Skip to main content
Erschienen in: EJNMMI Research 1/2024

Open Access 01.12.2024 | Short communication

A histogram of [18F]BBPA PET imaging differentiates non-neoplastic lesions from malignant brain tumors

verfasst von: Ziren Kong, Zhu Li, Junyi Chen, Yixin Shi, Nan Li, Wenbin Ma, Yu Wang, Zhi Yang, Zhibo Liu

Erschienen in: EJNMMI Research | Ausgabe 1/2024

Hinweise
Ziren Kong, Zhu Li, and Junyi Chen have contributed equally to this work.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
AUC
Area under the ROC curve
BAA
Boramino acids
BBPA
Trifluoroborate boronophenylalanine
BNCT
Boron neutron capture therapy
BPA
4‑Boronophenylalanine
GTR
Gross total resection
IDH
Isocitrate dehydrogenase
KPS
Karnofsky Performance Score
MRI
Magnetic resonance imaging
MTV
Metabolic tumor volume
PET
Positron emission tomography
RANO
Response Assessment in Neuro-Oncology
RECIST
Response Evaluation Criteria in Solid Tumors
ROI
Region of interest
SUV
Standard uptake value
TLA
Total lesion activity
T/N ratio
Tumor-to-normal brain ratio
WHO
World Health Organization

Introduction

Differentiating treatment response from tumor progression is fundamental but challenging for almost all oncological subjects, as the treatment strategy is effective and should be insisted in the former situation, while the therapeutic regimen is invalid and necessitates substitutions in the latter circumstances [1]. However, radiotherapy or immunotherapy may induce pseudo-progression, a transient increase of tumor volume due to tumor cell lysis or immune cell infiltration followed by delayed tumor shrinkage, and is difficult for early clinical and radiological identification [1, 2]. In malignant brain tumors, 10–30% of tumors showed pseudo-progression following radiotherapy, immunotherapy and targeted therapy [36], some of which were not restricted to the recent onset of treatment [7, 8]. In addition, alternative non-neoplastic conditions such as radiation necrosis or inflammation may also mimic neoplasms and warrant appropriate distinction [3]. Response Evaluation Criteria in Solid Tumors (RECIST) and Response Assessment in Neuro­Oncology (RANO) have been proposed [9, 10], yet the performance in distinguishing treatment response from tumor progression remains to be improved [1114].
Boramino acids (BAA) are a class of amino acid biosimilars with the boron trifluoride group (–BF3) to replace the carboxyl group (–COOH) of amino acids, which mimics the corresponding amino acid in biological recognition and transportation [15]. The 18F-19F isotope exchange reaction of boron trifluoride moiety allows the molecule to be mildly radiolabeled and can facilitate tumor theranostics through identical chemical structure (the only difference between positron emission tomography [PET] diagnosis and boron neutron capture therapy [BNCT] for treatment is 18F or 19F) [1520]. The first-in-human study of this class of PET tracers demonstrated sufficient safety, clean background and high tumor activity in malignant brain tumors [21, 22], validating the concept and potential clinical value of boron amino acids. Subsequently, trifluoroborate boronophenylalanine (BBPA) that replaced the carboxyl group (–COOH) of 4‑boronophenylalanine (BPA) with boron trifluoride group (-BF3) was synthesized and is recognized as the next generation of boron amino acids thanks to the doubled boron delivery efficiency [23].
This study raised a [18F]BBPA PET-based approach to differentiate non-neoplastic lesions from proliferating tumors, aiming to provide a non-invasive method to uncover true lesion property. A total of 21 patients were included and underwent [18F]BBPA PET and contrast-enhanced magnetic resonance imaging (MRI) scans. Both neoplastic and non-neoplastic lesions exhibited elevated [18F]BBPA radioactivity and cannot be distinguished by traditional parameters. Histograms of the standard uptake value (SUV) within region of interest (ROI) were plotted, and the malignant tumors exhibited a symmetrical distribution (similar to normal distribution), while the non-neoplastic lesions displayed a positive skewed (left deviated) distribution. Such difference can be further quantified by skewness and tendency, providing an alternative method for differential diagnosis.

Methods

[18F]BBPA PET/CT and MRI acquisition

[18F]BBPA PET/CT and MRI were performed within 1 week on separate days. For [18F]BBPA PET/CT, a dose of 3.7 MBq (0.1 mCi)/kg [18F]BBPA was intravenously given, and a PET/CT scan was acquired using a Biograph mCT Flow 64 scanner (Siemens, Germany) 30 min after injection. The PET image was transferred into an SUV map that was normalized by body weight and decay factor. For MRI, contrast-enhanced T1-weighted MRI (matrix 256 × 256, slice thickness 1 mm, gadolinium chelate 0.1 mmol/kg) and T2-weighted MRI (matrix 256 × 256, slice thickness 5–6 mm) were acquired from a 3.0 T Discovery MR750 scanner (GE, USA).
[18F]BBPA PET/CT image and T2-weighted MRI were co-registered to the thin-slice contrast-enhanced T1-weighted MRI to unify the origin and direction of images, allowing the same region of interest (ROI) refers to identical area in different image modality.

Patients enrollment

Patients that were suspected to have primary or metastatic brain tumors were enrolled under the following criteria: (1) age ≥ 18 years; (2) Karnofsky Performance Score (KPS) ≥ 80; (3) suspected to have malignant gliomas or metastatic brain tumors based on medical history, clinical and radiological evaluation; (4) no contradictions for PET/CT and MRI scan. The pathological diagnosis was established by two neuropathologists according to the 2021 WHO classification for central nervous system tumors [24]. The therapeutic strategies, including but not limited to, surgery, radiotherapy, pharmacological treatment, or close imaging follow-up, were determined by a multi-disciplinary team after PET/CT and MRI scans.

Tumor segmentation

Three spherical reference regions of interest (ROIref) with a diameter of 1 cm were manually placed on the contralateral area (mirroring the position of the tumor) to calculate the maximum and mean SUV of the normal brain (generating Nmax and Nmean, respectively) [22].
The ROI of the lesion was delineated by the definition of gross total resection (GTR) for brain tumors, which includes the contrast-enhanced region for significantly contrast-enhanced tumors or the region with abnormal T2-weighted signal for non-significantly contrast-enhanced tumors. The ROI was semi-automatically delineated and manually revised by a neurosurgeon on the thin-slice T1-weighted MRI using 3D Slicer (4.11.2, www.​slicer.​org). The ROI was subsequently applied to the co-registered BBPA PET images for feature calculation and histogram analysis.

Traditional feature calculation

Five traditional quantitative parameters, namely SUVmax, SUVmean, metabolic tumor volume (MTV), total lesion activity (TLA) and tumor-to-normal brain ratio (T/N ratio), were calculated [25]. SUVmax and SUVmean represent the maximum and mean SUV of ROI, while MTV and TLA calculate the volume and total radioactivity inside ROI. The T/N ratio was calculated as the ratio of SUVmax and Nmax.

Histogram plotting and quantification

The SUV of each voxel within ROI was documented as a number series, and a histogram was plotted to visualize the voxel value distribution. Skewness and tendency were defined to reflect the histogram characteristics:
$${\text{Skewness}}=\frac{\frac{1}{{{\text{N}}}_{{\text{p}}}}\sum_{{\text{i}}=1}^{{{\text{N}}}_{{\text{p}}}}({\text{X}}({\text{i}})-\overline{{\text{X}}}{)}^{3}}{{\left(\sqrt{\frac{1}{{{\text{N}}}_{{\text{p}}}}\sum_{{\text{i}}=1}^{{{\text{N}}}_{{\text{p}}}}({\text{X}}({\text{i}})-\overline{{\text{X}}}{)}^{2}}\right)}^{3}}$$
where X refers to all voxel values included in the ROI, \({{\text{N}}}_{{\text{p}}}\) refers to the number of voxel within ROI.
$${\text{Tendency}}={{\text{SUV}}}_{{\text{mean}}}-{{\text{SUV}}}_{{\text{median}}}$$
where SUVmean and SUVmedian refer to the mean and median SUV value within ROI.

Statistical analysis

Images were processed and segmented on 3D slicer (4.11.2, www.​slicer.​org). The Wilcoxon rank-sum test was applied to evaluate whether a parameter was significantly different in distinct circumstances. Statistical analysis were performed using Python (3.8.5, www.​python.​org) and R (4.0.4, www.​r-project.​org).

Results

Elevated [18F]BBPA activity in both neoplastic and non-neoplastic lesions

Twenty-one patients who were suspected of primary or recurrent malignant brain tumors were enrolled. Ten patients were primary brain tumors (all pathologically confirmed), 8 patients were metastatic brain tumors (5 pathologically confirmed, 3 diagnosed according to patient history and imaging characteristics), and 3 patients were non-neoplastic lesions (1 pathological confirmed, 2 verified based on history, imaging behavior and treatment outcome). The baseline characteristics of the enrolled patients are displayed in Table 1.
Table 1
Baseline characteristics of the enrolled patients
Characteristics
Population
Age (mean ± SD)
54.8 ± 12.5
Sex
 Male
11 (52.4%)
 Female
10 (47.6%)
Primary/recurrent diffuse gliomas
10 (47.6%)
WHO grade III
1 (4.8%)
IDH-mutant, 1p/19q-codeleted
1 (4.8%)
WHO grade IV
9 (42.9%)
IDH-wildtype
7 (33.3%)
IDH-mutant, 1p/19q-intact
1 (4.8%)
H3K27M-mutant
1 (4.8%)
Metastatic brain tumors
8 (38.1%)
Lung origin
2 (9.5%)
Breast origin
2 (9.5%)
Pancreatic origin
1 (4.8%)
Esophageal origin
1 (4.8%)
Renal origin
1 (4.8%)
Lymphatic origin
1 (4.8%)
Non-neoplastic lesion
3 (14.3%)
Radiation necrosis
2 (9.5%)
Viral encephalitis
1 (4.8%)
Unless otherwise noted, data in the table refers to the number and percentages of patients/tumors
SD standard deviation, WHO World Health Organization
All lesions exhibited elevated [18F]BBPA radioactivity, with SUVmax of 2.56 ± 0.57, T/N ratio of 19.7 ± 5.1 in the whole population. However, the traditional metabolic parameters (SUVmax, SUVmean, MTV, TLA and T/N ratio) were not able to distinguish neoplastic and non-neoplastic lesions (p = 0.269–0.975) SUVmax were 2.52 ± 0.61 and 2.75 ± 0.21, and T/N ratio were 19.2 ± 5.3 and 22.7 ± 2.2 in neoplastic and non-neoplastic lesions, respectively. Traditional [18F]BBPA metabolic parameters in neoplasms and non-neoplastic lesions are demonstrated in Table 2.
Table 2
Traditional metabolic parameters of [18F]BBPA in neoplasms and non-neoplastic lesions
Diagnosis
SUVmax
SUVmean
MTV
TLA
T/N ratio
Malignant brain tumor
2.52 ± 0.61
1.08 ± 0.31
29.6 ± 38.9
31.4 ± 35.6
19.2 ± 5.3
Non-neoplastic lesion
2.75 ± 0.21
0.89 ± 0.31
30.4 ± 50.9
17.3 ± 28.1
22.7 ± 2.2
P value
0.543
0.328
0.975
0.524
0.269
Statistical properties of each parameter were displayed as mean ± standard deviation. Independent sample t test was utilized to compare the differences between groups
SUV standard uptake value, MTV metabolic tumor volume, TLA total lesion activity, T/N ratio tumor-to-normal brain ratio

[18F]BBPA histogram distinguishes neoplastic and non-neoplastic lesions

The histogram that reflects the voxel value distribution within ROI was plotted to visualize the metabolic characteristics of [18F]BBPA-PET. The neoplastic lesions (including both primary and metastatic tumors) exhibited a symmetrical distribution that can be fitted as a normal distribution. On the other hand, the non-neoplastic lesions (radiation necrosis and viral encephalitis) displayed a positive skewed (left deviated) distribution which was conspicuously varied from a normal distribution. Flowchart and examples of [18F]BBPA-PET histogram are displayed in Fig. 1.
Skewness represents the extent of the histogram varied from a normal distribution, with positively skewed (left deviated) and negatively skewed (right deviated) distributions exhibiting positive and negative values, respectively. The neoplastic histograms revealed higher similarity to a normal distribution with a skewness of 0.145 ± 0.337, while the non-neoplastic cases were significantly positively skewed with a skewness of 0.935 ± 0.448 (P = 0.002). Tendency, calculated as the subtraction of SUVmean and SUVmedian, exhibited a significantly smaller value in neoplastic lesions than non-neoplastic lesions (0.001 ± 0.038 vs. 0.123 ± 0.021, P < 0.001). Statistical properties of skewness and tendency are illustrated in Table 3.
Table 3
Histogram parameters of [18F]BBPA in neoplasms and non-neoplastic lesions
Diagnosis
Skewness
Tendency
Malignant brain tumor
0.145 ± 0.337
0.001 ± 0.038
Non-neoplastic lesion
0.935 ± 0.448
0.123 ± 0.022
P value
0.002
< 0.001
Threshold
0.624
0.084
Statistical properties of each parameter were displayed as mean ± standard deviation. Independent sample t test was utilized to compare the differences between groups
The capability of [18F]BBPA histogram to distinguish neoplastic and non-neoplastic lesions was further verified in 3 recent clinical scenarios. In a newly diagnosed glioblastoma (World Health Organization [WHO] grade IV, isocitrate dehydrogenase [IDH] wild-type), [18F]BBPA histogram separated the central necrosis (skewness 1.019, tendency 0.064) from the ring-like proliferating tumors (skewness 0.191, tendency 0.013), whom metabolic characteristics was suggestive of glioblastoma. In a post-radiation metastatic breast cancer, [18F]BBPA histogram identified tumor progression (skewness −0.043, tendency −0.017) earlier than MRI. In another post-radiation metastatic lung cancer, [18F]BBPA histogram recognized the lesion as radiation necrosis instead of tumor recurrence (skewness 0.721, tendency 0.109) and guide patient management (no anti-tumor treatment was given and the lesion remained radiologically stable at 1 year follow-up). Images and histograms of the 3 cases are displayed in Fig. 2.

Discussion

Differentiating neoplastic and non-neoplastic lesions (i.e., inflammation, necrosis, anti-tumor immune response) remains a critical clinical issue at both initial diagnosis and treatment follow-up. Amino acid tracers such as [18F]FET were investigated to distinguish tumor progression and treatment-related changes, with a T/N ratio displayed accuracy of 0.70 and area under the ROC curve (AUC) of 0.75 at a cutoff value of 1.95 [26]. However, considerable situations were not identified by traditional parameters, and both neoplastic and non-neoplastic lesions exhibited elevated [18F]BBPA activity. Histogram was further proposed for differential diagnosis, and the SUV of a normal or neoplastic area with regional heterogeneity (e.g., [18F]FDG in the brain, [18F]FDG or [18F]FLT in head and neck squamous cell carcinoma) are expected to be normal distribution [27, 28]. The non-neoplastic lesions displayed positively skewed (left deviated) voxel value distribution that was visually differed from the normally distributed neoplastic lesions on the histogram, and can be further quantified by skewness and tendency, providing an alternative method for differential diagnosis. The clinical impact is further demonstrated in recent cases, in which [18F]BBPA PET identified the lesion properties earlier than traditional methods. Therefore, the histogram of [18F]BBPA PET might aid the differentiation of neoplastic and non-neoplastic lesions and ultimately facilitate the accurate treatment decisions.
The histogram analysis may be applied to other circumstances (i.e., other disease or radiotracers) with low background activity and high lesion uptake, and the segmentation is preferably conducted on alternative imaging modality rather than PET image (threshold-based PET segmentation would result in a clear boundary on histogram). However, the current study had several limitations including a small sample size (particularly for non-neoplastic lesions) and a short follow-up period (unable to demonstrate the prognostic value of [18F]BBPA histogram). For future works, a well-designed prospective study with balanced cohort and longitudinal follow-up is necessary to validate the findings, and an in-depth exploration of the mechanism underlying the [18F]BBPA histogram differences is necessitated. In conclusion, the histogram of [18F]BBPA PET can differentiate non-neoplastic lesions from proliferating tumors and would facilitate the precision diagnosis and patient management.

Acknowledgement

Not applicable.

Declarations

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of the Peking University Cancer Hospital (ID 2021KT38), and written informed consent was obtained from all participants.
Not applicable.

Competing interests

ZLiu is the consultant of Boomray Pharmaceuticals (Beijing) Co., Ltd.; other authors reported no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Literatur
2.
Zurück zum Zitat Nishino M, Hatabu H, Johnson BE, McLoud TC. State of the art: response assessment in lung cancer in the era of genomic medicine. Radiology. 2014;271(1):6–27.CrossRefPubMed Nishino M, Hatabu H, Johnson BE, McLoud TC. State of the art: response assessment in lung cancer in the era of genomic medicine. Radiology. 2014;271(1):6–27.CrossRefPubMed
3.
Zurück zum Zitat Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 2008;9(5):453–61.CrossRefPubMed Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 2008;9(5):453–61.CrossRefPubMed
4.
Zurück zum Zitat Chen X, Lim-Fat MJ, Qin L, et al. A comparative retrospective study of immunotherapy RANO versus standard RANO criteria in glioblastoma patients receiving immune checkpoint inhibitor therapy. Front Oncol. 2021;11:679331.CrossRefPubMedPubMedCentral Chen X, Lim-Fat MJ, Qin L, et al. A comparative retrospective study of immunotherapy RANO versus standard RANO criteria in glioblastoma patients receiving immune checkpoint inhibitor therapy. Front Oncol. 2021;11:679331.CrossRefPubMedPubMedCentral
5.
Zurück zum Zitat Wen PY, van den Bent M, Youssef G, et al. RANO 2.0: update to the response assessment in neuro-oncology criteria for high- and low-grade gliomas in adults. J Clin Oncol. 2023;41(33):5187–99.CrossRefPubMed Wen PY, van den Bent M, Youssef G, et al. RANO 2.0: update to the response assessment in neuro-oncology criteria for high- and low-grade gliomas in adults. J Clin Oncol. 2023;41(33):5187–99.CrossRefPubMed
6.
Zurück zum Zitat Youssef G, Rahman R, Bay C, et al. Evaluation of standard response assessment in neuro-oncology, modified response assessment in neuro-oncology, and immunotherapy response assessment in neuro-oncology in newly diagnosed and recurrent glioblastoma. J Clin Oncol. 2023;41(17):3160–71.CrossRefPubMed Youssef G, Rahman R, Bay C, et al. Evaluation of standard response assessment in neuro-oncology, modified response assessment in neuro-oncology, and immunotherapy response assessment in neuro-oncology in newly diagnosed and recurrent glioblastoma. J Clin Oncol. 2023;41(17):3160–71.CrossRefPubMed
7.
Zurück zum Zitat Nasseri M, Gahramanov S, Netto JP, et al. Evaluation of pseudoprogression in patients with glioblastoma multiforme using dynamic magnetic resonance imaging with ferumoxytol calls RANO criteria into question. Neuro Oncol. 2014;16(8):1146–54.CrossRefPubMedPubMedCentral Nasseri M, Gahramanov S, Netto JP, et al. Evaluation of pseudoprogression in patients with glioblastoma multiforme using dynamic magnetic resonance imaging with ferumoxytol calls RANO criteria into question. Neuro Oncol. 2014;16(8):1146–54.CrossRefPubMedPubMedCentral
8.
Zurück zum Zitat Yang S, Ma Y, Xu Y, et al. Dosimetric and clinical analysis of pseudo-progression versus recurrence after hypo-fractionated radiotherapy for brain metastases. Radiat Oncol. 2023;18(1):30.CrossRefPubMedPubMedCentral Yang S, Ma Y, Xu Y, et al. Dosimetric and clinical analysis of pseudo-progression versus recurrence after hypo-fractionated radiotherapy for brain metastases. Radiat Oncol. 2023;18(1):30.CrossRefPubMedPubMedCentral
9.
Zurück zum Zitat Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 2017;18(3):e143–52.CrossRefPubMedPubMedCentral Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 2017;18(3):e143–52.CrossRefPubMedPubMedCentral
10.
Zurück zum Zitat Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963–72.CrossRefPubMed Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963–72.CrossRefPubMed
11.
Zurück zum Zitat Tensaouti F, Khalifa J, Lusque A, et al. Response Assessment in neuro-oncology criteria, contrast enhancement and perfusion MRI for assessing progression in glioblastoma. Neuroradiology. 2017;59(10):1013–20.CrossRefPubMed Tensaouti F, Khalifa J, Lusque A, et al. Response Assessment in neuro-oncology criteria, contrast enhancement and perfusion MRI for assessing progression in glioblastoma. Neuroradiology. 2017;59(10):1013–20.CrossRefPubMed
12.
Zurück zum Zitat Rowe LS, Butman JA, Mackey M, et al. Differentiating pseudoprogression from true progression: analysis of radiographic, biologic, and clinical clues in GBM. J Neurooncol. 2018;139(1):145–52.CrossRefPubMedPubMedCentral Rowe LS, Butman JA, Mackey M, et al. Differentiating pseudoprogression from true progression: analysis of radiographic, biologic, and clinical clues in GBM. J Neurooncol. 2018;139(1):145–52.CrossRefPubMedPubMedCentral
13.
Zurück zum Zitat Rodriguez D, Chambers T, Warmuth-Metz M, et al. Evaluation of the implementation of the response assessment in neuro-oncology criteria in the HERBY trial of pediatric patients with newly diagnosed high-grade gliomas. AJNR Am J Neuroradiol. 2019;40(3):568–75.PubMedPubMedCentral Rodriguez D, Chambers T, Warmuth-Metz M, et al. Evaluation of the implementation of the response assessment in neuro-oncology criteria in the HERBY trial of pediatric patients with newly diagnosed high-grade gliomas. AJNR Am J Neuroradiol. 2019;40(3):568–75.PubMedPubMedCentral
14.
Zurück zum Zitat Chawla S, Bukhari S, Afridi OM, et al. Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma. NMR Biomed. 2022;35(7):e4719.CrossRefPubMedPubMedCentral Chawla S, Bukhari S, Afridi OM, et al. Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma. NMR Biomed. 2022;35(7):e4719.CrossRefPubMedPubMedCentral
16.
Zurück zum Zitat Li J, Shi Y, Zhang Z, et al. A metabolically stable boron-derived tyrosine serves as a theranostic agent for positron emission tomography guided boron neutron capture therapy. Bioconjug Chem. 2019;30(11):2870–8.CrossRefPubMed Li J, Shi Y, Zhang Z, et al. A metabolically stable boron-derived tyrosine serves as a theranostic agent for positron emission tomography guided boron neutron capture therapy. Bioconjug Chem. 2019;30(11):2870–8.CrossRefPubMed
17.
Zurück zum Zitat Lan X, Fan K, Cai W. First-in-human study of an (18)F-labeled boramino acid: a new class of PET tracers. Eur J Nucl Med Mol Imaging. 2021;48(10):3037–40.CrossRefPubMedPubMedCentral Lan X, Fan K, Cai W. First-in-human study of an (18)F-labeled boramino acid: a new class of PET tracers. Eur J Nucl Med Mol Imaging. 2021;48(10):3037–40.CrossRefPubMedPubMedCentral
18.
Zurück zum Zitat Liu Z, Ehlerding EB, Cai W, Lan X. One-step synthesis of an (18)F-labeled boron-derived methionine analog: a substitute for (11)C-methionine? Eur J Nucl Med Mol Imaging. 2018;45(4):582–4.CrossRefPubMedPubMedCentral Liu Z, Ehlerding EB, Cai W, Lan X. One-step synthesis of an (18)F-labeled boron-derived methionine analog: a substitute for (11)C-methionine? Eur J Nucl Med Mol Imaging. 2018;45(4):582–4.CrossRefPubMedPubMedCentral
19.
Zurück zum Zitat Chen J, Li C, Hong H, et al. Side chain optimization remarkably enhances the in vivo stability of (18)F-labeled glutamine for tumor imaging. Mol Pharm. 2019;16(12):5035–41.CrossRefPubMed Chen J, Li C, Hong H, et al. Side chain optimization remarkably enhances the in vivo stability of (18)F-labeled glutamine for tumor imaging. Mol Pharm. 2019;16(12):5035–41.CrossRefPubMed
20.
Zurück zum Zitat Chen M, Wang C, Wang X, Tu Z, Ding Z, Liu Z. An "AND" logic-gated prodrug micelle locally stimulates anti-tumor immunity. Adv Mater. 2023:e2307818. Chen M, Wang C, Wang X, Tu Z, Ding Z, Liu Z. An "AND" logic-gated prodrug micelle locally stimulates anti-tumor immunity. Adv Mater. 2023:e2307818.
21.
Zurück zum Zitat Li Z, Kong Z, Chen J, et al. (18)F-boramino acid PET/CT in healthy volunteers and glioma patients. Eur J Nucl Med Mol Imaging. 2021;48(10):3113–21.CrossRefPubMed Li Z, Kong Z, Chen J, et al. (18)F-boramino acid PET/CT in healthy volunteers and glioma patients. Eur J Nucl Med Mol Imaging. 2021;48(10):3113–21.CrossRefPubMed
22.
Zurück zum Zitat Kong Z, Li Z, Chen J, et al. Metabolic characteristics of [(18)F]fluoroboronotyrosine (FBY) PET in malignant brain tumors. Nucl Med Biol. 2022;106–107:80–7.CrossRefPubMed Kong Z, Li Z, Chen J, et al. Metabolic characteristics of [(18)F]fluoroboronotyrosine (FBY) PET in malignant brain tumors. Nucl Med Biol. 2022;106–107:80–7.CrossRefPubMed
24.
Zurück zum Zitat Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021;23(8):1231–51.CrossRefPubMedPubMedCentral Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021;23(8):1231–51.CrossRefPubMedPubMedCentral
25.
Zurück zum Zitat Kong Z, Zhang Y, Liu D, et al. Role of traditional CHO PET parameters in distinguishing IDH, TERT and MGMT alterations in primary diffuse gliomas. Ann Nucl Med. 2021;35(4):493–503.CrossRefPubMed Kong Z, Zhang Y, Liu D, et al. Role of traditional CHO PET parameters in distinguishing IDH, TERT and MGMT alterations in primary diffuse gliomas. Ann Nucl Med. 2021;35(4):493–503.CrossRefPubMed
26.
Zurück zum Zitat Maurer GD, Brucker DP, Stoffels G, et al. (18)F-FET PET imaging in differentiating glioma progression from treatment-related changes: a single-center experience. J Nucl Med. 2020;61(4):505–11.CrossRefPubMed Maurer GD, Brucker DP, Stoffels G, et al. (18)F-FET PET imaging in differentiating glioma progression from treatment-related changes: a single-center experience. J Nucl Med. 2020;61(4):505–11.CrossRefPubMed
27.
Zurück zum Zitat Scarpelli M, Eickhoff J, Cuna E, Perlman S, Jeraj R. Optimal transformations leading to normal distributions of positron emission tomography standardized uptake values. Phys Med Biol. 2018;63(3):035021.CrossRefPubMed Scarpelli M, Eickhoff J, Cuna E, Perlman S, Jeraj R. Optimal transformations leading to normal distributions of positron emission tomography standardized uptake values. Phys Med Biol. 2018;63(3):035021.CrossRefPubMed
28.
Zurück zum Zitat Proesmans S, Raedt R, Germonpré C, et al. Voxel-Based Analysis of [18F]-FDG brain PET in rats using data-driven normalization. Front Med (Lausanne). 2021;8:744157.CrossRefPubMed Proesmans S, Raedt R, Germonpré C, et al. Voxel-Based Analysis of [18F]-FDG brain PET in rats using data-driven normalization. Front Med (Lausanne). 2021;8:744157.CrossRefPubMed
Metadaten
Titel
A histogram of [18F]BBPA PET imaging differentiates non-neoplastic lesions from malignant brain tumors
verfasst von
Ziren Kong
Zhu Li
Junyi Chen
Yixin Shi
Nan Li
Wenbin Ma
Yu Wang
Zhi Yang
Zhibo Liu
Publikationsdatum
01.12.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
EJNMMI Research / Ausgabe 1/2024
Elektronische ISSN: 2191-219X
DOI
https://doi.org/10.1186/s13550-024-01069-7

Weitere Artikel der Ausgabe 1/2024

EJNMMI Research 1/2024 Zur Ausgabe