybrid study in patients with brain tumors

PET using O-(2-(18)F-fluoroethyl)-L-tyrosine ((18)F-FET) provides important diagnostic information in addition to that from conventional MR imaging on tumor extent and activity of cerebral gliomas. Recent studies suggest that perfusion-weighted MR imaging (PWI), especially maps of regional cerebral blood volume (rCBV), may provide similar diagnostic information. In this study, we directly compared (18)F-FET PET and PWI in patients with brain tumors.

METHODS

Fifty-six patients with gliomas were investigated using static (18)F-FET PET and PWI. For comparison, 8 patients with meningiomas were included. We generated a set of tumor and reference volumes of interest (VOIs) based on morphologic MR imaging and transferred these VOIs to the corresponding (18)F-FET PET scans and PWI maps. From these VOIs, tumor-to-brain ratios (TBR) were calculated, and normalized histograms were generated for (18)F-FET PET and rCBV maps. Furthermore, in rCBV maps and in (18)F-FET PET scans, tumor volumes, their spatial congruence, and the distance between the local hot spots were assessed.

RESULTS

For patients with glioma, TBR was significantly higher in (18)F-FET PET than in rCBV maps (TBR, 2.28 ± 0.99 vs. 1.62 ± 1.13; P < 0.001). Histogram analysis of the VOIs revealed that (18)F-FET scans could clearly separate tumor from background. In contrast, deriving this information from rCBV maps was difficult. Tumor volumes were significantly larger in (18)F-FET PET than in rCBV maps (tumor volume, 24.3 ± 26.5 cm(3) vs. 8.9 ± 13.9 cm(3); P < 0.001). Accordingly, spatial overlap of both imaging parameters was poor (congruence, 11.0%), and mean distance between the local hot spots was 25.4 ± 16.1 mm. In meningioma patients, TBR was higher in rCBV maps than in (18)F-FET PET (TBR, 5.33 ± 2.63 vs. 2.37 ± 0.32; P < 0.001) whereas tumor volumes were comparable.

CONCLUSION

In patients with cerebral glioma, tumor imaging with (18)F-FET PET and rCBV yields different information. (18)F-FET PET shows considerably higher TBRs and larger tumor volumes than rCBV maps. The spatial congruence of both parameters is poor. The locations of the local hot spots differ considerably. Taken together, our data show that metabolically active tumor tissue of gliomas as depicted by amino acid PET is not reflected by rCBV as measured with PWI.

Seasonal variations of EPG Levels in gastro-intestinal parasitic infection in a southeast asian controlled locale: a statistical analysis

We present a data based statistical study on the effects of seasonal variations in the growth rates of the gastro-intestinal (GI) parasitic infection in livestock. The alluded growth rate is estimated through the variation in the number of eggs per gram (EPG) of faeces in animals. In accordance with earlier studies, our analysis too shows that rainfall is the dominant variable in determining EPG infection rates compared to other macro-parameters like temperature and humidity. Our statistical analysis clearly indicates an oscillatory dependence of EPG levels on rainfall fluctuations. Monsoon recorded the highest infection with a comparative increase of at least 2.5 times compared to the next most infected period (summer). A least square fit of the EPG versus rainfall data indicates an approach towards a super diffusive (i. e. root mean square displacement growing faster than the square root of the elapsed time as obtained for simple diffusion) infection growth pattern regime for low rainfall regimes (technically defined as zeroth level dependence) that gets remarkably augmented for large rainfall zones. Our analysis further indicates that for low fluctuations in temperature (true on the bulk data), EPG level saturates beyond a critical value of the rainfall, a threshold that is expected to indicate the onset of the nonlinear regime. The probability density functions (PDFs) of the EPG data show oscillatory behavior in the large rainfall regime (greater than 500 mm), the frequency of oscillation, once again, being determined by the ambient wetness (rainfall, and humidity). Data recorded over three pilot projects spanning three measures of rainfall and humidity bear testimony to the universality of this statistical argument.

Authenticity preservation with histogram-based reversible data hiding and quadtree concepts

With the widespread use of identification systems, establishing authenticity with sensors has become an important research issue. Among the schemes for making authenticity verification based on information security possible, reversible data hiding has attracted much attention during the past few years. With its characteristics of reversibility, the scheme is required to fulfill the goals from two aspects. On the one hand, at the encoder, the secret information needs to be embedded into the original image by some algorithms, such that the output image will resemble the input one as much as possible. On the other hand, at the decoder, both the secret information and the original image must be correctly extracted and recovered, and they should be identical to their embedding counterparts. Under the requirement of reversibility, for evaluating the performance of the data hiding algorithm, the output image quality, named imperceptibility, and the number of bits for embedding, called capacity, are the two key factors to access the effectiveness of the algorithm. Besides, the size of side information for making decoding possible should also be evaluated. Here we consider using the characteristics of original images for developing our method with better performance. In this paper, we propose an algorithm that has the ability to provide more capacity than conventional algorithms, with similar output image quality after embedding, and comparable side information produced. Simulation results demonstrate the applicability and better performance of our algorithm.

Quantitative T2* imaging of metastatic human breast cancer to brain in the nude rat at 3 T

This study uses quantitative T(2)* imaging to track ferumoxides--protamine sulfate (FEPro)-labeled MDA-MB-231BR-Luc (231BRL) human breast cancer cells that metastasize to the nude rat brain. Four cohorts of nude rats were injected intracardially with FEPro-labeled, unlabeled or tumor necrosis factor-related apoptosis-inducing ligand(TRAIL)-treated (to induce apoptosis) 231BRL cells, or saline, in order to develop metastatic breast cancer in the brain. The heads of the rats were imaged serially over 3-4 weeks using gradient multi-echo and turbo spin-echo pulse sequences at 3 T with a solenoid receive-only 4-cm-diameter coil. Quantitative T(2)* maps of the whole brain were obtained by the application of single-exponential fitting to the signal intensity of T(2)* images, and the distribution of T(2)* values in brain voxels was calculated. MRI findings were correlated with Prussian blue staining and immunohistochemical staining for iron in breast cancer and macrophages. Quantitative analysis of T(2)* from brain voxels demonstrated a significant shift to lower values following the intracardiac injection of FEPro-labeled 231BRL cells, relative to animals receiving unlabeled cells, apoptotic cells or saline. Quartile analysis based on the T(2)* distribution obtained from brain voxels demonstrated significant differences (p < 0.0083) in the number of voxels with T(2)* values in the ranges 10-35  ms (Q1), 36-60  ms (Q2) and 61-86  ms (Q3) from 1 day to 3 weeks post-infusion of labeled 231BRL cells, compared with baseline scans. There were no significant differences in the distribution of T(2)* obtained from serial MRI in rats receiving unlabeled or TRAIL-treated cells or saline. Histologic analysis demonstrated isolated Prussian blue-positive breast cancer cells scattered in the brains of rats receiving labeled cells, relative to animals receiving unlabeled or apoptotic cells. Quantitative T(2)* analysis of FEPro-labeled metastasized cancer cells was possible even after the hypointense voxels were no longer visible on T(2)*-weighted images.

Dose-volume histogram quality assurance for linac-based treatment planning systems

Dose–volume histograms provide key information to radiation oncologists when they assess the adequacy of a patient treatment plan in radiation therapy. It is important therefore that all clinically relevant data be accurate. In this article we present the first quality assurance routine involving a direct comparison of planning system results with the results obtained from independent hand calculations. Given a known three-dimensional (3-D) structure such as a parallelepiped, a simple beam arrangement, and known physics beam data, a time-efficient and reproducible method for verifying the accuracy of volumetric statistics (DVH) from a radiation therapy treatment planning system (TPS) can be employed rapidly, satisfying the QA requirements for (TPS) commissioning, upgrades, and annual checks. Using this method, the maximum disagreement was only 1.7% for 6 MV and 1.3% for 18 MV photon energies. The average accuracy was within 0.6% for 6 MV and 0.4% for 18 MV for all depth-dose results. A 2% disagreement was observed with the treatment planning system DVH from defined volume comparison to the known structure dimensions.