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

2018 ◽  
Vol 48 (3) ◽  
pp. 680-686 ◽  
Author(s):  
Masanori Nakajo ◽  
Yoshihiko Fukukura ◽  
Hiroto Hakamada ◽  
Tomohide Yoneyama ◽  
Kiyohisa Kamimura ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Soft Tissue ◽  
Apparent Diffusion Coefficient ◽  
Soft Tissue Sarcomas ◽  
Histogram Analysis ◽  
Neurogenic Tumors ◽  
Apparent Diffusion

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

2020 ◽  
pp. 028418512095195
Author(s):  
Ji Hyun Hong ◽  
Won-Hee Jee ◽  
Sunyoung Whang ◽  
Chan-Kwon Jung ◽  
Yang-Guk Chung ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Soft Tissue ◽  
Apparent Diffusion Coefficient ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Histogram Analysis ◽  
Undifferentiated Sarcoma ◽  
Single Slice ◽  
Significant Difference ◽  
Apparent Diffusion

Background Making the preoperative diagnosis of soft-tissue lymphoma is important because the treatments for lymphoma and sarcoma are different. Purpose 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. Material and Methods 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. Results 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. Conclusion 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?

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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