Whole-tumor Apparent Diffusion Coefficient Research Articles

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

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. 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. 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). Whole tumor ADC histogram parameters have additional clinical value in predicting PR expression in meningiomas.

Refining the predictive value of preoperative apparent diffusion coefficient (ADC) by whole-tumor analysis for facial nerve outcomes in vestibular schwannomas.

Apparent diffusion coefficient (ADC) in MRI has been shown to correlate with postoperative House-Brackmann (HB) scores in patients with vestibular schwannoma despite limited methodology. To rectify limitations of single region of interest (ROI) sampling, we hypothesize that whole-tumor ADC histogram analysis will refine the predictive value of this preoperative biomarker related to postoperative facial nerve function. Of 155 patients who underwent resection of vestibular schwannoma (2014-2020), 125 patients were included with requisite clinical and radiographic data. After volumetric analysis and whole-tumor ADC histogram, regression tree analysis identified ADC cutoff for significant differences in HB grade. Outcomes were extent of resection, facial nerve function, hospital length of stay (LOS), and complications. Regression tree analysis defined three quantitative ADC groups (× 10-6 mm2/s) as high (> 2248.77; HB 1.7), mid (1468.44-2248.77; HB 3.1), and low (< 1468.44; HB 2.3) range (p 0.04). The mid-range ADC group had significantly worse postoperative HB scores and longer hospital LOS. Large tumor volume was independently predictive of lower rates of gross total resection (p <0.0001), higher postoperative HB score (p 0.002), higher rate of complications (p 0.04), and longer LOS (p 0.003). Whole-tumor histogram yielded a robust regression tree analysis that defined three ADC groups with significantly different facial nerve outcomes. This likely reflects tumor heterogeneity better than solid-tumor ROI sampling. Whole-tumor ADC warrants further study as a useful radiographic biomarker in patients with vestibular schwannoma who are considering surgical resection.

Multiparametric MR-based radiomics fusion combined with quantitative stratified ADC-defined tumor habitats for differentiating TNBC versus non-TNBC

Objective. To investigate the incremental value of quantitative stratified apparent diffusion coefficient (ADC) defined tumor habitats for differentiating triple negative breast cancer (TNBC) from non-TNBC on multiparametric MRI (mpMRI) based feature-fusion radiomics (RFF) model. Approach. 466 breast cancer patients (54 TNBC, 412 non-TNBC) who underwent routine breast MRIs in our hospital were retrospectively analyzed. Radiomics features were extracted from whole tumor on T2WI, diffusion-weighted imaging, ADC maps and the 2nd phase of dynamic contrast-enhanced MRI. Four models including the RFF model (fused features from all MRI sequences), RADC model (ADC radiomics feature), StratifiedADC model (tumor habitas defined on stratified ADC parameters) and combinational RFF-StratifiedADC model were constructed to distinguish TNBC versus non-TNBC. All cases were randomly divided into a training (n = 337) and test set (n = 129). The four competing models were validated using the area under the curve (AUC), sensitivity, specificity and accuracy. Main results. Both the RFF and StratifiedADC models demonstrated good performance in distinguishing TNBC from non-TNBC, with best AUCs of 0.818 and 0.773 in the training and test sets. StratifiedADC model revealed significant different tumor habitats (necrosis/cysts habitat, chaotic habitat or proliferative tumor core) between TNBC and non-TNBC with its top three discriminative parameters (p < 0.05). The integrated RFF-StratifiedADC model demonstrated superior accuracy over the other three models, with higher AUCs of 0.832 and 0.784 in the training and test set, respectively (p < 0.05). Significance. The RFF-StratifiedADC model through integrating various tumor habitats’ information from whole-tumor ADC maps-based StratifiedADC model and radiomics information from mpMRI-based RFF model, exhibits tremendous promise for identifying TNBC.

Whole-tumor histogram analysis of apparent diffusion coefficients for predicting lymphovascular space invasion in stage IB-IIA cervical cancer.

To investigate the value of apparent diffusion coefficient (ADC) histogram analysis based on whole tumor volume for the preoperative prediction of lymphovascular space invasion (LVSI) in patients with stage IB-IIA cervical cancer. Fifty consecutive patients with stage IB-IIA cervical cancer were stratified into LVSI-positive (n = 24) and LVSI-negative (n = 26) groups according to the postoperative pathology. All patients underwent pelvic 3.0T diffusion-weighted imaging with b-values of 50 and 800 s/mm2 preoperatively. Whole-tumor ADC histogram analysis was performed. Differences in the clinical characteristics, conventional magnetic resonance imaging (MRI) features, and ADC histogram parameters between the two groups were analyzed. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of ADC histogram parameters in predicting LVSI. ADCmax, ADCrange, ADC90, ADC95, and ADC99 were significantly lower in the LVSI-positive group than in the LVSI-negative group (all P-values < 0.05), whereas no significant differences were reported for the remaining ADC parameters, clinical characteristics, and conventional MRI features between the groups (all P-values > 0.05). For predicting LVSI in stage IB-IIA cervical cancer, a cutoff ADCmax of 1.75×10-3 mm2/s achieved the largest area under ROC curve (Az) of 0.750, followed by a cutoff ADCrange of 1.36×10-3 mm2/s and ADC99 of 1.75×10-3 mm2/s (Az = 0.748 and 0.729, respectively), and the cutoff ADC90 and ADC95 achieved an Az of <0.70. Whole-tumor ADC histogram analysis has potential value for preoperative prediction of LVSI in patients with stage IB-IIA cervical cancer. ADCmax, ADCrange, and ADC99 are promising prediction parameters.

Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma.

To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC). Clinical features, solid component ADC (ADCSC) and whole-tumor ADC histogram-derived parameters (volume, the ADCmean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal-Wallis test, and receiver operating characteristic curves. A significantly higher ADCSC and smaller volume were associated with SMBT than with CCC/EC. The ADCmean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADCSC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADCSC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADCmean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively. Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.

Estimation of colon cancer grade and metastatic lymph node involvement using DWI/ADC sequences.

The potential benefit of neoadjuvant chemotherapy (NAC) in colon cancer is under evaluation. There is a need to improve preoperative non-invasive diagnostics using techniques that provide more accurate staging information in assessing patient eligibility for NAC. To investigate the link between the tumor grade (pathohistological confirmed) and the N status (corresponding to lymph node involvement) with apparent diffusion coefficient (ADC) values. A total of 17 patients planned for surgical resection had a biopsy confirming colon carcinoma and participated in the study. Abdominal magnetic resonance imaging with diffusion-weighted imaging/ADC sequence was recorded before surgery. The tumor and all visible lymph nodes were manually delineated directly on a grayscale ADC map for every single slice and detected to access the total tumor and summarized lymph node volume. The mean ADC value was further calculated for the mean tumor and mean lymph node values. Low-grade tumors had a mean ADC equivalent to 1225 ± 170×10-6 mm2/s, and the coefficient of high-grade tumors was 1444 ± 69×10-6 mm2/s. The group of patients with positive lymph nodes in operative tissue samples (N+) exhibited lower mean ADC values (1023 ± 142×10-6 mm2/s) as opposed to the group without metastatic lymph nodes (N-) with ADC values of 1260 ± 231×10-6 mm2/s. The mean whole-tumor ADC is associated with the histological tumor grade, and the mean ADC value of whole-volume abdominal lymph nodes could assume real nodal infiltration.

Diagnostic value of whole-tumor apparent diffusion coefficient map radiomics analysis in predicting early recurrence of solitary hepatocellular carcinoma ≤ 5cm.

To evaluate the role of whole-tumor radiomics analysis of apparent diffusion coefficient (ADC) maps in predicting early recurrence (ER) of solitary hepatocellular carcinoma (HCC) ≤ 5cm and compare the diagnostic efficiency of whole-tumor and single-slice ADC measurements. One hundred and seventy patients with primary HCC were randomly divided into the training set (n = 119) and the test set (n = 51). The diagnostic efficiency was compared between the whole-tumor and single-slice ADC measurements. The clinical-radiological model was established by selected significant clinical characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The significant clinical-radiological risk factors and radiomics features were integrated to develop the combined model. Receiver operating characteristic (ROC) curves were used for evaluating the predictive performance. Cirrhosis, age, and albumin were significantly associated with ER in the clinical-radiological model selected by the random forest classifier. The diagnostic efficiency of the whole-tumor ADC measurements was slight higher than that of the single-slice (AUC = 0.602 and 0.586, respectively). The clinical-radiological model (AUC = 0.84 and 0.82 in the training and test sets, respectively) showed better diagnostic performance than the radiomics model (AUC = 0.70 and 0.69 in the training and test sets, respectively) in predicting ER. The combined model showed optimal predictive performance with the highest AUC values of 0.88 and 0.85 in the training and test sets, respectively. The whole-tumor ADC measurements performed better than the single-slice ADC measurements. The clinical-radiological model performed better than the radiomics model for predicting ER in patients with solitary HCC ≤ 5cm.

Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer.

BACKGROUNDWhole-tumor apparent diffusion coefficient (ADC) histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy (nCRT) response in patients with locally advanced rectal cancer (LARC).AIMTo evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC.METHODSThis is a single-center, retrospective study, which included 48 patients with LARC. All patients underwent a pre-treatment magnetic resonance imaging (MRI) scan for primary tumor staging and a second restaging MRI for response evaluation. The sample was distributed as follows: 18 responder patients (R) and 30 non-responders (non-R). Eight parameters derived from the whole-lesion histogram analysis (ADCmean, skewness, kurtosis, and ADC10th, 25th, 50th, 75th, 90th percentiles), as well as the ADCmean from the hot spot region of interest (ROI), were calculated for each patient before and after treatment. Then all data were compared between R and non-R using the Mann-Whitney U test. Two measures of diagnostic accuracy were applied: the receiver operating characteristic curve and the diagnostic odds ratio (DOR). We also reported intra- and interobserver variability by calculating the intraclass correlation coefficient (ICC).RESULTSPost-nCRT kurtosis, as well as post-nCRT skewness, were significantly lower in R than in non-R (both P < 0.001, respectively). We also found that, after treatment, R had a larger loss of both kurtosis and skewness than non-R (∆%kurtosis and ∆skewness, P < 0.001). Other parameters that demonstrated changes between groups were post-nCRT ADC10th, ∆%ADC10th, ∆%ADCmean, and ROI ∆%ADCmean. However, the best diagnostic performance was achieved by ∆%kurtosis at a threshold of 11.85% (Area under the receiver operating characteristic curve [AUC] = 0.991, DOR = 376), followed by post-nCRT kurtosis = 0.78 × 10-3 mm2/s (AUC = 0.985, DOR = 375.3), ∆skewness = 0.16 (AUC = 0.885, DOR = 192.2) and post-nCRT skewness = 1.59 × 10-3 mm2/s (AUC = 0.815, DOR = 168.6). Finally, intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement, ensuring the implementation of histogram analysis into routine clinical practice.CONCLUSIONWhole-tumor ADC histogram parameters, particularly kurtosis and skewness, are relevant biomarkers for predicting the nCRT response in LARC. Both parameters appear to be more reliable than ADCmean from one-slice ROI.

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study.

Simple SummaryMetachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases.This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign.

The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10-3mm2/s (VADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. VADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with VADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited VADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with VADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. VADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Differentiation of soft-tissue lymphoma from undifferentiated sarcoma: apparent diffusion coefficient histogram analysis.

Making the preoperative diagnosis of soft-tissue lymphoma is important because the treatments for lymphoma and sarcoma are different. To determine the reliability and accuracy of single-slice and whole-tumor apparent diffusion coefficient (ADC) histogram analysis when differentiating soft-tissue lymphoma from undifferentiated sarcoma. Patients with confirmed soft-tissue lymphoma or undifferentiated sarcoma who underwent 3-T magnetic resonance imaging (MRI), including diffusion-weighted imaging, were included. Single-slice and whole-tumor ADC histogram analyses were performed using software. Mean, standard deviation (SD), 5th and 95th percentiles, skewness, and kurtosis were compared between groups, and a receiver operating characteristic curve with area under the curve (AUC) was obtained. Thirteen patients with soft-tissue lymphoma and 12 patients with undifferentiated sarcoma were included. ADC histogram analysis of single-slice and whole-tumor, mean, SD, and 5th and 95th percentiles was significantly lower in lymphoma than in undifferentiated sarcoma. Whole-tumor analysis kurtosis was significantly higher in lymphoma than in undifferentiated sarcoma. All AUCs were high in single-slice and whole-tumor analysis: 0.987 vs. 1.000 in mean; 0.821 vs. 0.782 in SD; 0.949 vs. 0.949 in 5th percentile; and 1.000 vs. 1.000 in 95th percentile without significant difference. AUC of kurtosis in whole-tumor ADC histogram analysis was 0.750. Single-slice and whole-tumor ADC histogram analysis seems to be reliable and accurate for differentiating soft-tissue lymphoma from undifferentiated sarcoma.

Can whole-tumor apparent diffusion coefficient histogram analysis be helpful to evaluate breast phyllode tumor grades?

PurposeTo investigate whether whole-tumor apparent diffusion coefficient (ADC) histogram analysis could be helpful to evaluate breast phyllode tumor (PT) grades. Materials and methodsThis institutional review board-approved retrospective study included 56 PTs (23 benign lesions, 22 borderline lesions, and 11 malignant lesions) from August 2011 to November 2017. MRI was performed using a 1.5 T MR system equipped with a 4-channel SENSE breast coil. All cases were divided into two groups, benign PT (BPT) and borderline or malignant PT (BMPT). The conventional MR parameters included age, longest diameter, shape, margin, internal enhancement characteristics, cystic component of the tumor, wall of the cystic component, peritumoral edema on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI) and T2WI signal intensity, time-signal intensity curve (TIC) patterns and early-stage enhancement ratio (EER). The ADC values were determined in three different types of regions of interest (ROIs), including a circular ROI (ROI-c), single-slice ROI (ROI-s), and whole-tumor ROI (ROI-w). All ADC values were measured twice by Observer A and B (with a 2-week interval). The Ki-67 index was determined, and cases were classified into a “negative group” (Ki-67<14%) and a “positive group” (Ki-67≥14%). SPSS Statistics V21.0 was used for the statistical analyses. ResultsOur study included 23 cases of BPT and 33 cases of BMPT (including 22 borderline PTs and 11 malignant PTs). Only 23 patients in BMPT group had Ki-67 results, and 17 of these were positive. Regarding conventional MR features, significant differences were observed in the margin (P = 0.011), cystic component (P<0.001), peritumoral edema on T2WI (P<0.001), and cystic wall (P = 0.011) of the PT between the BPT and BMPT groups. Regarding the ADC value, good intraobserver agreement for ROI-c, ROI-s and ROI-w measurements was obtained. For the three different ROIs, the intraclass correlation coefficient (ICC) values were 0.905 for ROI-c (P > 0.05), 0.965 (P > 0.05) for ROI-s and 0.994 (P > 0.05) for ROI-w. ADC parameter indicated that the figure of ROI-s tended to be higher than the ROI-c and ROI-w, while the ROI-c and ROI-w values were similar. However, no significant difference was found in ADC values between the BPT and BMPT groups for ROI-c, ROI-s and mean ROI-w values and the 10th, 25th, 50th and 75th ROI-w. The areas under the ROC curves for the mean ROI-w and the 10th, 25th, 50th and 75th ROI-w were 0.568, 0.613, 0.567, 0.544, and 0.540, respectively. ConclusionBased on the results obtained in our study, the whole-tumor ADC histogram could not improve differentiation of the breast PT grade, while conventional MR images could provide more meaningful information, so morphological characteristics may be valuable than ADC value, and ADC could be used as a supplemental method to differentiate PT grades.

Multiple mathematical models of diffusion-weighted magnetic resonance imaging combined with prognostic factors for assessing the response to neoadjuvant chemotherapy and radiation therapy in locally advanced rectal cancer

PurposeTo investigate whether the apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and stretched exponential model (SEM) based on histogram analyses derived from the whole-tumor volume combined with prognostic factors can be used to assess the response to chemotherapy and radiation therapy (CRT) in locally advanced rectal cancer (LARC). Materials and methodsThis study included 60 patients with LARC who underwent diffusion-weighted imaging with 9b values (0–1000s/mm2) before CRT. Histograms derived from the whole-tumor volume were used to obtain the ADC, IVIM (Dslow, Dfast, and f), and SEM parameters (distributed diffusion coefficient (DDC) and α). The histogram metrics and prognostic factors before CRT were compared between pathological complete response (pCR) and non-pCR patients. The receiver operating characteristic (ROC) and the area under the ROC curve (AUC) were generated to analyze the histogram metrics and prognostic factors. ResultsA significant difference was only found in the tumor volume between the pCR and non-pCR groups (p = 0.033, AUC = 0.740). The ADC mean, DDC median, and most of the histogram metrics were significantly lower in the pCR group than the non-pCR group (p = 0.000–0.025), and AUC was highest for the ADC mean (0.890). Only the Dslow median differed significantly between the two groups (p = 0.023, AUC = 0.721). However, the Dfast, f, and α histogram metrics did not differ significantly between the pCR and non-pCR groups. The AUC for the ADC mean combined with the tumor volume was 0.908, with a sensitivity of 100% and specificity of 81%. The inter-observer agreements were good or excellent for the ADC and SEM histogram parameters but generally fair for IVIM. ConclusionThe whole-tumor ADC mean combined with the tumor volume was highly accurate for predicting pCR. The IVIM models were inferior to ADC and SEM at predicting pCR.

Whole-Tumor Quantitative Apparent Diffusion Coefficient Histogram and Texture Analysis to Differentiation of Minimal Fat Angiomyolipoma from Clear Cell Renal Cell Carcinoma

To evaluate the diagnostic value of whole-tumor quantitative apparent diffusion coefficient (ADC) histogram and texture analysis for differentiation of minimal fat angiomyolipoma (MFAML) from clear cell renal cell carcinoma (ccRCC). We retrospectively reviewed 27 patients with MFAML and 113 patients with ccRCC pathologically proven. All patients performed magnetic resonance imaging (MRI) including diffusion-weighted imaging (b = 0, 800s/mm2). Whole-tumor regions of interest were drawn on all slices of diffusion-weighted imaging to obtain histogram and texture parameters, including the mean ADC, median ADC, 10th, 25th, 75th, 90th percentiles ADC, standard deviation, skewness, kurtosis, and entropy. The Student's t test was used to compare the parameters between MFAML and ccRCC. Receiver operating characteristic curves analysis was used to compare the diagnostic value of each significant parameter. MFAML had significantly lower mean ADC, median ADC, 10th, 25th, 75th, 90th percentiles ADC than ccRCC (p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, respectively). Skewness of MFAML was significantly higher than that of ccRCC (p = 0.016). However, standard deviation, kurtosis, and entropy were not significantly different (p = 0.409, p = 0.085, p = 0.206, respectively). The 90th percentile ADC generated the highest AUC (AUC, 0.854; Sensitivity, 78.8%; Specificity, 81.5%) for differentiating MFAML from ccRCC. Whole-tumor ADC histogram and texture analysis could be considered a useful and noninvasive method to help differentiate MFAML of ccRCC with higher accuracy.

Whole-tumor apparent diffusion coefficient (ADC) histogram analysis to differentiate benign peripheral neurogenic tumors from soft tissue sarcomas.

Apparent diffusion coefficient (ADC) histogram analyses have been used to differentiate tumor grades and predict therapeutic responses in various anatomic sites with moderate success. To determine the ability of diffusion-weighted imaging (DWI) with a whole-tumor ADC histogram analysis to differentiate benign peripheral neurogenic tumors (BPNTs) from soft tissue sarcomas (STSs). Retrospective study, single institution. In all, 25 BPNTs and 31 STSs. Two-b value DWI (b-values = 0, 1000s/mm2 ) was at 3.0T. The histogram parameters of whole-tumor for ADC were calculated by two radiologists and compared between BPNTs and STSs. Nonparametric tests were performed for comparisons between BPNTs and STSs. P < 0.05 was considered statistically significant. The ability of each parameter to differentiate STSs from BPNTs was evaluated using area under the curve (AUC) values derived from a receiver operating characteristic curve analysis. The mean ADC and all percentile parameters were significantly lower in STSs than in BPNTs (P < 0.001-0.009), with AUCs of 0.703-0.773. However, the coefficient of variation (P = 0.020 and AUC = 0.682) and skewness (P = 0.012 and AUC = 0.697) were significantly higher in STSs than in BPNTs. Kurtosis (P = 0.295) and entropy (P = 0.604) did not differ significantly between BPNTs and STSs. Whole-tumor ADC histogram parameters except kurtosis and entropy differed significantly between BPNTs and STSs. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Whole-tumor apparent diffusion coefficient measurements in nephroblastoma: Can it identify blastemal predominance?

To explore the potential relation between whole-tumor apparent diffusion coefficient (ADC) parameters in viable parts of tumor and histopathological findings in nephroblastoma. Children (n = 52) with histopathologically proven nephroblastoma underwent diffusion-weighted magnetic resonance imaging (MRI) (1.5T) before preoperative chemotherapy. Of these, 25 underwent an additional MRI after preoperative chemotherapy, shortly before resection. An experienced reader performed the whole-tumor ADC measurements of all lesions, excluding nonenhancing areas. An experienced pathologist reviewed the postoperative specimens according to standard SIOP guidelines. Potential associations between ADC parameters and proportions of histological subtypes were assessed with Pearson's or Spearman's rank correlation coefficient depending on whether the parameters tested were normally distributed. In case the Mann-Whitney U-test revealed significantly different ADC values in a subtype tumor, this ADC parameter was used to derive a receiver operating characteristic (ROC) curve. The 25th percentile ADC at presentation was the best ADC metric correlated with proportion of blastema (Pearson's r = -0.303, P = 0.026). ADC after preoperative treatment showed moderate correlation with proportion stromal subtype at histopathology (r = 0.579, P = 0.002). By ROC analysis, the optimal threshold of median ADC for detecting stromal subtype was 1.362 × 10-3 mm2 /s with sensitivity and specificity of 100% (95% confidence interval [CI] 0.65-1.00) and 78.9% (95% CI 0.57-0.92), respectively. ADC markers in nephroblastoma are related to stromal subtype histopathology; however, identification of blastemal predominant tumors using whole-tumor ADC measurements is probably not feasible. 3 J. MAGN. RESON. IMAGING 2017;45:1316-1324.