Same-day post-therapy imaging with a new generation whole-body digital SPECT/CT in assessing treatment response to [177Lu]Lu-PSMA-617 in metastatic castration-resistant prostate cancer.

PURPOSE: Lutetium-177 [177Lu]Lu-PSMA-617 radioligand therapy (RLT) represents a significant advancement for metastatic castration-resistant prostate cancer (mCRPC), demonstrating improvements in radiographic progression free survival (rPFS) and overall survival (OS) with a low rate of associated side effects. Currently, most post-therapy SPECT/CT is conducted at 24 h after infusion. This study examines the clinical utility of a next-generation multi-detector Cadmium-Zinc-Telluride (CZT) SPECT/CT system (StarGuide) in same-day post-infusion assessment and early treatment response to [177Lu]Lu-PSMA-617. METHODS: In this retrospective study, 68 men with progressive mCRPC treated with [177Lu]Lu-PSMA-617 at our center from June 2022 to June 2023 were evaluated. Digital whole-body SPECT/CT imaging was performed after [177Lu]Lu-PSMA-617infusion (mean ± SD: 1.8 ± 0.6 h, range 1.1-4.9 h). Quantitative analysis of [177Lu]Lu-PSMA-617 positive lesions was performed in patients who underwent at least 2 post-therapy SPECT/CT, using liver parenchyma uptake as reference. Metrics including [177Lu]Lu-PSMA-617 positive total tumor volume (Lu-TTV), SUVmax and SUVmean were calculated. These quantitative metrics on post-infusion SPECT/CT images after cycles 1, 2 and 3 were correlated with overall survival (OS), prostate specific antigen-progression free survival (PSA-PFS) as defined by prostate cancer working group 3 (PCWG3), and PSA decrease over 50% (PSA50) response rates. RESULTS: 56 patients (means age 76.2 ± 8.1 years, range: 60-93) who underwent at least 2 post-therapy SPECT/CT were included in the image analysis. The whole-body SPECT/CT scans (~ 12 min per scan) were well tolerated, with 221 same-day scans performed (89%). At a median of 10-months follow-up, 33 (58.9%) patients achieved PSA50 after [177Lu]Lu-PSMA-617 treatment and median PSA-PFS was 5.0 months (range: 1.0-15 months) while median OS was not reached. Quantitative analysis of SPECT/CT images showed that 37 patients (66%) had > 30% reduction in Lu-TTV, associated with significantly improved overall survival (median not reached vs. 6 months, P = 0.008) and PSA-PFS (median 6 months vs. 1 months, P < 0.001). However, changes in SUVmax or SUVmean did not correlate with PSA-PFS or OS. CONCLUSION: We successfully implemented same-day post-therapy SPECT/CT after [177Lu]Lu-PSMA-617 infusions. Quantitation of 1-2 h post-therapy SPECT/CT images is a promising method for assessing treatment response. However, the approach is currently limited by its suboptimal detection of small tumor lesions and the necessity of incorporating a third-cycle SPECT/CT to mitigate the effects of any potential treatment-related flare-up. Further investigation in a larger patient cohort and prospective validation is essential to confirm these findings and to explore the role of SPECT/CT as a potential adjunct to PSMA PET/CT in managing mCRPC.

SPECT at the speed of PET: a feasibility study of CZT-based whole-body SPECT/CT in the post 177Lu-DOTATATE and 177Lu-PSMA617 setting.

PURPOSE: To evaluate the feasibility of using the StarGuide (General Electric Healthcare, Haifa, Israel), a new generation multi-detector cadmium-zinc-telluride (CZT)-based SPECT/CT, for whole-body imaging in the setting of post-therapy imaging of 177Lu-labeled radiopharmaceuticals. METHODS: Thirty-one patients (34-89 years old; mean ± SD, 65.5 ± 12.1) who were treated with either 177Lu-DOTATATE (n=17) or 177Lu-PSMA617 (n=14) as part of standard of care were scanned post-therapy with the StarGuide; some were also scanned with the standard GE Discovery 670 Pro SPECT/CT. All patients had either 64Cu-DOTATATE or 18F-DCFPyL PET/CT prior to first cycle of therapy for eligibility check. The detection/targeting rate (lesion uptake greater than blood pool uptake) of large lesions meeting RECIST 1.1 size criteria on post-therapy StarGuide SPECT/CT was evaluated and compared to the standard design GE Discovery 670 Pro SPECT/CT (when available) and pre-therapy PET by two nuclear medicine physicians with consensus read. RESULTS: This retrospective analysis identified a total of 50 post-therapy scans performed with the new imaging protocol from November 2021 to August 2022. The StarGuide system acquired vertex to mid-thighs post-therapy SPECT/CT scans with 4 bed positions, 3 min/bed and a total scan time of 12 min. In comparison, the standard GE Discovery 670 Pro SPECT/CT system typically acquires images in 2 bed positions covering the chest, abdomen, and pelvis with a total scan time of 32 min. The pre-therapy 64Cu-DOTATATE PET takes 20 min with 4 bed positions on GE Discovery MI PET/CT, and 18F-DCFPyL PET takes 8-10 min with 4-5 bed positions on GE Discovery MI PET/CT. This preliminary evaluation showed that the post-therapy scans acquired with faster scanning time using StarGuide system had comparable detection/targeting rate compared to the Discovery 670 Pro SPECT/CT system and detected large lesions defined by RECIST criteria on the pre-therapy PET scans. CONCLUSION: Fast acquisition of whole-body post-therapy SPECT/CT is feasible with the new StarGuide system. Short scanning time improves the patients' clinical experience and compliance which may lead to increased adoption of post-therapy SPECT. This opens the possibility to offer imaged-based treatment response assessment and personalized dosimetry to patients referred for targeted radionuclide therapies.

Mask-Guided Convolutional Neural Network for Breast Tumor Prognostic Outcome Prediction on 3D DCE-MR Images.

In this proof-of-concept work, we have developed a 3D-CNN architecture that is guided by the tumor mask for classifying several patient-outcomes in breast cancer from the respective 3D dynamic contrast-enhanced MRI (DCE-MRI) images. The tumor masks on DCE-MRI images were generated using pre- and post-contrast images and validated by experienced radiologists. We show that our proposed mask-guided classification has a higher accuracy than that from either the full image without tumor masks (including background) or the masked voxels only. We have used two patient outcomes for this study: (1) recurrence of cancer after 5 years of imaging and (2) HER2 status, for comparing accuracies of different models. By looking at the activation maps, we conclude that an image-based prediction model using 3D-CNN could be improved by even a conservatively generated mask, rather than overly trusting an unguided, blind 3D-CNN. A blind CNN may classify accurately enough, while its attention may really be focused on a remote region within 3D images. On the other hand, only using a conservatively segmented region may not be as good for classification as using full images but forcing the model's attention toward the known regions of interest.

Deep learning detection of prostate cancer recurrence with 18F-FACBC (fluciclovine, Axumin®) positron emission tomography.

PURPOSE: To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal 18F-FACBC (fluciclovine, Axumin®) PET scans based on the presence of tumor recurrence and/or metastases in patients with prostate cancer (PC) and biochemical recurrence (BCR). METHODS: A total of 251 consecutive 18F-fluciclovine PET scans were acquired between September 2017 and June 2019 in 233 PC patients with BCR (18 patients had 2 scans). PET images were labeled as normal or abnormal using clinical reports as the ground truth. Convolutional neural network (CNN) models were trained using two different architectures, a 2D-CNN (ResNet-50) using single slices (slice-based approach) and the same 2D-CNN and a 3D-CNN (ResNet-14) using a hundred slices per PET image (case-based approach). Models' performances were evaluated on independent test datasets. RESULTS: For the 2D-CNN slice-based approach, 6800 and 536 slices were used for training and test datasets, respectively. The sensitivity and specificity of this model were 90.7% and 95.1%, and the area under the curve (AUC) of receiver operating characteristic curve was 0.971 (p < 0.001). For the case-based approaches using both 2D-CNN and 3D-CNN architectures, a training dataset of 100 images and a test dataset of 28 images were randomly allocated. The sensitivity, specificity, and AUC to discriminate abnormal images by the 2D-CNN and 3D-CNN case-based approaches were 85.7%, 71.4%, and 0.750 (p = 0.013) and 71.4%, 71.4%, and 0.699 (p = 0.053), respectively. CONCLUSION: DL accurately classifies abnormal 18F-fluciclovine PET images of the pelvis in patients with BCR of PC. A DL classifier using single slice prediction had superior performance over case-based prediction.

Preclinical SPECT and SPECT-CT in Oncology.

Molecular imaging enables both spatial and temporal understanding of the complex biologic systems underlying carcinogenesis and malignant spread. Single-photon emission tomography (SPECT) is a versatile nuclear imaging-based technique with ideal properties to study these processes in vivo in small animal models, as well as to identify potential drug candidates and characterize their antitumor action and potential adverse effects. Small animal SPECT and SPECT-CT (single-photon emission tomography combined with computer tomography) systems continue to evolve, as do the numerous SPECT radiopharmaceutical agents, allowing unprecedented sensitivity and quantitative molecular imaging capabilities. Several of these advances, their specific applications in oncology as well as new areas of exploration are highlighted in this chapter.

Identifying tests related to breast cancer care in claims data.

To develop a method for calculating rates of testing for breast cancer recurrence in patients who have already undergone initial treatment for breast cancer, we calculated rates in a cohort of Medicare breast cancer patients and an age-matched noncancer cohort. We first used only tests with claims including diagnosis codes indicating invasive breast cancer and then used all tests regardless of diagnosis code. For each method, we calculated testing rates in the breast cancer cohort above the background rate in the noncancer population. The two methods provided similar estimates of testing prevalence and frequency, with exception of prevalence of CT.

Performance Comparison of Individual and Ensemble CNN Models for the Classification of Brain 18F-FDG-PET Scans.

The high-background glucose metabolism of normal gray matter on [18F]-fluoro-2-D-deoxyglucose (FDG) positron emission tomography (PET) of the brain results in a low signal-to-background ratio, potentially increasing the possibility of missing important findings in patients with intracranial malignancies. To explore the strategy of using a deep learning classifier to aid in distinguishing normal versus abnormal findings on PET brain images, this study evaluated the performance of a two-dimensional convolutional neural network (2D-CNN) to classify FDG PET brain scans as normal (N) or abnormal (A). METHODS: Two hundred eighty-nine brain FDG-PET scans (N; n = 150, A; n = 139) resulting in a total of 68,260 images were included. Nine individual 2D-CNN models with three different window settings for axial, coronal, and sagittal axes were trained and validated. The performance of these individual and ensemble models was evaluated and compared using a test dataset. Odds ratio, Akaike's information criterion (AIC), and area under curve (AUC) on receiver-operative-characteristic curve, accuracy, and standard deviation (SD) were calculated. RESULTS: An optimal window setting to classify normal and abnormal scans was different for each axis of the individual models. An ensembled model using different axes with an optimized window setting (window-triad) showed better performance than ensembled models using the same axis and different windows settings (axis-triad). Increase in odds ratio and decrease in SD were observed in both axis-triad and window-triad models compared with individual models, whereas improvements of AUC and AIC were seen in window-triad models. An overall model averaging the probabilities of all individual models showed the best accuracy of 82.0%. CONCLUSIONS: Data ensemble using different window settings and axes was effective to improve 2D-CNN performance parameters for the classification of brain FDG-PET scans. If prospectively validated with a larger cohort of patients, similar models could provide decision support in a clinical setting.

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain.

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods Prospective 18F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.

Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning.

Breast cancer is a leading cause of cancer death among women in the USA. Screening mammography is effective in reducing mortality, but has a high rate of unnecessary recalls and biopsies. While deep learning can be applied to mammography, large-scale labeled datasets, which are difficult to obtain, are required. We aim to remove many barriers of dataset development by automatically harvesting data from existing clinical records using a hybrid framework combining traditional NLP and IBM Watson. An expert reviewer manually annotated 3521 breast pathology reports with one of four outcomes: left positive, right positive, bilateral positive, negative. Traditional NLP techniques using seven different machine learning classifiers were compared to IBM Watson's automated natural language classifier. Techniques were evaluated using precision, recall, and F-measure. Logistic regression outperformed all other traditional machine learning classifiers and was used for subsequent comparisons. Both traditional NLP and Watson's NLC performed well for cases under 1024 characters with weighted average F-measures above 0.96 across all classes. Performance of traditional NLP was lower for cases over 1024 characters with an F-measure of 0.83. We demonstrate a hybrid framework using traditional NLP techniques combined with IBM Watson to annotate over 10,000 breast pathology reports for development of a large-scale database to be used for deep learning in mammography. Our work shows that traditional NLP and IBM Watson perform extremely well for cases under 1024 characters and can accelerate the rate of data annotation.

Geographic Variation in Postoperative Imaging for Low-Risk Breast Cancer.

Background: The objective of this study was to examine the presence and magnitude of US geographic variation in use rates of both recommended and high-cost imaging in young patients with early-stage breast cancer during the 18 month period after surgical treatment of their primary tumor. Methods: Using the Truven Health MarketScan Commercial Database, a descriptive analysis was conducted of geographic variation in annual rates of dedicated breast imaging and high-cost body imaging of 36,045 women aged 18 to 64 years treated with surgery for invasive unilateral breast cancer between 2010 and 2012. Multivariate hierarchical analysis examined the relationship between likelihood of imaging and patient characteristics, with metropolitan statistical area (MSA) serving as a random effect. Patient characteristics included age group, BRCA1/2 carrier status, family history of breast cancer, combination of breast surgery type and radiation therapy, drug therapy, and payer type. All MSAs in the United States were included, with areas outside MSAs within a given state aggregated into a single area for analytic purposes. Results: Descriptive analysis of rates of imaging use and intensity within MSA regions revealed wide geographic variation, irrespective of treatment cohort or age group. Increased probability of recommended postoperative dedicated breast imaging was primarily associated with age and treatment including both surgery and radiation therapy, followed by MSA region (odds ratio, 1.42). Increased probability of PET use-a high-cost imaging modality for which postoperative routine use is not recommended in the absence of specific clinical findings-was primarily associated with surgery type followed by MSA region (odds ratio, 1.82). Conclusions: In patients with breast cancer treated for low-risk disease, geography has effects on the rates of posttreatment imaging, suggesting that some patients are not receiving beneficial dedicated breast imaging, and high-cost nonbreast imaging may not be targeted to those groups most likely to benefit.

In Vivo PET Imaging of the Activated Immune Environment in a Small Animal Model of Inflammatory Arthritis.

BACKGROUND: Evolving immune-mediated therapeutic strategies for rheumatoid arthritis (RA) may benefit from an improved understanding of the complex role that T-cell activation plays in RA. This study assessed the potential of fluorine-18-labeled 9-ß-d-arabinofuranosylguanine ([18F]F-AraG) positron emission tomography (PET) imaging to report immune activation in vivo in an adjuvant-induced arthritis (AIA) small animal model. METHODS: Using positron emission tomography-computed tomography imaging, uptake of [18F]F-AraG in the paws of mice affected by arthritis at 6 (acute) and 20 (chronic) days following AIA induction in a single paw was assessed and compared to uptake in contralateral control paws. Fractions of T cells and B cells demonstrating markers of activation at the 2 time points were determined by flow cytometry. RESULTS: Differential uptake of [18F]F-AraG was demonstrated on imaging of the affected joint when compared to control at both acute and chronic time points with corresponding changes in markers of T-cell activation observed on flow cytometry. CONCLUSION: [18F]F-AraG may serve as an imaging biomarker of T-cell activation in inflammatory arthritis. Further development of this technique is warranted and could offer a tool to explore the temporal link between activated T cells and RA as well as to monitor immune-mediated therapies for RA in clinical trials.

Imaging Bone-Cartilage Interactions in Osteoarthritis Using [18F]-NaF PET-MRI.

PURPOSE: Simultaneous positron emission tomography-magnetic resonance imaging (PET-MRI) is an emerging technology providing both anatomical and functional images without increasing the scan time. Compared to the traditional PET/computed tomography imaging, it also exposes the patient to significantly less radiation and provides better anatomical images as MRI provides superior soft tissue characterization. Using PET-MRI, we aim to study interactions between cartilage composition and bone function simultaneously, in knee osteoarthritis (OA). PROCEDURES: In this article, bone turnover and remodeling was studied using [18F]-sodium fluoride (NaF) PET data. Quantitative MR-derived T1ρ relaxation times characterized the biochemical cartilage degeneration. Sixteen participants with early signs of OA of the knee received intravenous injections of [18F]-NaF at the onset of PET-MR image acquisition. Regions of interest were identified, and kinetic analysis of dynamic PET data provided the rate of uptake ( Ki) and the normalized uptake (standardized uptake value) of [18F]-NaF in the bone. Morphological MR images and quantitative voxel-based T1ρ maps of cartilage were obtained using an atlas-based registration technique to segment cartilage automatically. Voxel-by-voxel statistical parameter mapping was used to investigate the relationship between bone and cartilage. RESULTS: Increases in cartilage T1ρ, indicating degenerative changes, were associated with increased turnover in the adjoining bone but reduced turnover in the nonadjoining compartments. Associations between pain and increased bone uptake were seen in the absence of morphological lesions in cartilage, but the relationship was reversed in the presence of incident cartilage lesions. CONCLUSION: This study shows significant cartilage and bone interactions in OA of the knee joint using simultaneous [18F]-NaF PET-MR, the first in human study. These observations highlight the complex biomechanical and biochemical interactions in the whole knee joint in OA, which potentially could help assess therapeutic targets in treating OA.

Preclinical SPECT and SPECT/CT.

The molecular processes underlying carcinogenesis and malignant spread are the foundation of future drug development for the treatment of cancer. Understanding these processes requires study of the interaction of complex biologic systems in a way that spatially and temporally recapitulates that seen in humans. Likewise, once an anticancer agent is developed, its intended antitumor action and its unintended side-effects must be studied in a rigorous and reproducible manner prior to its introduction into the clinic, a process that can benefit from methods that elucidate specific molecular processes and that can be performed serially. Recent advances in small-animal models of cancer, radiochemistry of single photon emitting radionuclides, single photon emission tomography systems, and image reconstruction techniques have set the stage for an ever-increasing use of SPECT and SPECT/CT in preclinical oncology-related applications. Several of these advances as well as several specific applications in oncology are highlighted and areas needing further improvement are identified.

Prediction of future healthcare expenses of patients from chest radiographs using deep learning: a pilot study.

Our objective was to develop deep learning models with chest radiograph data to predict healthcare costs and classify top-50% spenders. 21,872 frontal chest radiographs were retrospectively collected from 19,524 patients with at least 1-year spending data. Among the patients, 11,003 patients had 3 years of cost data, and 1678 patients had 5 years of cost data. Model performances were measured with area under the receiver operating characteristic curve (ROC-AUC) for classification of top-50% spenders and Spearman ρ for prediction of healthcare cost. The best model predicting 1-year (N = 21,872) expenditure achieved ROC-AUC of 0.806 [95% CI 0.793-0.819] for top-50% spender classification and ρ of 0.561 [0.536-0.586] for regression. Similarly, for predicting 3-year (N = 12,395) expenditure, ROC-AUC of 0.771 [0.750-0.794] and ρ of 0.524 [0.489-0.559]; for predicting 5-year (N = 1779) expenditure ROC-AUC of 0.729 [0.667-0.729] and ρ of 0.424 [0.324-0.529]. Our deep learning model demonstrated the feasibility of predicting health care expenditure as well as classifying top 50% healthcare spenders at 1, 3, and 5 year(s), implying the feasibility of combining deep learning with information-rich imaging data to uncover hidden associations that may allude to physicians. Such a model can be a starting point of making an accurate budget in reimbursement models in healthcare industries.

First-in-human immunoPET imaging of HIV-1 infection using 89Zr-labeled VRC01 broadly neutralizing antibody.

A major obstacle to achieving long-term antiretroviral (ART) free remission or functional cure of HIV infection is the presence of persistently infected cells that establish a long-lived viral reservoir. HIV largely resides in anatomical regions that are inaccessible to routine sampling, however, and non-invasive methods to understand the longitudinal tissue-wide burden of HIV persistence are urgently needed. Positron emission tomography (PET) imaging is a promising strategy to identify and characterize the tissue-wide burden of HIV. Here, we assess the efficacy of using immunoPET imaging to characterize HIV reservoirs and identify anatomical foci of persistent viral transcriptional activity using a radiolabeled HIV Env-specific broadly neutralizing antibody, 89Zr-VRC01, in HIV-infected individuals with detectable viremia and on suppressive ART compared to uninfected controls (NCT03729752). We also assess the relationship between PET tracer uptake in tissues and timing of ART initiation and direct HIV protein expression in CD4 T cells obtained from lymph node biopsies. We observe significant increases in 89Zr-VRC01 uptake in various tissues (including lymph nodes and gut) in HIV-infected individuals with detectable viremia (N = 5) and on suppressive ART (N = 5) compared to uninfected controls (N = 5). Importantly, PET tracer uptake in inguinal lymph nodes in viremic and ART-suppressed participants significantly and positively correlates with HIV protein expression measured directly in tissue. Our strategy may allow non-invasive longitudinal characterization of residual HIV infection and lays the framework for the development of immunoPET imaging in a variety of other infectious diseases.

Positron Emission Tomography Imaging of Functional Transforming Growth Factor ß (TGFß) Activity and Benefit of TGFß Inhibition in Irradiated Intracranial Tumors.

PURPOSE: Transforming growth factor ß (TGFß) promotes cell survival by endorsing DNA damage repair and mediates an immunosuppressive tumor microenvironment. Thus, TGFß activation in response to radiation therapy is potentially targetable because it opposes therapeutic control. Strategies to assess this potential in the clinic are needed. METHODS AND MATERIALS: We evaluated positron emission tomography (PET) to image 89Zr -fresolimumab, a humanized TGFß neutralizing monoclonal antibody, as a means to detect TGFß activation in intracranial tumor models. Pathway activity of TGFß was validated by immunodetection of phosphorylated SMAD2 and the TGFß target, tenascin. The contribution of TGFß to radiation response was assessed by Kaplan-Meier survival analysis of mice bearing intracranial murine tumor models GL261 and SB28 glioblastoma and brain-adapted 4T1 breast cancer (4T1-BrA) treated with TGFß neutralizing monoclonal antibody, 1D11, and/or focal radiation (10 Gy). RESULTS: 89Zr-fresolimumab PET imaging detected engineered, physiological, and radiation-induced TGFß activation, which was confirmed by immunostaining of biological markers. GL261 glioblastoma tumors had a greater PET signal compared with similar-sized SB28 glioblastoma tumors, whereas the widespread PET signal of 4T1-BrA intracranial tumors was consistent with their highly dispersed histologic distribution. Survival of mice bearing intracranial tumors treated with 1D11 neutralizing antibody alone was similar to that of mice treated with control antibody, whereas 1D11 improved survival when given in combination with focal radiation. The extent of survival benefit of a combination of radiation and 1D11 was associated with the degree of TGFß activity detected by PET. CONCLUSIONS: This study demonstrates that 89Zr-fresolimumab PET imaging detects radiation-induced TGFß activation in tumors. Functional imaging indicated a range of TGFß activity in intracranial tumors, but TGFß blockade provided survival benefit only in the context of radiation treatment. This study provides further evidence that radiation-induced TGFß activity opposes therapeutic response to radiation.

Evaluation of primary breast cancers using dedicated breast PET and whole-body PET.

Metabolic imaging of the primary breast tumor with 18F-fluorodeoxyglucose ([18F]FDG) PET may assist in predicting treatment response in the neoadjuvant chemotherapy (NAC) setting. Dedicated breast PET (dbPET) is a high-resolution imaging modality with demonstrated ability in highlighting intratumoral heterogeneity and identifying small lesions in the breast volume. In this study, we characterized similarities and differences in the uptake of [18F]FDG in dbPET compared to whole-body PET (wbPET) in a cohort of ten patients with biopsy-confirmed, locally advanced breast cancer at the pre-treatment timepoint. Patients received bilateral dbPET and wbPET following administration of 186 MBq and 307 MBq [18F]FDG on separate days, respectively. [18F]FDG uptake measurements and 20 radiomic features based on morphology, tumor intensity, and texture were calculated and compared. There was a fivefold increase in SULpeak for dbPET (median difference (95% CI): 4.0 mL-1 (1.8-6.4 mL-1), p = 0.006). Additionally, spatial heterogeneity features showed statistically significant differences between dbPET and wbPET. The higher [18F]FDG uptake in dbPET highlighted the dynamic range of this breast-specific imaging modality. Combining with the higher spatial resolution, dbPET may be able to detect treatment response in the primary tumor during NAC, and future studies with larger cohorts are warranted.

PET/CT Imaging of Human TNFα Using [89Zr]Certolizumab Pegol in a Transgenic Preclinical Model of Rheumatoid Arthritis.

PURPOSE: Tumor necrosis factor alpha (TNFα) drives inflammation and bone degradation in patients with rheumatoid arthritis (RA). Some RA patients experience a rapid clinical response to TNFα inhibitors such as certolizumab pegol (CZP) while other patients show limited to no response. Current methods for imaging RA have limited sensitivity and do not assist in the selection of patients most likely to respond to immune-mediated therapy. Herein, we developed a novel positron emission tomography (PET) radiotracer for immuno-PET imaging of TNFα in transgenic human TNFα-expressing mice. PROCEDURES: CZP was modified with p-isothiocyanatobenzyl-deferoxamine (DFO) and radiolabeled with Zr-89. The biological activity of [89Zr]DFO-CZP was evaluated by HPLC and binding assay using human recombinant TNFα (hTNFα). The feasibility of specific immuno-PET imaging of human TNFα was assessed in a transgenic mouse model of RA that expresses human TNFα. This model resembles the progression of RA in humans by maintaining lower levels of circulating hTNFα and exhibits chronic arthritis in the forepaw and hind paw joints. The dosimetry of [89Zr]DFO-CZP in humans was estimated using microPET/CT imaging in Sprague Dawley rats. RESULTS: [89Zr]DFO-CZP was isolated with radiolabeling yields of 85 ± 6 % (n = 5) and specific activities ranging from 74 to 185 MBq/mg (n = 5). Following size exclusion purification, the radiochemical purity of [89Zr]DFO-CZP was greater than 97 %. [89Zr]DFO-CZP retained high immunoreactivity with more than 95 % of the radioactivity shifted into higher molecular weight complexes. Images showed increasing uptake of the tracer in forepaw and hind paw joints with disease progression. No uptake was observed in the model previously administered with an excess amount of unmodified CZP and in normal control mice, demonstrating in vivo specific uptake of [89Zr]DFO-CZP. CONCLUSION: The feasibility of immuno-PET imaging of human TNFα with [89Zr]DFO-CZP has been demonstrated in a preclinical model of RA.