FDG-PET Features Research Articles

Mitigating the impact of image processing variations on tumour [18F]-FDG-PET radiomic feature robustness

Radiomics analysis of [18F]-fluorodeoxyglucose ([18F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation complicates its clinical translation. In this work, we evaluated the robustness of tumour [18F]-FDG-PET radiomic features to 174 different variations in intensity resolution or voxel size, and determined whether implementing parameter range conditions or dependency corrections could improve their robustness. Using 485 patient images spanning three cancer types: non-small cell lung cancer (NSCLC), melanoma, and lymphoma, we observed features were more sensitive to intensity discretisation than voxel interpolation, especially texture features. In most of our investigations, the majority of non-robust features could be made robust by applying parameter range conditions. Correctable features, which were generally fewer than conditionally robust, showed systematic dependence on bin configuration or voxel size that could be minimised by applying corrections based on simple mathematical equations. Melanoma images exhibited limited robustness and correctability relative to NSCLC and lymphoma. Our study provides an in-depth characterisation of the sensitivity of [18F]-FDG-PET features to image processing variations and reinforces the need for careful selection of imaging biomarkers prior to any clinical application.

Baseline [18F]FDG PET/CT and MRI first-order breast tumor features do not improve pathological complete response prediction to neoadjuvant chemotherapy

PurposeTo verify the ability of pretreatment [18F]FDG PET/CT and T1-weighed dynamic contrast-enhanced MRI to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients.MethodsThis retrospective study includes patients with BC of no special type submitted to baseline [18F]FDG PET/CT, NAC and surgery. [18F]FDG PET-based features reflecting intensity and heterogeneity of tracer uptake were extracted from the primary BC and suspicious axillary lymph nodes (ALN), for comparative analysis related to NAC response (pCR vs. non-pCR). Multivariate logistic regression was performed for response prediction combining the breast tumor-extracted PET-based features and clinicopathological features. A subanalysis was performed in a patients’ subsample by adding breast tumor-extracted first-order MRI-based features to the multivariate logistic regression.ResultsA total of 170 tumors from 168 patients were included. pCR was observed in 60/170 tumors (20/107 luminal B-like, 25/45 triple-negative and 15/18 HER2-enriched surrogate molecular subtypes). Higher intensity and higher heterogeneity of [18F]FDG uptake in the primary BC were associated with NAC response in HER2-negative tumors (immunohistochemistry score 0, 1 + or 2 + non-amplified by in situ hybridization). Also, higher intensity of tracer uptake was observed in ALN in the pCR group among HER2-negative tumors. No [18F]FDG PET-based features were associated with pCR in the other subgroup analyses. A subsample of 103 tumors was also submitted to extraction of MRI-based features. When combined with clinicopathological features, neither [18F]FDG PET nor MRI-based features had additional value for pCR prediction. The only significant predictors were estrogen receptor status, HER2 expression and grade.ConclusionPretreatment [18F]FDG PET-based features from primary BC and ALN are not associated with response to NAC, except in HER2-negative tumors. As compared with pathological features, no breast tumor-extracted PET or MRI-based feature improved response prediction.

Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone.

Accurate differentiation of benign and malignant cervical lymph nodes is important for prognosis and treatment planning in patients with head and neck squamous cell carcinoma. We evaluated the diagnostic performance of magnetic resonance image (MRI) texture analysis and traditional 18F-deoxyglucose positron emission tomography (FDG-PET) features. This retrospective study included 21 patients with head and neck squamous cell carcinoma. We used texture analysis of MRI and FDG-PET features to evaluate 109 histologically confirmed cervical lymph nodes (41 metastatic, 68 benign). Predictive models were evaluated using area under the curve (AUC). Significant differences were observed between benign and malignant cervical lymph nodes for 36 of 41 texture features (p < 0.05). A combination of 22 MRI texture features discriminated benign and malignant nodal disease with AUC, sensitivity, and specificity of 0.952, 92.7%, and 86.7%, which was comparable to maximum short-axis diameter, lymph node morphology, and maximum standard uptake value (SUVmax). The addition of MRI texture features to traditional FDG-PET features differentiated these groups with the greatest AUC, sensitivity, and specificity (0.989, 97.5%, and 94.1%). The addition of the MRI texture feature to lymph node morphology improved nodal assessment specificity from 70.6% to 88.2% among FDG-PET indeterminate lymph nodes. Texture features are useful for differentiating benign and malignant cervical lymph nodes in patients with head and neck squamous cell carcinoma. Lymph node morphology and SUVmax remain accurate tools. Specificity is improved by the addition of MRI texture features among FDG-PET indeterminate lymph nodes. This approach is useful for differentiating benign and malignant cervical lymph nodes.

Qualitative flow metabolic phenotype of pancreatic cancer. A new prognostic biomarker?

BackgroundRetrospective analysis to investigate the relationship between the flow-metabolic phenotype and overall survival (OS) of pancreatic ductal adenocarcinoma (PDAC) and its potential clinical utility. MethodsPatients with histopathologically proven PDAC between 2005 and 2014 using tumor attenuation on routine pre-operative CECT as a surrogate for the vascularity and [18F]FDG-uptake as a surrogate for metabolic activity on [18F]FDG-PET. ResultsIn total, 93 patients (50 male, 43 female, median age 63) were included. Hypoattenuating PDAC with high [18F]FDG-uptake has the poorest prognosis (median OS 7 ± 1 months), compared to hypoattenuating PDAC with low [18F]FDG-uptake (median OS 11 ± 3 months; p = 0.176), iso- or hyperattenuating PDAC with high [18F]FDG-uptake (median OS 15 ± 5 months; p = 0.004) and iso- or hyperattenuating PDAC with low [18F]FDG-uptake (median OS 23 ± 4 months; p = 0.035). In multivariate analysis, surgery combined with tumor differentiation, tumor stage, systemic therapy and flow metabolic phenotype remained independent predictors for overall survival. DiscussionThe novel qualitative flow-metabolic phenotype of PDAC using a combination of CECT and [18F]FDG-PET features, predicted significantly worse survival for hypoattenuating-high uptake pancreatic cancers compared to the other phenotypes.

Baseline [18F]FDG PET features are associated with survival and toxicity in patients treated with CAR T cells for large B cell lymphoma.

Chimeric antigen receptor (CAR) T cells have established themselves as an effective treatment for refractory or relapsed large B cell lymphoma (LBCL). Recently, the sDmax, which corresponds to the distance separating the two farthest lesions standardized by the patient's body surface area, has appeared as a prognostic factor in LBCL. This study aimed to identify [18F]FDG-PET biomarkers associated with prognosis and predictive of adverse events in patients treated with CAR T cells. Patients were retrospectively included from two different university hospitals. They were being treated with CAR T cells for LBCL and underwent [18F]FDG-PET just before CAR T cell infusion. Lesions were segmented semi-automatically with a threshold of 41% of the maximal uptake. In addition to clinico-biological features, sDmax, total metabolic tumor volume (TMTV), SUVmax, and uptake intensity of healthy lymphoid organs and liver were collected. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method. The occurrence of adverse events, such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), was reported. Fifty-six patients were included. The median follow-up was 9.7 months. Multivariate analysis showed that TMTV (cut-off of 36 mL) was an independent prognostic factor for PFS (p < 0.001) and that sDmax (cut-off of 0.15 m-1) was an independent prognostic factor for OS (p = 0.008). Concerning the occurrence of adverse events, a C-reactive protein level > 35 mg/L (p = 0.006) and a liver SUVmean > 2.5 (p = 0.027) before CAR T cells were associated with grade 2 to 4 CRS and a spleen SUVmean > 1.9 with grade 2 to 4 ICANS. TMTV and sDmax had independent prognostic values, respectively, on PFS and OS. Regarding adverse events, the mean liver and spleen uptakes were associated with the occurrence of grade 2 to 4 CRS and ICANS, respectively. Integrating these biomarkers into the clinical workflow could be useful for early adaptation of patients management.

Alzheimer's Disease and Neurosyphilis: Meaningful Commonalities and Differences of Clinical Phenotype and Pathophysiological Biomarkers.

Neurosyphilis-associated cognitive and behavioral impairment- historically coined as "general paralysis of the insane"- share clinical and neuroradiological features with the neurodegenerative disease spectrum, in particular Alzheimer's disease (AD). Anatomopathological similarities have been extensively documented, i.e., neuronal loss, fibrillary alterations, and local amyloid-β deposition. Consequently, accurate classification and timely differential diagnosis may be challenging. To describe clinical, bio-humoral, brain MRI, FDG-PET, and amyloid-PET features in cases of neurosyphilis with an AD-like phenotypical presentation, as well as clinical outcome in terms of response to antibiotic therapy. We selected the studies comparing patients with AD and with neurosyphilis associated cognitive impairment, to investigate candidate biomarkers classifying the two neurological diseases. The neuropsychological phenotype of general paralysis, characterized by episodic memory impairment and executive disfunction, substantially mimics clinical AD features. Neuroimaging often shows diffuse or medial temporal cortical atrophy, thus contributing to a high rate of misdiagnosis. Cerebrospinal fluid (CSF)-based analysis may provide supportive diagnostic value, since increased proteins or cells are often found in neurosyphilis, while published data on pathophysiological AD candidate biomarkers are controversial. Finally, psychometric testing using cross-domain cognitive tests, may highlight a wider range of compromised functions in neurosyphilis, involving language, attention, executive function, and spatial ability, which are atypical for AD. Neurosyphilis should be considered a potential etiological differential diagnosis of cognitive impairment whenever imaging, neuropsychological or CSF features are atypical for AD, in order to promptly start antibiotic therapy and delay or halt cognitive decline and disease progression.

Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction.

Radiomics analysis provides a promising avenue towards the enabling of personalized radiotherapy. Most frequently, prognostic radiomics models are based on features extracted from medical images that are acquired before treatment. Here, we investigate whether combining data from multiple timepoints during treatment and from multiple imaging modalities can improve the predictive ability of radiomics models. We extracted radiomics features from computed tomography (CT) images acquired before treatment as well as two and three weeks after the start of radiochemotherapy for 55 patients with locally advanced head and neck squamous cell carcinoma (HNSCC). Additionally, we obtained features from FDG-PET images taken before treatment and three weeks after the start of therapy. Cox proportional hazards models were then built based on features of the different image modalities, treatment timepoints, and combinations thereof using two different feature selection methods in a five-fold cross-validation approach. Based on the cross-validation results, feature signatures were derived and their performance was independently validated. Discrimination regarding loco-regional control was assessed by the concordance index (C-index) and log-rank tests were performed to assess risk stratification. The best prognostic performance was obtained for timepoints during treatment for all modalities. Overall, CT was the best discriminating modality with an independent validation C-index of 0.78 for week two and weeks two and three combined. However, none of these models achieved statistically significant patient stratification. Models based on FDG-PET features from week three provided both satisfactory discrimination (C-index = 0.61 and 0.64) and statistically significant stratification (p=0.044 and p<0.001), but produced highly imbalanced risk groups. After independent validation on larger datasets, the value of (multimodal) radiomics models combining several imaging timepoints should be prospectively assessed for personalized treatment strategies.

Diagnostic Accuracy of Slow-Capillary Endobronchial Ultrasound Needle Aspiration in Determining PD-L1 Expression in Non-Small Cell Lung Cancer

The role of EBUS-TBNA in the diagnosis and staging of lung cancer is well established. EBUS-TBNA can be performed using different aspiration techniques. The most common aspiration technique is known as "suction". One alternative to the suction technique is the slow-pull capillary aspiration. To the best of our knowledge, no studies have assessed the diagnostic yield of slow-pull capillary EBUS-TBNA in PD-L1 amplification assessment in NSCLC. Herein, we conducted a single-centre retrospective study to establish the diagnostic yield of slow-pull capillary EBUS-TBNA in terms of PD-L1 in patients with NSCLC and hilar/mediastinal lymphadenopathies subsequent to NSCLC. Patients with hilar and/or mediastinal lymph node (LN) NSCLC metastasis, diagnosed by EBUS-TBNA between January 2021 and April 2022 at Pulmonology Unit of "Ospedali Riuniti di Ancona" (Ancona, Italy) were enrolled. We evaluated patient characteristics, including demographic information, CT scan/ FDG-PET features and final histological diagnoses, including PD-L1 assessment. A total of 174 patients underwent EBUS-TBNA for diagnosis of hilar/mediastinal lymphadenopathies between January 2021 and April 2022 in the Interventional Pulmonology Unit of the "Ospedali Riuniti di Ancona". Slow-pull capillary aspiration was adopted in 60 patients (34.5%), and in 30/60 patients (50.0%) NSCLC was diagnosed. EBUS-TBNA with slow-pull capillary aspiration provided adequate sampling for molecular biology and PD-L1 testing in 96.7% of patients (29/30); in 15/29 (51.7%) samples with more than 1000 viable cells/HPF were identified, whereas in 14/29 (48.3%) samples contained 101-1000 viable cells/HPF. These retrospective study shows that slow-pull capillary aspiration carries an excellent diagnostic accuracy, almost equal to that one reported in literature, supporting its use in EBUS-TBNA for PD-L1 testing in NSCLC.

Correlation Between Tumor Uptake on FDG-PET and Malignant Features in Esophageal Squamous Cell Carcinoma After Neoadjuvant Chemotherapy Followed by Surgery.

To identify the correlations between the 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET) images and the pathological features and recurrence among patients with esophageal squamous cell carcinoma (ESCC) who were administered neoadjuvant chemotherapy (NAC) followed by surgery. We assessed the correlations between the maximum standardized uptake value (SUVmax) of primary tumors as reflected on preoperative FDG-PET images, the pathological features, and cancer recurrence in 124 patients with locally advanced ESCC, who were treated with NAC and esophagectomy. The pre-NAC SUVmax significantly differed for the ypT status and venous invasion (VI). The post-NAC SUVmax (post-SUVmax) significantly differed for the ypT and ypN status, VI, lymphatic invasion (LI), pathological tumor response, down-staging, and recurrence. The decrease in SUVmax before and after NAC (ΔSUVmax) significantly differed for ypT status, LI, VI, pathological tumor response, down-staging, and recurrence. The survivals were significantly stratified according to the optimal cutoffs of SUVmax for predicting recurrence (post- and ΔSUVmax cutoffs: 4.2 and 30, respectively; all p<0.0001). Moreover, multivariate analysis showed that the post- and ΔSUVmax were independent predictive factors for recurrence-free survival. The SUVmax on preoperative FDG-PET can predict the degree of aggressiveness of the tumor in locally advanced ESCC treated with NAC.

Early Prediction and Monitoring of Treatment Response in Gastrointestinal Stromal Tumors by Means of Imaging: A Systematic Review.

Gastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms. Tyrosine kinase inhibitor (TKI) therapy is currently part of routine clinical practice for unresectable and metastatic disease. It is important to assess the efficacy of TKI treatment at an early stage to optimize therapy strategies and eliminate futile ineffective treatment, side effects and unnecessary costs. This systematic review provides an overview of the imaging features obtained from contrast-enhanced (CE)-CT and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT to predict and monitor TKI treatment response in GIST patients. PubMed, Web of Science, the Cochrane Library and Embase were systematically screened. Articles were considered eligible if quantitative outcome measures (area under the curve (AUC), correlations, sensitivity, specificity, accuracy) were used to evaluate the efficacy of imaging features for predicting and monitoring treatment response to various TKI treatments. The methodological quality of all articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies, v2 (QUADAS-2) tool and modified versions of the Radiomics Quality Score (RQS). A total of 90 articles were included, of which 66 articles used baseline [18F]FDG-PET and CE-CT imaging features for response prediction. Generally, the presence of heterogeneous enhancement on baseline CE-CT imaging was considered predictive for high-risk GISTs, related to underlying neovascularization and necrosis of the tumor. The remaining articles discussed therapy monitoring. Clinically established imaging features, including changes in tumor size and density, were considered unfavorable monitoring criteria, leading to under- and overestimation of response. Furthermore, changes in glucose metabolism, as reflected by [18F]FDG-PET imaging features, preceded changes in tumor size and were more strongly correlated with tumor response. Although CE-CT and [18F]FDG-PET can aid in the prediction and monitoring in GIST patients, further research on cost-effectiveness is recommended.

Imaging features of immune checkpoint inhibitor-related nephritis with clinical correlation: a retrospective series of biopsy-proven cases.

Imaging appearances of immune checkpoint inhibitor-related nephritis have not yet been described. The primary objective of this study is to describe the appearances of immunotherapy-related nephritis on computerized tomography (CT) and positron emission tomography (PET). The secondary objectives are to investigate the association of radiologic features with clinical outcomes. CT and PET-CT scans before the initiation of immunotherapy (baseline), at nephritis, and after resolution of pathology-proven nephritis cases were reviewed. Total kidney volume, renal parenchymal SUVmax, renal pelvis SUVmax, and blood pool SUVmean were obtained. Thirty-four patients were included. The total kidney volume was significantly higher at nephritis compared to baseline (464.7 ± 96.8 mL vs. 371.7 ± 187.7 mL; p < 0.001). Fifteen patients (44.1%) had > 30% increase in total kidney volume, which was associated with significantly higher renal toxicity grade (p = 0.007), higher peak creatinine level (p = 0.004), and more aggressive medical treatment (p = 0.011). New/increasing perinephric fat stranding was noted in 10 patients (29.4%) at nephritis. Among 8 patients with contrast-enhanced CT at nephritis, one (12.5%) developed bilateral wedge-shaped hypoenhancing cortical. On PET-CT, the renal parenchymal SUVmax-to-blood pool ratio was significantly higher at nephritis compared to baseline (2.13 vs. 1.68; p = 0.035). The renal pelvis SUVmax-to-blood pool SUVmean ratio was significantly lower at nephritis compared to baseline (3.47 vs. 8.22; p = 0.011). Bilateral increase in kidney size, new/increasing perinephric stranding, and bilateral wedge-shaped hypoenhancing cortical foci can occur in immunotherapy-related nephritis. On PET-CT, a diffuse increase in radiotracer uptake throughout the renal cortex and a decrease in radiotracer activity in the renal pelvis can be seen. • CT features of immune checkpoint inhibitor-related nephritis include an increase in kidney volume, new/increasing perinephric stranding, and bilateral ill-defined wedge-shaped hypoenhancing cortical foci. • FDG-PET features of immune checkpoint inhibitor-related nephritis include an increase in FDG uptake throughout the renal cortex and a decrease in FDG activity/excretion in the collecting system. • > 30% increase in total kidney volume is associated with worse toxicity grade and more aggressive medical management.

Bone marrow segmentation and radiomics analysis of [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

Background and objectivesThe last few years have been crucial in defining the most appropriate way to quantitatively assess [18F]FDG PET images in Multiple Myeloma (MM) patients to detect persistent tumor burden. The visual evaluation of images complements the assessment of Measurable Residual Disease (MRD) in bone marrow samples by multiparameter flow cytometry (MFC) or next-generation sequencing (NGS). The aim of this study was to quantify MRD by analyzing quantitative and texture [18F]FDG PET features. MethodsWhole body [18F]FDG PET of 39 patients with newly diagnosed MM were included in the database, and visually evaluated by experts in nuclear medicine. A segmentation methodology of the skeleton from CT images and an additional manual segmentation tool were proposed, implemented in a software solution including a graphical user interface. Both the compact bone and the spinal canal were removed from the segmentation to obtain only the bone marrow mask. SUV metrics, GLCM, GLRLM, and NGTDM parameters were extracted from the PET images and evaluated by Mann-Whitney U-tests and Spearman ρ rank correlation as valuable features differentiating PET+/PET- and MFC+/MFC- groups. Seven machine learning algorithms were applied for evaluating the classification performance of the extracted features. ResultsQuantitative analysis for PET+/PET- differentiating demonstrated to be significant for most of the variables assessed with Mann-Whitney U-test such as Variance, Energy, and Entropy (p-value = 0.001). Moreover, the quantitative analysis with a balanced database evaluated by Mann-Whitney U-test revealed in even better results with 19 features with p-values < 0.001. On the other hand, radiomics analysis for MFC+/MFC- differentiating demonstrated the necessity of combining MFC evaluation with [18F]FDG PET assessment in the MRD diagnosis. Machine learning algorithms using the image features for the PET+/PET- classification demonstrated high performance metrics but decreasing for the MFC+/MFC- classification. ConclusionsA proof-of-concept for the extraction and evaluation of bone marrow radiomics features of [18F]FDG PET images was proposed and implemented. The validation showed the possible use of these features for the image-based assessment of MRD.

Prognostic Value of Quantitative [18F]FDG-PET Features in Patients with Metastases from Soft Tissue Sarcoma.

Background: Prognostic biomarkers are pivotal for adequate treatment decision making. The objective of this study was to determine the added prognostic value of quantitative [18F]FDG-PET features in patients with metastases from soft tissue sarcoma (STS). Methods: Patients with metastases from STS, detected by (re)staging [18F]FDG-PET/CT at Leiden University Medical Centre, were retrospectively included. Clinical and histopathological patient characteristics and [18F]FDG-PET features (SUVmax, SUVpeak, SUVmean, total lesion glycolysis, and metabolic tumor volume) were analyzed as prognostic factors for overall survival using a Cox proportional hazards model and Kaplan–Meier methods. Results: A total of 31 patients were included. SUVmax and SUVpeak were significantly predictive for overall survival (OS) in a univariate analysis (p = 0.004 and p = 0.006, respectively). Hazard ratios (HRs) were 1.16 per unit increase for SUVmax and 1.20 per unit for SUVpeak. SUVmax and SUVpeak remained significant predictors for overall survival after correction for the two strongest predictive clinical characteristics (number of lesions and performance status) in a multivariate analysis (p = 0.02 for both). Median SUVmax and SUVpeak were 5.7 and 4.9 g/mL, respectively. The estimated mean overall survival in patients with SUVmax > 5.7 g/mL was 14 months; otherwise, it was 39 months (p < 0.001). For patients with SUVpeak > 4.9 g/mL, the estimated mean overall survival was 18 months; otherwise, it was 33 months (p = 0.04). Conclusions: In this study, SUVmax and SUVpeak were independent prognostic factors for overall survival in patients with metastases from STS. These results warrant further investigation of metabolic imaging with [18F]FDG-PET/CT in patients with metastatic STS.

PH-0106 FDG-PET features help predict distant metastases in oropharyngeal cancer patients with definitive RT

PH-0106 FDG-PET features help predict distant metastases in oropharyngeal cancer patients with definitive RT

Quantitative [18]FDG PET asymmetry features predict long-term seizure recurrence in refractory epilepsy

ObjectiveFluorodeoxyglucose-positron emission tomography (FDG-PET) is an established, independent, strong predictor of surgical outcome in refractory epilepsy. In this study, we explored the added value of quantitative [18F]FDG-PET features combined with clinical variables, including electroencephalography (EEG), [18F]FDG-PET, and magnetic resonance imaging (MRI) qualitative interpretations, to predict long-term seizure recurrence (mean post-op follow-up of 5.85 ± 3.77 years). MethodsMachine learning predictive models of surgical outcome were created using a random forest classifier trained on quantitative features in 89 patients with drug-refractory temporal lobe epilepsy evaluated at the Hospital of the University of Pennsylvania epilepsy surgery program (2003–2016). Quantitative features were calculated from asymmetry features derived from image processing using Advanced Normalization Tools (ANTs). ResultsThe best-performing model used quantification and had an out-of-bag accuracy of 0.71 in identifying patients with seizure recurrence (Engel IB or worse) which outperformed that using qualitative clinical data by 10%. This model is shared through open-source software for research use. In addition, several asymmetry features in temporal and extratemporal regions that were significantly associated with seizure freedom are identified for future study. SignificanceComplex quantitative [18F]FDG-PET imaging features can predict seizure recurrence in patients with refractory temporal lobe epilepsy. These initial retrospective results in a cohort with long-term follow-up suggest that using quantitative imaging features from regions in the epileptogenic network can inform the clinical decision-making process.

A FDG-PET radiomics signature detects esophageal squamous cell carcinoma patients who do not benefit from chemoradiation

Detection of patients with esophageal squamous cell carcinoma (ESCC) who do not benefit from standard chemoradiation (CRT) is an important medical need. Radiomics using 18-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a promising approach. In this retrospective study of 184 patients with locally advanced ESCC. 152 patients from one center were grouped into a training cohort (n = 100) and an internal validation cohort (n = 52). External validation was performed with 32 patients treated at a second center. Primary endpoint was disease-free survival (DFS), secondary endpoints were overall survival (OS) and local control (LC). FDG-PET radiomics features were selected by Lasso-Cox regression analyses and a separate radiomics signature was calculated for each endpoint. In the training cohort radiomics signatures containing up to four PET derived features were able to identify non-responders in regard of all endpoints (DFS p < 0.001, LC p = 0.003, OS p = 0.001). After successful internal validation of the cutoff values generated by the training cohort for DFS (p = 0.025) and OS (p = 0.002), external validation using these cutoffs was successful for DFS (p = 0.002) but not for the other investigated endpoints. These results suggest that pre-treatment FDG-PET features may be useful to detect patients who do not respond to CRT and could benefit from alternative treatment.

Association of metabolic and genetic heterogeneity in head and neck squamous cell carcinoma with prognostic implications: integration of FDG PET and genomic analysis

PurposeThe linkage between the genetic and phenotypic heterogeneity of the tumor has not been thoroughly evaluated. Herein, we investigated how the genetic and metabolic heterogeneity features of the tumor are associated with each other in head and neck squamous cell carcinoma (HNSC). We further assessed the prognostic significance of those features.MethodsThe mutant-allele tumor heterogeneity (MATH) score (n = 508), a genetic heterogeneity feature, and tumor glycolysis feature (GlycoS) (n = 503) were obtained from the HNSC dataset in the cancer genome atlas (TCGA). We identified matching patients (n = 33) who underwent 18F-fluorodeoxyglucose positron emission tomography (FDG PET) from the cancer imaging archive (TCIA) and obtained the following information from the primary tumor: metabolic, metabolic-volumetric, and metabolic heterogeneity features. The association between the genetic and metabolic features and their prognostic values were assessed.ResultsTumor metabolic heterogeneity and metabolic-volumetric features showed a mild degree of association with MATH (n = 25, ρ = 0.4~0.5, P < 0.05 for all features). The patients with higher FDG PET features and MATH died sooner. Combination of MATH and tumor metabolic heterogeneity features showed a better stratification of prognosis than MATH. Also, higher MATH and GlycoS were associated with significantly worse overall survival (n = 499, P = 0.002 and 0.0001 for MATH and GlycoS, respectively). Furthermore, both MATH and GlycoS independently predicted overall survival after adjusting for clinicopathologic features and the other (P = 0.015 and 0.006, respectively).ConclusionBoth tumor metabolic heterogeneity and metabolic-volumetric features assessed by FDG PET showed a mild degree of association with genetic heterogeneity in HNSC. Both metabolic and genetic heterogeneity features were predictive of survival and there was an additive prognostic value when the metabolic and genetic heterogeneity features were combined. Also, MATH and GlycoS were independent prognostic factors in HNSC; they can be used for precise prognostication once validated.

PATHOLOGIC ASSOCIATIONS OF FDG PET IN LEWY BODY DISEASE

Among individuals with Lewy body disease (LBD), pathologic correlates of clinical course include the presence and extent of coexisting Alzheimer's pathology and the presence of transitional or diffuse LBD. The objective of this study was to determine whether 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) signatures of LBD would be associated with these pathologies. 36 participants with pathologically-confirmed LBD who underwent antemortem FDG-PET were included in this study. We evaluated whether FDG-PET features of DLB, including the cingulate island sign (CIS) and occipital hypometabolism, varied by Lewy body stage (amygdala only, transitional, diffuse) and Braak neurofibrillary tangle stage. We performed a recursive partitioning tree algorithm to classify our subjects into distinctive sub-groups. The median imaging age was 73 and 75% were male. The median age at death was 75.5. 26 participants had diffuse LBD, 8 participants had transitional LBD, and 2 had amygdala only LBD. There was no difference in CIS or occipital hypometabolism by LBD type (transitional, diffuse). In contrast, those with a lower Braak stage (I-III) had a higher CIS compared to those with a higher Braak stage (IV-VI). For the CIS, there were 3 distinct groups based on the partitioning analysis (Braak I-II), (Braak III-IV) and Braak (V-VI). The partitioning found two groups for occipital hypometabolism (Braak I-III and Braak IV-VI). Among pathologically confirmed LBD patients, FDG-PET features varied by Braak neurofibrillary tangle stage, but not LBD subtype. Predicting the extent of AD pathology antemortem may allow for improved prognostication of the clinical course of patients with LBD.

EP-1907 Which FDG-PET features are robust enough for Radiomic studies in pancreatic cancer patients?

EP-1907 Which FDG-PET features are robust enough for Radiomic studies in pancreatic cancer patients?

Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions.

To evaluate the ability of CT and PET radiomics features to classify lung lesions as primary or metastatic, and secondly to differentiate histological subtypes of primary lung cancers. A cohort of 534 patients with lung lesions were retrospectively studied. Radiomics texture features were extracted using the LIFEx package from semiautomatically segmented PET and CT images. Histology data were recorded in all patients. The patient cohort was divided into a training and a validation group and linear discriminant analysis (LDA) was performed to classify the lesions using both direct and backward stepwise methods. The robustness of the procedure was tested by repeating the entire process 100 times with different assignments to the training and validation groups. Scoring metrics included analysis of the receiver operating characteristic curves in terms of area under the curve (AUC), sensitivity, specificity and accuracy. Radiomics features extracted from CT and PET datasets were able to differentiate primary tumours from metastases in both the training and the validation group (AUCs 0.79 ± 0.03 and 0.70 ± 0.04, respectively, from the CT dataset; AUCs 0.92 ± 0.01 and 0.91 ± 0.03, respectively, from the PET dataset). The AUC cut-off thresholds identified by LDA using direct and backward elimination strategies were -0.79 ± 0.06 and -0.81 ± 0.08, respectively (CT dataset) and -0.69 ± 0.05 and -0.68 ± 0.04, respectively (PET dataset). For differentiation between primary subgroups based on CT features, the AUCs in the training and validation groups were 0.81 ± 0.02 and 0.69 ± 0.04 for adenocarcinoma (Adc) vs. squamous cell carcinoma (Sqc) or "Other", 0.85 ± 0.02 and 0.70 ± 0.05 for Sqc vs. Adc or Other, and 0.77 ± 0.03 and 0.57 ± 0.05 for Other vs. Adc or Sqc. The same analyses for the PET data revealed AUCs of 0.90 ± 0.10 and 0.80 ± 0.04, 0.80 ± 0.02 and 0.61 ± 0.06, and 0.97 ± 0.01 and 0.88 ± 0.04, respectively. PET radiomics features were able to differentiate between primary and metastatic lung lesions and showed the potential to identify primary lung cancer subtypes.