Search

Objective To evaluate the predictive value of apparent diffusion coefficient (ADC) histogram in preoperative magnetic resonance imaging (MRI) of the 2 cm peritumoral edema zone for spatial pattern of recurrence in IDH wild-type glioblastoma (GB). Methods A case-control study was performed on 50 patients with IDH wild-type GB treated with standard protocol, diagnosed by pathology as tumor recurrence and accepted regular follow-up MRI in Department of Neurosurgery of Army Medical Center of PLA from January 2012 to December 2021. The postoperative spatial recurrence patterns were divided into the local recurrence group (n=28) and the non-local recurrence group (n=22) according to whether the distance between the recurrence foci and the operative cavity was more than 2 cm or not. According to the maximum level of tumor enhancement in axial planes of enhanced MRI, the corresponding level of ADC images were selected. Mazda software was used to outline the region of interest (ROI) along the edge of the 2 cm range of peritumoral edema for histogram analysis. The histogram parameters of the 2 groups with different spatial patterns of recurrence were statistically analyzed, and the area under the curve (AUC) was obtained by receiver operating characteristic curve to evaluate the diagnostic efficacy. Results Among the 50 patients with recurrent GB, progression-free survival (PFS) and overall survival (OS) in the local recurrence group were longer than those in the non-local recurrence group (median PFS: 6.6 vs 4.6 months; median OS: 15.4 vs 12.4 months), and the differences were statistically significant(PFS: log-rank Chi-square=4.325, P=0.038; OS: log-rank Chi-square=4.022, P=0.045). Variance, kurtosis and Perc.90% of the 9 features extracted from ADC histogram were statistically significant differences between the 2 groups (P < 0.05). Variance had the best diagnostic efficiency (AUC: 0.804, sensitivity: 75.00%, specificity: 81.82%). Among the 4 multivariate logistic regression models constructed based on the variance, kurtosis and Perc.90%, the model constructed by the combination of variance, kurtosis and Perc.90% had the best diagnostic efficiency (AUC: 0.878, sensitivity: 78.57%, specificity: 86.36%). Conclusion The characteristic parameters of the preoperative ADC histogram in peritumoral edema zone can be used as an imaging marker to predict the different spatial patterns of postoperative recurrence in patients with IDH wild-type GB.

[Abstract] Objective To investigate the feasibility and value of histogram analysis based on two- dimensional gray-scale ultrasonography in the differential diagnosis of medullary thyroid carcinoma (MTC) and thyroid adenoma (TA). Methods The preoperative ultrasound images of 86 newly diagnosed MTC patients and 100 TA patients treated in the Cancer Hospital of Chinese Academy of Medical Sciences from January 2015 to October 2021 were collected. Histograms were performed based on the regions of interest (ROIs) delineated manually by two radiologists, thereafter, mean, variance, skewness, kurtosis, percentiles (1st, 10th, 50th, 90th, 99th) were generated. The histogram parameters between the MTC group and the TA group were compared, and the independent predictors were screened by multivariate logistic regression analysis. Receiver operating characteristic (ROC) analysis was used to compare the individual diagnostic efficacy and joint diagnostic efficacy of independent predictors. Results Multivariate regression analysis showed that mean, skewness, kurtosis and 50th percentile were independent factors. The skewness and kurtosis in the MTC group were significantly higher than those in the TA group, and the mean and 50th percentile were significantly lower than those in the TA group. The area under the individual ROC curve of mean, skewness, kurtosis and 50th percentile is 0.654-0.778. The area under the combined ROC curve is 0.826. Conclusion Histogram analysis based on two-dimensional gray-scale ultrasonography is a promising tool to distinguish MTC from TA, in which the joint diagnosis value of mean, skewness, kurtosis and 50th percentile is the highest. [ABSTRACT FROM AUTHOR]

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To explore histogram parameters and texture features of benign and malignant breast lesion on turbo inversion recovery magnitude(Tirm) sequence, and evaluate which parameter could help best differentiate benign from malignant breast lesion. Methods This retrospective study included 100 breast cancer patients who underwent conventional MRI and confirmed pathologically. Texture features were derived from the gray level co-occurrence matrix(GLCM), and entropy, energy, correlation, inertia, inverse difference moment, cluster prominence and mean value, skewness, kurtosis of histogram parameters were calculated. Then we assessed the diagnosis efficacy with these parameters among the variety kinds of benign and malignant breast lesions respectively, and establish the receiver-operating characteristic curve(ROC). We assessed the differences between benign and malignant groups by the Yoden index combined with clinic for the cut-off values. Results The differences of correlation, cluster prominence, mean value and kurtosis were statistically significant between the benign and malignant groups(all P < 0.001); the differences of energy, entropy, inertia, inverse difference moment and skewness were not statistically significant(all P > 0.05). The results of ROC with correlation, cluster prominence, mean value and kurtosis on the diagnosis of benign and malignant breast lesions were statistically significant, respectively(P < 0.001). Combined the four parameters on diagnosis benign and malignant lesions, the AUC was 0.868, the sensitivity was 90.57% and the specificity was 72.34%. Conclusion The histogram analysis and texture analysis based on Tirm sequence could be used for the differential diagnosis of benign and malignant breast lesions. Correlation, cluster prominence, mean value and kurtosis have certain significance in the diagnosis. The combined diagnosis could improve the differential ability of benign and malignant breast lesions.

Objective: To investigate the feasibility and value of histogram analysis based on two-dimensional gray-scale ultrasonography in the differential diagnosis of medullary thyroid carcinoma (MTC) and thyroid adenoma (TA). Methods: The preoperative ultrasound images of 86 newly diagnosed MTC patients and 100 TA patients treated in the Cancer Hospital of Chinese Academy of Medical Sciences from January 2015 to October 2021 were collected. Histograms were performed based on the regions of interest (ROIs) delineated manually by two radiologists, thereafter, mean, variance, skewness, kurtosis, percentiles (1st, 10th, 50th, 90th, 99th) were generated. The histogram parameters between the MTC group and the TA group were compared, and the independent predictors were screened by multivariate logistic regression analysis. Receiver operating characteristic (ROC) analysis was used to compare the individual diagnostic efficacy and joint diagnostic efficacy of independent predictors. Results: Multivariate regression analysis showed that mean, skewness, kurtosis and 50th percentile were independent factors. The skewness and kurtosis in the MTC group were significantly higher than those in the TA group, and the mean and 50th percentile were significantly lower than those in the TA group. The area under the individual ROC curve of mean, skewness, kurtosis and 50th percentile is 0.654-0.778. The area under the combined ROC curve is 0.826. Conclusion: Histogram analysis based on two-dimensional gray-scale ultrasonography is a promising tool to distinguish MTC from TA, in which the joint diagnosis value of mean, skewness, kurtosis and 50th percentile is the highest.

Objective: To evaluate the functional changes of lateral pterygoid muscle (LPM) in the patients with temporomandibular disorders (TMD) using histogram texture analysis. Methods: A total of 28 TMD patients, including 15 males and 13 females, aged (26.2±12.1) years (15-62 years), were enrolled in the outpatient clinic of Department of Radiology, Hainan Hospital of Chinese PLA General Hospital from May 2016 to April 2020. At the same time, 13 volunteers with normal temporomandibular joint [6 males and 7 females, aged (23.5±3.3) years (20-30) years] were recruited. All participants were performed with temporomandibular joint MRI scan, and the subjects were classified into 3 groups according to the disc displacement: TMD with disc without displacement (TMD-DwoD), TMD with disc displacement without reduction (TMD-DDwoR) and NC with disc displacement without reduction (NC-DwoD) for each temporomandibular joint. The area, signal intensity, coefficient of variation (CV) of mean signal intensity and histogram texture of the superior belly of LPM (SBLPM) and inferior belly of LPM (IBLPM) were measured using regions of interests. Results: The area of SILPM presented significantly smaller in TMD-DwoD [(67.36±30.23) mm 2 ] compared with that in NC-DwoD [(91.42±45.01) mm 2 ] ( P <0.05) and in TMD-DDwoR group [(72.27±37.53) mm 2 ] ( P >0.05). The mean signal intensity of SBLPM was identified significantly higher in TMD-DwoD (304.20±29.90) and TMD-DDwoR (315.06±40.20) compared with that in NC-DwoD (269.79±25.54) ( P <0.05). The CV of mean signal intensity of SBLPM presented significantly higher in TMD-DwoD (8.89±2.60) compared with that in NC-DwoD (6.76±1.82) and TMD-DDwoR (7.63±1.62) ( P <0.05). The skew and kurtosis showed no significantly differences among three groups for SBLPM ( P >0.05). The mean signal intensity and CV for IBLPM presented significantly higher in TMD-DwoD (286.69±23.48 and 9.96±2.24, respectively) and TMD-DDwoR (275.98±30.55 and 10.25±1.96, respectively) compared with that in NC-DwoD (243.56±17.04 and 8.49±1.94, respectively) ( P <0.05). The skewness showed significantly lower in TMD-DwoD (0.90±0.94) and TMD-DDwoR (0.73±0.97) compared with that in NC-DwoD (1.40±1.03) ( P <0.05). The area and kurtosis for IBLPM showed no significant differences among three groups ( P >0.05). Conclusions: The MR histogram texture analysis could primarily be considered as a tool to evaluate the LPM function in TMD patients.

Objective: To assess the value of stretched-exponential and mono-exponential diffusion-weighted imaging (DWI) in predicting the aggressiveness of PCa., Methods: This retrospective study included 36 cases of PCa with 48 lesions in the peripheral zone diagnosed by DWI with b-values of 0, 500, 1000, and 2000 s/mm2. We reconstructed the apparent diffusion coefficient (ADC), distributed diffusion coefficient (DDC) and α maps on the post-processing workstation, performed a histogram analysis on the largest slice of PCa on T2WI and Spearman's rank-order analysis on the correlation of the histogram variables with Gleason grade grouping (GG). Then, we assessed the values of the histogram variables in differentiating low-grade from high-grade PCa using the receiver operating characteristic (ROC) curve., Results: The percentile and mean ADCs and DDCs were correlated with GG (ρ: 0.392－0.641) but not the α value, skewnesses and kurtosises (ρ: 0.055－0.266). High-grade PCa exhibited significantly lower 10th-, 25th-, 50th- and 75th-percentile and mean ADCs (490 ± 141, 591 ± 137, 695 ± 137, 781 ± 139 and 888 ± 135 mm2/s) and DDCs (420 ± 146, 534 ± 167, 666 ± 182, 787 ± 190 and 912 ± 175 mm2/s) than low-grade PCa (ADCs: 636 ± 74, 727 ± 86, 825 ± 85, 907 ± 85 and 975 ± 117 mm2/s; DDCs: 542 ± 80, 666 ± 93, 806 ± 108, 910 ± 110 and 1023 ± 105 mm2/s), but there were no statistically significant differences between low- and high-grade PCa in the α value (0.67 ± 0.042 vs 0.64 ± 0.036), kurtosises (ADC 0.105 vs 0.078; DDC －0.027 vs －0.401) or skewnesses (ADC －0.042 vs 0.067; DDC －0.058 vs 0.162). Both 10th-percentile ADCs and DDCs showed a higher efficiency than the mean ones in differentiating high- from low-grade PCa, though with no statistically significant difference (P > 0.05)., Conclusions: Histogram variables DDCs and ADCs, rather than the α value, can be used to predict the aggressiveness of PCa, even more efficiently at the 10th percentile than on the mean.

Objective: To discuss the correlation between histogram analysis of quantitative mono-exponential, bi-exponential and diffusion kurtosis models in diffusion weighted imaging and the Gleason score of prostate cancer, and evaluate the application value and diagnostic efficiency in identifying low and high grade prostate cancer. Methods: A total of 50 patients with histologically confirmed as prostate cancer were examined from May 2015 to May 2016 in the Second Affiliated Hospital of Soochow University using DWI performed at 3.0 T with an extended b-value range from 0 to 2 000 s/mm(2). Data were post-processed by whole tumor histogram analysis,the ROI was manually drown in DWI (b=1 000 s/mm(2)) step by step along the outline of cancer, and quantitative analysis were performed respectively by mono-exponential, bi-exponential and diffusion kurtosis models for quantification of apparent diffusion coefficients (ADCs), diffusivity D, pseudo-diffusivity D(*), perfusion fraction f, diffusion coefficients by non-Gaussian distribution (D(k)) and kurtosis coefficient (K).Then the histogram analysis was performed to get the mean, median, 25th percentile, 75th percentile, skewness and kurtosis. The correlation between histogram analysis results of these quantitative parameters and Gleason score of prostate cancer were evaluated by Spearman correlation coefficient. The diagnostic performance of histogram analysis results of each quantitative parameters in identifying low (Gleason score≤6) and high (Gleason score>6) grade prostate cancer was performed by comparing the area under the ROC curve and the curve values. Results: The values of ADC, D and D(k) (mean, median, 25th, 75th) were negatively correlated with Gleason score of prostate cancer ( r value was -0.388--0.624, P< 0.05). The values of D (skewness and kurtosis) had a certain correlation with Gleason score of prostate cancer ( r value were 0.413 and 0.402, P< 0.05). The histogram analysis results of D(*) and f had no statistically significant correlation with Gleason score of prostate cancer ( P> 0.05). The values of K (mean, median, 25th, kurtosis) were positively correlated with Gleason score of prostate cancer ( r value was 0.423-0.699, P <0.05). The diagnostic efficiency of histogram analysis results of these quantitative parameter values in identifying low and high grade prostate cancer showed that the ADC (median), D (25th), D(k) (mean) and K (25th) had a larger area under the curve, and were 0.844, 0.873, 0.815, and 0.919 respectively, the differences of area under the curve between any two of these parameters above were not statistically significant (all P >0.05). Conclusions: The quantitative parameters of three diffusion models (ADC, D, D(k), K) in DWI are all related to the Gleason score of prostate cancer, but in the differential diagnosis of low and high grade prostate cancer, the diagnostic efficacy of mono-exponential model is sufficient. The more complex model such as bi-exponential and diffusion kurtosis may complement it in other ways.