Simultaneous mapping of vasculature, hypoxia and proliferation using DSC-MRI, 18F-FMISO PET, and 18F-FLT PET in relation to contrast enhancement in newly diagnosed glioblastoma

Solène Collet, Jean Sébastien Guillamo, David Hassanein Berro, Ararat Chakhoyan, Jean-Marc Constans, Emmanuèle Lechapt-Zalcman, Jean-Michel Derlon, Mathieu Hatt, Dimitris Visvikis, Stéphane Guillouet, et al.

► To cite this version:

Solène Collet, Jean Sébastien Guillamo, David Hassanein Berro, Ararat Chakhoyan, Jean-Marc Constans, et al. Simultaneous mapping of vasculature, hypoxia and proliferation using DSC-MRI, 18F-FMISO PET, and 18F-FLT PET in relation to contrast enhancement in newly diagnosed glioblastoma. Journal of Nuclear Medicine, 2021, 62 (10), pp.1349-1356. 10.2967/jnumed.120.249524. hal-03123047

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Simultaneous mapping of vasculature, hypoxia and proliferation using DSC-MRI, ¹⁸F-FMISO PET, and ¹⁸F-FLT PET in relation to contrast enhancement in newly diagnosed glioblastoma

Collet Solène<fnref n="1" />,<fnref n="2" />, Guillamo Jean-Sébastien<fnref n="1" />,<fnref n="3" />,<fnref n="4" />*, Berro David Hassanein<fnref n="1" />,<fnref n="5" />*, Chakhoyan Ararat<fnref n="1" />,
Constans Jean-marc<fnref n="1" />, <fnref n="6" />, Lechapt-Zalcman Emmanuèle<fnref n="1" />,<fnref n="7" />,<fnref n="8" />, Derlon Jean-Michel<fnref n="1" />, Hatt Mathieu<fnref n="9" />,
Visvikis Dimitris<fnref n="9" />, Guillouet Stéphane<fnref n="10" />, Perrio Cécile<fnref n="10" />, Bernaudin Myriam<fnref n="1" /> and Valable Samuel<fnref n="1" />

**Immediate Open Access:** Creative Commons Attribution 4.0 International License (CC BY) allows users to share and adapt with attribution, excluding materials credited to previous publications.

License: https://creativecommons.org/licenses/by/4.0/.

Details: https://jnm.snmjournals.org/page/permissions.

<figure><img src="image_2.png" /></figure>

<footnote n="1">¹ Normandie Univ, UNICAEN, CEA, CNRS, ISTCT/CERVOxy group, GIP CYCERON, Caen</footnote>

<footnote n="2">² Radiophysics Department, Centre François Baclesse, Caen</footnote>

<footnote n="3">³ Department of Neurology, CHU de Caen</footnote>

<footnote n="4">⁴ Department of Neurology, CHU de Nimes</footnote>

<footnote n="5">⁵ Department of Neurosurgery, CHU de Caen</footnote>

<footnote n="6">⁶ Department of Neuroradiology, CHU de Caen</footnote>

<footnote n="7">⁷ Department of Pathology, CHU de Caen</footnote>

<footnote n="8">⁸ Department of Neuropathology, GHU Paris Psychiatry and Neuroscience, Paris</footnote>

<footnote n="9">⁹ LaTIM, INSERM, UMR 1101, Univ Brest, Brest</footnote>

<footnote n="10">¹⁰ Normandie Univ, UNICAEN, CEA, CNRS, ISTCT/LDM-TEP group, GIP CYCERON, Caen</footnote>
**Corresponding author:** Dr S. Valable, ISTCT, CERVOxy group, GIP CYCERON, Bd Henri Becquerel, BP5229, 14074 CAEN, France. Phone: 33231470108; fax: 33231470222. E-mail: samuel.valable@cnrs.fr; orcid # 0000-0003-0355-0270

**First author:** Dr S. Collet, Department of Radiophysics, Centre François Baclesse, 3 Avenue du Général Harris, 14000 Caen. Phone: 33231470230; fax: 33231470222. E-mail: s.collet@baclesse.unicancer.fr

* equal contribution

Word count in the MS: 5403

**Running title:** multimodal imaging of glioblastoma
ABSTRACT

Conventional MRI plays a key role in the management of patients with high grade glioma but multiparametric MRI and PET tracers could provide further information to better characterize the tumor metabolism and heterogeneity, by identifying the regions having a high risk of recurrence.

In this study, we focused on the proliferation, hypervascularization and hypoxia, all factors considered as factors of poor prognosis. They were assessed by measuring the uptake of ¹⁸F-FLT, the rCBV maps and the uptake of ¹⁸F-FMISO, respectively. For each modality, the volumes and high uptake sub-volumes (hotspots) were semi-automatically segmented and compared to contrast enhancement (CE) volume on T1w-Gd images, commonly used in the management of patient with glioblastoma. Methods: DSC MRI (31 patients), ¹⁸F-FLT PET (20 patients) and/or ¹⁸F-FMISO PET (20 patients), for a total of 31 patients, were performed on pre-operative glioblastoma patients.

Volumes and hotspots were segmented on SUV maps for ¹⁸F-FLT (using FLAB) and ¹⁸F-FMISO (using mean contralateral + 3.3 SD) PET and on rCBV maps (using mean contralateral + 1.96 SD) for DSC MRI and overlaid on T1w-Gd images. For each modality, the percentage of peripheral volumes and the peripheral hotspot outside the CE volume were calculated. Results: All tumors showed high proliferation, hypervascularization and hypoxic regions. Images also showed pronounced heterogeneity of both tracers uptake and rCBV maps, within each individual case. Overlaid volumes on T1w-Gd images showed that some proliferative, hypervascularization and hypoxic regions extended beyond the CE volume but with marked differences between patients. The ranges of peripheral volume outside the CE volume were [1.6% - 155.5 %], [1.5% - 89.5%] and [3.1% - 78.0%] for ¹⁸F-FLT, rCBV and ¹⁸F-FMISO respectively. All patients had hyperproliferative hotspots outside CE volume, whereas hotspots of hypervasculature and hypoxia were mainly
detected within the enhancing region. **Conclusion:** The spatial analysis of the multiparametric maps with the segmented volumes and hotspots provides valuable information to optimize the management and treatment of the patients with glioblastoma.

**Key words:** Proliferation, vasculature, hypoxia, MRI, PET, glioblastoma.
INTRODUCTION

Despite the use of aggressive treatments (1), glioblastoma remains one of the deadliest human
cancers characterized by a 5 years survival of 6.8% (2). Glioblastoma are highly heterogeneous
tumors characterized by a strong interpatient heterogeneity illustrated at both, the molecular
(3,4) and the macroscopic levels. More importantly, glioblastoma are also characterized by a
pronounced intratumoral heterogeneity (5) which is macroscopically visible on conventional MRI
with regions of necrosis and contrast enhancement (6), and has been associated to a large range
of response to therapies (7).

Among the various pathophysiological parameters that may influence patient survival, two keys
parameters (proliferation and invasion) were considered to be predictive of patient survival (8).
Interestingly, the dynamic interactions between tumor cells, vasculature and hypoxia is also
considered as a key feature that s tumor growth (9–11).

While various works have been performed to address the spatial relationship between pairs of
parameters (10,12–17), the concomitant and quantitative measurement of these three
parameters remains challenging and has been performed only on histology (9) which lacks the
overall overview of the entire 3D tumor volume.

Until now, conventional MRI with the so-called contrast enhancement (CE) area remains the
most used imaging modality to characterize glioblastoma and guide treatments. However,
multiparametric imaging is most appropriate to assess biological tumor heterogeneity and more
specifically, to quantitatively analyze these three compartments together.

Specific imaging markers of tumor activity have emerged recently, providing additional specific
information to further characterize the tumor and its environment (18,19). In the field of neuro-
oncology, these markers include those derived from multiparametric MRI such as perfusion, diffusion and magnetic resonance spectroscopy. For PET imaging, radiotracers reflecting cell proliferation as ¹⁸F-3'-deoxy-3'-¹⁸F-fluorothymidine (¹⁸F-FLT) (13,20,21), but also amino acid tracers as ¹¹C-Methionine, O-(2-¹⁸F-fluoroethyl)-l-tyrosine (¹⁸F-FET), or 3,4-dihydroxy-6-¹⁸F-fluoro-l-phenylalanine (¹⁸F-FDOPA) (22) that can specifically differentiate true tumor boundaries from equivocal lesions based on the degree of hypoxia as ¹⁸F-fluoromisonidazole (¹⁸F-FMISO) (10,12,23), have emerged as the most pertinent for this tumor type.

However, to the best of our knowledge, a spatial analysis of these three parameters, i.e., proliferation, hypervascularization and hypoxia has never been reported using noninvasive imaging for newly diagnosed glioblastoma and only a few papers have performed such characterization in other tumor location (24,25). Interestingly, the most proliferative, vascularized and hypoxic sub-volumes (i.e. “hotspots”) could also represent regions of high risk of relapse and consequently their identification is of real interest to overcome resistance to therapies such as surgery or radiation therapy.

Therefore, in this study, we aimed to spatially evaluate the volumes and the hotspots of proliferation, hypervascularization and hypoxia, by using ¹⁸F-FLT PET, relative Cerebral Blood Volume (rCBV)-MRI and ¹⁸F-FMISO PET respectively, relative to CE volume in preoperative glioblastoma patients.
# MATERIALS AND METHODS

## Patients

Patients with *de novo* glioblastoma were included from two prospective clinical trials funded by INCA (Institut National du Cancer) and approved by the local ethics committee and AFSSAPS agreement (ClinicalTrials.gov identifier: NCT00850278 and NCT01200134). Thirty one patients were included in the Caen University Hospital based on inclusion criteria: presenting histopathologically proven grade IV gliomas based on WHO criteria; eligible in the final analysis with MR and PET imaging modalities, age ≥18 years, KPS ≥50, normal blood cell count, normal biological hepatic functions and a signed informed consent for a voluntary participation in research. Patients underwent ¹⁸F-FLT-PET (n=20) and/or ¹⁸F-FMISO-PET (n=20) and multiparametric MRI (n=31)(Table 1) within the same week and prior to surgery. Thereafter, patients underwent surgery, resection or biopsy depending on the location of the tumor, and the specimens were histopathologically evaluated by an experienced neuropathologist (ELZ) and only patients with established diagnosis of glioblastoma were analyzed.

## Image Acquisition

MR Imaging was performed on a 1.5 Tesla GEMS version HDXt 15.0. After scout-view and coronal T2-weighted imaging, an axial FLAIR (Fluid Attenuated Inversion Recovery) sequence was performed (24 slices, slice spacing 5.5 mm, pixel resolution 0.47×0.47 mm, TR/TE=9 602/150 ms). For DSC-MRI, a dynamic gradient-echo T2*-weighted EPI images was used (14 slices, 35 repetitions, slice spacing: 7 mm, pixel resolution 2.19×2.19 mm, TR/TE=2 280/60 ms) to track a bolus of 0.1 mmol/kg of Gadolinium-DOTA (0.1 mmol/kg, Dotarem, Guerbet). An injection delay of 20 s was applied to obtain an accurate estimate of the baseline signal intensity prior to the arrival of the contrast agent and the acquisition lasted 1min20.
Immediately thereafter, a 3DT1-weighted sequence, 3DT1w-Gd (124 slices, slice spacing 1.5 mm, pixel resolution 1.01×1.01 mm², TR/TE=17/3 ms) was performed to evaluate the contrast enhancement.

$^{18}$F-FLT and $^{18}$F-FMISO were both produced by the LDM-TEP group (ISTCT, GIP CYCERON) and synthesized as previously described (12,13,26). Data within a brain-focused field of view were acquired on two consecutive days 40 minutes ($^{18}$F-FLT) and 2 hours ($^{18}$F-FMISO) after the intravenous injection of 5 MBq/kg (both tracers) and lasted 10 minutes ($^{18}$F-FLT, to match a clinically feasible approach) and 20 minutes ($^{18}$F-FMISO). Acquisitions were performed on a General Electric Discovery VCT 64 PET/CT scanner (CYCERON platform). The CT-based attenuation-corrected PET images were reconstructed with an OSEM 2D algorithm (9 subsets and 2 iterations) and filtered in 3D with a Butterworth filter on 1.95×1.95×3.27 mm voxel size. Standardized uptake values (SUV, g/mL) were calculated using the measured concentration of tissue (counts, kBq/mL) divided by the injected activity in kBq/g of body weight.

## Image Analysis

MRI image analysis was performed with in-house macros based on the ImageJ software (27). PET analyses were performed with PMOD 3.1® software.

rCBV maps were computed using DSC-MRI. Variations of the T2* signal in the tissue was calculated with in-house macros based on ImageJ software as: $\Delta R_2^*(t) = -1.\ln(S(t)/S_0)/TE$, with: $S_0=\text{signal intensity before contrast agent injection}$, $S(t)=\text{signal intensity over time and TE is Echo time}$. Then, CBV maps were generated by integrating the area under the γ-variate fitted curves to avoid an effect of recirculation (28). Images were then normalized by dividing CBV maps with the mean value of the normal-appearing contralateral side to obtain rCBV maps.
**Co-registration:** rCBV maps, FLAIR, 3DT1w-Gd and ¹⁸F-FMISO PET images were co-registered with trilinear interpolation, rigid matching and normalized mutual information on ¹⁸F-FLT PET images (PMOD 3.1® software).

**Volume Segmentation:** In the present study, we had to tune the segmentation for each imaging modality since none of the various methods we used was considered pertinent enough for the three imaging modalities, which differed in term of contrast/tumor ratio, signal intensity. Also, we paid attention to the accuracy of the segmentation modality for all patients in one imaging modality. When two methods were almost similar, and to avoid any over-interpretation of our results, the most restrictive one was retained.

**Volume Segmentation For ¹⁸F-FLT PET:** The visual inspection easily enabled us to eliminate 40% SUV max, which underestimates volume, whereas Mean Contra + 3.3SD overestimates volume (Supplementary Fig.1). A semi-automated algorithm previously validated for ¹⁸F-FLT PET images named FLAB -Fuzzy Locally Adaptive Bayesian- was exploited, using 2 or 3 classes (29–33).

**Volume Segmentation For ¹⁸F-FMISO PET:** We considered FLAB, 1.2 T/B (Tissue/Blood), 40% SUVmax and Mean Contra + 3.3SD, and compared them to each other quantitatively as well as through visual inspection.

As exemplified in Supplementary Fig. 2, the standard 1.2 T/B segmentation failed for some cases. For 40% of SUVmax, it was visually striking that an overestimation of hypoxic volumes occurred. We then performed either FLAB segmentation or a semi-automated segmentation using a statistical approached based on the Mean Contra + 3.3 SD (34). As compared to T/B approach, we believe this segmentation could be very well suited in the routine situation because it does not require to draw and process radioactive blood. Thus, with the exception of a few pixels
removed by hiding with FLAIR hypersignal, no further manual intervention was necessary, making
this technique particularly well suited. This statistical approach, Mean Contra + 3.3 SD, seems
suitable for a tracer like FMISO with an average uptake and a large standard deviation in the
healthy brain parenchyma and a poor tumor/contralateral contrast.

FLAB and Mean Contra +3.3 SD led to very similar results (Supplementary Fig. 2). In some cases,
FLAB provided slightly larger hypoxic volumes than Mean Contra + 3.3 SD; we retained the less
permissive strategy.

*Volume Segmentation For CBV:* The situation relates to FMISO situation since a blood volume is present in the healthy brain tissue, the statistical approach resulted in accurate segmentation compared to the naked eyes. We used an already published methodology (35) assuming a threshold at 2 or 3 greater than the signal of the Normal Appearing White Matter (NAWM). We also compared to a contralateral ROI composed of both grey and white matter with a threshold at Mean Contra + 1.96 SD. For both methods, only the region included into the FLAIR region were retained. The two methods provided very similar results but Mean Contra + 1.96 SD was less permissive (Supplementary Fig.3).

*Hotspot Segmentation:* For each modality, the hotspot area was defined as the 95th centile of histogram distribution in the 3D-ROI defined on FLAIR hypersignal to include all voxels that may extend to gadolinium (Gd) enhancement ROI.

All segmented areas were then used as 3D-ROI for further studies.

*Peripheral Volume And Hotspot Calculation:* After the segmentation process, for each modality and each patient, we defined a peripheral volume and peripheral hotspot as the percentage of
the ROI of the modality of interest that is outside the volume of CE using a Boolean operation.

This volume was calculated using the following equations.

$$ Peripheral\ volume(\%) = \frac{ROI\ of\ the\ volume\ of\ the\ modality\ NOT\ ROI\ of\ CE}{ROI\ of\ CE} * 100 $$

$$ Peripheral\ hotspot(\%) = \frac{ROI\ of\ the\ hot\ spot\ of\ the\ modality\ NOT\ ROI\ of\ CE}{ROI\ of\ CE} * 100 $$
## RESULTS

All tumors were confirmed to be a glioblastoma by the pathologist and exhibited a marked CE on 3DT1w-Gd images, elevated rCBV, and pronounced ¹⁸F-FLT and ¹⁸F-FMISO uptakes. Fig. 1 shows representative example of two glioblastoma patients with the multimodal imaging, 3DT1w-Gd, ¹⁸F-FLT PET, rCBV maps, ¹⁸F-FMISO PET. Based on visual inspection by an expert in PET imaging, a marked intra-tumoral heterogeneity of tracer uptake on both PET images was observed. Since the contrast enhancement is the main target of treatments (surgery; radiation therapy), we then paid attention to the spatial relationship between each modality and CE region.

## Analyses of the Peripheral Volume Outside the Contrast Enhancement Region

Thresholded regions of proliferation with ¹⁸F-FLT, of hypervascularization with rCBV and of hypoxia with ¹⁸F-FMISO (Fig. 2 top) were overlaid on the T1w-Gd images (Fig. 2 bottom). Fig. 2 and the peripheral volume calculated (Fig. 3, patient #20) illustrated that the volume of FLT uptake extended far from CE area (139%). The similar situation also occurs for FMISO uptake, but less pronounced where the peripheral volume was 43%, while for CBV, only 11% of the segmented area extended in the non-enhancing area. A representative example of the three modalities segmentation overlayed on the T1w-Gd is provided (Supplementary Fig.4).

The peripheral volume outside CE volume calculated for each patient (Fig. 3) clearly demonstrated that extension of metabolic areas beyond the CE volume was highly variable. The range of peripheral volumes for ¹⁸F-FLT, rCBV and ¹⁸F-FMISO were respectively [1.6 - 155.5 %], [1.5 - 89.5%] and [3.1 - 78.0%]. More precisely, over the 20 patients investigated with FLT, 9/20 had a % comprised between 0 and 20%; 5/20 between 20 and 40% but 6/20 had % of peripheral volume greater than 40%. For CBV, 17/31 had a % comprised between 0 and 20%; 10/31 between 20 and
40 % but 4/31 had % of peripheral volume greater than 40%. For ¹⁸F-FMISO, 10/20 had a value comprised between 0 and 20%; 7/20 between 20 and 40 % but 3/20 had % of peripheral volume greater than 40%.

## Analyses of the Peripheral Hotspots Outside the Contrast Enhancement Region

Considering the strong intra-tumor heterogeneity observed on multiparametric imaging, we were interested in further identifying sub-volumes in the tumor that could be likely associated with resistance and early recurrence. Hyper-proliferative, hypervascularized and severe hypoxic hotspots were thresholded (Fig. 4, top) and overlaid on the T1w-Gd images (Fig. 4, bottom). In this example, a percentage of the hyperproliferative (18%), the hypervascular (11%) and the most hypoxic region (3%) were located outside the CE region.

The peripheral hotspots outside CE volume were calculated for each patient (Fig. 5) and showed that all patients had hyperproliferative volume outside CE volume [8.8 – 32.5 %]. More precisely, 1/20 had less than 10%, 15/20 had values comprised between 10 and 20%, and 4/20 between 20 and 40 %. Concerning hypervascularized hotspots [0 – 25.2%], in 23/31 patients the hotspot fraction less than 5%, 7/31 had a value comprised between 5 and 20% and in 1/31 it was 25%. Last, most hypoxic area was mainly detected in the enhancement region [0 – 5.7%], since 14/20 patients had less than 1% of the FMISO hotspot outside the CE volume, and the others around 5%.

## DISCUSSION

In the context of glioblastoma, intertumoral and intratumoral heterogeneity has been attributed to the failure of standardized treatments. Among the factors influencing tumor growth, the study of the *in vivo* relationship between proliferation, angiogenesis and hypoxia remains of
great interest relative to the conventional aggressive region defined on contrast enhancement
MRI. In the context of glioblastoma, the present study is the first one to show spatial distribution
of each modality together and relative to contrast enhancement.

In the literature and based on multivariate analyses, it has been shown that each parameter
was independently associated with the tumor volume (17,26,36). In gliomas, elevated ¹⁸F-FLT
uptake has been shown to be correlated with Ki67 immunostaining expression and to reflect
proliferation (37–39). In gliomas also, high hypoxia has been shown to be a poor prognosis factor
(40).

As exemplified on Fig. 1, our results confirm that all three analyzed parameters are interlinked
and that an increase of each parameter occurs concomitantly (9,11). Increased rCBV along with
hypoxia might indicate tumor-induced angiogenesis to counteract changes in oxygenation that
occur along with the metabolic demand of proliferating cells.

Following a visual inspection, each modality on Fig. 1 clearly showed a variable uptake
distribution that would need to be exploited. As previously published (9,10,17), it confirms that
the heterogeneity can be mapped using multimodal imaging.

However, our results on peripheral volume also showed that active tumor tissues were already
present in areas that could be considered as non-pathologic according to contrast enhancement
on MRI, and which therefore might not be targeted by the treatment. This is especially true for
¹⁸F-FLT PET, which clearly showed that proliferating cells extended outside the enhancement
region on T1w-Gd images, as shown in earlier publications (36).

One of our main results is that FLT PET volume was greater than the other volumes. The spatial
analysis shows that ¹⁸F-FLT volume encompasses the rCBV, contrast enhancement and the ¹⁸F-
FMISO volume for the most patients. This result is in line with those already published, which also demonstrated that in most cases, ¹⁸F-FLT uptake was larger than tumor volume assessed by anatomical MRI. In glioma, elevated ¹⁸F-FLT uptake is correlated with Ki67 and reflects proliferation (37,38). This result strengthens the hypothesis that the tumoral proliferation is the driving force of the other parameters analyzed in this study, namely angiogenesis and hypoxia.

Various papers have discussed the dependency of FLT uptake to the integrity of the BBB see for review (41). It is recognized that a major limiting factor of ¹⁸F-FLT uptake is the transport mechanism and leakage via the disrupted blood-tumor barrier could result in increased uptake. In a recent paper from Watkins et al. (42), they suggest that only a small number of glioma cells could be sufficient to damage the integrity of the BBB which might explain the ability of FLT to detect proliferating cells in non-enhancing regions of the tumor.

The hotspots analysis showed that all tumors had hyperproliferative area that extended outside the CE volume, while hypervascularized or severe hypoxic areas were mostly included within the CE volume. This result concurs with a recent publication using ¹¹C-Methionine and demonstrating the presence of metabolic tumor volume following gross tumor resection (43).

These results strengthen the fact that tumor cells have already infiltrated the non-enhancing tissue and ought to be included in the surgical treatment or in the definition of the Biological Target Volume for radiotherapy (43).

Nowadays, the standard surgical treatment of glioblastoma is the removal of CE area (44). As we showed that metabolically active areas are visible outside the enhancement volume, removal of the CE volume could contribute in explaining a rapid recurrence of glioblastoma. We suggest to
resect glioblastoma beyond CE volume and until functional limits while preserving the quality of life (45). This information may also be used as a parameter for improving the accuracy of the biopsy analysis and if biopsy and imaging concur, it could be used to improve the quality of resection.

This information contributes to the definition of gross target volumes for radiotherapy, integrating these findings in the concept of Biological Target Volume (46). This could lead to a better tumor control, as it is known that the majority of relapses occurs within the irradiation field (47,48), illustrating the radiation resistance of some areas within the irradiated volume. It is assumed that the current radiotherapy regimen does not guarantee the curative doses necessary to counteract radioresistance of some areas of the tumor identified as hotspots in this paper and which may contribute to the failure of the conventional treatments (49). Radiotherapy is likely to be optimized by specifically targeting these unfavorable biological characteristics (49,50).

This study has some limitations. We only studied each modality with respect to T1 enhancement and we did not perform voxel-wise analyses between the various modalities. However, the main goal of the present analysis was to make a study as simple as possible relative to T1 enhancement that could provide useful information to the physician at the individual level to adapt or tune therapeutic strategies. Also the use of other PET tracers such as amino acid tracers (¹¹C-Met; ¹⁸F-FET; ¹⁸F-FDOPA) might also provide very accurate information in mapping region potentially involved in tumor recurrence. For the hot spot-study, given the method of calculation for each patient, a potential overinterpretation of low activity could occur. As a consequence comparison of our results to include the peripheral volumes or the hot-spot in the therapeutic strategies to stereotaxic biopsies would also be of great importance. We are now
incorporating this strategy in ongoing clinical trials.

## CONCLUSION

Even if it is difficult to draw a general overview for each individual patient, this study underlines the complementary value of using different multiparametric imaging to assess tumor heterogeneity, and to define tumor volume and sub volumes that are likely to be resistant to conventional therapies.

## DISCLOSURE

Authors declare that they have no conflict of interest.

## ACKNOWLEDGEMENTS

Conseil Régional de Normandie, Elen Fund, Institut National du Cancer (grant #RECF1475), Agence Nationale de la Recherche-Labex IRON (ANR-11-LABX-0018-01). We are grateful for Dr Aurélien Corroyer-Dulmont for his helpful comments.

## KEY POINTS

**QUESTION:** Identify the spatial relationship between proliferation, vascularization and hypoxia in preoperative glioblastoma patients, with respect to the contrast enhancement area on T1w-Gd images.

**PERTINENT FINDINGS:** Clinical trials demonstrating the heterogeneity of the three parameters measured, namely proliferation, vasculature and hypoxia over the classically used contrast-enhancement.

**IMPLICATIONS FOR PATIENT CARE:** This study clearly demonstrates the significance of incorporating more functional parameters for patient management.
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<table><caption>Table 1: flowchart of the study (patients were followed by 18F-FLT PET along with MRI and/or by 18F-FMISO PET along with MRI)</caption><thead><tr><th rowspan="3">Patient #</th><th colspan="3">"FLT" study</th></tr><tr><th colspan="3">"Hyponco" study</th></tr><tr><th><sup>18</sup>F-FLT PET</th><th>rCBV MRI</th><th><sup>18</sup>F-FMISO PET</th></tr></thead><tbody><tr><td>2</td><td>✓</td><td>✓</td><td></td></tr><tr><td>3</td><td>✓</td><td>✓</td><td></td></tr><tr><td>4</td><td>✓</td><td>✓</td><td></td></tr><tr><td>5</td><td>✓</td><td>✓</td><td></td></tr><tr><td>6</td><td>✓</td><td>✓</td><td></td></tr><tr><td>7</td><td>✓</td><td>✓</td><td></td></tr><tr><td>8</td><td>✓</td><td>✓</td><td></td></tr><tr><td>9</td><td>✓</td><td>✓</td><td></td></tr><tr><td>10</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>11</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>12</td><td>✓</td><td>✓</td><td></td></tr><tr><td>13</td><td>✓</td><td>✓</td><td></td></tr><tr><td>14</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>15</td><td></td><td>✓</td><td>✓</td></tr><tr><td>16</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>17</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>18</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>19</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>20</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>21</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>22</td><td></td><td>✓</td><td>✓</td></tr><tr><td>23</td><td></td><td>✓</td><td>✓</td></tr><tr><td>24</td><td></td><td>✓</td><td>✓</td></tr><tr><td>25</td><td></td><td>✓</td><td>✓</td></tr><tr><td>26</td><td></td><td>✓</td><td>✓</td></tr><tr><td>27</td><td></td><td>✓</td><td>✓</td></tr><tr><td>28</td><td></td><td>✓</td><td>✓</td></tr><tr><td>29</td><td></td><td>✓</td><td>✓</td></tr><tr><td>30</td><td></td><td>✓</td><td>✓</td></tr><tr><td>31</td><td></td><td>✓</td><td>✓</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Figure 1: multimodal imaging of two glioblastoma patients with 3DT1w-Gd, ¹⁸F-FLT PET, rCBV MRI and ¹⁸F-FMISO PET.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2: example of proliferative volume in green, hypervascularized volume in blue and hypoxic volume in pink segmented (top) and overlaid 3DT1w-Gd image (bottom).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3: percentage of peripheral volume of ¹⁸F-FLT, rCBV and ¹⁸F-FMISO outside contrast enhancement volume.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4: example of proliferative hotspot (in green), hypervascularized hotspot (in blue) and hypoxic hotspot (in pink) segmented (top) and overlaid on T1w-Gd image (bottom).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 5: percentage of peripheral hotspots volume of ¹⁸F-FLT, rCBV and ¹⁸F-FMISO outside contrast enhancement (CE).</figcaption></figure>
Graphical Abstract

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>**Supplemental Figure 1:** Representative example of the various segmentation methodologies used for FLT images.</figcaption></figure>

Panel A represents 40% of SUV max segmentation (yellow ROI) overlaid on FLT image, panel B represents Mean Contralateral + 3.3 SD (blue ROI) overlaid on FLT image. Panel C represents FLAB (red ROI) overlaid on FLT image.
<figure><img src="image_12.png" /><figcaption>**Supplemental Figure 2:** Representative examples of the various segmentation methodologies used for FMISO images. Panel A represents 1.2 T/B segmentation (cyan ROI) overlaid on FMISO image. Panel B represents 40% of SUV max segmentation (yellow ROI) overlaid on FMISO image. Panel C represents mean Contralateral + 3.3 SD (blue ROI) overlaid on FMISO image. Panel D represents FLAB (red ROI) overlaid on FMISO image</figcaption></figure>
<figure><img src="image_13.png" /></figure>

**Supplemental Figure 3:** Representative examples of the two segmentation strategies used for rCBV segmentation. Yellow overlay represents a threshold at 2-3 time greater than NAWM and the Cyan overlay represents a mean contralateral +1.96 SD. Arrows refer to suspicious regions that are excluded after masking with the FLAIR ROI.
<figure><img src="image_14.png" /><figcaption>**Supplemental Figure 4** : Multimodal imaging of a glioblastoma patient and the respective segmentation registered and overlayed on the T1w-Gd image.</figcaption></figure>