In this study, we investigated the association between DW parameters measured at different Td values (including ADC, sADC, IVIM, and non-Gaussian DWI), CEST parameters, and histopathological features, especially tumor proliferative markers, using murine xenograft models of human breast cancer. We found that the combined ADC change using different Td values had a significant positive correlation with APT SI. Previous studies have reported that time-dependent DWI or CEST imaging might provide information about tumor microstructure indicating tumor proliferation in oncologic imaging 28–32. Comparisons of APT SI and IVIM parameters in cervical cancers 33, gliomas 34, and hepatocellular carcinomas 35, as well as comparisons of APT SI and diffusion kurtosis imaging in rectal carcinomas 36, have been reported. However, the relationship between time-dependent DWI parameters, as tissue level markers, and CEST parameters, as molecular level markers, is still unknown, to the best of our knowledge.
We used two different human breast cancer cell lines: an estrogen-dependent tumor cell line (MCF-7) and an aggressive, triple negative breast tumor cell line (MDA-MB-231). The MDA-MB-231 tumor cells showed smaller cell sizes, larger necrotic areas, higher Ki-67 expression, and greater MVD, suggesting histopathological malignancy, which is consistent with previous studies 37–41. Although these histopathological features appear very different, single measurements of diffusion, IVIM, and CEST quantitative parameters were not useful to distinguish between the two different tumor types. Only ADC values at the shortest Td (2.5 ms using OGSE), sADC change and combined ADC change showed statistically significant differentiation between the two xenograft groups.
The observed decrease in ADC and sADC values with Td in breast tumors was in agreement with the literature, as were the results of our previous investigation 11,32,42. This confirms the hypothesis that diffusion hindrance in tumors increases when the Td value increases, as more molecules might hit the increased number of obstacles such as cell membranes. Our results also confirm that ADC value is a Td-dependent quantitative marker and thus that standardization of acquisition parameters in diffusion MRI is important, especially for multicenter studies 43. Indeed, although the Td settings available on clinical MRI scanners used to be similar, the availability of new, stronger gradient hardware now allows access to a broad range of Td values while maintaining high b values. In general, the previous studies implicated that the ADC values obtained at very short Td might diminish the contrast for differentiation between lesion types (i.e., malignant vs. benign lesions) because of a reduced sensitivity to diffusion hindrance 11,32. Our study revealed that only ADC values at the shortest Td (2.5 ms using OGSE) can distinguish between two xenograft models. The ADC results obtained at short Td values might capture the movement of water molecules in all tissue compartments on the cellular and subcellular scales, whereas the ADC acquired at longer Td values might increase the diffusion MRI signal’s emphasis on the extracellular space. In this study, we used a tumor model that mixed cellular and necrotic components in various proportions and ROIs on the whole tumor. The variation of ADC values is particularly large in the MDA-MB-231 group with a large necrotic area. As the Td is increased, the water molecule displacement becomes larger, and then ADC values at long Td values may indicate an increase in SD, reflecting the heterogeneity of the tissue. We considered that the ADC values at Td = 2.5ms, which captures the diffusion motion on a small scale, captures the small cell size (many cell membranes) of the MDA-MB-231 tumors, resulting in significantly lower ADC values in the MDA-MB-231 groups. These results also suggest that setting an appropriate Td is troubling, however the comparison of ADC values between short and long Td values might be simple useful parameters to reveal additional information about micro tissue structure, such as cell size, cell density, or cell membranes 44.
A previous study that compared between benign and malignant tumors revealed greater ADC change in malignant than benign lesions 32,42. In this study, we assumed that more malignant pathological features like active proliferation, tight structure, and small cell spacing hinder the diffusion of water molecules inside tissue and ultimately decrease the ADC value in MDA-MB-231 tumors. However, in this study, the more aggressive MDA-MB-231 tumors had less sADC change and combined ADC change than the MCF-7 tumors. The presence of a larger necrotic area because of MDA-MB-231’s aggressiveness is assumed to be one of the causes of the lower sADC change and combined ADC change observed in those tumors. There should be almost no structure in necrotic areas, which means that diffusion should be less hindered and almost free. Indeed, the areas with small sADC changes corresponded to the necrotic areas in some tumors, as shown in Figs. 6 and 7. Investigation of Td’s dependence on tumor characterization provides new insight to supplement standard ADC values, and maps of ADC changes were useful for highlighting tumor features such as heterogeneity, as shown in this study. Furthermore, our result that the ADC change, calculated using standard ADC values with b values of 0 and 600 s/mm2, were not able to distinguish between two different xenografts is an interesting point. The shifted ADC change, excluding the signal of b value of 0 and using relatively high b value, is suggested to reflect non-Gaussian diffusion effect and to be more useful to estimate the tumor microstructure, because the behavior of DW signals is non-Gaussian, especially in highly restricted tissues such as cancers with cell proliferation.
A strong positive correlation between Ki-67max and combined ADC change was found in MCF-7 tumors (R = 0.82, P < 0.05). Cells with high Ki-67 expression tend to have high cellular proliferation and tight tissue structure, which hinders diffusion of water molecules inside and between these cells. No statistically significant correlation between tumor proliferative markers and combined ADC changes was found in MDA-MB-231 tumors. The MDA-MB-231 cell line has been known to express very high levels of Ki-67: almost 100% of these cells stain positively for Ki-67 37, in contrast to human breast cancer. We also found in our study that MDA-MB-231 cell lines had abnormal expression and a small standard deviation of Ki-67 LI (75.5%±3.3%), so caution is required when extrapolating results obtained with xenograft models to clinical cancer when considering the association between the Ki-67 marker and MR parameters. Indeed, our single measurement of the ADC value for each Td value had no correlation with any tumor proliferative biomarkers. It has often been found that ADC values are linked to both cell density and proliferation rate (measured by Ki-67) in tumors 45,46. However, ADC can also be affected by other tissue characteristics, such as vascularity and extracellular water diffusivity. The rate of change of ADC with Td appears to be more promising than the ADC value itself for highlighting differences in the tumor environment. Furthermore, the differences between ADC values observed in various tissue types at different Td values might reflect functional differences in diffusion hindrance (e.g., related to membrane permeability to water 44) in addition to microstructure (related to cell geometry) 47,48.
The tendencies of decreased ADCo and increased K with Td were stronger at Td = 9 ms than 27.6 ms in both xenograft models, further confirming the increase in diffusion hindrance. No significant differences of f, D*, or the time dependence of IVIM parameters were found between the two tumor groups. The differences between various IVIM models (the exponential model and sinc model) 49,50 might explain some time-dependence of IVIM parameters. In this study, we used an exponential model that is valid as long as Td is sufficiently long, but there is a general consensus that this model might not be accurate at small time scales, explaining the moderate correlation between f and MVD found only at long Td (27.6 ms) and not at short Td. IVIM MRI might provide quantitative parameters of angiogenesis, like f, which should increase with the proliferation of neovascularity in some tumors. Tumor angiogenesis is essential for tumor growth and metastasis, and the establishment of a noninvasive imaging tool is essential to obtain the degree of angiogenesis noninvasively. As f is expected to be useful as an indicator of the degree of angiogenesis, further investigation with a larger group of subjects is desirable.
There was no significant association between APT SI and Ki-67 LI in our study. One reason for this result might be the abnormally high rate of Ki-67-positive cells, which was found especially in the MDA-MB-231 model. Second, Ki-67 LI was calculated as the mean positive cell count in the cellular area, and it therefore could not be directly compared with the mean APT SI value, which was calculated from the ROI covering the whole tumor including necrosis. Although several previous studies have reported positive correlations between APT SI and Ki-67 LI in gliomas 29, meningiomas 30, and rectal carcinomas 31, no correlation was found in rectal adenocarcinomas 51. There have been few studies on the utility of APT imaging in the differential diagnosis of different breast tumors 26,28, and further investigation will be required to determine whether there is an association between APT SI and Ki-67 LI.
The positive correlation found between cellular area and APT SI (as shown in Fig. 5) is consistent with the general view that mobile protons in the cytoplasm (and mobile proteins and peptides in tumors 52) are the major sources of APT signals. Malignant tumors with high Ki-67 expression have higher cellularity and increased cytosolic protein in the cytoplasm, resulting in higher APT SI 3. In contrast, in our study, a negative association was observed between the Ki-67 positive rate and APT SI (Fig. 5). The motivations for establishing the Ki-67 positive ratio (i.e. the ratio of Ki-67-positive area divided by the cellular area) were to reduce bias in ROI selection and to analyze the value equivalent to the conventional Ki-67LI in ROIs covering the whole tumor using Halo. Because Ki-67 is usually localized to the nucleus when detected by immunohistochemistry (an unusual cell membrane and cytoplasmic pattern of Ki-67 reactivity has been rarely described 53), a large Ki-67-positive ratio means a small cytoplasmic area. In Jiang’s report 54, primary central nervous system lymphomas (PCNSL) had significantly lower APT SI than high-grade gliomas, and this finding was hypothetically attributed to higher nucleus–cytoplasm ratios (N/C) in PCNSLs, which would reduce the amount of cytoplasmic protein. No significant correlation between APT and the Ki-67 positive ratio was observed in MCF-7 cells, which have a small N/C and varying Ki-67 positive cell rates. The decreased APT SI signal in MDA-MB-231 tumors may have been caused by high N/C, but this interpretation is not straightforward.
A moderate positive correlation between combined ADC change and APT SI was found in our study, suggesting a link between tumor microstructure at the cellular scale and molecular status. Both parameters have been reported to reflect the tumor proliferative potential, as shown by Ki-67 expression, reflecting pathological malignancy, although diffusion MRI and CEST rely on totally different mechanisms. The sADC change maps and APT maps (Figs. 5 and 6) look very similar, including a signal decrease in the necrotic area. Because the ADC values using 2 b values did not correlate significantly with APT SI, ADC change using multiple Td values may be useful as additional information to conventional DW parameters for distinguishing and evaluating two different types of malignancies, as in the present study.
The limitations of this study include that the number of mice was relatively small and that xenograft models were used. Xenograft models provide a whole organism environment for tumor growth, but using immunocompromised mice and differences associated with the implantation site might be limitations. For example, the observed aberrant expression of Ki-67 is different from that in human breast cancer. Thus, we cannot directly extrapolate our results to humans. Further investigation is warranted to verify the associations of DWI and CEST parameters with histopathological biomarkers. Then, in this study, we are not able to compare simply between ADC0 − 600 change and sADC200 − 1500 change because the using Td to calculate these change values were different. The maximal b value in OGSE was 600 s/mm2 for maintaining the same TE as those in PGSE, hence the sADC200 − 1500 change (high key b value of 1500 s/mm2) was calculated at Td =9 ms and 27.6 ms.
In conclusion, the associations of combined ADC change with different Td values, API SI, and histopathological parameters such as Ki-67 expression and cellularity were found. These results indicate that the Td-dependent DWI and CEST parameters are useful for investigating the microstructure of breast cancers. Furthermore, diffusion parameter values, especially sADC, are dependent on Td, confirming that this Td dependence is strongly associated with the degree of diffusion hindrance, which increases with the Td. These results indicate the importance of reporting Td in future studies.