Whole-tumor histogram analysis of apparent diffusion coefficient in differentiating intracranial solitary fibrous tumor/hemangiopericytoma from angiomatous meningioma

2019 ◽  
Vol 112 ◽  
pp. 186-191 ◽  
Author(s):  
Wenle He ◽  
Xiang Xiao ◽  
Xiaodan Li ◽  
Yihao Guo ◽  
Liuji Guo ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Solitary Fibrous Tumor ◽  
Histogram Analysis ◽  
Apparent Diffusion ◽  
Fibrous Tumor

scholarly journals The Relationship Between The Apparent Diffusion Coefficient and The Ki-67 Proliferation Index in Intracranial Solitary Fibrous Tumor/Hemangiopericytoma

2021 ◽  
Author(s):  
Shenglin Li ◽  
Qing Zhou ◽  
Peng Zhang ◽  
Shize Ma ◽  
Caiqiang Xue ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Solitary Fibrous Tumor ◽  
Proliferation Index ◽  
Ki 67 ◽  
Solitary Fibrous Tumors ◽  
Average Maximum ◽  
Apparent Diffusion ◽  
Grade Ii ◽  
Fibrous Tumor

Abstract Objiective: This study evaluated the value of the apparent diffusion coefficient (ADC) in distinguishing grade II and III intracranial solitary fibrous tumors /hemangiopericytomas and explored the correlation between ADC and Ki-67. Methods The preoperative MRIs of 37 patients treated for solitary fibrous tumor/hemangiopericytoma (grade II, n = 15 and grade III, n = 22) in our hospital from 2011 to October 2020 were retrospectively analyzed. We compared the difference between the minimum, average, maximum, and relative ADCs based on tumor grade and examined the correlation between ADC and Ki-67. Receiver operating characteristic curve analysis was used to analyze the diagnostic efficiency of the ADC. Results There were significant differences in the average, minimum, and relative ADCs between grade II and III patients. The optimal cutoff value for the relative ADC value to differentiate grade II and III tumors was 0.998, which yielded an area under the curve of 0.879. The Ki-67 proliferation indexes of grade II and III tumors were significantly different, and the average (r = -0.427), minimum (r = -0.356), and relative (r = -0.529) ADCs were significantly negatively correlated with the Ki-67 proliferation index. Conclusions ADC can be used to differentiate grade II and III intracranial solitary fibrous tumors/hemangiopericytomas. Our results can be used to formulate a personalized surgical treatment plan before surgery.

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

2019 ◽  
Vol 114 ◽  
pp. 25-31
Author(s):  
Yuan Guo ◽  
Wen-Jie Tang ◽  
Qing-cong Kong ◽  
Ying-ying Liang ◽  
Xiao-rui Han ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Phyllode Tumor ◽  
Histogram Analysis ◽  
Apparent Diffusion

scholarly journals Volumetric voxelwise apparent diffusion coefficient histogram analysis for differentiation of the fourth ventricular tumors

2018 ◽  
Vol 31 (6) ◽  
pp. 554-564 ◽  
Author(s):  
Seyedmehdi Payabvash ◽  
Tarik Tihan ◽  
Soonmee Cha
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Predictive Value ◽  
Fourth Ventricle ◽  
Histogram Analysis ◽  
Mri Findings ◽  
Patient Age ◽  
Patient Âgé ◽  
Pilocytic Astrocytomas ◽  
Apparent Diffusion

Purpose We applied voxelwise apparent diffusion coefficient (ADC) histogram analysis in addition to structural magnetic resonance imaging (MRI) findings and patients’ age for differentiation of intraaxial posterior fossa tumors involving the fourth ventricle. Participants and methods Pretreatment MRIs of 74 patients with intraaxial brain neoplasm involving the fourth ventricle, from January 1, 2004 to December 31, 2015, were reviewed. The tumor solid components were segmented and voxelwise ADC histogram variables were determined. Histogram-driven variables, structural MRI findings, and patient age were combined to devise a differential diagnosis algorithm. Results The most common neoplasms were ependymomas ( n = 21), medulloblastoma ( n = 17), and pilocytic astrocytomas ( n = 13). Medulloblastomas followed by atypical teratoid/rhabdoid tumors had the lowest ADC histogram percentile values; whereas pilocytic astrocytomas and choroid plexus papillomas had the highest ADC histogram percentile values. In a multivariable multinominal regression analysis, the ADC 10th percentile value from voxelwise histogram was the only independent predictor of tumor type ( p < 0.001). In separate binary logistic regression analyses, the 10th percentile ADC value, tumor morphology, enhancement pattern, extension into Luschka/Magendie foramina, and patient age were predictors of different tumor types. Combining these variables, we devised a stepwise diagnostic model yielding 71% to 82% sensitivity, 91% to 95% specificity, 75% to 78% positive predictive value, and 89% to 95% negative predictive value for differentiation of ependymoma, medulloblastoma, and pilocytic astrocytoma. Conclusion We have shown how the addition of quantitative voxelwise ADC histogram analysis of the tumor solid component to structural findings and patient age can help with accurate differentiation of intraaxial posterior fossa neoplasms involving the fourth ventricle based on pretreatment MRI.

Differentiation of brain metastases originating from lung and breast cancers using apparent diffusion coefficient histogram analysis and the relation of histogram parameters with Ki-67

2021 ◽  
pp. 197140092110490
Author(s):  
Mustafa Bozdağ ◽  
Ali Er ◽  
Sümeyye Ekmekçi
Keyword(s):  
Lung Cancer ◽  
Diffusion Coefficient ◽  
Brain Metastases ◽  
Apparent Diffusion Coefficient ◽  
Roc Analysis ◽  
Small Cell ◽  
Histogram Analysis ◽  
Small Cell Lung ◽  
Ki 67 ◽  
Apparent Diffusion

Purpose A fast, reliable and non-invasive method is required in differentiating brain metastases (BMs) originating from lung cancer (LC) and breast cancer (BC). The aims of this study were to assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating BMs originated from LC and BC, and then to investigate further the association of ADC histogram parameters with Ki-67 index in BMs. Methods A total of 55 patients (LC, N = 40; BC, N = 15) with BMs histopathologically confirmed were enrolled in the study. The LC group was divided into small-cell lung cancer (SCLC; N = 15) and non-small-cell lung cancer (NSCLC; N = 25) groups. ADC histogram parameters (ADCmax, ADCmean, ADCmin, ADCmedian, ADC10, ADC25, ADC75 and ADC90, skewness, kurtosis and entropy) were derived from ADC maps. Mann–Whitney U-test, independent samples t-test, receiver operating characteristic (ROC) analysis and Spearman correlation analysis were used for statistical assessment. Results ADC histogram parameters did not show significant differences between LC and BC groups ( p > 0.05). Subgroup analysis showed that various ADC histogram parameters were found to be statistically lower in the SCLC group compared to the NSCLC and BC groups ( p < 0.05). ROC analysis showed that ADCmean and ADC10 for differentiating SCLC BMs from NSCLC, and ADC25 for differentiating SCLC BMs from BC achieved optimal diagnostic performances. Various histogram parameters were found to be significantly correlated with Ki-67 ( p < 0.05). Conclusion Histogram analysis of ADC maps may reflect tumoural proliferation potential in BMs and can be useful in differentiating SCLC BMs from NSCLC and BC BMs.

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