Amide Proton Transfer Imaging for Differentiating Gliomas and Meningiomas and Assessing Tumor Infiltration

Podsumowanie

This study evaluates Amide Proton Transfer (APT) imaging combined with conventional magnetic resonance imaging (MRI) to differentiate meningiomas from low- and high-grade gliomas and assess imaging features related to tumor infiltration, demonstrating its potential to improve preoperative classification and provide imaging-based information that may inform surgical planning.

Streszczenie

Differentiating gliomas from meningiomas and assessing imaging features related to glioma infiltration remain challenging with conventional magnetic resonance imaging (MRI). Amide Proton Transfer (APT) imaging, a chemical exchange saturation transfer technique, can detect changes in tissue protein content and pH, offering potential functional insights into brain tumors. This retrospective study evaluated APT imaging combined with conventional MRI in 105 patients with pathologically confirmed brain tumors (55 meningiomas, 20 low-grade gliomas [LGGs], and 30 high-grade gliomas [HGGs]). APT-derived mean signal intensity (APT_mean) and the ratios of APT abnormal area to T2 hyperintense area (RAPT/T2) and to contrast-enhancing area (RAPT/E) were measured and compared among groups. APTmean was significantly higher in HGGs (median 4.60%) than in LGGs (2.95%) and meningiomas (2.70%, P < 0.001). RAPT/T2 and RAPT/E were also markedly elevated in HGGs. For distinguishing HGGs from meningiomas, APTmean achieved excellent diagnostic performance (area under the curve [AUC] = 0.969), compared with RAPT/E (0.836) and RAPT/T2 (0.800). For differentiating LGGs from meningiomas, RAPT/E (AUC = 0.781) and RAPT/T2 (AUC = 0.734) showed good performance, whereas APT_mean did not differ significantly. For glioma grading (HGG vs. LGG), APTmean yielded an AUC of 0.916, superior to contrast enhancement degree (0.823). These findings indicate that APT imaging effectively distinguishes meningiomas from gliomas of different grades and provides valuable imaging-based information for inferring the extent of tumor infiltration. The combination of APTmean and spatial extent ratios provides a practical imaging tool for improving preoperative diagnosis and supporting surgical planning, particularly in challenging atypical cases.

Wprowadzenie

Intracranial tumors are among the most common disorders of the nervous system. Their incidence is increasing globally, posing a serious threat to human health¹. Based on histopathological origin, intracranial tumors can be classified into two major categories: primary and secondary (metastatic). Among primary intracranial tumors, meningioma and glioma represent the two most prevalent types with distinctly different origins and biological behaviors². Glioma, originating from glial cells, is the most common malignant intracranial tumor, characterized by infiltrative growth, ill-defined borders, a high tendency for recurrence, and a poor prognosis^3,4. The World Health Organization (WHO) classifies gliomas into grades I-IV based on their malignancy. Among these, high-grade gliomas (HGGs, WHO grades III-IV) are highly invasive and are associated with a short patient survival period^5,6. In contrast, meningioma originates from arachnoid cap cells. The vast majority are benign (WHO grade I), grow slowly, and have clear boundaries with a distinct demarcation from the surrounding brain tissue. Following complete surgical resection, patients typically have a favorable prognosis^7,8. Therefore, accurate preoperative imaging differentiation between these two tumor types, along with assessment of imaging features related to the extent of glioma infiltration, holds critical clinical significance. It is essential for formulating individualized surgical plans, determining safe resection margins, and ultimately improving patient outcomes.

Currently, magnetic resonance imaging (MRI) has become a widely used modality for preoperative diagnosis and assessment of intracranial tumors due to its superior soft tissue resolution, multi-parameter imaging capabilities, and the advantage of being non-ionizing^9,10. Conventional MRI sequences, such as T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI), can clearly reveal the tumor's location, size, morphology, internal structure, and its relationship with adjacent tissues^11,12. Typical meningiomas appear on MRI as well-defined extra-axial masses, exhibiting iso- or slightly hypointense signals on T1WI, iso- or slightly hyperintense signals on T2WI, with significant and homogeneous enhancement post-contrast, often accompanied by a "dural tail sign"¹³. In contrast, high-grade gliomas (HGGs) typically present as irregularly shaped, poorly defined masses. They are often surrounded by extensive peritumoral edema visible on T2WI/FLAIR sequences and demonstrate heterogeneous ring-like or garland-like enhancement after contrast administration^14,15.

However, conventional MRI still faces numerous challenges in clinical practice. Regarding differential diagnosis, some cases present with atypical features. For instance, certain HGGs may exhibit a relatively well-defined, homogeneously enhancing "pseudocapsule," making them difficult to distinguish from meningiomas. Conversely, some high-grade, cellular atypical or anaplastic meningiomas (WHO grades II-III), due to their invasive growth patterns, may also radiologically mimic gliomas¹⁶. Furthermore, conventional MRI has limited ability to accurately assess the presumed extent of glioma infiltration. The hyperintense region observed on T2WI/FLAIR sequences has been pathologically confirmed to be an admixture of tumor cell infiltration, vasogenic edema, and reactive gliosis. Conventional MRI is unable to effectively distinguish these components¹⁷. Similarly, the enhancing region visualized on CE-T1WI primarily represents the tumor core with a disrupted blood-brain barrier. A significant number of proliferative tumor cells have already infiltrated beyond this enhancing region, into and sometimes beyond the peritumoral T2 hyperintense area within the normal-appearing brain parenchyma^18,19. This mismatch between the radiological and pathological boundaries is a key contributor to the high post-surgical recurrence rate of gliomas. Consequently, there is an urgent clinical need for novel molecular imaging tools that can more directly and sensitively reflect tumor cellularity and metabolic activity.

Amide Proton Transfer (APT) imaging is an emerging molecular magnetic resonance imaging technique. It operates based on the Chemical Exchange Saturation Transfer (CEST) mechanism^20,21. By selectively saturating the amide protons (-NH) on the backbone of mobile proteins and peptides within tissues and transferring this magnetization to surrounding water molecules, it indirectly detects changes in tissue protein content and intracellular/extracellular pH^22,23. In the tumor microenvironment, malignant cells typically exhibit intense proliferative activity, leading to a significant increase in protein anabolism²⁴. Concurrently, enhanced glycolysis (the Warburg effect) in tumor tissues often results in acidification of the extracellular microenvironment²⁵. These pathophysiological alterations generally cause APT signals to appear characteristically hyperintense in high-grade malignancies. Furthermore, APT has demonstrated potential value in differentiating between glioma recurrence and radiation necrosis, offering complementary information to conventional MRI and other functional imaging techniques²⁶. This is because recurrent tumor tissue, characterized by high cellular density and active metabolism, exhibits high APT signals. In contrast, radiation necrosis, composed primarily of cellular debris and inflammatory reaction, presents with low APT signals. These findings have been corroborated by clinical studies demonstrating the superiority of APT over conventional MRI in distinguishing tumor recurrence from treatment-related changes²⁷.

However, current research predominantly focuses on glioma grading or differentiation from other intracranial pathologies, such as metastases or lymphoma^28,29,30. Systematic studies specifically investigating the utility of APT imaging for distinguishing meningiomas from gliomas of varying grades, particularly those encompassing both low-grade and high-grade gliomas, remain relatively limited. Furthermore, whether the area of APT signal abnormality may serve as an imaging surrogate that better correlates with the extent of cellular infiltration in gliomas, especially high-grade gliomas (HGGs), warrants further in-depth investigation.

Clinically, APT imaging may be valuable for preoperative differentiation of HGGs from meningiomas and for assessing imaging features related to glioma infiltration, especially when conventional MRI is equivocal. However, routine implementation faces practical challenges: prolonged acquisition time increases motion risk; signal quantification is sensitive to field inhomogeneities and post-processing algorithms, requiring standardized protocols; and APT metrics should complement, not replace, conventional MRI due to potential overlap in some LGGs and benign meningiomas. For adoption, we recommend starting with challenging cases and gradually integrating into routine practice once local expertise and quality assurance are established. Ultimately, APT imaging offers unique biological information but is best used judiciously within a multiparametric MRI framework.

Prior work by Zhang et al.³¹ has preliminarily explored the utility of APT imaging in distinguishing meningiomas from gliomas, proposing the RAPT/T2 and RAPT/E ratios as potential markers of tumor infiltration. However, that study was limited by a relatively small sample size (50 patients) and did not systematically evaluate the diagnostic performance of APT parameters compared with conventional MRI features across all clinically relevant pairwise distinctions, using comprehensive receiver operating characteristic (ROC) curve analyses. Furthermore, the clinical implications of APT-defined spatial extent abnormalities for surgical and radiotherapy planning were not thoroughly discussed. Based on this rationale, this study aims to retrospectively analyze APT and conventional MRI data from patients with pathologically confirmed meningiomas and gliomas of different grades. We systematically compare APT parameters between these two tumor types to evaluate the diagnostic efficacy of APT imaging for the preoperative differentiation of meningiomas from gliomas. Concurrently, by comparing the spatial extent of the APT abnormality region with that of the T2WI hyperintensity and CE-T1WI enhancement areas, this study preliminarily explores the potential application value of APT imaging as a radiological tool for inferring the infiltrative growth boundary of tumors. The objective is to provide clinicians with imaging-derived information that may strengthen the foundation for surgical planning, although such inferences await pathological validation.

Protokół

This study protocol adhered to the ethical principles outlined in the Declaration of Helsinki and was approved by the Shenzhen Bao'an District Songgang People's Hospital ethics review board (Approval number: IRB-YJ-2025-045). Given its retrospective design, the study involved only the analysis of archived clinical and imaging data. All patient-identifiable information was strictly anonymized.

Research design and subjects

This study is a retrospective diagnostic analysis. We retrospectively collected clinical and imaging data from brain tumor patients admitted to the Neurosurgical Oncology Departments of Songgang People's Hospital, Bao'an District, Shenzhen, and Shenzhen Second People's Hospital between January 2024 and June 2025. All patients had pathologically confirmed diagnoses post-surgery. A total of 105 patients were finally included, all of whom underwent both conventional MRI and APT imaging examinations preoperatively. According to the 2021 WHO Classification of Tumors of the Central Nervous System³², the patients were divided into three groups: Meningioma Group: 55 cases, pathologically confirmed as WHO grades I-II. Low-Grade Glioma (LGG) Group: 20 cases, pathologically confirmed as WHO grades I-II. HGG Group: 30 cases, pathologically confirmed as WHO grades III-IV. All patients underwent both conventional MRI and APT imaging examinations preoperatively. The detailed study flowchart is presented in Figure 1.

Inclusion and exclusion criteria

Inclusion criteria were (1) patients undergoing initial surgical resection with a definitive histopathological diagnosis of meningioma (WHO grades I-II), LGGs (WHO grades I-II), or HGGs (WHO grades III-IV); (2) completion of non-contrast head MRI, T1-weighted contrast-enhanced scanning, and APT imaging within one week prior to surgery; (3) availability of complete imaging data with good image quality, free from significant artifacts, suitable for diagnostic evaluation and measurements; (4) availability of complete clinical data and follow-up records.

Exclusion criteria were (1) patients who had received any anti-tumor therapy (e.g., radiotherapy, chemotherapy, targeted therapy, or immunotherapy) prior to surgery; (2) patients with recurrent brain tumors or the presence of other intracranial space-occupying lesions; (3) patients with contraindications to MRI examination; (4) images with severe motion artifacts or susceptibility artifacts that would affect subsequent data analysis; (5) pathological diagnosis of central nervous system tumor types other than those specified above.

Sample size calculation

This study was a retrospective diagnostic analysis, and the sample size was primarily determined by all available cases that met the inclusion criteria during the study period. To evaluate whether the current sample size was sufficient to detect statistically significant differences in APT parameters between groups, we referred to differences in APT signal values between different-grade gliomas and meningiomas reported in previous literature³³. Setting α = 0.05 and a statistical power of 0.80, and using a two-sample mean comparison formula for estimation, we determined that a minimum of 15 samples per group was required. Based on the final inclusion of 105 patients (55 meningiomas, 20 LGGs, 30 HGGs), a post-hoc power analysis showed that the power to detect differences in APTmean among the three groups exceeded 0.95 with the current sample size. This indicates that the study sample size was adequate to support statistical inference.

Image acquisition and post processing

MRI scanning protocol:

All patients were scanned using a magnetic resonance (MR) scanner with a standard 8-channel head coil ³⁴. The scanning sequences and parameters were as follows:

Conventional sequences: Axial T1-weighted imaging (T1WI) was performed using a three-dimensional spoiled gradient-echo sequence (3D T1-SPGR, repetition time/echo time [TR/TE] = 8.5/3.4 ms, flip angle = 12°, slice thickness = 1.2 mm, interslice gap = 0 mm, field of view [FOV] = 24 cm × 24 cm, matrix = 256 × 256). Axial T2-weighted imaging (T2WI) was acquired with a fast spin-echo sequence (TR/TE = 4,500/102 ms, slice thickness = 5 mm, interslice gap = 1.5 mm, FOV = 24 cm × 24 cm, matrix = 384 × 224). Fluid-attenuated inversion recovery (FLAIR) images were obtained with repetition time/echo time/inversion time (TR/TE/TI) = 9,000/120/2,250 ms.

Contrast-enhanced scanning: After intravenous bolus injection of gadopentetate dimeglumine at a standard dose of 0.1 mmol/kg body weight and a flow rate of 2 mL/s, followed by a 20 mL saline flush, axial, sagittal, and coronal contrast-enhanced T1WI images were acquired using the same parameters as the precontrast 3D T1-SPGR sequence.

APT imaging sequence: APT imaging was performed using a two-dimensional gradient-echo pulse sequence with the following parameters: TR/TE = 3,000/3.5 ms, slice thickness = 5 mm, FOV = 24 cm × 24 cm, matrix = 128 × 128, number of excitations = 2, and a total acquisition time of 4 min and 32 s. A continuous-wave saturation pulse with a duration of 2 s and a power (B₁) of 2 µT was applied at frequency offsets ranging from −6 to +6 parts per million (ppm) (in steps of 0.5 ppm), including the water resonance frequency at 0 ppm. Under the main magnetic field (B₀), the saturation pulse was specifically applied at the downfield resonance frequency of +3.5 ppm relative to the water proton resonance (0 ppm), corresponding to the amide proton chemical exchange rate. Prior to APT acquisition, B₀ inhomogeneity was corrected using a water saturation shift referencing (WASSR) approach with saturation parameters identical to the main APT scan but at a reduced B₁ amplitude of 0.5 µT. After scanning, APT-weighted images and magnetization transfer ratio asymmetry (MTRasym) maps at 3.5 ppm were automatically generated by the scanner's built-in software by calculating the asymmetry of the magnetization transfer ratio: MTRasym(3.5 ppm)=[S(−3.5 ppm) − S(+3.5 ppm)] / S(0 ppm) where S(Δω) represents the signal intensity with the saturation pulse applied at the indicated frequency offset Δω relative to the water resonance. The positive MTRasym value at 3.5 ppm primarily reflects the amide proton transfer effect, as the magnetization transfer (MT) and nuclear Overhauser effects are approximately symmetric about the water resonance and thus canceled out by the asymmetry calculation.

Image analysis:

All imaging data were independently analyzed by two neuroradiologists with over 5 years of experience using an image-viewing software workstation. Region of Interest (ROI) delineation was performed according to the following standardized procedures:

Tumor solid region: On the contrast-enhanced T1WI images, an ROI was manually drawn along the enhancing margin of the tumor, carefully avoiding necrotic, cystic, and peritumoral edema areas. The ROI was drawn to encompass the entire solid enhancing component on the slice with the maximum tumor diameter.

T2 Hyperintense region: On the T2WI images, an ROI was manually drawn to encompass the entire hyperintense area, including both the tumor and the surrounding peritumoral edema. The boundary was defined as the visible margin at which the hyperintensity met the normal-appearing brain parenchyma.

APT Abnormal signal region: On the APT-weighted MTRasym maps, using the contralateral normal-appearing white matter as a reference, an ROI was manually drawn to include all areas with signal intensity higher than the mean signal of the reference normal tissue plus 2 standard deviations. This threshold-based approach was applied to the same slice as the maximum tumor diameter. The contralateral reference ROI was placed in the centrum semiovale at a mirror location to the tumor, with an area of approximately 100 mm².

For consistency, each ROI was measured three times on consecutive slices centered at the maximum tumor diameter, and the average value was recorded for analysis. All ROI delineations were performed independently by the two reviewers, and any discrepancies were resolved by consensus. Interobserver agreement was assessed using the intraclass correlation coefficient (ICC), with an ICC > 0.80 indicating excellent agreement.

The following parameters were measured and calculated:

Mean APT Signal Intensity (APT_mean): The average MTRasym value at 3.5 ppm within the ROI of the tumor solid region.

Regional ratio calculation:

RAPT/T2: The ratio of the area of the APT abnormal signal region to the area of the T2WI hyperintense region (RAPT / RT2).

RAPT/E: The ratio of the area of the APT abnormal signal region to the area of the T1WI contrast-enhancing region (RAPT / RE).

Clinical outcome measures

The primary evaluation measures of this study were imaging parameters and their diagnostic efficacy, rather than therapeutic clinical outcomes. Primary Imaging Outcomes: APT_mean values for each group (meningioma, LGGs, HGG), and the RAPT/T2 and RAPT/E ratios for the LGG and HGG groups.

Statistical analysis

Continuous variables were tested for normality using the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard deviation (SD) and compared with one-way analysis of variance (ANOVA) (Bonferroni post-hoc). Non-normal data and ordinal data are presented as M (Q₁, Q₃) and compared using the Kruskal-Wallis H test (Dunn's test with Bonferroni correction). Categorical data are presented as n (%) and compared using the Chi-square or Fisher’s exact test. ROC curves were plotted to evaluate diagnostic performance. All tests were two-sided; P < 0.05 was considered significant.

Wyniki

Charakterystyka wyjściowa i kliniczna

Zaobserwowano istotne różnice pomiędzy trzema grupami pacjentów pod względem wieku, wyniku w skali Karnofsky'ego (KPS), początkowych objawów oraz stopnia wg WHO (P < 0,01). Pacjenci w grupie HGG byli starsi, mieli niższe wyniki KPS i częściej zgłaszali ogniskowe deficyty neurologiczne jako pierwszy objaw. Z kolei u pacjentów w grupie LGG początkowym objawem były głównie napady drgawkowe. Nie stwierdzono istotnych różnic pomiędzy trzema ...

Dyskusja

Accurately distinguishing between gliomas and meningiomas preoperatively and precisely delineating presumed tumor infiltration boundaries on imaging constitute core challenges in neuro-oncology, directly impacting treatment strategies and patients' ultimate prognosis. This study, based on APT, an emerging molecular magnetic resonance technique, systematically evaluated its application value in differentiating gliomas of various grades from meningiomas. Furthermore, by introducing the spatial extent ratio parameter, w...

Materiały

Lista materiałów użytych w tym artykule

Nazwa	Firma	Numer katalogowy	Komentarze
3.0T MR Scanner	GE Healthcare	Discovery 750W	Used for all conventional and APT imaging sequences.
Contrast Agent (Gadopentetate Dimeglumine)	Bayer Healthcare	Magnevist	Standard dose of 0.1 mmol/kg body weight, administered intravenously at 2 mL/s.
Head Coil	GE Healthcare	Standard 8-channel	Receive-only head coil for signal acquisition.
Image Analysis Software	MicroDicom Ltd.	MicroDicom Viewer (version 3.0)	Used for ROI delineation and parameter measurement by neuroradiologists.
Post-processing Software	GE Healthcare	FuncTool (built-in)	Automatically generates APT-weighted images and MTRasym maps on the scanner.
Statistical Analysis Software	IBM Corp.	SPSS (version 25.0)	Used for all statistical analyses, including ROC curves and group comparisons.