Yue Guan

Whole-Lesion Histogram Analysis of Apparent Diffusion Coefficient for the Assessment of Cervical Cancer.

Objective The aim of this study was to explore the application of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) values of cervical cancer. Methods A total of 54 women (mean age, 53 years) with cervical cancers underwent 3-T diffusion-weighted imaging with b values of 0 and 800 s/mm2 prospectively. Whole-lesion histogram analysis of ADC values was performed. Paired sample t test was used to compare differences in ADC histogram parameters between cervical cancers and normal cervical tissues. Receiver operating characteristic curves were constructed to identify the optimal threshold of each parameter. Results All histogram parameters in this study including ADCmean, ADCmin, ADC10%–ADC90%, mode, skewness, and kurtosis of cervical cancers were significantly lower than those of normal cervical tissues (all P < 0.0001). ADC90% had the largest area under receiver operating characteristic curve of 0.996. Conclusions Whole-lesion histogram analysis of ADC maps is useful in the assessment of cervical cancer.

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

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.

Application of CT texture analysis in predicting histopathological characteristics of gastric cancers

ObjectivesTo explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers.MethodsPreoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed.ResultsMean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, −0.231 ~ −0.324, 0.228 ~ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = −0.265, −0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion.ConclusionCT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers.Key Points• CT texture analysis is noninvasive and effective for gastric cancer.• Portal venous CT images correlated significantly with differentiation degree and Lauren classification.• Standard deviation, entropy and minimum attenuation in arterial phase reflect vascular invasion.

Whole-Lesion Apparent Diffusion Coefficient-Based Entropy-Related Parameters for Characterizing Cervical Cancers: Initial Findings.

RATIONALE AND OBJECTIVES This study aimed to develop whole-lesion apparent diffusion coefficient (ADC)-based entropy-related parameters of cervical cancer to preliminarily assess intratumoral heterogeneity of this lesion in comparison to adjacent normal cervical tissues. MATERIALS AND METHODS A total of 51 women (mean age, 49 years) with cervical cancers confirmed by biopsy underwent 3-T pelvic diffusion-weighted magnetic resonance imaging with b values of 0 and 800 s/mm2 prospectively. ADC-based entropy-related parameters including first-order entropy and second-order entropies were derived from the whole tumor volume as well as adjacent normal cervical tissues. Intraclass correlation coefficient, Wilcoxon test with Bonferroni correction, Kruskal-Wallis test, and receiver operating characteristic curve were used for statistical analysis. RESULTS All the parameters showed excellent interobserver agreement (all intraclass correlation coefficients  > 0.900). Entropy, entropy(H)0, entropy(H)45, entropy(H)90, entropy(H)135, and entropy(H)mean were significantly higher, whereas entropy(H)range and entropy(H)std were significantly lower in cervical cancers compared to adjacent normal cervical tissues (all P <.0001). Kruskal-Wallis test showed that there were no significant differences among the values of various second-order entropies including entropy(H)0, entropy(H)45, entropy(H)90, entropy(H)135, and entropy(H)mean. All second-order entropies had larger area under the receiver operating characteristic curve than first-order entropy in differentiating cervical cancers from adjacent normal cervical tissues. Further, entropy(H)45, entropy(H)90, entropy(H)135, and entropy(H)mean had the same largest area under the receiver operating characteristic curve of 0.867. CONCLUSION Whole-lesion ADC-based entropy-related parameters of cervical cancers were developed successfully, which showed initial potential in characterizing intratumoral heterogeneity in comparison to adjacent normal cervical tissues.

Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness

To explore the role of whole‐volume apparent diffusion coefficient (ADC)‐based entropy parameters in the preoperative assessment of gastric cancers aggressiveness.

Predicting the nodal status in gastric cancers: The role of apparent diffusion coefficient histogram characteristic analysis

PURPOSE To explore the application of histogram analysis in preoperative T and N staging of gastric cancers, with a focus on characteristic parameters of apparent diffusion coefficient (ADC) maps. MATERIALS AND METHODS Eighty-seven patients with gastric cancers underwent diffusion weighted magnetic resonance imaging (b=0, 1000s/mm2), which generated ADC maps. Whole-volume histogram analysis was performed on ADC maps and 7 characteristic parameters were obtained. All those patients underwent surgery and postoperative pathologic T and N stages were determined. RESULTS Four parameters, including skew, kurtosis, s-sDav and sample number, showed significant differences among gastric cancers at different T and N stages. Most parameters correlated with T and N stages significantly and worked in differentiating gastric cancers at different T or N stages. Especially skew yielded a sensitivity of 0.758, a specificity of 0.810, and an area under the curve (AUC) of 0.802 for differentiating gastric cancers with and without lymph node metastasis (P<0.001). All the parameters, except AUClow, showed good or excellent inter-observer agreement with intra-class correlation coefficients ranging from 0.710 to 0.991. CONCLUSION Characteristic parameters derived from whole-volume ADC histogram analysis could help assessing preoperative T and N stages of gastric cancers.

Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT

Purpose To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram and texture analysis in predicting tumor recurrence of advanced cervical cancer treated with concurrent chemo-radiotherapy (CCRT). Methods 36 women with pathologically confirmed advanced cervical squamous carcinomas were enrolled in this prospective study. 3.0 T pelvic MR examinations including diffusion weighted imaging (b = 0, 800 s/mm2) were performed before CCRT (pre-CCRT) and at the end of 2nd week of CCRT (mid-CCRT). ADC histogram and texture features were derived from the whole volume of cervical cancers. Results With a mean follow-up of 25 months (range, 11 ∼ 43), 10/36 (27.8%) patients ended with recurrence. Pre-CCRT 75th, 90th, correlation, autocorrelation and mid-CCRT ADCmean, 10th, 25th, 50th, 75th, 90th, autocorrelation can effectively differentiate the recurrence from nonrecurrence group with area under the curve ranging from 0.742 to 0.850 (P values range, 0.001 ∼ 0.038). Conclusions Pre- and mid-treatment whole-lesion ADC histogram and texture analysis hold great potential in predicting tumor recurrence of advanced cervical cancer treated with CCRT.

Texture analysis of CT images in predicting malignancy risk of gastrointestinal stromal tumours.

AIM To explore the role of texture analysis of computed tomography (CT) images in predicting the malignancy risk of gastrointestinal stromal tumours (GISTs). MATERIALS AND METHODS Seventy-eight patients with histopathologically confirmed GISTs underwent preoperative CT. Texture analysis was performed on unenhanced and contrast-enhanced CT images, respectively. Fourteen CT texture parameters were obtained and compared among GISTs at different malignancy risks with one-way analysis of variance or independent-samples Kruskal-Wallis test. Correlations between CT texture parameters and malignancy risk were analysed with Spearmans correlation test. Diagnostic performance of CT texture parameters in differentiating GISTs at low/very low malignancy risk was tested with receiver operating characteristic (ROC) analysis. RESULTS Three parameters on unenhanced images (r=-0.268-0.506), four parameters on arterial phase (r=-0.365-0.508), and six parameters on venous phase (r=-0.343-0.481) imaging correlated significantly with malignancy risk of GISTs, respectively (all p<0.05). For identifying GISTs at low/very low malignancy risk, three parameters on unenhanced images (area under ROC curve [AUC], 0.676-0.802), four parameters on arterial phase (AUC, 0.637-0.811), and six parameters on venous phase (AUC, 0.636-0.791) imaging showed significant diagnostic performance, respectively (all p<0.05), especially maximum frequency on both unenhanced and contrast-enhanced images (AUC, 0.791-0.811). CONCLUSION Texture analysis of CT images holds great potential to predict the malignancy risk of GISTs preoperatively.

CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers

To investigate the ability of CT texture analysis to assess and predict the expression statuses of E-cadherin, Ki67, VEGFR2 and EGFR in gastric cancers, the enhanced CT images of 139 patients with gastric cancer were retrospectively reviewed. The region of interest was manually drawn along the margin of the lesion on the largest slice in the arterial and venous phases, which yielded a series of texture parameters. Our results showed that the standard deviation, width, entropy, entropy (H), correlation and contrast from the arterial and venous phases were significantly correlated with the E-cadherin expression level in gastric cancers (all P < 0.05). The skewness from the arterial phase and the mean and autocorrelation from the venous phase were negatively correlated with the Ki67 expression level in gastric cancers (all P < 0.05). The width, entropy and contrast from the venous phase were positively correlated with the VEGFR2 expression level in gastric cancers (all P < 0.05). No significant correlation was found between the texture features and EGFR expression level. CT texture analysis, which had areas under the receiver operating characteristic curve (AUCs) ranging from 0.612 to 0.715, holds promise in predicting E-cadherin, Ki67 and VEGFR2 expression levels in gastric cancers.

Texture Analysis as Imaging Biomarker for recurrence in advanced cervical cancer treated with CCRT

This prospective study explored the application of texture features extracted from T2WI and apparent diffusion coefficient (ADC) maps in predicting recurrence of advanced cervical cancer patients treated with concurrent chemoradiotherapy (CCRT). We included 34 patients with advanced cervical cancer who underwent pelvic MR imaging before, during and after CCRT. Radiomic feature extraction was performed by using software at T2WI and ADC maps. The performance of texture parameters in predicting recurrence was evaluated. After a median follow-up of 31 months, eleven patients (32.4%) had recurrence. At four weeks after CCRT initiated, the most textural parameters (four T2 textural parameters and two ADC textural parameters) showed significant difference between the recurrence and nonrecurrence group (P values range, 0.002~0.046). Among them, RunLengthNonuniformity (RLN) from T2 and energy from ADC maps were the best selected predictors and together yield an AUC of 0.885. The support vector machine (SVM) classifier using ADC textural parameters performed best in predicting recurrence, while combining T2 textural parameters may add little value in prognosis. T2 and ADC textural parameters have potential as non-invasive imaging biomarkers in early predicting recurrence in advanced cervical cancer treated with CCRT.