Fractional order calculus model-derived histogram metrics for assessing pathological complete response to neoadjuvant chemotherapy in locally advanced rectal cancer.

AIM: This study evaluates the value of diffusion fractional order calculus (FROC) model for the assessment of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes. MATERIALS AND METHODS: Ninety-eight patients were prospectively included. Every patient received MRI scans before and after nCRT using a 3.0-Tesla MRI machine. Parameters of the FROC model, including the anomalous diffusion coefficient (D), intravoxel diffusion heterogeneity (ß), spatial parameter (µ), and the standard apparent diffusion coefficient (ADC), were calculated. Changes in median values (ΔX-median) and ratio (rΔX-median) were calculated. Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance. RESULTS: Pre-treatmentß-10th percentile values were significantly lower in the pCR group compared to the non-pCR group (p < 0.001). The Δß-median showed higher diagnostic accuracy (AUC = 0.870) and sensitivity (76.67 %) for predicting tumor response compared to MRI tumor regression grading (mrTRG) scores (AUC = 0.722; sensitivity = 90.0 %). DISCUSSION: The use of FROC alongside comprehensive tumor histogram analysis was found to be practical and effective in evaluating the tumor response to nCRT in LARC patients.

Combination of intravoxel incoherent motion histogram parameters and clinical characteristics for predicting response to neoadjuvant chemoradiation in patients with locally advanced rectal cancer.

OBJECTIVE: To explore the value of histogram parameters derived from intravoxel incoherent motion (IVIM) for predicting response to neoadjuvant chemoradiation (nCRT) in patients with locally advanced rectal cancer (LARC). METHODS: A total of 112 patients diagnosed with LARC who underwent IVIM-DWI prior to nCRT were enrolled in this study. The true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) calculated from IVIM were recorded along with the histogram parameters. The patients were classified into the pathological complete response (pCR) group and the non-pCR group according to the tumor regression grade (TRG) system. Additionally, the patients were divided into low T stage (yp T0-2) and high T stage (ypT3-4) according to the pathologic T stage (ypT stage). Univariate logistic regression analysis was implemented to identify independent risk factors, including both clinical characteristics and IVIM histogram parameters. Subsequently, models for Clinical, Histogram, and Combined Clinical and Histogram were constructed using multivariable binary logistic regression analysis for the purpose of predicting pCR. The area under the receiver operating characteristic (ROC) curve (AUCs) was employed to evaluate the diagnostic performance of the three models. RESULTS: The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the pCR group compared with the non-pCR group (all P < 0.05). The value of D*_ entropy was significantly lower in the pCR group compared with the non-pCR group (P < 0.05). The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the low T stage group compared with the high T stage group (all P < 0.05). The value of D*_ entropy was significantly lower in the low T stage group compared with the high T stage group (P < 0.05). The ROC curves indicated that the Combined Clinical and Histogram model exhibited the best diagnostic performance in predicting the pCR patients with AUCs, sensitivity, specificity, and accuracy of 0.916, 83.33%, 85.23%, and 84.82%. CONCLUSIONS: The histogram parameters derived from IVIM have the potential to identify patients who have achieved pCR. Moreover, the combination of IVIM histogram parameters and clinical characteristics enhanced the diagnostic performance of IVIM histogram parameters.

Sinonasal adenoid cystic carcinoma: preoperative apparent diffusion coefficient histogram analysis in prediction of prognosis and Ki-67 proliferation status.

PURPOSE: To investigate the value of preoperative apparent diffusion coefficient (ADC) histogram analysis in predicting the prognosis of patients with sinonasal adenoid cystic carcinoma (ACC) and the correlation between ADC histogram parameters and Ki-67 labeling index (LI). MATERIALS AND METHODS: The study enrolled 66 patients with sinonasal ACC who were surgically resected and confirmed by histopathology. The disease-free survival (DFS) was evaluated with clinical-pathologic and radiologic characteristics using the Cox proportion hazard model. Spearman correlation analysis was used to evaluate the correlation between ADC histogram parameters and Ki-67 LI. The predictive performance of ADC histogram parameters for Ki-67 LI was assessed using the receiver operating characteristic (ROC) curve. RESULTS: Multivariable analysis showed Ki-67 LI (hazard ratio: 9.279; 95% confidence interval 1.099-78.338; P = 0.041) and ADCskewness (hazard ratio: 5.942; 95% confidence interval 1.832-19.268; P = 0.003) were significant independent predictors of DFS. The combination of these two variables achieved the predictive ability with a C-index of 0.717 (95% confidence interval 0.607-0.826). ADCmean and all ADC percentiles (10th, 50th, and 90th) significantly and inversely correlated with Ki-67 LI of ACC (Correlation coefficients = - 0.574 to - 0.591, Ps < 0.001). Among the ADC histogram parameters, the ADC50th showed superior performance for the differentiation of the high from low Ki-67 LI groups with an area under the curve (AUC) of 0.834 and an accuracy of 80.30%. CONCLUSION: ADC histogram analysis had predictive value for DFS and Ki-67 LI, which may be a valuable biomarker for prognosis and proliferation status for ACC in clinical practice.

Predicting the Ki-67 proliferation index in cervical cancer: a preliminary comparative study of four non-Gaussian diffusion-weighted imaging models combined with histogram analysis.

Background: The prognosis for patients with cervical cancer (CC) is strongly correlated with the Ki-67 proliferation index (PI). However, the Ki-67 PI obtained through biopsy has certain limitations. The non-Gaussian distribution diffusion model of magnetic resonance imaging (MRI) may play an important role in characterizing tissue heterogeneity. At present, there are limited data available concerning the prediction of Ki-67 PI using models based on histogram features of non-Gaussian diffusion distribution. This study aimed to determine whether preoperative histogram features from multiple non-Gaussian models of diffusion-weighted imaging can predict the Ki-67 PI in patients with CC. Methods: Our cross-sectional prospective study recruited a total of 53 patients suspected of having CC who underwent 3.0-T MRI at Sun Yat-sen Memorial Hospital of Sun Yat-sen University between January 2022 and January 2023. Fifteen b values (0-4,000 s/mm2) were used for diffusion-weighted imaging. A total of nine parameters from four non-Gaussian diffusion-weighted imaging models, including continuous-time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM), were used. Whole-tumor volumetric histogram analysis of these parameters was then obtained. In logistic regression, significant histogram characteristics were statistically examined across two groups to build the final prediction model. To assess diagnostic parameters of the proposed model in the diagnosis of the Ki-67 PI, along with the sensitivity, specificity, and diagnostic accuracy of these various parameters from the four models, receiver operating feature analysis was applied. Results: Among the 53 patients (55.3±9.6 years, ranging from 23 to 79 years) included in the study, 15 had a Ki-67 PI ≤50% and 38 had a Ki-67 PI >50%. Univariable analysis determined that 12 histogram features were statistically different between the two groups. In multivariable logistic regression, we ultimately selected 6 histogram features to construct the final prediction model, with CTRW_α_10th percentile [odds ratio (OR) =0.955; 95% confidence interval (CI): 0.92-0.99; P=0.019], CTRW_α_robust mean absolute deviation (OR =0.893; 95% CI: 0.81-0.99; P=0.028), and CTRW_α_uniformity (OR =0.000, 95% CI: 0.00-0.90, P=0.047) being the independent predictive variables. The area under the curve of the combined prediction model was 0.845 (95% CI: 0.74-0.95), with a sensitivity of 78.9% (95% CI: 0.63-0.90), a specificity of 86.7% (95% CI: 0.60-0.98), an accuracy of 81.1% (95% CI: 0.68-0.91), a positive predictive value of 93.8% (95% CI: 0.79-0.99), and a negative predictive value of 61.9% (95% CI: 0.38-0.82). Conclusions: The histogram features of multiple non-Gaussian diffusion-weighted imaging can help to predict the Ki-67 PI of CC, providing a new method for the noninvasive evaluation of critical biological features of CC.

Assessing the Histological Malignancy Grade of Olfactory Neuroblastoma Using the Apparent Diffusion Coefficient Histogram Analysis.

Introduction Olfactory neuroblastoma (ONB) is a rare malignant tumor of the upper nasal cavity. The Hyams classification is an important histological grading system for diagnosing recurrence and predicting survival in ONB. This study aimed to evaluate the utility of apparent diffusion coefficient (ADC) histogram analysis in distinguishing between high-grade and low-grade ONB based on the Hyams classification system. Methods This retrospective study included 17 patients (11 males, six females; mean age 54 years, range 29-84) diagnosed with ONB who underwent pretreatment magnetic resonance imaging (MRI) including diffusion-weighted imaging between December 2017 and September 2022. Two board-certified radiologists outlined the regions of interest on ADC maps of the tumors. Mean, minimum, maximum ADC, standard deviation, skewness, kurtosis, and entropy were calculated from the ADC histograms. Patients were divided into low-grade (Hyams I-II) and high-grade (Hyams III-IV) groups based on histopathological evaluation by a board-certified pathologist. ADC histogram parameters were compared between the two groups using Mann-Whitney U tests. Two-sided p-values of < 0.05 were considered statistically significant. Results The study included 10 low-grade (two grade I, eight grade II) and seven high-grade (five grade III, one grade III/IV, one grade IV) ONB cases. Comparison between the low-grade and high-grade groups showed no statistically significant differences in any of the ADC histogram parameters analyzed: mean ADC (median 1.02 vs 0.95; p = 0.591), minimum ADC (0.84 vs 0.78; p = 0.494), maximum ADC (1.06 vs 1.19; p = 0.625), standard deviation (0.09 vs 0.14; p = 0.433), skewness (-0.48 vs -0.75; p = 0.133), kurtosis (2.79 vs 3.12; p = 0.161), and entropy (4.69 vs 5.06; p = 0.315). Conclusion This study demonstrated that ADC histogram analysis was unable to differentiate between high-grade and low-grade ONB based on the Hyams classification. The findings suggest that preoperative grading of ONB malignancy using ADC histogram parameters is challenging. Thus, grading based on preoperative imaging evaluation is difficult.

Whole-lesion CT histogram analysis as an advanced technique in the portal venous phase: differentiating lipid poor adrenal adenomas from pheochromocytomas.

PURPOSE: Adrenal computed tomography (CT) has limitation due to imaging overlaps inthe washout characteristics of pheochromocytomas and adenomas (especially lipid-poor). The aim of this study was to investigate the distinguishability of lipid-poor adrenal adenomas and pheochromocytomas using whole-lesion CT histogram analysis. MATERIALS AND METHODS: Histopathologically proven 24 lipid-poor adenomas and 29 pheochromocytomas (total 53 lesions in 53 patients) were included in this retrospective study. Data obtained from standard and volumetric examinations of the lesions by dedicated adrenal CT were compared between the two groups using univariate analysis. Parameters that showed differences were further evaluated using multivariate logistic regression analysis. RESULTS: Univariate analysis revealed significant differences between the two groups in terms of lesion size, lesion volume, percentage of relative wash out, peak HU values and the percentage of voxels with attenuation ≥ 100 HU, ≥ 110 HU and ≥ 120 HU (p = 0.0001, P = 0.0001, P = 0.01, P = 0.008, p = 0.04, p = 0.02, p = 0.02, respectively). Multivariate analysis revealed lesion size ≥ 22.05 mm (OR: 22; p < 0.0001), the percentage of voxels with attenuation ≥ 120 HU being ≥ 9% (OR: 3.27; p = 0.04), peak HU value ≥ 161.5 HU (OR: 4.40; p = 0.01) as risk factors for pheochromocytomas. CONCLUSIONS: Whole lesion CT histogram analysis can be used to differentiate pheochromocytomas from lipid-poor adenomas. Lesion volume, the percentage of voxels with attenuation ≥ 120 HU and peak HU values are independent parameters that can assist in this differentiation. These findings may help avoid unnecessary biopsies and surgeries for lipid-poor adenomas, while identifying pheochromocytoma risk may improve perioperative patient management. Our results should be validated by future prospective studies.

The diagnostic value of histogram analysis of DWI and DKI for the mismatch repair status of rectal adenocarcinoma.

Objectives: To compare the diagnostic value of histogram analysis derived from diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating the mismatch repair (MMR) status of rectal adenocarcinoma. Methods: DWI and DKI were performed in 124 patients with rectal adenocarcinoma, which were divided into deficient mismatch repair (dMMR) group and proficient mismatch repair (pMMR) group. The patients' general clinical information, pathology and image characteristics were compared. The histogram analysis of apparent diffusion coefficient (ADC), diffusion kurtosis (K) and diffusion coefficient (D)derived from DWI and DKI at b values of 1000 and 2000 s/mm2 were calculated. The diagnostic efficacy of quantitative parameters for MMR in rectal adenocarcinoma was compared. Results: The mean, 50th, 75th and 90th in ADC quantitative parameters of dMMR group were lower when the b value was 2000 s/mm2 (all P < 0.05). With b value of 1000 s/mm2, the 10th, 25th, and 50th in the dMMR group were lower, and the skewness was higher (all P < 0.05). D values (10th, 25th and 50th) derived from DKI quantitative parameters were lower in the dMMR group. The K values (75th, 90th and Kskewness) were higher in the dMMR group, while Kkurtosis was lower (all P < 0.05). The results of multivariate logistic regression analysis showed that ADC75th(b = 2000 s/mm2), ADCskewness (b = 1000 s/mm2) and Kskewness were the statistical significant parameters (P = 0.014, 0.036 and 0.002, respectively), and the AUC values were 0.713, 0.818 and 0.835, respectively. Conclusion: Histogram analysis derived from DWI and DKI can be good predictor of MMR. Kskewness is the strongest independent factor for predicting MMR.

Whole-liver histogram analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging for predicting progression in patients with cirrhosis.

Background: Liver cirrhosis, as the terminal phase of chronic liver disease fibrosis, is associated with high morbidity and mortality. Traditional methods for assessing liver function, such as clinical scoring systems, offer only a global evaluation and may not accurately reflect regional liver function variations. This study aimed at evaluating the diagnostic potential of whole-liver histogram analysis of gadobenate dimeglumine (Gd-BOPTA)-enhanced magnetic resonance imaging (MRI) for predicting the progression of cirrhosis. Methods: In this retrospective study, 265 consecutive patients with cirrhosis admitted to the Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University from August 2012 to September 2019 were enrolled. After the exclusion criteria were applied, 117 patients (84 males and 33 females) were divided into Child-Pugh A cirrhosis (n=43), Child-Pugh B cirrhosis (n=49), and Child-Pugh C cirrhosis (n=25). After correction for liver signal intensity with the spleen was completed, 19 histogram features of the whole liver were extracted and modeled to evaluate liver function, with the Child-Pugh class being incorporated as a clinical parameter. Receiver operating characteristic (ROC) curves were used to assess the diagnosis capability and determine the optimal cutoffs after a mean follow-up of 42.3±19.1 (range, 8-93) months. The association between significant histogram features and the cumulative incidence of hepatic insufficiency was analyzed with the adjusted Kaplan-Meier curve model. Results: Among 117 patients (12%), 14 developed hepatic insufficiency through a period of follow-up. Five features, including the median (P<0.01), 90th percentile (P<0.01), root mean squared (P<0.01), mean (P<0.01), and 10th percentile (P<0.05), were significantly different between the groups with and without hepatic insufficiency according to the Kruskal-Wallis test; in the ROC curve analysis, the area under the curve (AUC) of these features was 0.723 [95% confidence interval (CI): 0.653-0.793], 0.722 (95% CI: 0.652-0.792), 0.722 (95% CI: 0.652-0.792), 0.721 (95% CI: 0.651-0.791), and 0.674 (95% CI: 0.600-0.748) after correction, respectively (all P values <0.05). Median, 90th percentile, root mean squared, and mean were found to be significant factors in predicting liver insufficiency. The adjusted Kaplan-Meier curves revealed that patients with a feature level less than the cutoff, as compared to those with a level above the cutoff, showed a statistically shorter progression-free survival and higher incidences of hepatic insufficiency for significant features of median (cutoff =26.001; 21.28% versus 5.71%; P=0.02), 90th percentile (cutoff =86.263; 20.41% versus 5.88%; P<0.01), root mean squared (cutoff =1,028.477; 19.15% versus 7.14%; P=0.049), and mean (cutoff =27.484; 19.15% versus 7.14%; P=0.049). Patients with a 10th percentile less than -39.811 also showed a higher cumulative incidence of hepatic insufficiency than did those with a value higher than the cutoff (0.18% versus 7.46%; P=0.22). Conclusions: Whole-liver histogram analysis of Gd-BOPTA-enhanced MRI may serve as a noninvasive analytical method to predict hepatic insufficiency in patients with cirrhosis.

Nomogram of magnetic resonance imaging (MRI) histogram analysis to predict telomerase reverse transcriptase promoter mutation status in glioblastoma.

Background: Telomerase reverse transcriptase promoter (pTERT) status is a strong biomarker to diagnose and predict the prognosis of glioblastoma (GBM). In this study, we explored the predictive value of preoperative magnetic resonance imaging (MRI) histogram analysis in the form of nomogram for evaluating pTERT mutation status in GBM. Methods: The clinical and imaging data of 181 patients with GBM at our hospital between November 2018 and April 2023 were retrospectively assessed. We used the molecular sequencing results to classify the datasets into pTERT mutations (C228T and C250T) and pTERT-wildtype groups. FireVoxel software was used to extract preoperative T1-weighted contrast-enhanced (T1C) histogram parameters of GBM patients. The T1C histogram parameters were compared between groups. Univariate and multivariate logistic regression analyses were used to construct the nomogram, and the predictive efficacy of model was evaluated using calibration and decision curves. Receiver operating characteristic curve was used to assess model performance. Results: Patient age and percentage of unenhanced tumor area showed statistically significant differences between the pTERT mutation and pTERT-wildtype groups (P<0.001). Among the T1C histogram features, the maximum, standard deviation (SD), variance, coefficient of variation (CV), skewness, 5th, 10th, 25th, 95th and 99th percentiles were statistically significantly different between groups (P=0.000-0.040). Multivariate logistic regression analysis showed that age, percentage of unenhanced tumor area, SD and CV were independent risk factors for predicting pTERT mutation status in GBM patients. The logistic regression model based on these four features showed a better sample predictive performance, and the area under the curve (AUC) [95% confidence interval (CI)], accuracy, sensitivity, specificity were 0.842 (0.767-0.917), 0.796, 0.820, and 0.729, respectively. There were no significant differences in the T1C histogram parameters between the C228T and C250T groups (P=0.055-0.854). Conclusions: T1C histogram parameters can be used to evaluate pTERT mutations status in GBM. A nomogram based on conventional MRI features and T1C histogram parameters is a reliable tool for the pTERT mutation status, allowing for non-invasive radiological prediction before surgery.

Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy.

BACKGROUND: There is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC. METHODS: Seventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model. RESULTS: ADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively. CONCLUSIONS: Whole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.

MRI features and tumor-infiltrating CD8 + T cells-based nomogram for predicting meningioma recurrence risk.

OBJECTIVE: This study was based on MRI features and number of tumor-infiltrating CD8 + T cells in post-operative pathology, in predicting meningioma recurrence risk. METHODS: Clinical, pathological, and imaging data of 102 patients with surgically and pathologically confirmed meningiomas were retrospectively analyzed. Patients were divided into recurrence and non-recurrence groups based on follow-up. Tumor-infiltrating CD8 + T cells in tissue samples were quantitatively assessed with immunohistochemical staining. Apparent diffusion coefficient (ADC) histogram parameters from preoperative MRI were quantified in MaZda. Considering the high correlation between ADC histogram parameters, we only chose ADC histogram parameter that had the best predictive efficacy for COX regression analysis further. A visual nomogram was then constructed and the recurrence probability at 1- and 2-years was determined. Finally, subgroup analysis was performed with the nomogram. RESULTS: The risk factors for meningioma recurrence were ADCp1 (hazard ratio [HR] = 0.961, 95% confidence interval [95% CI]: 0.937 ~ 0.986, p = 0.002) and CD8 + T cells (HR = 0.026, 95%CI: 0.001 ~ 0.609, p = 0.023). The resultant nomogram had AUC values of 0.779 and 0.784 for 1- and 2-years predicted recurrence rates, respectively. The survival analysis revealed that patients with low CD8 + T cells counts or ADCp1 had higher recurrence rates than those with high CD8 + T cells counts or ADCp1. Subgroup analysis revealed that the AUC of nomogram for predicting 1-year and 2-year recurrence of WHO grade 1 and WHO grade 2 meningiomas was 0.872 (0.652) and 0.828 (0.751), respectively. CONCLUSIONS: Preoperative ADC histogram parameters and tumor-infiltrating CD8 + T cells may be potential biomarkers in predicting meningioma recurrence risk. CLINICAL RELEVANCE STATEMENT: The findings will improve prognostic accuracy for patients with meningioma and potentially allow for targeted treatment of individuals who have the recurrent form.

A Combination of Amide Proton Transfer, Tumor Blood Flow, and Apparent Diffusion Coefficient Histogram Analysis Is Useful for Differentiating Malignant from Benign Intracranial Tumors in Young Patients: A Preliminary Study.

PURPOSE: To evaluate the amide proton transfer (APT), tumor blood flow (TBF), and apparent diffusion coefficient (ADC) combined diagnostic value for differentiating intracranial malignant tumors (MTs) from benign tumors (BTs) in young patients, as defined by the 2021 World Health Organization classification of central nervous system tumors. METHODS: Fifteen patients with intracranial MTs and 10 patients with BTs aged 0-30 years underwent MRI with APT, pseudocontinuous arterial spin labeling (pCASL), and diffusion-weighted imaging. All tumors were evaluated through the use of histogram analysis and the Mann-Whitney U test to compare 10 parameters for each sequence between the groups. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The APT maximum, mean, 10th, 25th, 50th, 75th, and 90th percentiles were significantly higher in MTs than in BTs; the TBF minimum (min) was significantly lower in MTs than in BTs; TBF kurtosis was significantly higher in MTs than in BTs; the ADC min, 10th, and 25th percentiles were significantly lower in MTs than in BTs (all p < 0.05). The APT 50th percentile (0.900), TBF min (0.813), and ADC min (0.900) had the highest area under the curve (AUC) values of the parameters in each sequence. The AUC for the combination of these three parameters was 0.933. CONCLUSIONS: The combination of APT, TBF, and ADC evaluated through histogram analysis may be useful for differentiating intracranial MTs from BTs in young patients.

Prediction of O(6)-methylguanine-DNA methyltransferase promoter methylation status in IDH-wildtype glioblastoma using MRI histogram analysis.

To evaluate the utility of magnetic resonance imaging (MRI) histogram parameters in predicting O(6)-methylguanine-DNA methyltransferase promoter (pMGMT) methylation status in IDH-wildtype glioblastoma (GBM). From November 2021 to July 2023, forty-six IDH-wildtype GBM patients with known pMGMT methylation status (25 unmethylated and 21 methylated) were enrolled in this retrospective study. Conventional MRI signs (including location, across the midline, margin, necrosis/cystic changes, hemorrhage, and enhancement pattern) were assessed and recorded. Histogram parameters were extracted and calculated by Firevoxel software based on contrast-enhanced T1-weighted images (CET1). Differences and diagnostic performance of conventional MRI signs and histogram parameters between the pMGMT-unmethylated and pMGMT-methylated groups were analyzed and compared. No differences were observed in the conventional MRI signs between pMGMT-unmethylated and pMGMT-methylated groups (all p > 0.05). Compared with the pMGMT-methylated group, pMGMT-unmethylated showed a higher minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50, and coefficient of variation (CV) (all p < 0.05). Among all significant CET1 histogram parameters, minimum achieved the best distinguishing performance, with an area under the curve of 0.836. CET1 histogram parameters could provide additional value in predicting pMGMT methylation status in patients with IDH-wildtype GBM, with minimum being the most promising parameter.

Histogram analysis of intravoxel incoherent motion imaging: Correlation with molecular prognostic factors and combined subtypes of breast cancer.

PURPOSE: To look for links between diffusion and IVIM parameters and different molecular subtypes and prognostic factors through histogram analysis. MATERIALS AND METHODS: A total of 139 patients with breast cancer who had pre-operative MRI examinations were enrolled in this retrospective study. Histograms of the diffusion and IVIM parameters were analyzed for the whole tumor, and an association was investigated between the parameters and the different molecular prognostic factors and subtypes using the nonparametric test, Spearman's rank correlation, and receiver operating characteristic (ROC) curve. RESULTS: The histogram metrics of the diffusion and IVIM parameters were significantly different for molecular prognostic factors such as human epidermal receptor factor-2 (HER2), progesterone receptor, estrogen receptor, and ki-67. All histogram metrics displayed a poor correlation with all groups (r = -0.28-0.29). There were significant differences in the histogram metrics for the Luminal B-HER2 (-) vs. HER2-positive (non-luminal) subtypes in the mean and 10th percentile D, with the area under the curves (AUCs) of 0.742 and 0.700, respectively, and for the Luminal A and HER2-positive (non-luminal) subtypes in the 90th percentile and entropy of D*, with AUCs of 0.769 and 0.727, respectively. CONCLUSION: The histogram metrics of IVIM parameters exhibited links with breast cancer prognosis factors and combined subtypes.

Volumetric apparent diffusion coefficient histogram analysis in term neonatal asphyxia treated with hypothermia.

OBJECTIVES: Our aim is to estimate the long-term neurological sequelae and prognosis in term neonatal asphyxia treated with hypothermia via volumetric apparent diffusion coefficient (ADC) map histogram analysis (HA). METHODS: Brain MRI studies of 83 term neonates with asphyxia who received whole-body hypothermia treatment and examined between postnatal (PN) fourth and sixth days were retrospectively re-evaluated by 2 radiologists. Volumetric HA was performed for the areas frequently affected in deep and superficial asphyxia (thalamus, lentiform nucleus, posterior limb of internal capsule, corpus callosum forceps major, and perirolandic cortex-subcortical white matter) on ADC map. The quantitative ADC values were obtained separately for each region. Qualitative-visual (conventional) MRI findings were also re-evaluated. Neonates were examined neurodevelopmentally according to the Revised Brunet-Lezine scale. The distinguishability of long-term neurodevelopmental outcomes was statistically investigated. RESULTS: With HA, the adverse neurodevelopmental outcomes could only be distinguished from mild-moderated impairment and normal development at the thalamus with 10th percentile ADC (P = .02 and P = .03, respectively) and ADCmin (P = .03 and P = .04, respectively). Also with the conventional MRI findings, adverse outcome could be distinguished from mild-moderated impairment (P = .04) and normal development (P = .04) via cytotoxic oedema of the thalamus, corpus striatum, and diffuse cerebral cortical. CONCLUSION: The long-term adverse neurodevelopmental outcomes in newborns with asphyxia who received whole-body hypothermia treatment can be estimated similarly with volumetric ADC-HA and the conventional assessment of the ADC map. ADVANCES IN KNOWLEDGE: This study compares early MRI ADC-HA with neurological sequelae in term newborns with asphyxia who received whole-body hypothermia treatment. We could not find any significant difference in predicting adverse neurological sequelae between the visual-qualitative evaluation of the ADC map and HA.

A novel quantitative method to evaluate lumbar disc degeneration: MRI histogram analysis.

PURPOSE: This study aimed to use MRI histogram analysis to routine MRI sequences to evaluate lumbar disc degeneration (LDD), illustrate the correlation between this novel method and the traditional Pfirrmann classification method, and more importantly, perform comprehensive agreement analysis of MRI histogram analysis in various situations to evaluate its objectivity and stability. METHODS: Lumbar MRI images from 133 subjects were included in this study. LDD was classified into grades by Pfirrmann classification and was measured as peak separation value by MRI histogram analysis. Correlation analysis between the two methods was performed and cutoff values were determined. In addition, the agreement analysis of peak separation value was performed by intraclass correlation coefficient (ICC) in four scenarios, including inter-resolution, inter-observer, inter-regions of interest (ROI) and inter-slice. RESULTS: Peak separation values were strongly correlated with Pfirrmann grades (r = - 0.847). The inter-resolution agreements of peak separation value between original image resolution of 2304 × 2304 and compressed image resolutions (1152 × 1152, 576 × 576, 288 × 288) were good to excellent (ICCs were 0.916, 0.876 and 0.822), except 144 × 144 was moderate (ICC = 533). The agreements of inter-observer (ICC = 0.982) and inter-ROI (ICC = 0.915) were excellent. Compared with the mid-sagittal slice, the inter-slice agreements were good for the first adjacent slices (ICCs were 0.826 and 0.844), and moderate to good for the second adjacent slices (ICC = 0.733 and 0.753). CONCLUSION: MRI histogram analysis, used in routine MRI sequences, demonstrated a strong correlation with Pfirrmann classification and good agreements in various scenarios, expanding the range of application and providing an effective, objective and quantitative tool to evaluate LDD.

The value of whole tumor apparent diffusion coefficient histogram parameters in predicting meningiomas progesterone receptor expression.

PURPOSE: This study investigated the value of whole tumor apparent diffusion coefficient (ADC) histogram parameters and magnetic resonance imaging (MRI) semantic features in predicting meningioma progesterone receptor (PR) expression. MATERIALS AND METHODS: The imaging, pathological, and clinical data of 53 patients with PR-negative meningiomas and 52 patients with PR-positive meningiomas were retrospectively reviewed. The whole tumor was outlined using Firevoxel software, and the ADC histogram parameters were calculated. The differences in ADC histogram parameters and MRI semantic features were compared between the two groups. The predictive values of parameters for PR expression were assessed using receiver operating characteristic curves. The correlation between whole-tumor ADC histogram parameters and PR expression in meningiomas was also analyzed. RESULTS: Grading was able to predict the PR expression in meningiomas (p = 0.012), though the semantic features of MRI were not (all p > 0.05). The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were able to predict meningioma PR expression (all p < 0.05). The predictive performance of the combined histogram parameters improved, and the combination of grade and histogram parameters provided the optimal predictive value, with an area under the curve of 0.849 (95%CI: 0.766-0.911) and sensitivity, specificity, ACC, PPV, and NPV of 73.08%, 81.13%, 77.14%, 79.20%, and 75.40%, respectively. The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were positively correlated with PR expression (all p < 0.05). CONCLUSION: Whole tumor ADC histogram parameters have additional clinical value in predicting PR expression in meningiomas.

The Relationship between Breast Cancer Subtypes, Prognostic Factors, and Apparent Diffusion Coefficient Histogram Analysis.

BACKGROUND: Diffusion Magnetic Resonance Imaging (MRI) is a useful method to evaluate tumor biology and tumor microstructure. The apparent diffusion coefficient (ADC) value correlates negatively with the cellular density of the tumor. OBJECTIVE: This study aimed to investigate the effectiveness of the ADC histogram analysis in showing the relationship between breast cancer prognostic factors and ADC parameters. METHODS: This study is a retrospective observational descriptive study. ADC histogram parameters were evaluated in all tumor volumes of 67 breast cancer patients. Minimum, 5, 10, 25, 50, 75, 90, 95 percentiles, maximum, mean, median ADC values, kurtosis, and skewness were calculated. Breast MRI examinations were performed on a 3T MR scanner. We evaluated the fibroglandular tissue density of bilateral breasts, background enhancement, localization of masses, multifocality-multicentricity, shape, rim, internal contrast enhancement, and kinetic curve on breast MRI. BIRADS scoring was performed according to breast MRI. Pathologically, histologic type, histologic grade, HER 2, Ki 67, ER-, and PR status were evaluated. RESULTS: A significant correlation was found between tumor volume and ADC scores. There is a significant correlation between min ADC values (p< 0.031), max ADC (p< 0.001), and skewness (p< 0.019). A significant correlation was found between tumor kurtosis and lymph nodes (p< 0.029). There was a significant difference in ADC values depending on ER-and PRstatus. (for ER p = 0.004, p = 0.018, p = 0.010, p = 0.008, p = 0.004, p = 0.004, p = 0.02, p = 0.02 and p = 0.038, for PR p < 0.001, p = 0.028, p = 0.011, p = 0.001, p < 0.001, p =<0.001, p < 0.001, and p < 0.001, respectively; p < 0.05). These values were lower in ER-and PR-positive status than in ER-and PR-negative receptor status. According to HER2 status, there was a statistically significant difference in ADC

To characterize small renal cell carcinoma using diffusion relaxation correlation spectroscopic imaging and apparent diffusion coefficient based histogram analysis: a preliminary study.

PURPOSE: To study the capability of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) on subtype classification and grade differentiation for small renal cell carcinoma (RCC). Histogram analysis for apparent diffusion coefficient (ADC) was studied for comparison. MATERIALS AND METHODS: A total of 61 patients with small RCC (< 4 cm) were included in the retrospective study. MRI data were reviewed, including a multi-b (0-1500 s/mm2) multi-TE (51-200 ms) diffusion weighted imaging (DWI) sequence. Region of interest (ROI) was delineated manually on DWI to include solid tumor. For each patient, a D-T2 spectrum was fitted and segmented into 5 compartments, and the volume fractions VA, VB, VC, VD, VE were obtained. ADC mapping was calculated, and histogram parameters ADC 90th, 10th, median, standard deviation, skewness and kurtosis were obtained. All MRI metrices were compared between clear cell RCC (ccRCC) and non-ccRCC group, and between high-grade and low-grade group. Receiver operator curve analysis was used to assess the corresponding diagnostic performance. RESULTS: Significantly higher ADC 90th, ADC 10th and ADC median, and significantly lower DR-CSI VB was found for ccRCC compared to non-ccRCC. Significantly lower ADC 90th, ADC median and significantly higher VB was found for high-grade RCC compared to low-grade. For identifying ccRCC from non-ccRCC, VB showed the highest area under curve (AUC, 0.861) and specificity (0.882). For differentiating high- from low-grade, ADC 90th showed the highest AUC (0.726) and specificity (0.786), while VB also displayed a moderate AUC (0.715). CONCLUSION: DR-CSI may offer improved accuracy in subtype identification for small RCC, while do not show better performance for small RCC grading compared to ADC histogram.

Whole-tumor histogram analysis of synthetic magnetic resonance imaging predicts isocitrate dehydrogenase mutation status in gliomas.

Background: An accurate assessment of isocitrate dehydrogenase (IDH) status in patients with glioma is crucial for treatment planning and is a key factor in predicting patient outcomes. In this study, we investigated the potential value of whole-tumor histogram metrics derived from synthetic magnetic resonance imaging (MRI) in distinguishing IDH mutation status between astrocytoma and glioblastoma. Methods: In this prospective study, 80 glioma patients were enrolled from September 2019 to June 2022. All patients underwent pre- and post-contrast synthetic MRI scan protocol. Immunohistochemistry (IHC) staining or gene sequencing were used to assess IDH mutation status in tumor tissue samples. Whole-tumor histogram metrics, including T1, T2, proton density (PD), etc., were extracted from the quantitative maps, while radiological features were assessed by synthetic contrast-weighted maps. Basic clinical features of the patients were also evaluated. Differences in clinical, radiological, and histogram metrics between IDH-mutant astrocytoma and IDH-wildtype glioblastoma were analyzed using univariate analyses. Variables with statistical significance in univariate analysis were included in multivariate logistic regression analysis to develop the combined model. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to assess the diagnostic performance of metrics and models. Results: The histopathologic analysis revealed that of the 80 cases, 41 were classified as IDH-mutant astrocytoma and 39 as IDH-wildtype glioblastoma. Compared to IDH-wildtype glioblastoma, IDH-mutant astrocytoma showed significantly lower T1 [10th percentile (10th), mean, and median] and post-contrast PD (10th, 90th percentile, mean, median, and maximum) values as well as higher post-contrast T1 (cT1) (10th, mean, median, and minimum) values (all P<0.05). The combined model (T1-10th + cT1-10th + age) was developed by integrating the independent influencing factors of IDH-mutant astrocytoma using the multivariate logistic regression. The diagnostic performance of this model [AUC =0.872 (0.778-0.936), sensitivity =75.61%, and specificity =89.74%] was superior to the clinicoradiological model, which was constructed using age and enhancement degree (AUC =0.822 (0.870-0.898), P=0.035). Conclusions: The combined model constructed using histogram metrics derived from synthetic MRI could be a valuable preoperative tool to distinguish IDH mutation status between astrocytoma and glioblastoma, and subsequently, could assist in the decision-making process of pretreatment.