Differentiating Glioma Recurrence and Pseudoprogression by APTw CEST MRI.

PurposeRadiological assessment of primary brain neoplasms, both high (HGG) and low grade tumors (LGG), based on contrast-enhancement alone can be inaccurate. We evaluated the radiological value of amide proton transfer weighted (APTw) MRI as an imaging complement for pre-surgical radiological diagnosis of brain tumors.MethodsTwenty-six patients were evaluated prospectively; (22 males, 4 females, mean age 55 years, range 26–76 years) underwent MRI at 3T using T1-MPRAGE pre- and post-contrast administration, conventional T2w, FLAIR, and APTw imaging pre-surgically for suspected primary/secondary brain tumor. Assessment of the additional value of APTw imaging compared to conventional MRI for correct pre-surgical brain tumor diagnosis. The initial radiological pre-operative diagnosis was based on the conventional contrast-enhanced MR images. The range, minimum, maximum, and mean APTw signals were evaluated.Conventional normality testing was performed; with boxplots/outliers/skewness/kurtosis and a Shapiro–Wilk’s test. Mann-Whitney U for analysis of significance for mean/max/min and range APTw signal. A logistic regression model was constructed for mean, max, range and Receiver Operating Characteristic (ROC) curves calculated for individual and combined APTw signalsResultsConventional radiological diagnosis prior to surgery/biopsy was HGG (8 patients), LGG (12 patients), and metastasis (6 patients). Using the mean and maximum: APTw signal would have changed the pre-operative evaluation the diagnosis in 8 of 22 patients (two LGGs excluded, two METs excluded). Using a cut off value of >2.0% for mean APTw signal integral, 4 of the 12 radiologically suspected LGG would have been diagnosed as high grade glioma, which was confirmed by histopathological diagnosis. APTw mean of >2.0% and max >2.48% outperformed four separate clinical radiological assessments of tumor type, P-values = .004 and = .002, respectively.ConclusionsUsing APTw-images as part of the daily clinical pre-operative radiological evaluation may improve diagnostic precision in differentiating LGGs from HGGs, with potential improvement of patient management and treatment.

Predicting Preoperative Pathologic Grades of Bladder Cancer Using Intravoxel Incoherent Motion and Amide Proton Transfer-Weighted Imaging

H3K27M-mutant associated brainstem glioma (BSG) carries a very poor prognosis. We aimed to predict H3K27M mutation status by amide proton transfer-weighted (APTw) imaging and radiomic features. Eighty-one BSG patients with APTw imaging at 3T MR and known H3K27M status were retrospectively studied. APTw values (mean, median, and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student's T/Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine learning algorithm (support vector machine) in randomly selected train (n = 64) and test (n = 17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks, and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm. BSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, sensitivity of 0.92, and specificity of 0.80 in the test set. Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71-0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, sensitivity of 0.88, and specificity of 0.85 for predicting H3K27M-mutation status. BSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTw-derived radiomics could accurately predict a H3K27M-mutant status in BSG patients.

The aim of the study is to investigate the efficacy of amide proton transfer-weighted (APT) imaging combined with serum squamous cell carcinoma antigen (SCC-Ag) in grading cervical cancer. Sixty-three patients with surgically confirmed cervical SCC were enrolled and categorized into 3 groups: highly differentiated (G1), moderately differentiated (G2), and poorly differentiated (G3). The diagnostic efficacies of APT imaging and serum SCC-Ag, alone or in combination, for grading cervical SCC were compared. The APT values measured by the 2 observers were in excellent agreement (intraclass correlation coefficient >0.75). Mean (± standard deviation) APT values for the high, moderate, and poor differentiation groups were 2.542 ± 0.215% (95% confidence interval [CI]: 2.423-2.677), 2.784 ± 0.175% (95% CI: 2.701-2.856), and 3.120 ± 0.221% (95% CI: 2.950-3.250), respectively. APT values for groups G2 and G3 were significantly higher than those for G1 ( P < 0.05). APT values for identifying cervical SCC in groups G1 and G2, G2 and G3, and G1 and G3, had areas under the receiver operating characteristic curve, sensitivities, and specificities of 0.815 (95% confidence interval [CI]: 0.674-0.914), 82.1%, and 72.2%, 0.882 (95% CI: 0.751-0.959), 70.6%, and 92.7%, and 0.961 (95% CI: 0.835-0.998), 94.1%, and 94.4%, respectively. APT values were significantly and positively correlated with the histological grade of cervical SCC (Spearman's correlation [ rs ] = 0.731, P < 0.01). Serum SCC-Ag levels for the high, moderate, and poor differentiation groups were 1.60 (0.88-4.63) ng/mL, 4.10 (1.85-6.98) ng/mL, and 26.10 (9.65-70.00) ng/mL, respectively. The differences were statistically significant only between groups G1 and G3 and G2 and G3 ( P < 0.05), whereas the differences between groups G1 and G2 were not statistically significant ( P > 0.05). Spearman's analysis revealed a positive correlation between SCC-Ag levels and the histological grade of cervical SCC ( rs = 0.573, P < 0.01). The diagnostic efficacy of APT imaging for the histological grading of cervical SCC was better than that of serum SCC-Ag, and the discriminatory efficacy of the combination of the 2 parameters was better than that of either alone. The diagnostic efficacy of APT imaging was better than that of serum SCC-Ag, and the combined diagnostic utility of APT and SCC-Ag was better than that of the individual parameters.

To quantify the accuracy of amide proton transfer-weighted (APTw) MRI for identifying active glioma after treatment via radiographically guided stereotactic tissue validation.Experimental Design: Twenty-one patients who were referred for surgery for MRI features concerning for tumor progression versus treatment effect underwent preoperative APTw imaging. Stereotactic biopsy samples were taken from regions of interest with varying APTw signal intensities. The relationship between final clinical pathology and the histopathology of each of the 64 specimens was analyzed relative to APTw results. Analysis of confirmed recurrent tumor or treatment effect tissue was used to perform ROC analysis. Eighteen of 21 patients had recurrent tumor, and 3 had treatment effect on clinical pathology. In 12 patients, there were multiple histopathologic assignments confirmed within the same tumor. Of the 64 total specimens, 20 specimens were active glioma, 27 mixed active and quiescent glioma, and 17 quiescent/no identifiable tumor. APTw signal intensity and histopathologic assignment, cellularity, and proliferation index had significant positive correlations (R = 0.651, 0.580, and 0.458, respectively; all P < 0.001). ROC analysis with a 1.79% APTw intensity cutoff differentiated active from nonactive tumor (AUC of 0.881) with 85.1% sensitivity and 94.1% specificity. Analysis of clinical pathology showed the mean APTw intensity for each patient had 94.4% sensitivity and 100% positive predictive value for identifying recurrent glioma at this cutoff. APTw imaging hyperintensity may be a marker of active malignant glioma. It is able to distinguish between regions of heterogeneous abnormality on anatomic brain MRI with high sensitivity and specificity.

&lt;div&gt;AbstractPurpose:&lt;p&gt;To quantify the accuracy of amide proton transfer-weighted (APTw) MRI for identifying active glioma after treatment via radiographically guided stereotactic tissue validation.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Experimental Design:&lt;/b&gt; Twenty-one patients who were referred for surgery for MRI features concerning for tumor progression versus treatment effect underwent preoperative APTw imaging. Stereotactic biopsy samples were taken from regions of interest with varying APTw signal intensities. The relationship between final clinical pathology and the histopathology of each of the 64 specimens was analyzed relative to APTw results. Analysis of confirmed recurrent tumor or treatment effect tissue was used to perform ROC analysis.&lt;/p&gt;Results:&lt;p&gt;Eighteen of 21 patients had recurrent tumor, and 3 had treatment effect on clinical pathology. In 12 patients, there were multiple histopathologic assignments confirmed within the same tumor. Of the 64 total specimens, 20 specimens were active glioma, 27 mixed active and quiescent glioma, and 17 quiescent/no identifiable tumor. APTw signal intensity and histopathologic assignment, cellularity, and proliferation index had significant positive correlations (&lt;i&gt;R&lt;/i&gt; = 0.651, 0.580, and 0.458, respectively; all &lt;i&gt;P&lt;/i&gt; &lt; 0.001). ROC analysis with a 1.79% APTw intensity cutoff differentiated active from nonactive tumor (AUC of 0.881) with 85.1% sensitivity and 94.1% specificity. Analysis of clinical pathology showed the mean APTw intensity for each patient had 94.4% sensitivity and 100% positive predictive value for identifying recurrent glioma at this cutoff.&lt;/p&gt;Conclusions:&lt;p&gt;APTw imaging hyperintensity may be a marker of active malignant glioma. It is able to distinguish between regions of heterogeneous abnormality on anatomic brain MRI with high sensitivity and specificity.&lt;/p&gt;&lt;/div&gt;

The purpose of this study was to evaluate the value of amide proton transfer-weighted (APTw) MRI radiomic features for the differentiation of tumor recurrence from treatment effect in malignant gliomas. Eighty-six patients who had suspected tumor recurrence after completion of chemoradiation or radiotherapy, and who had APTw-MRI data acquired at 3 T, were retrospectively analyzed. Using a fluid-attenuated inversion recovery (FLAIR) image-based mask, radiomics analysis was applied to the processed APTw and structural MR images. A chi-square automatic interaction detector decision tree was used for classification analysis. Models with and without APTw features were built using the same strategy. Tenfold cross-validation was applied to obtain the overall classification performance of each model. Sixty patients were confirmed as having tumor recurrence, and the remainder were confirmed as having treatment effect, at median time points of 190 and 171 days after therapy, respectively. There were 525 radiomic features extracted from each of the processed APTw and structural MR images. Based on these, the APTw-based model yielded the highest accuracy (86.0%) for the differentiation of tumor recurrence from treatment effect, compared with 74.4%, 76.7%, 83.7%, and 76.7% for T1 w, T2 w, FLAIR, and Gd-T1 w, respectively. Model classification accuracy was 82.6% when using the combined structural MR images (T1 w, T2 w, FLAIR, Gd-T1 w), and increased to 89.5% when using these structural plus APTw images. The corresponding sensitivity and specificity were 85.0% and 76.9% for the combination of structural MR images, and 85.0% and 100% after adding APTw image features. Adding APTw-based radiomic features increased MRI accuracy in the assessment of the treatment response in post-treatment malignant gliomas.

Sleep deprivation leads to hippocampal injury. Proteostasisdisturbanceis an important mechanism linking sleep deprivation and hippocampalinjury. However, identifying noninvasive imaging biomarkers for hippocampalproteostasis disturbance remains challenging. Amide proton transfer-weighted(APTw) imaging is a chemical exchange saturation transfer techniquebased on the amide protons in proteins and peptides. We aimed to explorethe ability of APTw imaging in detecting sleep deprivation-inducedhippocampal proteostasis disturbance and its biological significance,as well as its biological basis. In vitro, the feasibility of APTwimaging in detecting changes of the protein state was evaluated, demonstratingthat APTw imaging can detect alterations in the protein concentration,conformation, and aggregation state. In vivo, the hippocampal APTwsignal declined with increased sleep deprivation time and was significantlylower in sleep-deprived rats than that in normal rats. This signalwas positively correlated with the number of surviving neurons countedin Nissl staining and negatively correlated with the expression ofglucose-regulated protein 78 evaluated in immunohistochemistry. Differentiallyexpressed proteins in proteostasis network pathways were identifiedin the hippocampi of normal rats and sleep-deprived rats via massspectrometry proteomics analysis, providing the biological basis forthe change of the hippocampal APTw signal in sleep-deprived rats.These findings demonstrate that APTw imaging can detect hippocampalproteostasis disturbance induced by sleep deprivation and reflectthe extent of neuronal injury and endoplasmic reticulum stress.

To evaluate the assumption in amide proton transfer weighted (APTw) imaging that the APT dominates over the relayed nuclear Overhauser enhancement (rNOE) and other CEST effects such as those from amines/guanidines, thereby providing imaging of mobile proteins/peptides. We introduced two auxiliary asymmetric analysis metrics that can vary the relative contributions from amine/guanidinium CEST and other effects. By comparing these metrics with the conventional asymmetric analysis metric on healthy rat brains, we can approximately assess the contribution from amines/guanidines to APTw and determine whether the APT dominates over the rNOE effect. To further investigate the molecular origin of APTw, we used samples of dialyzed tissue homogenates to eliminate small metabolites and supernatants of homogenates to separate lipids from other components. When the APTw signal is positive using high saturation amplitudes (e.g., 2-3 μT), the contributions from amines/guanidines are significant and cannot be ignored. The APTw signal from the dialyzed homogenates and the controls has negligible changes, indicating that it primarily originates from macromolecules rather than small metabolites. Additionally, the APTw signals with low saturation amplitudes (e.g., 1 μT) were negative in tissue homogenates but positive in their supernatants, suggesting that proteins contribute positively to APTw signals, whereas lipids contribute negatively to it. The positive APTw signal using high saturation amplitudes could have significant contributions from soluble proteins through CEST, including amide/amine/guanidine proton transfer effects. In contrast, the negative APTw signal using low saturation amplitudes has significant contribution from lipids through rNOE.

To investigate the value of pre-treatment amide proton transfer-weighted (APTw) imaging for predicting survival of patients with nasopharyngeal carcinoma (NPC). Pre-treatment APTw imaging was performed in 77 NPC patients and the mean, 90th percentile, skewness, and kurtosis of APT asymmetry (APTmean, APT90, APTskewness, and APTkurtosis, respectively) were obtained from the primary tumor. Associations of APTw parameters with locoregional relapse-free survival (LRRFS), distant metastasis-free survival (DMFS), and disease-free survival (DFS) after 2 years were assessed by univariable Cox regression analysis and significant APTw parameters, together with age, sex, treatment, and stage as confounding variables, were added to the multivariable model. Kaplan-Meier analysis was used to determine the prognostic significance of patients with high or low APT values based on a threshold value from receiver operating characteristic curve analysis. Locoregional relapse, distant metastases, and disease relapse occurred in 14/77 (18%), 10/77 (13%), and 20/77 (26%) patients, respectively, at a median follow-up of 48.3 (10.6-67.4) months. Univariable analysis showed significant associations of LRRFS with APTskewness (HR = 1.98; p = 0.034), DMFS with APTmean (HR = 2.44; p = 0.033), and APT90 (HR = 1.93; p = 0.009), and DFS with APTmean (HR = 2.01; p = 0.016), APT90 (HR = 1.68; p = 0.009), and APTskewness (HR = 1.85; p = 0.029). In multivariable analysis, the significant predictors for DMFS were APT90 (HR = 3.51; p = 0.004) and nodal stage (HR = 5.95; p = 0.034) and for DFS were APT90 (HR = 1.97; p = 0.010) and age (HR = 0.92; p = 0.014). An APT90 ≥ 4.38% was associated with a significantly poorer DFS at 2 years than APT90 < 4.38% (66% vs. 91%; HR = 4.01; p = 0.005). APTw imaging may potentially predict survival in patients with NPC. • APTw imaging may provide new markers to predict survival in nasopharyngeal carcinoma. • APT90 is an independent predictor of distant metastases-free survival and disease-free survival. • The APThigh group is at higher risk of disease relapse than the APTlow group.

To evaluate the utility of amide proton transfer-weighted (APTw) MRI imaging and its derived radiomics in classifying adult-type diffuse glioma. In this prospective study, APTw imaging was performed on 129 patients with adult-type diffuse gliomas. The mean APTw-related metrics (chemical exchange saturation transfer ratio (CESTR), CESTR normalized with the reference value (CESTRnr), and relaxation-compensated inverse magnetization transfer ratio (MTRRex)) and radiomic features within 3D tumor masks were extracted. APTw-radiomics models were developed using a support vector machine (SVM) classifier. Sensitivity analysis with tumor area of interest, different histogram cutoff values, and other classifiers were conducted. CESTR, CESTRnr, and MTRRex in glioblastomas were all significantly higher (p < 0.0003) than those of oligodendrogliomas and astrocytomas, with no significant difference between oligodendrogliomas and astrocytomas. The APTw-related metrics for IDH-wildtype and high-grade gliomas were significantly higher (p < 0.001) than those for the IDH-mutant and low-grade gliomas, with area under the curve (AUCs) of 0.88 for CESTR. The CESTR-radiomics models demonstrated accuracies of 84% (AUC 0.87), 83% (AUC 0.83), 90% (AUC 0.95), and 84% (AUC 0.86) in predicting the IDH mutation status, differentiating glioblastomas from astrocytomas, distinguishing glioblastomas from oligodendrogliomas, and determining high/low grade prediction, respectively, but showed poor performance in distinguishing oligodendrogliomas from astrocytomas (accuracy 63%, AUC 0.63). The sensitivity analysis affirmed the robustness of the APTw signal and APTw-derived radiomics prediction models. APTw imaging, along with its derived radiomics, presents a promising quantitative approach for prediction IDH mutation and grading adult-type diffuse glioma. Amide proton transfer-weighted imaging, a quantitative imaging biomarker, coupled with its derived radiomics, offers a promising non-invasive approach for predicting IDH mutation status and grading adult-type diffuse gliomas, thereby informing individualized clinical diagnostics and treatment strategies. • This study evaluates the differences of different amide proton transfer-weighted metrics across three molecular subtypes and their efficacy in classifying adult-type diffuse glioma. • Chemical exchange saturation transfer ratio normalized with the reference value and relaxation-compensated inverse magnetization transfer ratio effectively predicts IDH mutation/grading, notably the first one. • Amide proton transfer-weighted imaging and its derived radiomics holds potential to be used as a diagnostic tool in routine clinical characterizing adult-type diffuse glioma.

BackgroundNon-invasive preoperative prediction of lymphovascular space invasion (LVSI) in cervical cancer (CC) is clinically important for guiding surgical planning and adjuvant therapy, while avoiding the risks associated with invasive procedures. However, current studies using amide proton transfer-weighted (APTw) MRI for LVSI prediction typically analyze only the mean values from a limited number of intratumoral regions of interest (ROIs), which fails to fully capture tumor heterogeneity. This study investigates the added value of whole-tumor APTw habitat radiomics in predicting LVSI and its advantages over conventional analysis methods.MethodsThis prospective study included consecutive adult patients with suspected CC who underwent APTw MRI between December 2022 and December 2024; a portion of the cohort has been reported previously. APTw values were extracted using two methods: (1) the conventional approach, calculating the mean signal from three ROIs on a representative slice; and (2) habitat radiomics, involving whole-tumor segmentation, k-means clustering to identify functional subregions, and radiomic feature extraction. Pathological assessment of LVSI from hysterectomy specimens served as the reference standard. Multivariable logistic regression identified variables associated with LVSI and developed diagnostic models. Model robustness was evaluated by 5-fold cross-validation, with AUC and DeLong’s test used for performance assessment.ResultsAmong 124 patients (74 LVSI−, 50 LVSI+), the APTw_h3 model achieved a higher AUC (0.796 [95% CI: 0.709–0.882]) for predicting LVSI positivity than the clinical-radiological model (AUC = 0.733, 95% CI: 0.638–0.817). The combined model integrating clinical, radiological, and APTw_h3 features achieved the highest AUC (0.903, 95% CI: 0.841–0.952), which was significantly higher than those of both the clinical-radiological and APTw_h3 models (both P < 0.001). Moreover, the addition of APTw_h3 to the clinical-radiological model improved sensitivity (88% vs. 82%) and specificity (83.8% vs. 64.9%) for determining LVSI positivity.ConclusionWhole-tumor APTw habitat radiomics demonstrates superior performance over conventional mean-value APTw analysis for preoperative prediction of LVSI in CC. Notably, integrating habitat radiomic features with clinical and radiological parameters further improves predictive accuracy, demonstrating potential for enhanced individualized patient management.

Malignant gliomas remain among the most challenging brain tumors to manage, with high recurrence rates and difficulty in distinguishing progression from treatment-related changes. While the peritumoral region offers valuable insights into tumor behavior, its quantitative MRI characteristics remain largely underexplored. This study investigated the diagnostic and prognostic value of multiparametric MRI biomarkers from noncontrast-enhancing peritumoral regions, particularly the surrounding nonenhancing FLAIR hyperintensity (SNFH). Thirty-eight patients with treated malignant gliomas underwent amide proton transfer-weighted (APTw) imaging, T1 and T2 mapping, and diffusion-weighted imaging. Histogram analysis of signal intensities from the enhancing tumor, immediate SNFH (≤ 10 mm), and distant SNFH regions was performed. Progression cases typically showed APTw hyperintensity in enhancing tumor cores and surrounding SNFH, while response cases showed minimal hyperintensity to isointensity in these regions. Repeated measures ANOVA revealed significant parameter variation across the tumor and SNFH regions, with APTw showing the largest dynamic range. Logistic regression models trained on histogram features from APTw and T1 maps achieved a maximum test area under the curve (AUC) of 0.79 in differentiating progression from response. In survival analysis, parameters including higher APTw skewness was associated with better overall survival (hazard ratio = 0.32; p = 0.006). These findings demonstrate that APTw and T1 imaging biomarkers from peritumoral edema regions reflect key features of tumor invasion and metabolic activity. Their combined application has potential to improve diagnostic accuracy and inform prognosis in posttreatment high-grade gliomas.

Identification of ovarian endometriotic cysts in cystic lesions of the ovary by amide proton transfer-weighted imaging and R2∗ mapping

Amide Proton Transfer-Weighted MRI and Diffusion-Weighted Imaging in Bladder Cancer: A Complementary Tool to the VI-RADS