Whole Volume Apparent Diffusion Coefficient (ADC) Histogram as a Quantitative Imaging Biomarker to Differentiate Breast Lesions: Correlation with the Ki-67 Proliferation Index
Yuan GuoQingcong KongLi-qi LiWenjie TangWanli ZhangGuan-yuan NingJun XueQianwei ZhouYingying LiangMei WuXinqing Jiang
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Objectives . To evaluate the value of the whole volume apparent diffusion coefficient (ADC) histogram in distinguishing between benign and malignant breast lesions and differentiating different molecular subtypes of breast cancers and to assess the correlation between ADC histogram parameters and Ki‐67 expression in breast cancers. Methods . The institutional review board approved this retrospective study. Between September 2016 and February 2019, 189 patients with 84 benign lesions and 105 breast cancers underwent magnetic resonance imaging (MRI). Volumetric ADC histograms were created by placing regions of interest (ROIs) on the whole lesion. The relationships between the ADC parameters and Ki‐67 were analysed using Spearman’s correlation analysis. Results . Of the 189 breast lesions included, there were significant differences in patient age ( P < 0.001) and lesion size ( P = 0.006) between the benign and malignant lesions. The results also demonstrated significant differences in all ADC histogram parameters between benign and malignant lesions (all P < 0.001). The median and mean ADC histogram parameters performed better than the other ADC histogram parameters (AUCs were 0.943 and 0.930, respectively). The receiver operating characteristic (ROC) analysis revealed that the 10th percentile ADC value and entropy could determine the human epidermal growth factor receptor 2 (HER‐2) status (both P = 0.001) and estrogen receptor (ER)/progesterone receptor (PR) status ( P = 0.020 and P = 0.041, respectively). Among all breast cancer lesions, 35 tumours in the low‐proliferation group (Ki − 67 < 14 % ) and 70 tumours in the high‐proliferation group (Ki − 67 ≥ 14) were analysed with ROC curves and correlation analyses. The ROC analysis revealed that entropy and skewness could determine the Ki‐67 status ( P = 0.007 and P < 0.001, respectively), and there were weak correlations between ADC entropy ( r = 0.383) and skewness ( r = 0.209) and the Ki‐67 index. Conclusion . The volumetric ADC histogram could serve as an imaging marker to determine breast lesion characteristics and may be a supplemental method in predicting tumour proliferation in breast cancer.Keywords:
Imaging biomarker
Ki-67
Proliferation index
Background: Diffusion Weighted Imaging and Diffusion Tensor Imaging is an advanced technique in MRI that shows the diffusion in brain of ischemic stroke disease. Diffusion Weighted Imaging (DWI) shows the lesions without gadolinium contrast agent and produce Apparent Diffusion Coefficient values. Whereas, Diffusion Tensor Imaging (DTI) shows connectivity’s of central nervous system that cannot be seen by using conventional MRI. Diffusion Tensor Imaging produces Fractional Anisotropy values. Purpose:This study has aim to analyze the Apparent Diffusion Coefficient values and Fractional Anisotropy values in Stroke Ischemic disease. Methods: Total samples used are 14 samples, consist of 7 (50%) man and 7 (50%) woman with ischemic stroke disease. Each sample deals by Diffusion Weighted Imaging and Diffusion Tensor Imaging sequences. The Region of Interest (ROI) is placed in ischemic stroke lesions and contra lateral side of lesions. Results: The result shows that 9 samples of brain tissue lesions located in the right side and 5 samples in the left side. Right lesions have the average ADC stroke: 0.001748; normal ADC: 0.000954; FA stroke: 0.144522; and normal FA: 0.426111. While, left lesions have the average ADC strokes 0.000979; normal ADC: 0.000835; FA stroke: 0.2556; and normal FA 0.4324. Conclusion: So, the conclusion of this study is Apparent Diffusion Coefficient (ADC) values in case of ischemic stroke can decreases or increases depend on the age of stroke. While, the Fractional Anisotropy (FA) values will decrease without being affected by age of stroke.
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The Ki-67 proliferation index (PI) is a prognostic factor in neuroendocrine tumors (NETs) and defines tumor grade. Analysis of Ki-67 PI requires calculation of Ki-67-positive and Ki-67-negative tumor cells, which is highly subjective. To overcome this, we developed a deep learning-based Ki-67 PI algorithm (KAI) that objectively calculates Ki-67 PI. Our study material consisted of NETs divided into training (n = 39), testing (n = 124), and validation (n = 60) series. All slides were digitized and processed in the Aiforia® Create (Aiforia Technologies, Helsinki, Finland) platform. The ICC between the pathologists and the KAI was 0.89. In 46% of the tumors, the Ki-67 PIs calculated by the pathologists and the KAI were the same. In 12% of the tumors, the Ki-67 PI calculated by the KAI was 1% lower and in 42% of the tumors on average 3% higher. The DL-based Ki-67 PI algorithm yields results similar to human observers. While the algorithm cannot replace the pathologist, it can assist in the laborious Ki-67 PI assessment of NETs. In the future, this approach could be useful in, for example, multi-center clinical trials where objective estimation of Ki-67 PI is crucial.
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Prostate specific antigen (PSA) remains the most used test to assess the response after therapies including the radiation therapy (RT). Apparent diffusion coefficient (ADC) derived from the conventional diffusionweighted imaging (DWI), as a part of noncontrast or biparametric MRI (bpMRI) (T2-weighted and DWI), offers diagnostic accuracy and cancer detection rate equivalent to that of multiparametric MRI. Cellular changes induced by RT can be quali-qualitatively demonstrated as early as 3months after RT as an increase in the signal intensity of the tumor on the ADC map. ADC, in association with PSA, represents a potential biomarker imaging for evaluating treatment efficacy in PCa both during and shortly after RT.
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Tumor Ki-67 expression reflects prognosis and cancer grade, and biopsy-based preoperative assessment of Ki-67 expression is key to treatment. Apparent diffusion coefficient (ADC) values obtained with this imaging may noninvasively predict Ki-67 by reflecting tumor cell density and limited water molecule movement from irregular alignment. This study aimed to investigate the ability of ADC values to predict Ki-67 expression in intrahepatic cholangiocarcinoma (ICC).
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Objective
This study aimed to elucidate the difference in diffusion-weighted magnetic resonance imaging (DWI) and diffusion tensor imaging (DTI) parameters between endometrioid endometrial adenocarcinoma (EEA) and uterine serous adenocarcinoma (SA).Methods
Data of patients with pathologically confirmed EEA or SA who underwent DWI and DTI scanning between May 2013 and July 2016 were retrospectively analyzed. Apparent diffusion coefficient (ADC) value from DWI and ADC from DTI (ADCT) map and fractional anisotropy (FA) values from DTI were analyzed and compared statistically. The correlation between ADC and ADCT was analyzed by Pearson correlation analysis. Apparent diffusion coefficient, ADCT, and FA between the 2 groups were compared using independent t test. The effect of ADC, ADCT, and FA in distinguishing EEA and SA was evaluated by receiver operator characteristic curve.Result
Thirty-three patients were enrolled into the study, including 13 cases of SA and 20 cases of EEA. Pearson correlation analysis suggested that the value of ADC was highly related with ADCT in both the SA group (r = 0.812, P = 0.001) and the EEA group (r = 0.858, P < 0.001). The value of ADC and ADCT in the SA group was significantly lower than that in the EEA group; FA was significantly higher than that in the EEA group. Receiver operator characteristic curve analysis showed that ADC and ADCT have high sensitivity and specificity; FA has low sensitivity and high specificity.Conclusions
We suggest that both DWI and DTI could be used in distinguishing EEA from SA. Apparent diffusion coefficient and ADCT possess potential diagnostic value with high sensitivity and specificity.Cite
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Background: Diffusion Weighted Imaging and Diffusion Tensor Imaging is an advanced technique in MRI that shows the diffusion in brain of ischemic stroke disease. Diffusion Weighted Imaging (DWI) shows the lesions without gadolinium contrast agent and produce Apparent Diffusion Coefficient values. Whereas, Diffusion Tensor Imaging (DTI) shows connectivity’s of central nervous system that cannot be seen by using conventional MRI. Diffusion Tensor Imaging produces Fractional Anisotropy values. Purpose:This study has aim to analyze the Apparent Diffusion Coefficient values and Fractional Anisotropy values in Stroke Ischemic disease. Methods: Total samples used are 14 samples, consist of 7 (50%) man and 7 (50%) woman with ischemic stroke disease. Each sample deals by Diffusion Weighted Imaging and Diffusion Tensor Imaging sequences. The Region of Interest (ROI) is placed in ischemic stroke lesions and contra lateral side of lesions. Results: The result shows that 9 samples of brain tissue lesions located in the right side and 5 samples in the left side. Right lesions have the average ADC stroke: 0.001748; normal ADC: 0.000954; FA stroke: 0.144522; and normal FA: 0.426111. While, left lesions have the average ADC strokes 0.000979; normal ADC: 0.000835; FA stroke: 0.2556; and normal FA 0.4324. Conclusion: So, the conclusion of this study is Apparent Diffusion Coefficient (ADC) values in case of ischemic stroke can decreases or increases depend on the age of stroke. While, the Fractional Anisotropy (FA) values will decrease without being affected by age of stroke.
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Abstract BACKGROUND Apparent diffusion coefficient (ADC) is a quantitative measure reflecting observed net movement of water calculated from a diffusion-weighted image (DWI), correlating with tumor cellularity. The higher cellularity of high-grade gliomas results in diffusion restriction and reduced ADC values, whereas the lower cellularity of low-grade gliomas (LGGs) gives higher ADC values. Here we examine changes in ADC values in patients with LGGs treated with the type 2 RAF inhibitor DAY101 (formerly TAK580). METHODS Historical, baseline, and on-treatment brain MRIs for 9 patients enrolled on a phase 1 study of DAY101 in children and young adults with radiographically recurrent or progressive LGG harboring MAPK pathway alterations were obtained, de-identified and independently evaluated for ADC changes. Time points included baseline, first follow-up, and best response. Data processing of ADC estimates was performed using pmod molecular image software package. ADC changes were displayed as a histogram with mean values. Results were based upon a single read paradigm. RESULTS There was a clear shift to lower ADC values for the solid component of tumors, reflecting changes in cellularity and tissue organization, while necrosis correlated with a shift toward higher ADC values. DWI reveals reduced ADCs in responding tumors, with the percent change in ADC from baseline correlating with deeper RANO responses. CONCLUSION DWI analysis reveals reductions in ADC values that correlates with treatment response and a shift toward more normal cellularity in tumors treated with DAY101. Changes in ADC may represent a novel imaging biomarker, reflecting biological response to DAY101 treatment.
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Diffusion weighted magnetic resonance imaging (DWI) is a powerful tool for evaluation of microstructural anomalies in numerous central nervous system pathologies. Diffusion tensor imaging (DTI) allows for the magnitude and direction of water self diffusion to be estimated by sampling the apparent diffusion coefficient (ADC) in various directions. Clinical DWI and DTI performed at a single level of diffusion weighting, however, does not allow for multiple diffusion compartments to be elicited. Furthermore, assumptions made regarding the precise number of diffusion compartments intrinsic to the tissue of interest have resulted in a lack of consensus between investigations. To overcome these challenges, a stretched-exponential model of diffusion was applied to examine the diffusion coefficient and "heterogeneity index" within highly compartmentalized brain tumors. The purpose of the current study is to expand on the stretched-exponential model of diffusion to include directionality of both diffusion heterogeneity and apparent diffusion coefficient. This study develops the mathematics of this new technique along with an initial application in quantifying spinal cord regeneration following acute injection of epidermal neural crest stem cell (EPI-NCSC) grafts.
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To study the correlation between apparent diffusion coefficient (ADC) and Ki-67, a marker of tumor activity, in the diagnosis of gliomas.Conventional magnetic resonance imaging (MRI), enhanced scanning, and diffusion-weighted imaging were performed in 76 patients with pathologically confirmed gliomas. The ADC values were measured at tumor parenchyma and the corresponding contralateral normal brain. The relatively ADC (rADC) values of the tumor parenchyma were calculated. The correlation of the ADC values with tumor grades was analyzed. The expression of Ki-67 was detected by immunohistochemical staining. The correlation between ADC value and Ki-67 in the diagnosis of gliomas was analyzed.The ADC values and rADC values of high-grade gliomas were significantly lower than those of low-grade gliomas. The ADC values of tumor parenchyma were inversely associated with the degree of malignancy of the gliomas (r=-0.898, r=-0.868; P<0.01). The expression of Ki-67 was significantly higher in high-grade gliomas than that in low-grade gliomas. The Ki-67 labeling index in grade 3 and 4 gliomas were (29.48 ± 19.78)% and (31.21 ± 17.50)%, respectively. Both of them were significantly higher than Ki-67 labeling index in low-grade (grade 1 and 2) gliomas [(2.33 ± 2.20)%] (P<0.01). Nevertheless, the Ki-67 labeling index showed no significant difference between grade 3 and 4 gliomas (P>0.05). The expression of Ki-67 was negatively correlated with the ADC values and rADC values in tumor parenchyma (r=-0.627, r=-0.607; P<0.01).The ADC and rADC values of tumor parenchyma can indirectly reflect the proliferation and malignancy of gliomas and therefore can be useful for the grading of glioma.
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