The predictive capacity of apparent diffusion coefficient (ADC) in response assessment of brain metastases following radiation

Abstract Background Brain metastases are particularly common in patients with small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC showing a less aggressive clinical course and lower chemo- and radio sensitivity compared to SCLC. Early adequate therapy is highly desirable and depends on a reliable classification of tumor type. The apparent diffusion coefficient is a noninvasive neuroimaging marker with the potential to differentiate between major histological subtypes. Here we determine the sensitivity and specificity of the apparent diffusion coefficient to distinguish between NSCLC and SCLC. Methods We enrolled all NSCLC and SCLC patients diagnosed between 2008 and 2019 at the University Medical Center Göttingen. Cranial MR scans were visually inspected for brain metastases and the ratio of the apparent diffusion coefficient (ADC) was calculated by dividing the ADC measured within the solid part of a metastasis by a reference ADC extracted from an equivalent region in unaffected tissue on the contralateral hemisphere. Results Out of 411 enrolled patients, we detected 129 patients (83 NSCLC, 46 SCLC) with sufficiently large brain metastases with histologically classified lung cancer and no hemorrhage. We analyzed 185 brain metastases, 84 of SCLC and 101 of NSCLC. SCLC brain metastases showed an ADC ratio of 0.68 ± 0.12 SD, and NSCLC brain metastases showed an ADC ratio of 1.47 ± 0.31 SD. Receiver operating curve statistics differentiated brain metastases of NSCLC from SCLC with an area under the curve of 0.99 and a 95% CI of 0.98 to 1, p < 0.001. Youden's J cut-point is 0.97 at a sensitivity of 0.989 and a specificity of 0.988. Conclusions In patients with lung cancer and brain metastases with solid tumor parts, ADC ratio enables an ad hoc differentiation of SCLC and NSCLC, easily achieved during routine neuroradiological examination. Non-invasive MR imaging enables an early-individualized management of brain metastases from lung cancer. Trial registration: The study was registered in the German Clinical Trials Register (DRKS00023016).

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Diagnostic value of apparent diffusion coefficient in differentiating between high-grade gliomas and brain metastases

Acta Radiologica ◽

10.1177/0284185117727787 ◽

2017 ◽

Vol 59 (5) ◽

pp. 599-605 ◽

Cited By ~ 15

Author(s):

Ionut Caravan ◽

Cristiana Augusta Ciortea ◽

Alexandra Contis ◽

Andrei Lebovici

Keyword(s):

Diffusion Coefficient ◽

Brain Metastases ◽

Apparent Diffusion Coefficient ◽

Curve Analysis ◽

High Grade ◽

Peritumoral Edema ◽

Roc Curve Analysis ◽

Apparent Diffusion ◽

Adc Values ◽

The Mean

Background High-grade gliomas (HGGs) and brain metastases (BMs) can display similar imaging characteristics on conventional MRI. In HGGs, the peritumoral edema may be infiltrated by the malignant cells, which was not observed in BMs. Purpose To determine whether the apparent diffusion coefficient values could differentiate HGGs from BMs. Material and Methods Fifty-seven patients underwent conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) before treatment. The minimum and mean ADC in the enhancing tumor (ADCmin, ADCmean) and the minimum ADC in the peritumoral region (ADCedema) were measured from ADC maps. To determine whether there was a statistical difference between groups, ADC values were compared. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff ADC value for distinguishing between HGGs and BMs. Results The mean ADCmin values in the intratumoral regions of HGGs were significantly higher than those in BMs. No differences were observed between groups regarding ADCmean values. The mean ADCmin values in the peritumoral edema of HGGs were significantly lower than those in BMs. According to ROC curve analysis, a cutoff value of 1.332 × 10−3 mm2/s for the ADCedema generated the best combination of sensitivity (95%) and specificity (84%) for distinguishing between HGGs and BMs. The same value showed a sensitivity of 95.6% and a specificity of 100% for distinguishing between GBMs and BMs. Conclusion ADC values from DWI were found to distinguish between HGGs and solitary BMs. The peritumoral ADC values are better than the intratumoral ADC values in predicting the tumor type.

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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

The Neuroradiology Journal ◽

10.1177/19714009211049082 ◽

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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The effect of region of interest strategies on apparent diffusion coefficient assessment in patients treated with palliative radiation therapy to brain metastases

Acta Oncologica ◽

10.3109/0284186x.2015.1061211 ◽

2015 ◽

Vol 54 (9) ◽

pp. 1529-1534 ◽

Cited By ~ 7

Author(s):

Faisal Mahmood ◽

Helle H. Johannesen ◽

Poul Geertsen ◽

Giske F. Opheim ◽

Rasmus H. Hansen

Keyword(s):

Diffusion Coefficient ◽

Radiation Therapy ◽

Brain Metastases ◽

Apparent Diffusion Coefficient ◽

Region Of Interest ◽

Palliative Radiation ◽

Apparent Diffusion ◽

Palliative Radiation Therapy

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

Canadian Association of Radiologists Journal ◽

10.1177/0846537120933837 ◽

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.

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