Whole-Lesion DCE-MRI Intensity Histogram Analysis for Diagnosis in Patients with Suspected Lung Cancer

2020 ◽  
Author(s):  
Wei Wu ◽  
Shuchang Zhou ◽  
Daniel S. Hippe ◽  
Haining Liu ◽  
Yujin Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Histogram Analysis ◽  
Dce Mri ◽  
Intensity Histogram

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.

scholarly journals Quantitative Volumetric CT-Histogram Analysis in N-Staging of 18F-FDG-Equivocal Patients with Lung Cancer

2014 ◽  
Vol 55 (4) ◽  
pp. 559-564 ◽  
Author(s):  
P. Flechsig ◽  
C. Kratochwil ◽  
L. H. Schwartz ◽  
D. Rath ◽  
J. Moltz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Histogram Analysis ◽  
Volumetric Ct ◽  
18F Fdg ◽  
N Staging

Differential diagnosis of central lymphoma and high-grade glioma: dynamic contrast-enhanced histogram

2020 ◽  
Vol 61 (9) ◽  
pp. 1221-1227
Author(s):  
Han-wen Zhang ◽  
Gui-wen Lyu ◽  
Wen-jie He ◽  
Yi Lei ◽  
Fan Lin ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Histogram Analysis ◽  
High Grade ◽  
Diagnostic Efficiency ◽  
Roc Curve Analysis ◽  
Diagnostic Efficacy ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Significant Difference

Background In clinical diagnosis, some central nervous system lymphomas (CNSL) are difficult to distinguish from high-grade gliomas (HGG). Purpose To evaluate the diagnostic efficacy of the histogram analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in the identification of CNSL and HGG. Material and Methods In all, 43 patients diagnosed with HGG (n = 28) and CNSL (n = 15) by histopathology underwent DCE-MRI scanning. Differences in histogram parameters based on DCE-MRI between HGG and CNSL were analyzed by Mann–Whitney U test. In addition, receiver operating characteristic (ROC) analysis was performed. Short-term follow-up of patients was performed using Kaplan–Meier analysis to explore the survival rates of HGG and CNSL. Results For the ROC curve analysis, we demonstrate that the 10th percentile of Ktrans (area under the curve [AUC] = 0.912, sensitivity = 86.7%, specificity = 92.9%), Kep (AUC = 0.940, sensitivity = 93.3%, specificity = 79.6%), Ve (AUC = 0.907, sensitivity = 86.7%, specificity = 89.3%), and AUC (AUC = 0.904, sensitivity = 86.7%, specificity = 92.9%) were significantly different between the CNSL and HGG groups ( P < 0.001), with high diagnostic efficiency. Table 2 shows that the histogram features based on AUC maps (10th, 25th, median, 75th, 90th, and mean) were always significantly higher in the CNSL group than in the HGG group ( P < 0.001). There was no significant difference in Vp or in the 75th, 90th and mean of Ktrans, Kep, and Ve between the CNSL and HGG groups ( P > 0.05). Conclusion A histogram analysis of DCE-MRI identified significant differences between HGG and CNSL, and this will help in the clinical differential diagnosis of these conditions.

Histogram Analysis of ADC Maps for Differentiating Brain Metastases From Different Histological Types of Lung Cancers

2020 ◽  
pp. 084653712093383
Author(s):  
Mustafa Bozdağ ◽  
Ali Er ◽  
Akın Çinkooğlu
Keyword(s):  
Lung Cancer ◽  
Diffusion Coefficient ◽  
Brain Metastases ◽  
Apparent Diffusion Coefficient ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Histogram Analysis ◽  
Small Cell Lung ◽  
Apparent Diffusion

Purpose: Our study aimed to investigate the role of histogram analysis derived from apparent diffusion coefficient (ADC) maps in brain metastases (BMs) from lung cancer for differentiating histological subtype. Methods: A total of 61 BMs (45 non-small cell lung cancer [NSCLC] comprising 32 adenocarcinoma [AC], 13 squamous cell carcinoma [SCC], and 16 small-cell lung cancer [SCLC]) in 50 patients with histopathologically confirmed lung cancer were retrospectively included in this study. Pretreatment cranial diffusion-weighted imaging was performed, and the corresponding ADC maps were generated. Regions of interest were drawn on solid components of the BM on all slices of the ADC maps to obtain parameters, including ADCmax, ADCmean, ADCmin, ADCmedian, ADCrange, skewness, kurtosis, entropy, ADC10, ADC25, ADC75, and ADC90. Apparent diffusion coefficient histogram parameters were compared among histological type groups. Kruskal-Wallis, Mann-Whitney U, chi-square tests, and receiver-operating characteristic (ROC) curve were used for statistical assessment. Results: ADCmin, ADC10, and ADC25 were found to be significantly different among AC, SCC, and SCLC groups; these parameters were higher for AC group, moderate for SCC group, and significantly lower for SCLC group. Skewness and kurtosis were not significantly different among all groups. The ROC analysis for differentiating BMs of NSCLC from SCLC showed that ADC25 achieved the highest area under the curve at 0.922 with 93.02% sensitivity and 81.25% specificity. Conclusion: Apparent diffusion coefficient histogram analysis of BMs from lung cancer has significant prognostic value in differentiating histological subtypes of lung cancer.

Correlation Between Quantitative Perfusion Histogram Parameters of DCE-MRI and PTEN, P-Akt and m-TOR in Different Pathological Types of Lung Cancer

2020 ◽  
Author(s):  
Bingqian Zhang ◽  
Zhenhua Zhao ◽  
Ya'nan Huang ◽  
Haijia Mao ◽  
Mingyue Zou ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Preoperative Imaging ◽  
Resonance Imaging ◽  
Small Cell Lung ◽  
Dce Mri ◽  
Quantitative Perfusion ◽  
Pathway Gene ◽  
Contrast Enhanced ◽  
Pathological Subtypes ◽  
Activation Status

Abstract Background: To explore if the quantitative perfusion histogram parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) correlates with the PTEN, P-Akt and m-TOR protein in lung cancer.Methods: Thirty‐three patients with 33 lesions who had been diagnosed with lung cancer were enrolled in this study. They were divided into three groups: squamous cell carcinoma (SCC, 15 cases), adenocarcinoma (AC, 12 cases) and small cell lung cancer (SCLC, 6 cases). Preoperative imaging (conventional imaging and DCE-MRI) was performed on all patients. The Exchange model was used to measure the phar- macokinetic parameters, including Ktrans, Vp, Kep, Ve and Fp, and then the histogram parameters meanvalue, skewness, kurtosis, uniformity, energy, entropy, quantile of above five parameters were analyzed. The expression of PTEN, P-Akt and m-TOR were assessed by immunohistochemistry. Spearman correlation analysis was used to compare the correlation between the quantitative perfusion histogram parameters and PTEN, P-Akt and m-TOR in different pathological subtypes of lung cancer.Results: The expression of m-TOR (P = 0.013) and P-Akt (P = 0.002) in AC was significantly higher than those in SCC. Vp (uniformity) in SCC group, Ktrans (uniformity), Ve (kurtosis, Q10, Q25) in AC group, Fp (skewness, kurtosis, energy), Ve (Q75, Q90, Q95) in SCLC group was positively correlated with PTEN, and Fp (entropy) in the SCLC group was negatively correlated with PTEN (P <0.05); Kep (Q5, Q10) in the SCLC group was positively correlated with P-Akt, and Kep (energy) in the SCLC group was negatively correlated with P-Akt (P < 0.05); Kep (Q5) in SCC group and Vp (meanvalue, Q75, Q90, Q95) in SCLC group was positively correlated with m-TOR, and Ve (meanvalue) in SCC group was negatively correlated with m-TOR (P < 0.05).Conclusions: The quantitative perfusion histogram parameters of DCE-MRI was correlated with PTEN, P-Akt and m-TOR in different pathological types of lung cancer, which may be used to indirectly evaluate the activation status of P13K / Akt / mTOR signal pathway gene in lung cancer, and provide important reference for clinical treatment.

scholarly journals Histogram Analysis of Perfusion Parameters from Dynamic Contrast-Enhanced MR Imaging with Tumor Characteristics and Therapeutic Response in Locally Advanced Rectal Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Dong Myung Yeo ◽  
Soon Nam Oh ◽  
Moon Hyung Choi ◽  
Sung Hak Lee ◽  
Myung Ah Lee ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Kras Mutation ◽  
Therapeutic Response ◽  
Locally Advanced ◽  
Advanced Rectal Cancer ◽  
Locally Advanced Rectal Cancer ◽  
Histogram Analysis ◽  
Tumor Characteristics ◽  
Dce Mri ◽  
Contrast Enhanced

Purpose. To explore the role of histogram analysis of perfusion parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on entire tumor volume in discriminating tumor characteristics and predicting therapeutic response in rectal cancer. Materials and Methods. Thirty-seven DCE-MRIs of locally advanced rectal cancer patients who received chemoradiation therapy (CRT) before surgery were analyzed by pharmacokinetic model for quantification and histogram analysis of perfusion parameters. The results were correlated with tumor characteristics including EGFR expression, KRAS mutation, and CRT response based on the pathologic tumor regression grade (TRG). Results. The area under the contrast agent concentration-time curve (AUC) skewness was significantly lower in patients with node metastasis. The vp histogram parameters were significantly higher in group with perineural invasion (PNI). The receiver operating characteristics (ROC) curve analyses showed that mode vp revealed the best diagnostic performance of PNI. The values of Ktrans and kep were significantly higher in the group with KRAS mutation. ROC curve analyses showed that mean and mode Ktrans demonstrated excellent diagnostic performance of KRAS mutation. DCE-MRI parameters did not demonstrate statistical significance in correlating with TRG. Conclusion. These preliminary results suggest that a larger proportion of higher AUC skewness was present in LN metastasis group and a higher vp histogram value was present in rectal cancer with PNI. In addition, Ktrans and kep histogram parameters showed difference according to the KRAS mutation, demonstrating the utility of the histogram of perfusion parameters derived from DCE-MRI as potential imaging biomarkers of tumor characteristics and genetic features.

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