PET Modalities Research Articles

68Ga]Ga-FAPI04 Outperforms [18F]FDG PET/CT for Detecting Nodal Metastasis of Head and Neck Squamous Cell Carcinoma: A Single-Center Experience.

This study assesses fibroblast activated protein inhibitor (FAPI) targeted PET/CT imaging against [18F]FDG PET/CT (FDG PET) for detecting nodal involvement in head and neck squamous cell carcinoma (HNSCC), intending to improve diagnostic precision for metastatic lymph nodes and lay the groundwork for future investigations. Methods: Patients diagnosed with HNSCC were retrospectively enrolled. All patients underwent [68Ga]Ga-FAPI04 PET/CT (FAPI PET) and FDG PET within 6 d. Primary tumor, lymph nodes, and tracer uptake were visually and quantitatively compared. The metastatic lymph nodes were evaluated using patient-and lesion-based analyses, with biopsy or postoperative histopathological examination as the reference. Results: The cohort includes 24 patients (17 men, 7 women; mean age 60 ± 11.8 years) who underwent FDG and FAPI PET for preoperative diagnostic workup or restaging due to known recurrence of HNSCC. Lesions included 24 primary tumors, 54 cervical lymph nodes, and 5 metastases. Primary tumors exhibited significant uptake on both PET modalities (median maximum standardized uptake value [SUVmax]: FDG 19.4 ± 11.6, FAPI 16.9 ± 4.6), with no statistically significant difference (p > 0.5). For lymph nodes, FAPI and FDG PET showed median SUVmax of 9.18 ± 6.77 and 9.67 ± 6.5, respectively. The patient-based analysis found FDG PET sensitivity at 88.2% and FAPI PET at 94.1%, with FAPI PET specificity significantly higher (85.7% vs. 42.8% for FDG PET). Lesion-based analysis revealed FAPI PET sensitivity and specificity at 84.2% and 93.7%, respectively, contrasting FDG PET's at 81.5% and 25%, respectively. Conclusion: This study underscores the efficacy of FAPI PET in detecting primary tumors in HNSCC. Furthermore, FAPI PET shows improved specificity over FDG PET for metastatic lymph nodes advocating further investigations for integrating FAPI PET into HNSCC clinical protocols for its enhanced precision in detecting metastatic lymph nodes.

Organomics: A Concept Reflecting the Importance of PET/CT Healthy Organ Radiomics in Non-Small Cell Lung Cancer Prognosis Prediction Using Machine Learning.

Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and additional value of information extracted from healthy organs in addition to tumoral tissue using ML algorithms. This study included PET/CT images of 154 patients collected from available online databases. The gross tumor volume and 33 volumes of interest defined on healthy organs were segmented using nnU-Net deep learning-based segmentation. Subsequently, 107 radiomic features were extracted from PET and CT images (Organomics). Clinical information was combined with PET and CT radiomics from organs and gross tumor volumes considering 19 different combinations of inputs. Finally, different feature selection (FS; 5 methods) and ML (6 algorithms) algorithms were tested in a 3-fold data split cross-validation scheme. The performance of the models was quantified in terms of the concordance index (C-index) metric. For an input combination of all radiomics information, most of the selected features belonged to PET Organomics and CT Organomics. The highest C-index (0.68) was achieved using univariate C-index FS method and random survival forest ML model using CT Organomics + PET Organomics as input as well as minimum depth FS method and CoxPH ML model using PET Organomics as input. Considering all 17 combinations with C-index higher than 0.65, Organomics from PET or CT images were used as input in 16 of them. The selected features and C-indices demonstrated that the additional information extracted from healthy organs of both PET and CT imaging modalities improved the ML performance. Organomics could be a step toward exploiting the whole information available from multimodality medical images, contributing to the emerging field of digital twins in health care.

Joint diffusion: mutual consistency-driven diffusion model for PET-MRI co-reconstruction

Objective. Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. But PET suffers from a low signal-to-noise ratio, while MRI are time-consuming. To address time-consuming, an effective strategy involves reducing k-space data collection, albeit at the cost of lowering image quality. This study aims to leverage the inherent complementarity within PET-MRI data to enhance the image quality of PET-MRI. Approach. A novel PET-MRI joint reconstruction model, termed MC-Diffusion, is proposed in the Bayesian framework. The joint reconstruction problem is transformed into a joint regularization problem, where data fidelity terms of PET and MRI are expressed independently. The regular term, the derivative of the logarithm of the joint probability distribution of PET and MRI, employs a joint score-based diffusion model for learning. The diffusion model involves the forward diffusion process and the reverse diffusion process. The forward diffusion process adds noise to transform a complex joint data distribution into a known joint prior distribution for PET and MRI simultaneously, resembling a denoiser. The reverse diffusion process removes noise using a denoiser to revert the joint prior distribution to the original joint data distribution, effectively utilizing joint probability distribution to describe the correlations of PET and MRI for improved quality of joint reconstruction. Main results. Qualitative and quantitative improvements are observed with the MC-Diffusion model. Comparative analysis against LPLS and Joint ISAT-net on the ADNI dataset demonstrates superior performance by exploiting complementary information between PET and MRI. The MC-Diffusion model effectively enhances the quality of PET and MRI images. Significance. This study employs the MC-Diffusion model to enhance the quality of PET-MRI images by integrating the fundamental principles of PET and MRI modalities and leveraging their inherent complementarity. Furthermore, utilizing the diffusion model to learn the joint probability distribution of PET and MRI, thereby elucidating their latent correlation, facilitates a more profound comprehension of the priors obtained through deep learning, contrasting with black-box prior or artificially constructed structural similarities.

Predicting conversion from mild cognitive impairment to Alzheimer's disease: a multimodal approach.

Successively predicting whether mild cognitive impairment patients will progress to Alzheimer's disease is of significant clinical relevance. This ability may provide information that can be leveraged by emerging intervention approaches and thus mitigate some of the negative effects of the disease. Neuroimaging biomarkers have gained some attention in recent years and may be useful in predicting the conversion of mild cognitive impairment to Alzheimer's disease. We implemented a novel multi-modal approach that allowed us to evaluate the potential of different imaging modalities, both alone and in different degrees of combinations, in predicting the conversion to Alzheimer's disease of mild cognitive impairment patients. We applied this approach to the imaging data from the Alzheimer's Disease Neuroimaging Initiative that is a multi-modal imaging dataset comprised of MRI, Fluorodeoxyglucose PET, Florbetapir PET and diffusion tensor imaging. We included a total of 480 mild cognitive impairment patients that were split into two groups: converted and stable. Imaging data were segmented into atlas-based regions of interest, from which relevant features were extracted for the different imaging modalities and used to construct machine-learning models to classify mild cognitive impairment patients into converted or stable, using each of the different imaging modalities independently. The models were then combined, using a simple weight fusion ensemble strategy, to evaluate the complementarity of different imaging modalities and their contribution to the prediction accuracy of the models. The single-modality findings revealed that the model, utilizing features extracted from Florbetapir PET, demonstrated the highest performance with a balanced accuracy of 83.51%. Concerning multi-modality models, not all combinations enhanced mild cognitive impairment conversion prediction. Notably, the combination of MRI with Fluorodeoxyglucose PET emerged as the most promising, exhibiting an overall improvement in predictive capabilities, achieving a balanced accuracy of 78.43%. This indicates synergy and complementarity between the two imaging modalities in predicting mild cognitive impairment conversion. These findings suggest that β-amyloid accumulation provides robust predictive capabilities, while the combination of multiple imaging modalities has the potential to surpass certain single-modality approaches. Exploring modality-specific biomarkers, we identified the brainstem as a sensitive biomarker for both MRI and Fluorodeoxyglucose PET modalities, implicating its involvement in early Alzheimer's pathology. Notably, the corpus callosum and adjacent cortical regions emerged as potential biomarkers, warranting further study into their role in the early stages of Alzheimer's disease.

PET-MRI Applications and Future Prospects in Psychiatry.

This article reviews the synergistic application of positron emission tomography-magnetic resonance imaging (PET-MRI) in neuroscience with relevance for psychiatry, particularly examining neurotransmission, epigenetics, and dynamic imaging methodologies. We begin by discussing the complementary insights that PET and MRI modalities provide into neuroreceptor systems, with a focus on dopamine, opioids, and serotonin receptors, and their implications for understanding and treating psychiatric disorders. We further highlight recent PET-MRI studies using a radioligand that enables the quantification of epigenetic enzymes, specifically histone deacetylases, in the brain in vivo. Imaging epigenetics is used to exemplify the impact the quantification of novel molecular targets may have, including new treatment approaches for psychiatric disorders. Finally, we discuss innovative designs involving functional PET using [18F]FDG (fPET-FDG), which provides detailed information regarding dynamic changes in glucose metabolism. Concurrent acquisitions of fPET-FDG and functional MRI provide a time-resolved approach to studying brain function, yielding simultaneous metabolic and hemodynamic information and thereby opening new avenues for psychiatric research. Collectively, the review underscores the potential of a multimodal PET-MRI approach to advance our understanding of brain structure and function in health and disease, which could improve clinical care based on objective neurobiological features and treatment response monitoring. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Using imaging modalities to predict nanoparticle distribution and treatment efficacy in solid tumors: The growing role of ultrasound.

Nanomedicine in oncology has not had the success in clinical impact that was anticipated in the early stages of the field's development. Ideally, nanomedicines selectively accumulate in tumor tissue and reduce systemic side effects compared to traditional chemotherapeutics. However, this has been more successful in preclinical animal models than in humans. The causes of this failure to translate may be related to the intra- and inter-patient heterogeneity of the tumor microenvironment. Predicting whether a patient will respond positively to treatment prior to its initiation, through evaluation of characteristics like nanoparticle extravasation and retention potential in the tumor, may be a way to improve nanomedicine success rate. While there are many potential strategies to accomplish this, prediction and patient stratification via noninvasive medical imaging may be the most efficient and specific strategy. There have been some preclinical and clinical advances in this area using MRI, CT, PET, and other modalities. An alternative approach that has not been studied as extensively is biomedical ultrasound, including techniques such as multiparametric contrast-enhanced ultrasound (mpCEUS), doppler, elastography, and super-resolution processing. Ultrasound is safe, inexpensive, noninvasive, and capable of imaging the entire tumor with high temporal and spatial resolution. In this work, we summarize the in vivo imaging tools that have been used to predict nanoparticle distribution and treatment efficacy in oncology. We emphasize ultrasound imaging and the recent developments in the field concerning CEUS. The successful implementation of an imaging strategy for prediction of nanoparticle accumulation in tumors could lead to increased clinical translation of nanomedicines, and subsequently, improved patient outcomes. This article is categorized under: Diagnostic Tools In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery Emerging Technologies.

3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer’s disease sub-scores

Different aspects of cognitive functions are affected in patients with Alzheimer’s disease. To date, little is known about the associations between features from brain-imaging and individual Alzheimer’s disease (AD)-related cognitive functional changes. In addition, how these associations differ among different imaging modalities is unclear. Here, we trained and investigated 3D convolutional neural network (CNN) models that predicted sub-scores of the 13-item Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS–Cog13) based on MRI and FDG–PET brain-imaging data. Analysis of the trained network showed that each key ADAS–Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Furthermore, different association patterns were observed in MRI and FDG–PET modalities. According to MRI, cognitive sub-scores were typically associated with structural changes of subcortical regions, including amygdala, hippocampus, and putamen. Comparatively, according to FDG–PET, cognitive functions were typically associated with metabolic changes of cortical regions, including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. These findings brought insights into complex AD etiology and emphasized the importance of investigating different brain-imaging modalities.

Construction and Application of a PD-L1-Targeted Multimodal Diagnostic and Dual-Functional Theranostics Nanoprobe.

In recent years, PD-L1 has been primarily utilized as an immune checkpoint marker in cancer immunotherapy. However, due to tumor heterogeneity, the response rate to such therapies often falls short of expectations. In addition to its role in immunotherapy, PD-L1 serves as a specific target on the surface of tumor cells for targeted diagnostic and therapeutic interventions. There is an absence of a fully developed PD-L1-targeted diagnostic and therapeutic probe for clinical use, which constrains the exploration and clinical exploitation of this target. In this study, we engineered a PD-L1-targeted probe with multimodal imaging and dual therapeutic functionalities utilizing organic melanin nanoparticles. Functionalization with the WL12-SH peptide endowed the nanoprobe with specific targeting capabilities. Subsequent radiolabeling with 89Zr (half-life: 100.8 hours) and chelation of Mn2+ ions afforded the probe the capacity for simultaneous PET and MRI imaging modalities. Cellular uptake assays revealed pronounced specificity, with -positive cells exhibiting significantly higher uptake than -negative counterparts (p < 0.05). Dual-modal PET/MRI imaging delineated rapid and sustained accumulation at the neoplastic site, yielding tumor-to-non-tumor (T/NT) signal ratios at 24 hours post-injection of 16.67±3.45 for PET and 6.63±0.64 for MRI, respectively. We conjugated the therapeutic radionuclide 131I (half-life: 8.02 days) to the construct and combined low-dose radiotherapy and photothermal treatment (PTT), culminating in superior antitumor efficacy while preserving a high safety profile. The tumors in the cohort receiving the dual-modality therapy exhibited significantly reduced volume and weight compared to those in the control and monotherapy groups. We developed and applied a novel -targeted multimodal theranostic nanoprobe, characterized by its high specificity and superior imaging capabilities as demonstrated in PET/MRI modalities. Furthermore, this nanoprobe facilitates potent therapeutic efficacy at lower radionuclide doses when used in conjunction with PTT.

Multitask connected U-Net: automatic lung cancer segmentation from CT images using PET knowledge guidance.

Lung cancer is a predominant cause of cancer-related mortality worldwide, necessitating precise tumor segmentation of medical images for accurate diagnosis and treatment. However, the intrinsic complexity and variability of tumor morphology pose substantial challenges to segmentation tasks. To address this issue, we propose a multitask connected U-Net model with a teacher-student framework to enhance the effectiveness of lung tumor segmentation. The proposed model and framework integrate PET knowledge into the segmentation process, leveraging complementary information from both CT and PET modalities to improve segmentation performance. Additionally, we implemented a tumor area detection method to enhance tumor segmentation performance. In extensive experiments on four datasets, the average Dice coefficient of 0.56, obtained using our model, surpassed those of existing methods such as Segformer (0.51), Transformer (0.50), and UctransNet (0.43). These findings validate the efficacy of the proposed method in lung tumor segmentation tasks.

Role of Positron Emission Tomography/Computed Tomography in Epithelial Ovarian Cancer.

Ovarian cancer (OC) is the most lethal gynecological malignancy with majority of cases diagnosed in advanced stages and associated with high morbidity and mortality. Positron emission tomography/computed tomography (PET/CT) has emerged as an integral part of the management of several nongynecological cancers. We used PubMed search engine using MeSH words "ovarian cancer" and "PET/CT" and reviewed the current status of PET/CT in epithelial OC. Its application related to ovarian tumor including adnexal mass evaluation, baseline staging, as a triaging tool for upfront surgery or neoadjuvant chemotherapy, for response assessment and prognostication, and for relapse detection and treatment planning has been highlighted. we highlight the current guidelines and newer upcoming PET modalities and radiotracers.

Analyzing subcortical structures in Alzheimer's disease using ensemble learning

Alzheimer's disease (AD) is a neurological condition that causes significant cognitive deterioration within the brain. Early detection can lead to an early diagnosis of the illness. Structural and subcortical analyses are required for better diagnosis of disease and detection to gain a deeper understanding of the connections between neuroregions. As a result, in this paper, we have included the fusion of PET and MRI modalities, which can provide better visualization of the result. Second, we used the Ensemble Model (EM) and other machine learning (ML) methods to analyze the different subcortical structures to determine which region is more critical in the detection of AD when compared to other subtypes. The results revealed that the Hippocampus, the Amygdala in the left and right Hemispheres, and the neuroregion were the most effective in detecting AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN).

Deep Learning Denoising Improves and Homogenizes Patient [18F]FDG PET Image Quality in Digital PET/CT.

Given the constant pressure to increase patient throughput while respecting radiation protection, global body PET image quality (IQ) is not satisfactory in all patients. We first studied the association between IQ and other variables, in particular body habitus, on a digital PET/CT. Second, to improve and homogenize IQ, we evaluated a deep learning PET denoising solution (Subtle PETTM) using convolutional neural networks. We analysed retrospectively in 113 patients visual IQ (by a 5-point Likert score in two readers) and semi-quantitative IQ (by the coefficient of variation in the liver, CVliv) as well as lesion detection and quantification in native and denoised PET. In native PET, visual and semi-quantitative IQ were lower in patients with larger body habitus (p < 0.0001 for both) and in men vs. women (p ≤ 0.03 for CVliv). After PET denoising, visual IQ scores increased and became more homogeneous between patients (4.8 ± 0.3 in denoised vs. 3.6 ± 0.6 in native PET; p < 0.0001). CVliv were lower in denoised PET than in native PET, 6.9 ± 0.9% vs. 12.2 ± 1.6%; p < 0.0001. The slope calculated by linear regression of CVliv according to weight was significantly lower in denoised than in native PET (p = 0.0002), demonstrating more uniform CVliv. Lesion concordance rate between both PET series was 369/371 (99.5%), with two lesions exclusively detected in native PET. SUVmax and SUVpeak of up to the five most intense native PET lesions per patient were lower in denoised PET (p < 0.001), with an average relative bias of -7.7% and -2.8%, respectively. DL-based PET denoising by Subtle PETTM allowed [18F]FDG PET global image quality to be improved and homogenized, while maintaining satisfactory lesion detection and quantification. DL-based denoising may render body habitus adaptive PET protocols unnecessary, and pave the way for the improvement and homogenization of PET modalities.

Radionuclide Tracers for Myocardial Perfusion Imaging and Blood Flow Quantification

Myocardial perfusion imaging by nuclear cardiology is widely validated for the diagnosis, risk stratification, and management of patients with suspected or known coronary artery disease. Numerous radiopharmaceuticals are available for single-photon emission computed tomography and PET modalities. Each tracer shows advantages and limitations that should be taken into account in performing an imaging examination. This review aimed to summarize the state-of-the-art radiotracers used for myocardial perfusion imaging and blood flow quantification, highlighting the new technologic advances and promising possible applications.

A Novel Lung Nodule Accurate Segmentation of PET-CT Images Based on Convolutional Neural Network and Graph Model

Positron Emission Tomography and Computed Tomography(PET/CT) imaging could obtain functional metabolic feature information and anatomical localization information of the patient body. However, tumor segmentation in PET/CT images is significantly challenging for fusing of dual-modality characteristic information. In this work, we have proposed a novel deep learning-based graph model network which can automatically fuse dual-modality information for tumor area segmentation. Our method rationally utilizes the advantage of each imaging modality(PET: the superior contrast, CT: the superior spatial resolution). We formulate this task as a Conditional Random Field(CRF) based on multi-scale fusion and dual-modality co-segmentation of object image with a normalization term which balances the segmentation divergence between PET and CT. This mechanism considers that the spatial varying characteristics acquire different scales, which encode various feature information over different modalities. The ability of our method was evaluated to detect and segment tumor regions with different fusion approaches using a dataset of PET/CT clinical tumor images. The results illustrated that our method effectively integrates both PET and CT modalities information, deriving segmentation accuracy result of 0.86 in DSC and the sensitivity of 0.83, which is 3.61% improvement compared to the W-Net.

Endpoints and outcomes: the challenge of biomarker choice for VCI trials

AbstractBackgroundClinical trials for Alzheimer’s disease (AD) have been significantly enhanced by the development of a suite of biomarkers ranging from PET and MRI modalities to CSF and now, plasma. Such biomarkers have refined trials by enabling the specific inclusion of participants with the targeted pathology. Clinical trials for vascular cognitive impairment and dementia (VCID) will require similar development and validation of biomarkers to be used for inclusion, stratification, target engagement, and efficacy in trials.MethodsWe have explored fluid biomarkers for VCID using two longitudinal cohorts: UK‐ADRC and MarkVCID. Using the Quanterix Simoa assays, we have examined) neurodegenerative (NfL), inflammatory (GFAP, IL1b, TNFa, IL6, IL10), and angiogenic (PlGF, VEGF‐A, MMP9) biomarkers in plasma. We examined associations of these plasma biomarkers with disease phenotypes: white matter hyperintensities (WMH) and cerebrovascular reactivity (CVR) on MRI, learning and memory assessments, and neuropathologic hallmarks including amyloid plaques, tangles, amyloid angiopathy (CAA), brain arteriolosclerosis and microinfarcts.ResultsIn association with WMH, a presumed neuroimaging indicator of small vessel disease, we found that PlGF and WMH were positively correlated. Further, higher plasma GFAP was significantly associated with decreased CVR. For cognition, PlGF was associated with decreased attention and processing speed as assessed by Trail Making Test‐A; anti‐inflammatory cytokine IL10 was positively correlated with performance on verbal memory and verbal fluency. As has been shown previously, we observed that plasma NfL was negatively associated verbal fluency. Finally, when examining cerebrovascular pathologies, we found PlGF was positively associated with CAA, while GFAP was positively associated with chronic small vessel pathology like microinfarcts; IL8, an endothelial‐derived cytokine, was positively associated with more severe brain arteriolosclerosis.ConclusionA number of candidate plasma biomarkers have been identified for potential use in clinical trials for VCID and these warrant further validation and characterization. While angiogenic markers like PlGF were associated with WMH, small vessel pathology may be more strongly associated with inflammation as indicated by GFAP and IL8. Verbal memory and fluency may be negatively affected by inflammation, and NfL remains a robust biomarker for cognitive test performance.

GSOR8 Presentation Time: 9:35 AM: Impact of External Beam Radiotherapy Site on Cervical Cancer Outcomes for Patients Undergoing Brachytherapy Boost at a Single Institution

<h3>Purpose</h3> For patients undergoing definitive radiation treatment for cervical cancer, historical evidence demonstrates that a delay in overall time to complete therapy beyond 7-9 weeks yields poorer survival outcomes. Moreover, retrospective data suggests that treatment with external beam radiotherapy (EBRT) and HDR brachytherapy at different institutions corresponds to increased likelihood of treatment delays and worse survival. It is unclear, however, whether these treatment-related factors continue to impact cervical cancer outcomes in contemporary cohorts with modern diagnostic imaging and treatment techniques. We investigated treatment-related factors and associated outcomes in a contemporary cohort of patients undergoing definitive radiation treatment for cervical cancer. <h3>Materials and Methods</h3> We conducted a retrospective cohort study using a single institution treatment database to identify patients who underwent HDR brachytherapy as part of definitive radiation treatment for cervical cancer between 2011 and 2021. Patients were categorized as having undergone EBRT at the same institution or at an outside facility. Patients who initially underwent surgery with hysterectomy were excluded. All patients underwent staging using PET and/or MRI imaging modalities and underwent MRI prior to HDR brachytherapy as well as CT and/or MRI with brachytherapy. We utilized Cox regression models to analyze the impact of treatment-related factors on recurrence-free and overall survival. <h3>Results</h3> The observational cohort included N = 138 patients with cervical cancer who underwent HDR brachytherapy as part of definitive radiation treatment during the study period including 64 (46.4%) who underwent EBRT at an outside facility. The majority of patients (72%) had FIGO 2018 Stage III or IV disease at diagnosis and median tumor size was 5.3 cm in greatest dimension (IQR: 4.0-6.5). Nearly all patients (95.6%) received concurrent chemotherapy. Median total treatment time was 50 days (IQR: 46-57). Patients who underwent outside EBRT were more likely to have a treatment delay of >8 weeks total treatment time (56% vs. 9%, p < 0.001). At a median follow up of 34 months, outside EBRT was not associated with a difference in recurrence-free survival (HR 0.97, 95% CI: 0.49-1.93) or overall survival (HR 0.96, 95% CI: 0.43-2.15) on univariate or multivariate analyses. Similarly, no difference in outcomes was observed when stratifying analyses by tumor size (> or < 5 cm) or stage at diagnosis. <h3>Conclusions</h3> In this observational cohort patients undergoing HDR brachytherapy for cervical cancer at a single institution, there was no difference in outcomes based on whether EBRT was delivered at the same facility despite outside EBRT being associated with increased total treatment time. Future prospective studies should investigate the optimal window of total treatment time for patients with cervical undergoing definitive radiation treatment in the era of modern diagnostic modalities, contemporary HDR technique, and concurrent chemotherapy.

Using Radiomics to Improve the Diagnostic Accuracy of Indeterminate Residual Primary Disease on Restaging PET/CT Imaging Following Radiation Therapy for Head and Neck Cancers

<h3>Purpose/Objective(s)</h3> Identifying residual disease (RD) in the primary site of head and neck cancer (HNC) patients treated with radiotherapy (RT) on the 3-month post RT PET/CT scans can be challenging. Radiologists have to account for RT-related inflammation versus RD. Accurate identification is key to determine the need for morbid salvage versus close observation. In this study, we hypothesized a clinical/radiomics model could complement a radiologist's interpretation in these uncertain cases. <h3>Materials/Methods</h3> We identified 182 patients with HNC treated at our institution from 2016-2020. We reviewed the 3-month post-RT PET/CT scan interpretations to obtain the radiologists' RD assessment. We classified assessments as "certain" or "uncertain." "Certain" assessments included words like "complete metabolic response," "no RD," "RD present" while "uncertain" assessments provided a differential. For uncertain cases, predictions were inferred as the most likely diagnosis ("likely radiation changes but can't rule out RD" was no RD present). If no clarification was provided, then the prediction was marked as RD present. We extracted radiomics features from the CT and PET modalities using open-source software. We gathered clinical data such as HPV status, T stage, etc. Feature reduction was performed by assessing single feature performance on a logistic regression model. Features with high correlation (>0.9) measured using Pearson coefficient were discarded. Recursive feature elimination was performed on the remaining features (only 5-10 features were allowed in the final model). We trained an explainable boosting machine model using the "certain" data and tested with "uncertain" data. Ground truth was obtained from follow-up imaging or pathology. <h3>Results</h3> Ninety-six assessments were "certain" and 86 were "uncertain." RD was present in 10 and 9 cases, respectively. The performances for the radiologists and our models are summarized in Table 1. The Clinical/CT radiomics model reduced the number of false positives from 22 to 8 at the cost of only one additional false negative. The ensemble of our CT and PET models did not improve performance. Table 1 <h3>Conclusion</h3> Even expert radiologists may struggle with identifying RD given post-RT inflammatory changes. Our Clinical/CT radiomics model greatly improved specificity with only a minor drop in sensitivity in this uncertain cohort. Machine learning models may have utility in complementing clinical experience to improve diagnostic accuracy for this challenging scenario.

Correspondence between gene expression and neurotransmitter receptor and transporter density in the human brain

Neurotransmitter receptors modulate signaling between neurons. Thus, neurotransmitter receptors and transporters play a key role in shaping brain function. Due to the lack of comprehensive neurotransmitter receptor/transporter density datasets, microarray gene expression measuring mRNA transcripts is often used as a proxy for receptor densities. In the present report, we comprehensively test the spatial correlation between gene expression and protein density for a total of 27 neurotransmitter receptors, receptor binding-sites, and transporters across 9 different neurotransmitter systems, using both PET and autoradiography radioligand-based imaging modalities. We find poor spatial correspondences between gene expression and density for all neurotransmitter receptors and transporters except four single-protein metabotropic receptors (5-HT1A, CB1, D2, and MOR). These expression-density associations are related to gene differential stability and can vary between cortical and subcortical structures. Altogether, we recommend using direct measures of receptor and transporter density when relating neurotransmitter systems to brain structure and function.

PET Imaging of Fibroblast Activation Protein in Various Types of Cancer Using 68Ga-FAP-2286: Comparison with 18F-FDG and 68Ga-FAPI-46 in a Single-Center, Prospective Study.

PET imaging that targets fibroblast activation protein (FAP) on the surface of cancer-associated fibroblasts has yielded promising tumor diagnostic results. FAP-2286 contains cyclic peptides as FAP-binding motifs to optimize tumor retention compared with the small-molecule FAP inhibitor (FAPI) series (FAPI-04/46). The aim of this study was to evaluate the diagnostic accuracy of 68Ga-FAP-2286 to detect primary and metastatic lesions in patients with various types of cancer, compared with 18F-FDG and 68Ga-FAP-2286. Methods: Sixty-four patients with 15 types of cancer underwent 68Ga-FAP-2286 PET/CT for initial assessment or detection of recurrence. For comparison, 63 patients underwent paired 68Ga-FAP-2286 and 18F-FDG PET/CT and 19 patients underwent paired 68Ga-FAP-2286 and 68Ga-FAPI-46 PET/CT. Lesion uptake was quantified as SUVmax and tumor-to-background ratio. The Wilcoxon matched-pairs signed-rank test was used to compare SUVmax between PET modalities, and the McNemar test was used to compare lesion detectability. Results: Uptake of 68Ga-FAP-2286 was significantly higher than that of 18F-FDG in primary tumors (median SUVmax, 11.1 vs. 6.9; P < 0.001), lymph node metastases (median SUVmax, 10.6 vs. 6.2; P < 0.001), and distant metastases, resulting in improved image contrast and lesion detectability. All primary tumors (46/46) were clearly visualized by 68Ga-FAP-2286 PET/CT, whereas 9 of the 46 lesions could not be visualized by 18F-FDG PET/CT. The lesion detection rate of 68Ga-FAP-2286 PET/CT was superior to that of 18F-FDG PET/CT for involved lymph nodes (98% [105/107] vs. 85% [91/107], P = 0.001) and bone and visceral metastases (95% [162/171] vs. 67% [114/171], P < 0.001). 68Ga-FAP-2286 yielded tumor uptake and lesion detection rates similar to those of 68Ga-FAPI-46 in a subcohort of 19 patients. Conclusion: 68Ga-FAP-2286 is a promising FAP-inhibitor derivative for safe cancer diagnosis, staging, and restaging. It may be a better alternative to 18F-FDG for the cancer types that exhibit low-to-moderate uptake of 18F-FDG, which include gastric, pancreatic, and hepatic cancers. In addition, 68Ga-FAP-2286 and 68Ga-FAPI-46 yielded comparable clinical results.

Association of Depressive Symptoms and Cognition in Older Adults Without Dementia Across Different Biomarker Profiles.

Depression is a late-life risk factor for cognitive decline. Evidence suggests an association between Alzheimer's disease (AD) associated pathologic changes and depressive symptoms. To investigate the influence of AT(N) biomarker profile (amyloid-β [A], p-tau [T], and neurodegeneration [N]) and gender on cross-sectional associations between subclinical depressive symptoms and cognitive function among older adults without dementia. Participants included 868 individuals without dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Depressive symptoms were measured using the Geriatric Depression Scale (GDS). ADNI neuropsychological composite scores assessed memory and executive function (EF). PET, cerebrospinal fluid, and MRI modalities classified the study sample into biomarker profiles: normal biomarkers (A-T-N-), AD continuum (A+T±N±), and suspect non-AD pathology (SNAP; A-T±N-or A-T-N±). Multivariate regression models were used to investigate associations between GDS and cognitive domains. GDS was negatively associated with memory (β= -0.156, p < 0.001) and EF (β= -0.147, p < 0.001) in the whole sample. When classified by biomarker profile, GDS was negatively associated with memory and EF in AD continuum (memory: β= -0.174, p < 0.001; EF: β= -0.129 p = 0.003) and SNAP (memory: β= -0.172, p = 0.005; EF: β= -0.197, p = 0.001) subgroups. When stratified by sex, GDS was negatively associated with memory (β= -0.227, p < 0.001) and EF (β= -0.205, p < 0.001) in men only. The association between subclinical depressive symptoms and cognitive function is highly influenced by the AT(N) biomarker profile.