Does multiparametric imaging with 18F-FDG-PET/MRI capture spatial variation in immunohistochemical cancer biomarkers in head and neck squamous cell carcinoma?
Ontology highlight
ABSTRACT: Background
The purpose of this study is to test if functional multiparametric imaging with 18F-FDG-PET/MRI correlates spatially with immunohistochemical biomarker status within a lesion of head and neck squamous cell carcinoma (HNSCC), and also whether a biopsy with the highest FDG uptake was more likely to have the highest PD-L1 expression or the highest percentage of vital tumour cells (VTC) compared with a random biopsy.Methods
Thirty-one patients with HNSCC were scanned on an integrated PET/MRI scanner with FDG prior to surgery in this prospective study. Imaging was quantified with SUV, ADC and Ktrans. A 3D-morphometric MRI scan of the specimen was used to co-register the patient and the specimen scans. All specimens were sectioned in consecutive slices, and slices from six different locations were selected randomly from each tumour. Core biopsies were performed to construct TMA blocks for IHC staining with the ten predefined biomarkers. The spatial correlation was assessed with a partial correlation analysis.Results
Twenty-eight patients with a total of 33 lesions were eligible for further analysis. There were significant correlations between the three imaging biomarkers and some of the IHC biomarkers. Moreover, a biopsy taken from the most FDG-avid part of the tumour did not have a statistically significantly higher probability of higher PD-L1 expression or VTC, compared with a random biopsy.Conclusion
We found statistically significant correlations between functional imaging parameters and key molecular cancer markers.
SUBMITTER: Rasmussen JH
PROVIDER: S-EPMC7341803 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectivesTo assess (I) correlations between diffusion-weighted (DWI), intravoxel incoherent motion (IVIM), dynamic contrast-enhanced (DCE) MRI, and 18F-FDG-PET/CT imaging parameters capturing tumor characteristics and (II) their predictive value of locoregional recurrence-free survival (LRFS) and overall survival (OS) in patients with head and neck squamous cell carcinoma (HNSCC) treated with (chemo)radiotherapy.MethodsBetween 2014 and 2018, patients with histopathologically proven HNSCC, planned for curative (chemo) radiotherapy, were prospectively included. Pretreatment clinical, anatomical, and functional imaging parameters (obtained by DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT) were extracted for primary tumors (PT) and lymph node metastases. Correlations and differences between parameters were assessed. The predictive value of LRFS and OS was assessed, performing univariable, multivariable Cox and CoxBoost regression analyses.ResultsIn total, 70 patients were included. Significant correlations between 18F-FDG-PET parameters and DWI-/DCE volume parameters were found (r > 0.442, p < 0.002). The combination of HPV (HR = 0.903), intoxications (HR = 1.065), PT ADCGTV (HR = 1.252), Ktrans (HR = 1.223), and Ve (HR = 1.215) was predictive for LRFS (C-index = 0.546; p = 0.023). N-stage (HR = 1.058), HPV positivity (HR = 0.886), hypopharyngeal tumor location (HR = 1.111), ADCGTV (HR = 1.102), ADCmean (HR = 1.137), D* (HR = 0.862), Ktrans (HR = 1.106), Ve (HR = 1.195), SUVmax (HR = 1.094), and TLG (HR = 1.433) were predictive for OS (C-index = 0.664; p = 0.046).ConclusionsFunctional imaging parameters, performing DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT, yielded complementary value in capturing tumor characteristics. More specific, intoxications, HPV-negative status, large tumor volume-related parameters, high permeability (Ktrans), and high extravascular extracellular space (Ve) parameters were predictive for adverse locoregional recurrence-free survival and adverse overall survival. Low cellularity (high ADC) and high metabolism (high SUV) were additionally predictive for decreased overall survival. These different predictive factors added to estimated locoregional and overall survival.Key points• Parameters of DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT were able to capture complementary tumor characteristics. • Multivariable analysis revealed that intoxications, HPV negativity, large tumor volume and high vascular permeability (Ktrans), and extravascular extracellular space (Ve) were complementary predictive for locoregional recurrence. • In addition to predictive parameters for locoregional recurrence, also high cellularity (low ADC) and high metabolism (high SUV) were complementary predictive for overall survival.
| S-EPMC7813703 | biostudies-literature
Project description:PurposeTo assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions.MethodsA population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar's test.ResultsEight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508).ConclusionA radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions.
| S-EPMC8803815 | biostudies-literature
Project description:PurposeThis study aimed to explore the utility of hippocampal radiomics using multiparametric simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) for early diagnosis of Alzheimer's disease (AD).MethodsA total of 53 healthy control (HC) participants, 55 patients with amnestic mild cognitive impairment (aMCI), and 51 patients with AD were included in this study. All participants accepted simultaneous PET/MRI scans, including 18F-fluorodeoxyglucose (18F-FDG) PET, 3D arterial spin labeling (ASL), and high-resolution T1-weighted imaging (3D T1WI). Radiomics features were extracted from the hippocampus region on those three modal images. Logistic regression models were trained to classify AD and HC, AD and aMCI, aMCI and HC respectively. The diagnostic performance and radiomics score (Rad-Score) of logistic regression models were evaluated from 5-fold cross-validation.ResultsThe hippocampal radiomics features demonstrated favorable diagnostic performance, with the multimodal classifier outperforming the single-modal classifier in the binary classification of HC, aMCI, and AD. Using the multimodal classifier, we achieved an area under the receiver operating characteristic curve (AUC) of 0.98 and accuracy of 96.7% for classifying AD from HC, and an AUC of 0.86 and accuracy of 80.6% for classifying aMCI from HC. The value of Rad-Score differed significantly between the AD and HC (p < 0.001), aMCI and HC (p < 0.001) groups. Decision curve analysis showed superior clinical benefits of multimodal classifiers compared to neuropsychological tests.ConclusionMultiparametric hippocampal radiomics using PET/MRI aids in the identification of early AD, and may provide a potential biomarker for clinical applications.
| S-EPMC11017421 | biostudies-literature
Project description:Background and purposePET/MRI with 18F-FDG has demonstrated the advantages of simultaneous PET and MR imaging in head and neck cancer imaging, MRI allowing excellent soft-tissue contrast, while PET provides metabolic information. The aim of this study was to evaluate the added value of gadolinium contrast-enhanced sequences in the tumor delineation of head and neck cancers on 18F-FDG-PET/MR imaging.Materials and methodsConsecutive patients who underwent simultaneous head and neck 18F-FDG-PET/MR imaging staging or restaging followed by surgery were retrospectively included. Local tumor invasion and lymph node extension were assessed in 45 head and neck anatomic regions using 18F-FDG-PET/MR imaging by 2 rater groups (each one including a radiologist and a nuclear medicine physician). Two reading sessions were performed, one without contrast-enhanced sequences (using only T1WI, T2WI, and PET images) and a second with additional T1WI postcontrast sequences. The results were compared with the detailed histopathologic analysis, used as reference standard. The κ concordance coefficient between the reading sessions and sensitivity and specificity for each region were calculated.ResultsThirty patients were included. There was excellent agreement between the contrast-free and postgadolinium reading sessions in delineating precise tumor extension in the 45 anatomic regions studied (Cohen κ = 0.96, 95% CI = [0.94-0.97], P < .001). The diagnostic accuracy did not differ between contrast-free and postgadolinium reading sessions, being 0.97 for both groups and both reading sessions. For the 2 rater groups, there was good sensitivity for both contrast-free (0.83 and 0.85) and postgadolinium reading sessions (0.88 and 0.90, respectively). Moreover, there was excellent specificity (0.98) for both groups and reading sessions.ConclusionsGadolinium chelate contrast administration showed no added value for accurate characterization of head and neck primary tumor extension and could possibly be avoided in the PET/MR imaging head and neck workflow.
| S-EPMC7661078 | biostudies-literature
Project description:PurposeTo investigate whether a machine learning (ML)-based radiomics model applied to 18F-FDG PET/MRI is effective in molecular subtyping of breast cancer (BC) and specifically in discriminating triple negative (TN) from other molecular subtypes of BC.MethodsEighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneous 18F-FDG PET/MRI of the breast. A 3D segmentation of BC lesion was performed on T2w, DCE, DWI and PET images. Quantitative diffusion and metabolic parameters were calculated and radiomics features extracted. Data were selected using the LASSO regression and used by a fine gaussian support vector machine (SVM) classifier with a 5-fold cross validation for identification of TNBC lesions.ResultsEight radiomics models were built based on different combinations of quantitative parameters and/or radiomic features. The best performance (AUROC 0.887, accuracy 82.8%, sensitivity 79.7%, specificity 86%, PPV 85.3%, NPV 80.8%) was found for the model combining first order, neighborhood gray level dependence matrix and size zone matrix-based radiomics features extracted from ADC and PET images.ConclusionA ML-based radiomics model applied to 18F-FDG PET/MRI is able to non-invasively discriminate TNBC lesions from other BC molecular subtypes with high accuracy. In a future perspective, a "virtual biopsy" might be performed with radiomics signatures.
| S-EPMC9406327 | biostudies-literature
Project description:To know tumor PD-L1 expression through IHC or the FDG-PET related radiomics, we investigated the association between programmed cell death protein 1 ligand (PD-L1) expression and immunohistochemical (IHC) biomarkers or textural features of 18F-fluoro-2-deoxdeoxyglucose positron emission tomography (18F-FDG PET) in 53 oropharyngeal or hypopharyngeal cancer patients who were ready to undergo radiotherapy-based treatment. Differences in textural features or biomarkers between tumors with and without PD-L1 expression were tested using a Mann-Whitney U test. The predicted values for PD-L1 expression were examined using logistic regression analysis. The mean percentages of tumor PD-L1 expression were 6.2 ± 13.5. Eighteen tumors had PD-L1 expression ≥5%, whereas 30 tumors ≥1%. Using a 5% cutoff, the p16 staining percentage and the textural index of correlation were two factors associated with PD-L1 expression. The odds ratios (ORs) were 17.00 (p = 0.028) and 0.009 (p = 0.015), respectively. When dichotomizing PD-L1 at 1%, the p16 and Ki-67 staining percentages were two predictors for PD-L1 expression with ORs of 11.41 (p = 0.035) and 757.77 (p = 0.045). p16 and Ki-67 staining percentages and several PET/CT-derived textural features can provide supplemental information to determine tumor PD-L1 expression in HNCs.
| S-EPMC5758832 | biostudies-literature
Project description:Knowledge of the within-subject variability of 18F-FDG PET/MRI measurements is necessary for proper interpretation of quantitative PET or MRI metrics in the context of therapeutic efficacy assessments with integrated PET/MRI scanners. The goal of this study was to determine the test-retest repeatability of these metrics on PET/MRI, with comparison to similar metrics acquired by PET/CT. Methods: This prospective study enrolled subjects with pathology-proven pelvic malignancies. Baseline imaging consisted of PET/CT immediately followed by PET/MRI, using a single 370-MBq 18F-FDG dose. Repeat imaging was performed within 7 d using an identical imaging protocol, with no oncologic therapy between sessions. PET imaging on both scanners consisted of a list-mode acquisition at a single pelvic station. The MRI consisted of 2-point Dixon imaging for attenuation correction, standard sequences for anatomic correlation, and diffusion-weighted imaging. PET data were statically reconstructed using various frame durations and minimizing uptake time differences between sessions. SUV metrics were extracted for both PET/CT and PET/MRI in each imaging session. Apparent diffusion coefficient (ADC) metrics were extracted for both PET/MRI sessions. Results: The study cohort consisted of 14 subjects (13 female, 1 male) with various pelvic cancers (11 cervical, 2 rectal, 1 endometrial). For SUVmax, the within-subject coefficient of variation (wCV) appeared higher for PET/CT (8.5%-12.8%) than PET/MRI (6.6%-8.7%) across all PET reconstructions, though with no significant repeatability differences (all P values ≥ 0.08) between modalities. For lean body mass-adjusted SUVpeak, the wCVs appeared similar for PET/CT (9.9%-11.5%) and PET/MRI (9.2%-11.3%) across all PET reconstructions, again with no significant repeatability differences (all P values ≥ 0.14) between modalities. For PET/MRI, the wCV for ADCmedian of 3.5% appeared lower than the wCVs for SUVmax (6.6%-8.7%) and SULpeak (9.2%-11.3%), though without significant repeatability differences (all P values ≥ 0.23). Conclusion: For solid tumors of the pelvis, the repeatability of the evaluated SUV and ADC metrics on 18F-FDG PET/MRI is both acceptably high and similar to previously published values for 18F-FDG PET/CT and MRI, supporting the use of 18F-FDG PET/MRI for quantitative oncologic treatment response assessments.
| S-EPMC6681694 | biostudies-literature
Project description:Tumor hypoxia and perfusion are independent prognostic indicators of patient outcome. We developed the methodology for and investigated the utility of multiparametric imaging of tumor hypoxia and perfusion with 18F-fluoromisonidazole (18F-FMISO) dynamic PET (dPET) in head and neck cancer. Methods: One hundred twenty head and neck cancer patients underwent 0- to 30-min 18F-FMISO dPET in a customized immobilization mask, followed by 10-min static acquisitions starting at 93 ± 6 and 160 ± 13 min after injection. A total of 248 lesions (≥2 cm3) were analyzed. Voxelwise pharmacokinetic modeling was conducted using an irreversible 1-plasma 2-tissue-compartment model to calculate surrogate biomarkers of tumor hypoxia (k3), perfusion (K1), and 18F-FMISO distribution volume. The analysis was repeated with truncated dPET datasets. Results: Substantial inter- and intratumor heterogeneity was observed for all investigated metrics. Equilibration between the blood and unbound 18F-FMISO was rapid in all tumors. 18F-FMISO distribution volume deviated from the expected value of unity, causing discrepancy between k3 maps and total 18F-FMISO uptake and reducing the dynamic range of total 18F-FMISO uptake for quantifying the degree of hypoxia. Both positive and negative trends between hypoxia and perfusion were observed in individual lesions. All investigated metrics were reproducible when calculated from a truncated 20-min dataset. Conclusion:18F-FMISO dPET provides the data necessary to generate parametric maps of tumor hypoxia, perfusion, and radiotracer distribution volume. These data clarify the ambiguity in interpreting 18F-FMISO uptake and improve the characterization of lesions. We show total acquisition times can be reduced to 20 min, facilitating the translation of 18F-FMISO dPET into the clinic.
| S-EPMC5493008 | biostudies-literature
Project description:PurposeTo investigate the diagnostic performance of integrated whole-body 18F-FDG PET/MRI for detecting bone marrow involvement (BMI) in indolent lymphoma compared with 18F-FDG PET or MRI alone.MethodsPatients with treatment-naive indolent lymphoma who underwent integrated whole-body 18F-FDG PET/MRI and bone marrow biopsy (BMB) were prospectively enrolled. Agreement between PET, MRI, PET/MRI, BMB, and the reference standard was assessed using kappa statistics. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of each method were calculated. A receiver operating characteristic (ROC) curve was used to determine the area under the curve (AUC). AUCs of PET, MRI, PET/MRI, and BMB were compared using the DeLong test.ResultsFifty-five patients (24 males and 31 females; mean age: 51.1 ± 10.1 years) were included in this study. Of these 55 patients, 19 (34.5%) had BMI. Two patients were upstaged as extra bone marrow lesions were detected via PET/MRI. 97.1% (33/34) of participants were confirmed as BMB-negative in the PET-/MRI-group. PET/MRI (parallel test) and BMB showed excellent agreement with the reference standard (k = 0.843, 0.918), whereas PET and MRI showed moderate agreement (k = 0.554, 0.577). The sensitivity, specificity, accuracy, PPV, and NPV for identifying BMI in indolent lymphoma were 52.6%, 97.2%, 81.8%, 90.9%, and 79.5%, respectively, for PET; 63.2%, 91.7%, 81.8%, 80.0%, and 82.5%, respectively, for MRI; 89.5%, 100%, 96.4%, 100%, and 94.7%, respectively, for BMB; and 94.7%, 91.7%, 92.7%, 85.7%, and 97.1%, respectively, for PET/MRI (parallel test). According to ROC analysis, the AUCs of PET, MRI, BMB, and PET/MRI (parallel test) for detecting BMI in indolent lymphomas were 0.749, 0.774, 0.947, and 0.932, respectively. The DeLong test showed significant differences between the AUCs of PET/MRI (parallel test) and those of PET (P = 0.003) and MRI (P = 0.004). Regarding histologic subtypes, the diagnostic performance of PET/MRI for detecting BMI in small lymphocytic lymphoma was lower than that in follicular lymphoma, which was in turn lower than that in marginal zone lymphoma.ConclusionIntegrated whole-body 18F-FDG PET/MRI showed excellent sensitivity and accuracy for detecting BMI in indolent lymphoma compared with 18F-FDG PET or MRI alone, demonstrating that 18F-FDG PET/MRI is an optimal method and a reliable alternative to BMB.Trial registrationClinicalTrials.gov (NCT05004961 and NCT05390632).
| S-EPMC10040668 | biostudies-literature
Project description:BackgroundPreoperative grading gliomas is essential for therapeutic clinical decision-making. Current non-invasive imaging modality for glioma grading were primarily focused on magnetic resonance imaging (MRI) or positron emission tomography (PET) of the tumor region. However, these methods overlook the peritumoral region (PTR) of tumor and cannot take full advantage of the biological information derived from hybrid-imaging. Therefore, we aimed to combine multiparameter from hybrid 18F-fluorodeoxyglucose (18F-FDG) PET/MRI of the solid component and PTR were combined for differentiating high-grade glioma (HGG) from low-grade glioma (LGG).MethodsA total of 76 patients with pathologically confirmed glioma (41 HGG and 35 LGG) who underwent simultaneous 18F-FDG PET, arterial spin labelling (ASL), and diffusion-weighted imaging (DWI) with hybrid PET/MRI were retrospectively enrolled. The relative maximum standardized uptake value (rSUVmax), relative cerebral blood flow (rCBF), and relative minimum apparent diffusion coefficient (rADCmin) for the solid component and PTR at different distances outside tumoral border were compared. Receiver operating characteristic (ROC) curves were applied to assess the grading performance. A nomogram for HGG prediction was constructed.ResultsHGGs displayed higher rSUVmax and rCBF but lower rADCmin in the solid component and 5 mm-adjacent PTR, lower rADCmin in 10 mm-adjacent PTR, and higher rCBF in 15- and 20-mm-adjacent PTR. rSUVmax in solid component performed best [area under the curve (AUC) =0.865] as a single parameter for grading. Combination of rSUVmax in the solid component and adjacent 20 mm performed better (AUC =0.881). Integration of all 3 indicators in the solid component and adjacent 20 mm performed the best (AUC =0.928). The nomogram including rSUVmax, rCBF, and rADCmin in the solid component and 5-mm-adjacent PTR predicted HGG with a concordance index (C-index) of 0.906.ConclusionsMultiparametric 18F-FDG PET/MRI from the solid component and PTR performed excellently in differentiating HGGs from LGGs. It can be used as a non-invasive and effective tool for preoperative grade stratification of patients with glioma, and can be considered in clinical practice.
| S-EPMC11320556 | biostudies-literature