In this study, we investigated the role of ADCCV derived from PET/MRI as a heterogeneity index in discriminating BM from NABP. The main finding of this study was that, ADCCV is more effective to discriminate BM from NABP compared to conventional ADC parameters. Besides, ADCCV was highly correlated with SUVmax from simultaneous derived PET/MRI system.
Integrated PET / MRI scanners with the recent developments, new opportunities have emerged for quantitative molecular imaging. PET / MRI provides multimodal analysis of concurrently acquired functional parameters that can contribute to better characterization of tumor biology and also help identify markers to predict response to therapy [13,14].
Due to the high 18F-FDG uptake of the cerebral cortex and the low spatial resolution of PET imaging, FDG PET/CT imaging has limitations, especially in the detection of small metastases in the brain. Sensitivity of FDG PET/CT in brain imaging is low. In retrospective comparative studies, it is stated that FDG PET/CT imaging at the time of diagnosis can capture up to 61% of metastatic brain lesions that can be detected by MRI . Therefore, PET / MR imaging may be preferred in brain metastasis scanning because of the high soft tissue contrast of MR imaging.
SUVmax measured by PET and ADC measured by MRI allow assessment of water diffusion and glucose metabolism in tumor cells. The results of the present study show that ADCCV exhibits an improved correlation with SUVmax. Moreover, it provides better quantitative separation between BM and NABP, as compared to common MRI metrics.
In this study, ADCmean showed a significant negative correlation with SUVmax; however, ADCCV showed higher correlation with SUVmax than ADCmean parameter. There are previously reported oncologic studies of the inverse correlation found between ADC and SUV. Several of these studies reported significant strong inverse correlation between ADCmean and SUVpeak in rectal cancer , a significant inverse correlation between ADCmean and SUVmean in gastrointestinal stromal tumor , and recently an inverse correlation between ADC and PET SUV in liver tumors .
Tumor heterogeneity consists of marked differences in cell mix, size, and arrangement. Heterogeneity also plays a role in micro-environmental factors (including oxygenation, pH, interstitial pressure, blood flow), metabolism, and gene expression. This deep heterogeneity is extremely important for prognosis, treatment planning, and drug distribution, which ultimately affects patient outcomes. There are a number of ways to investigate tumor heterogeneity, some of which include functional and molecular imaging, which can be applied to clinical data .
The characterization of tissues can be improved using histogram-based assessments of the distribution of ADC values. Histogram approaches have multiple advantages, including volume-of-interest (VOI) assessments, thus avoiding the subjectivity that is inherent with ROI placements. Importantly, histograms can provide additional metrics that reflect the texture of lesions, thereby allowing heterogeneity of ADC distribution within tissue to be assessed . Tissue heterogeneity analysis is rapidly evolving by various methods. Despite most of the tools currently offered are often complex and computationally costly, it is an easy to calculate texture parameter of ADCCV. Several studies have used CV as an index of heterogeneity in recent years.
In a study in liver metastasis, the results of this study show that ADCCV can significantly distinguish between liver metastasis and normal-appearing liver . Similar to our study, there was a good correlation between ADCCV and SUV peak in this study. Significant differences in CV diffusion index was found in another study about hepatocellular carcinoma in fresh liver explants .
PET / CT and DWI share applications in clinical oncology. While both SUV and ADC correlate with cellularity, SUV is also associated with several other pathological markers such as mitotic count, presence or absence of necrosis . For this reason, PET / MRI oncological evaluation is also valuable when these two parameters (SUV and ADC) are obtained together in the same examination. In a study by Nakajo et al. , 44 patients with breast cancer underwent preoperative PET/CT and DWI within an average of 17 days between examinations, and both SUVmax and ADC were significantly associated (p < 0.05) with histologic grade (independently), nodal status, and vascular invasion. This finding suggests that SUVmax and ADC correlate with several of the pathologic prognostic factors and that both values may have the same potential for being predictive of the prognosis of breast cancer.
In oncology, imaging has a very important place in evaluating response to treatment. For this reason, many studies are aimed at understanding the structure and heterogeneity of the tumor. Therefore, it is essential to develop quantitative imaging methods and objective biomarkers to improve the diagnosis of brain metastasis. As a volume-independent index of heterogeneity, ADCCV can be considered as a potential biomarker that quantitatively differentiates BM from NABP. Tissue heterogeneity has been proposed as a basis for a biomarker for tumors [3,4,24].
This hybrid PET / MRI study shows a significant negative correlation between metabolic activity on 18F-FDG PET and water diffusion over DWI in brain metastasis, possibly because both parameters are directly related to tumor cellularity. The correlation found between SUVs and ADCmean, ADCCV values supports the idea that high cellularity due to tumor proliferation results in greater metabolic activity and restricts water diffusion.
This study has several limitations. First, this was a retrospective study and performed on a relatively small study population. Another limitation was the difficulty in determining the limits of the lesions due to the limited resolution of PET. The accuracy of the results obtained from our study should be supported by using different software in larger patient groups and with multi-center studies. The last limitation of our study was that brain metastases originated from different sources.