Pilot Feasibility Study: 18 F-DPA-714 PET/CT Macrophage Imaging in Triple-Negative Breast Cancers (EITHICS).

ABSTRACT: Tumor-associated macrophages are targets of interest in triple-negative breast cancer (TNBC). The translocator protein 18 kDa (TSPO) is a sensitive marker for macrophages and holds potential relevance in TNBC stratification. This pilot prospective study (EITHICS, NCT04320030) aimed to assess the potential of TSPO PET/CT imaging using 18 F-DPA-714 in primary TNBC, compared with immunohistochemistry, autoradiography, and TSPO polymorphism. PATIENTS AND METHODS: Thirteen TNBC patients were included. They underwent TSPO genotyping (HAB, MAB, LAB), 18 F-FDG PET/CT, and breast MRI. Semiquantitative PET parameters were computed. VOIs were defined on the tumor lesion, healthy breast tissue, and pectoral muscle to obtain SUV, tumor-to-background ratio (TBR), and time-activity curves (TACs). Additionally, immunohistochemistry, 3 H-DPA-714, and 3 H-PK-11195 autoradiography were conducted. RESULTS: The majority of TNBC tumors (11/13, 84%) had a preponderance of M2-polarized macrophages with a median proportion of 82% (range, 44%-94%). 18 F-DPA-714 PET/CT clearly identified TNBC tumors with an excellent TBR. Three distinct patterns of 18 F-DPA-714 TACs were identified, categorized as "above muscular," "equal to muscular," and "below muscular" with reference to the muscular background. For the "above muscular" group (2 HAB and 2 MAB), "equal muscular" group (3 HAB, 3 MAB, and 1 LAB), and "below muscular" group (1 LAB and 1 MAB), tumor TACs showed a 18 F-DPA-714 accumulation slope of 1.35, 0.62, and 0.22, respectively, and a median SUV mean of 4.02 (2.09-5.31), 1.66 (0.93-3.07), and 0.61 (0.43-1.02). CONCLUSIONS: This study successfully demonstrated TNBC tumor targeting by 18 F-DPA-714 with an excellent TBR, allowing to stratify 3 patterns of uptake potentially influenced by the TSPO polymorphism status. Further studies in larger populations should be performed to evaluate the prognostic value of this new biomarker.

Between Pathological Prostate Cancer Lymph Nodes and Sentinel Nodes: Which Information Is the Leader?

PURPOSE: On the basis of the concept of sentinel lymph node biopsy (SLNB), SLNs should contain decisive information for clinical outcomes. In localized prostate cancer patients, this study assessed retrospectively clinical outcome after radical laparoscopic prostatectomy associated with SLNB and extensive pelvic lymph node dissection. METHODS: A total of 231 consecutive patients of intermediate to high risk were analyzed. Recurrence-free survival (RFS) was assessed with Kaplan-Meier curves. Various pathological parameters were analyzed using univariable and multivariable analyses through Cox regression analysis. The study was approved and registered under 2007-R41. RESULTS: The median follow-up was 7.1 years (95% confidence interval, 6.6-7.5). In total, 38/231 (16.5%) patients were pN1. Of these 38 patients, 27 had only SLN involvement (SLNI), 10 patients had both SLN and non-SLNI, and 1 patient had isolated non-SLNI, indicating a false-negative (FN). If the updated Briganti nomogram threshold set at >7% for recommending extensive pelvic lymph node dissection had been applied to these patients, we would have missed 44% (12/27) of patients with SLNI and 50% (5/10) of patients with SLNI and non-SLNI, as well as the FN patient. At the time of final follow-up, 84/231 (36.5%) patients had recurrence. In multivariable analysis, and regarding node status, the most significant prognostic factor was SLN with macrometastases and/or micrometastases, respectively, P = 10-3 and P < 10-3. No more information was obtained with non-SLN status. Probabilities of RFS between negative and positive SLN patients presented a major significant difference (P < 10-15) with a risk of event 8.75 times more frequent if SLN was involved than if it was metastasis-free. CONCLUSIONS: SLNB seems to contain decisive information for the clinical outcome of patients with localized intermediate- and high-risk prostate cancer patients. The question raised is thus whether immediate additional postoperative treatment should be offered to patients with metastatic SLN.

Quality Assurance Considerations in Radiopharmaceutical Therapy Dosimetry Using PLANETDose: An International Atomic Energy Agency Study.

Implementation of radiopharmaceutical therapy dosimetry varies depending on the clinical application, dosimetry protocol, software, and ultimately the operator. Assessing clinical dosimetry accuracy and precision is therefore a challenging task. This work emphasizes some pitfalls encountered during a structured analysis, performed on a single-patient dataset consisting of SPECT/CT images by various participants using a standard protocol and clinically approved commercial software. Methods: The clinical dataset consisted of the dosimetric study of a patient administered with [177Lu]Lu-DOTATATE at Tygerberg Hospital, South Africa, as a part of International Atomic Energy Agency-coordinated research project E23005. SPECT/CT images were acquired at 5 time points postinjection. Patient and calibration images were reconstructed on a workstation, and a calibration factor of 122.6 Bq/count was derived independently and provided to the participants. A standard dosimetric protocol was defined, and PLANETDose (version 3.1.1) software was installed at 9 centers to perform the dosimetry of 3 treatment cycles. The protocol included rigid image registration, segmentation (semimanual for organs, activity threshold for tumors), and dose voxel kernel convolution of activity followed by absorbed dose (AD) rate integration to obtain the ADs. Iterations of the protocol were performed by participants individually and within collective training, the results of which were analyzed for dosimetric variability, as well as for quality assurance and error analysis. Intermediary checkpoints were developed to understand possible sources of variation and to differentiate user error from legitimate user variability. Results: Initial dosimetric results for organs (liver and kidneys) and lesions showed considerable interoperator variability. Not only was the generation of intermediate checkpoints such as total counts, volumes, and activity required, but also activity-to-count ratio, activity concentration, and AD rate-to-activity concentration ratio to determine the source of variability. Conclusion: When the same patient dataset was analyzed using the same dosimetry procedure and software, significant disparities were observed in the results despite multiple sessions of training and feedback. Variations due to human error could be minimized or avoided by performing intensive training sessions, establishing intermediate checkpoints, conducting sanity checks, and cross-validating results across physicists or with standardized datasets. This finding promotes the development of quality assurance in clinical dosimetry.

Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies.

In metastatic breast cancer, bone metastases are prevalent and associated with multiple complications. Assessing their response to treatment is therefore crucial. Most deep learning methods segment or detect lesions on a single acquisition while only a few focus on longitudinal studies. In this work, 45 patients with baseline (BL) and follow-up (FU) images recruited in the context of the EPICUREseinmeta study were analyzed. The aim was to determine if a network trained for a particular timepoint can generalize well to another one, and to explore different improvement strategies. Four networks based on the same 3D U-Net framework to segment bone lesions on BL and FU images were trained with different strategies and compared. These four networks were trained 1) only with BL images 2) only with FU images 3) with both BL and FU images 4) only with FU images but with BL images and bone lesion segmentations registered as input channels. With the obtained segmentations, we computed the PET Bone Index (PBI) which assesses the bone metastases burden of patients and we analyzed its potential for treatment response evaluation. Dice scores of 0.53, 0.55, 0.59 and 0.62 were respectively obtained on FU acquisitions. The under-performance of the first and third networks may be explained by the lower SUV uptake due to treatment response in FU images compared to BL images. The fourth network gives better results than the second network showing that the addition of BL PET images and bone lesion segmentations as prior knowledge has its importance. With an AUC of 0.86, the difference of PBI between two acquisitions could be used to assess treatment response. Clinical relevance- To assess the response to treatment of bone metastases, it is crucial to detect and segment them on several acquisitions from a same patient. We proposed a completely automatic method to detect and segment these metastases on longitudinal 18F-FDG PET/CT images in the context of metastatic breast cancer. We also proposed an automatic PBI to quantitatively assess the evolution of the bone metastases burden of patient and to automatically evaluate their response to treatment.

Deformable image registration with deep network priors: a study on longitudinal PET images.

Objective.This paper proposes a novel approach for the longitudinal registration of PET imaging acquired for the monitoring of patients with metastatic breast cancer. Unlike with other image analysis tasks, the use of deep learning (DL) has not significantly improved the performance of image registration. With this work, we propose a new registration approach to bridge the performance gap between conventional and DL-based methods: medical image registration method regularized by architecture (MIRRBA).Approach.MIRRBAis a subject-specific deformable registration method which relies on a deep pyramidal architecture to parametrize the deformation field. Diverging from the usual deep-learning paradigms,MIRRBAdoes not require a learning database, but only a pair of images to be registered that is used to optimize the network's parameters. We appliedMIRRBAon a private dataset of 110 whole-body PET images of patients with metastatic breast cancer. We used different architecture configurations to produce the deformation field and studied the results obtained. We also compared our method to several standard registration approaches: two conventional iterative registration methods (ANTs and Elastix) and two supervised DL-based models (LapIRN and Voxelmorph). Registration accuracy was evaluated using the Dice score, the target registration error, the average Hausdorff distance and the detection rate, while the realism of the registration obtained was evaluated using Jacobian's determinant. The ability of the different methods to shrink disappearing lesions was also computed with the disappearing rate.Main results.MIRRBA significantly improved all metrics when compared to DL-based approaches. The organ and lesion Dice scores of Voxelmorph improved by 6% and 52% respectively, while the ones of LapIRN increased by 5% and 65%. Regarding conventional approaches, MIRRBA presented comparable results showing the feasibility of our method.Significance.In this paper, we also demonstrate the regularizing power of deep architectures and present new elements to understand the role of the architecture in DL methods used for registration.

Vaccine-Related Lymph Nodes: The Emerging Pitfalls of 18F-Fluorocholine and 68Ga-PSMA-11 PET/CT in the Era of COVID-19 Vaccination.

PURPOSE: Vaccination against coronavirus disease 2019 (COVID-19) is currently under worldwide deployment. The consequences of this vaccination can be seen in radiology and nuclear medicine explorations with visualization of axillary lymph nodes (LNs), as observed on ultrasonography, MRI, or 18F-FDG PET/CT.We aimed to evaluate on PET/CT the incidence of vaccine-related LNs and their characteristics after COVID-19 vaccination, using several radiopharmaceuticals different from 18F-FDG. PATIENTS AND METHODS: Between February and July 2021, all consecutive patients undergoing a whole-body PET/CT for any indication using a different radiopharmaceutical from 18F-FDG were eligible for inclusion if they had received at least 1 dose of the COVID-19 vaccine. The radiopharmaceutical administered and vaccine type were recorded for each patient. The incidence of positive vaccine-related axillary and supraclavicular LNs on PET/CT was our primary finding, along with the nodes characteristics. Statistical analyses were performed for patients with prostate cancer (PCa) to determine certain interaction factors that were associated with the detection of vaccine-related LNs. RESULTS: Of the 226 patients in our cohort study, 120 patients underwent an 18F-fluorocholine PET/CT, 79 a 68Ga-PSMA-11 PET/CT, 6 an 18F-FDOPA PET/CT, and 21 a 68Ga-DOTATOC PET/CT. A total of 67.3% of patients (152/226) received BNT162b2mRNA (Pfizer-BioNTech), 26.5% (60/226) ChAdOx1-S (AstraZeneca), 4.9% (11/226) mRNA-1273 (Moderna), and 1.3% (3/226) Ad26.COV2.S (Janssen). The incidence of positive vaccine-related axillary and supraclavicular LNs was 42.5% (51/120 patients) on PET/CT using 18F-fluorocholine and 12.7% (10/79 patients) with 68Ga-PSMA-11. None of our patients undergoing 18F-FDOPA or 68Ga-DOTATOC PET/CT presented any vaccine-related lymphadenopathy. Vaccine-related LNs were statistically associated with the nature of the radiopharmaceutical (P < 10-4), with the number of vaccine doses received (P = 0.041), with a short delay between vaccination and PET/CT realization (P < 10-5), and with a higher prostate-specific antigen level for patients with PCa (P = 0.032), but not with age or vaccine type. The vaccine-related nodes appeared in 85% of the cases, in the 30 days after vaccine injection, were limited in size and uptake, and were most often limited to the axilla level 1 area. CONCLUSIONS: Detecting positive LNs after COVID-19 vaccination is not an exclusive 18F-FDG PET/CT pattern but is common on 18F-fluorocholine and possible on 68Ga-PSMA-11 PET/CT. Confronting PET/CT findings with clinical data (such as date and site of injection) seems essential in the current pandemic context, just as it does for the radiopharmaceuticals used in PCa to avoid PET/CT misinterpretation and incorrect patient treatment. For 18F-FDOPA or 68Ga-DOTATOC PET/CT, this seems to have a lesser impact.

Generation of clinical 177Lu SPECT/CT images based on Monte Carlo simulation with GATE.

PURPOSE: Patient-specific dosimetry in MRT relies on quantitative imaging, pharmacokinetic assessment and absorbed dose calculation. The DosiTest project was initiated to evaluate the uncertainties associated with each step of the clinical dosimetry workflow through a virtual multicentric clinical trial. This work presents the generation of simulated clinical SPECT datasets based on GATE Monte Carlo modelling with its corresponding experimental CT image, which can subsequently be processed by commercial image workstations. METHODS: This study considers a therapy cycle of 6.85 GBq 177Lu-labelled DOTATATE derived from an IAEA-Coordinated Research Project (E23005) on "Dosimetry in Radiopharmaceutical therapy for personalised patient treatment". Patient images were acquired on a GE Infinia-Hawkeye 4 gamma camera using a medium energy (ME) collimator. Simulated SPECT projections were generated based on experimental time points and validated against experimental SPECT projections using flattened profiles and gamma index. The simulated projections were then incorporated into the patient SPECT/CT DICOM envelopes for processing and their reconstruction within a commercial image workstation. RESULTS: Gamma index passing rate (2% - 1 pixel criteria) between 95 and 98% and average gamma between 0.28 and 0.35 among different time points revealed high similarity between simulated and experimental images. Image reconstruction of the simulated projections was successful on HERMES and Xeleris workstations, a major step forward for the initiation of a multicentric virtual clinical dosimetry trial based on simulated SPECT/CT images. CONCLUSIONS: Realistic 177Lu patient SPECT projections were generated in GATE. These modelled datasets will be circulated to different clinical departments to perform dosimetry in order to assess the uncertainties in the entire dosimetric chain.

Automatic Segmentation of Metastatic Breast Cancer Lesions on 18F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment.

Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICUREseinmeta study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SULpeak, Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dice score of 0.66 on baseline acquisitions. The second network obtained a mean Dice score of 0.58 on follow-up acquisitions. SULpeak, with a 32% decrease between baseline and follow-up, was the biomarker best able to assess patients' response (sensitivity 87%, specificity 87%), followed by TLG (43% decrease, sensitivity 73%, specificity 81%) and PBI (8% decrease, sensitivity 69%, specificity 69%). Our networks constitute promising tools for the automatic segmentation of lesions in patients with metastatic breast cancer allowing treatment response assessment with several biomarkers.

Targeting Stereotactic Body Radiotherapy on Metabolic PET- and Immuno-PET-Positive Vertebral Metastases.

(1) Background: Stereotactic body radiotherapy (SBRT) for vertebral metastases (VM) allows the delivery of high radiation doses to tumors while sparing the spinal cord. We report a new approach to clinical target volume (CTV) delineation based on anti-carcinoembryonic antigen (CEA) positron emission tomography (pretargeted immuno-PET; "iPET") in patients with metastatic breast cancer (BC) or medullary thyroid cancer (MTC). (2) Methods: All patients underwent iPET, spine magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET-CT) using 18F-deoxyglucose (FDG) for BC or 18F-dihydroxy-phenylalanine (F-DOPA) for MTC. Vertebrae locations and vertebral segments of lesions were recorded and the impact on CTV delineation was evaluated. (3) Results: Forty-six VM eligible for SBRT following iPET were evaluated in eight patients (five BC, three MTC). Eighty-one vertebral segments were detected using MRI, 26 with FDG or F-DOPA PET/CT, and 70 using iPET. iPET was able to detect more lesions than MRI for vertebral bodies (44 vs. 34). iPET-based delineation modified MRI-based CTV in 70% (32/46) of cases. (4) Conclusion: iPET allows a precise mapping of affected VM segments, and adds complementary information to MRI in the definition of candidate volumes for VM SBRT. iPET may facilitate determining target volumes for treatment with stereotactic body radiotherapy in metastatic vertebral disease.

Radiomics for radiation oncologists: are we ready to go?

Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.

Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer.

18FDG PET/CT imaging is commonly used in diagnosis and follow-up of metastatic breast cancer, but its quantitative analysis is complicated by the number and location heterogeneity of metastatic lesions. Considering that bones are the most common location among metastatic sites, this work aims to compare different approaches to segment the bones and bone metastatic lesions in breast cancer.Two deep learning methods based on U-Net were developed and trained to segment either both bones and bone lesions or bone lesions alone on PET/CT images. These methods were cross-validated on 24 patients from the prospective EPICUREseinmeta metastatic breast cancer study and were evaluated using recall and precision to measure lesion detection, as well as the Dice score to assess bones and bone lesions segmentation accuracy.Results show that taking into account bone information in the training process allows to improve the precision of the lesions detection as well as the Dice score of the segmented lesions. Moreover, using the obtained bone and bone lesion masks, we were able to compute a PET bone index (PBI) inspired by the recognized Bone Scan Index (BSI). This automatically computed PBI globally agrees with the one calculated from ground truth delineations.Clinical relevance- We propose a completely automatic deep learning based method to detect and segment bones and bone lesions on 18FDG PET/CT in the context of metastatic breast cancer. We also introduce an automatic PET bone index which could be incorporated in the monitoring and decision process.

Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images.

Semi-automatic measurements are performed on 18FDG PET-CT images to monitor the evolution of metastatic sites in the clinical follow-up of metastatic breast cancer patients. Apart from being time-consuming and prone to subjective approximation, semi-automatic tools cannot make the difference between cancerous regions and active organs, presenting a high 18FDG uptake.In this work, we combine a deep learning-based approach with a superpixel segmentation method to segment the main active organs (brain, heart, bladder) from full-body PET images. In particular, we integrate a superpixel SLIC algorithm at different levels of a convolutional network. Results are compared with a deep learning segmentation network alone. The methods are cross-validated on full-body PET images of 36 patients and tested on the acquisitions of 24 patients from a different study center, in the context of the ongoing EPICUREseinmeta study. The similarity between the manually defined organ masks and the results is evaluated with the Dice score. Moreover, the amount of false positives is evaluated through the positive predictive value (PPV).According to the computed Dice scores, all approaches allow to accurately segment the target organs. However, the networks integrating superpixels are better suited to transfer knowledge across datasets acquired on multiple sites (domain adaptation) and are less likely to segment structures outside of the target organs, according to the PPV.Hence, combining deep learning with superpixels allows to segment organs presenting a high 18FDG uptake on PET images without selecting cancerous lesion, and thus improves the precision of the semi-automatic tools monitoring the evolution of breast cancer metastasis.Clinical relevance- We demonstrate the utility of combining deep learning and superpixel segmentation methods to accurately find the contours of active organs from metastatic breast cancer images, to different dataset distributions.

From a PMT-based to a SiPM-based PET system: a study to define matched acquisition/reconstruction parameters and NEMA performance of the Biograph Vision 450.

BACKGROUND: The purpose of this work was to propose an approach based on noise measurement to adapt present clinical acquisition and reconstruction parameters adapted to a PMT-based system (Biograph mCT) to a SiPM-based system (Biograph Vision 450) sharing identical geometrical properties. The NEMA performance (NEMA) of the recently released Biograph Vision 450 PET/CT (Vision) was also derived. METHODS: All measurements were conducted on Vision and Biograph mCT with TrueV (mCT). A full NEMA-based performance was derived for Vision only. The adaptation of acquisition and reconstruction parameters from mCT to Vision was done using the NEMA image quality phantom. The noise level reached using mCT was set as the reference value for six different numbers of net true coincidences. The noise level computed using Vision was matched to the reference noise level (within 0.01%) using a different reconstruction set-up to determine the potential reduction of count numbers for the same noise level. RESULTS: Vision sensitivity was 9.1 kcps/MBq for a timing resolution of 213 ps at 5.3 kBq/mL. The NEMA-based CR for the 10-mm sphere was better than 75% regardless the reconstruction set-up studied. The mCT reference noise properties could be achieved using Vision with a scan time reduction (STR) of 1.34 with four iterations and a 440 × 440 matrix size (or STR = 1.89 with a 220 × 220 matrix size) together with a 3D CR improvement of 53% for the 10-mm sphere (24% using 220 × 220). CONCLUSION: The Vision exhibited improved NEMA performances compared to mCT. Using the proposed approach, the time acquisition could be divided by almost two, while keeping the same noise properties as that of mCT with a marked improvement of contrast recovery.

Prognostic Impact of Pretherapeutic FDG-PET in Localized Anal Cancer.

Due to the heterogeneity of tumour mass segmentation methods and lack of consensus, our study evaluated the prognostic value of pretherapeutic positron emission tomography with fluorodeoxyglucose (FDG-PET) metabolic parameters using different segmentation methods in patients with localized anal squamous cell carcinoma (SCC). Eighty-one patients with FDG-PET before radiochemotherapy were retrospectively analyzed. Semiquantitative data were measured with three fixed thresholds (35%, 41% and 50% of Maximum Standardized Uptake Value (SUVmax)) and four segmentation methods based on iterative approaches (Black, Adaptive, Nestle and Fitting). Metabolic volumes of primary anal tumour (P-MTV) and total tumour load (T-MTV: P-MTV+ lymph node MTV) were calculated. The primary endpoint was event-free survival (EFS). Seven multivariate models were created to compare FDG-PET tumour volumes prognostic impact. For all segmentation thresholds, PET metabolic volume parameters were independent prognostic factor and T-MTV variable was consistently better associated with EFS than P-MTV. Patient's sex was an independent variable and significantly correlated with EFS. With fixed threshold segmentation methods, 35% of SUVmax threshold seemed better correlated with EFS and the best cut-off for discrimination between a low and high risk of event occurrence was 40 cm3. Determination of T-MTV by FDG-PET using fixed threshold segmentation is useful for predicting EFS for primary anal SCC. If these data are confirmed in larger studies, FDG-PET could contribute to individualized patient therapies.

Haute Couture or Ready-to-Wear? Tailored Pelvic Radiotherapy for Prostate Cancer Based on Individualized Sentinel Lymph Node Detection.

Prostate cancer (PCa) pelvic radiotherapy fields are defined by guidelines that do not consider individual variations in lymphatic drainage. We examined the feasibility of personalized sentinel lymph node (SLN)-based pelvic irradiation in PCa. Among a SLN study of 202 patients, we retrospectively selected 57 patients with a high risk of lymph node involvement. Each single SLN clinical target volume (CTV) was individually segmented and pelvic CTVs were contoured according to Radiation Therapy Oncology Group (RTOG) guidelines. We simulated a radiotherapy plan delivering 46 Gy and calculated the dose received by each SLN. Among a total of 332 abdominal SLNs, 305 pelvic SLNs (beyond the aortic bifurcation) were contoured (mean 5.4/patient). Based on standard guidelines, CTV missed 67 SLNs (22%), mostly at the common iliac level (40 SLNs). The mean distance between iliac vessels and the SLN was 11mm, and despite a 15mm margin around the iliac vessels, 9% of SLNs were not encompassed by the CTV. Moreover, 42 SLNs (63%) did not receive 95% of the prescribed dose. Despite a consensus on contouring guidelines, a significant proportion of SLNs were not included in the pelvic CTV and did not receive the prescribed dose. A tailored approach based on individual SLN detection would avoid underdosing pelvic lymph nodes that potentially contain tumor cells.

Initial Clinical Results of a Novel Immuno-PET Theranostic Probe in Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer.

This prospective study evaluated the imaging performance of a novel pretargeting immunologic PET (immuno-PET) method in patients with human epidermal growth factor receptor 2 (HER2)-negative, carcinoembryonic antigen (CEA)-positive metastatic breast cancer, compared with CT, bone MRI, and 18F-FDG PET. Methods: Twenty-three patients underwent whole-body immuno-PET after injection of 150 MBq of 68Ga-IMP288, a histamine-succinyl-glycine peptide given after initial targeting of a trivalent anti-CEA, bispecific, antipeptide antibody. The gold standards were histology and imaging follow-up. Tumor SUVs (SUVmax and SUVmean) were measured, and tumor burden was analyzed using total tumor volume and total lesion activity. Results: The total lesion sensitivity of immuno-PET and 18F-FDG PET were 94.7% (1,116/1,178) and 89.6% (1,056/1,178), respectively. Immuno-PET had a somewhat higher sensitivity than CT or 18F-FDG PET in lymph nodes (92.4% vs. 69.7% and 89.4%, respectively) and liver metastases (97.3% vs. 92.1% and 94.8%, respectively), whereas sensitivity was lower for lung metastases (48.3% vs. 100% and 75.9%, respectively). Immuno-PET showed higher sensitivity than MRI or 18F-FDG PET for bone lesions (95.8% vs. 90.7% and 89.3%, respectively). In contrast to 18F-FDG PET, immuno-PET disclosed brain metastases. Despite equivalent tumor SUVmax, SUVmean, and total tumor volume, total lesion activity was significantly higher with immuno-PET than with 18F-FDG PET (P = 0.009). Conclusion: Immuno-PET using anti-CEA/anti-IMP288 bispecific antibody, followed by 68Ga-IMP288, is a potentially sensitive theranostic imaging method for HER2-negative, CEA-positive metastatic breast cancer patients and warrants further research.

Preliminary results of a 68 Ga-PSMA PET/CT prospective study in prostate cancer patients with occult recurrence: Diagnostic performance and impact on therapeutic decision-making.

BACKGROUND: In this prospective study (NCT03443609), we investigated the impact of 68Ga-PSMA-11 PET-CT on the treatment plan and therapeutic response obtained for patients with prostate cancer (PCa) presenting a recurrence with a low rising PSA. METHODS: One hundred thirty hormone-naive (PSA < 1.5 ng/mL) patients were enrolled. All patients received radical treatment. PET images were recorded 1 and 2 hours after injection of tracer and interpreted by two independent nuclear physicians. Six months after treatment ended, a PSA assay was requested to evaluate the therapeutic efficacy of the treatment based on PSMA results. RESULTS: Data analysis for the first 52 included patients has been completed. 68Ga-PSMA-11-positive lesions were detected in 38/52 (73.1%) patients. Ninety-four lesions were detected as follows, 53/94 in lymph nodes (56.4%), 25/94 in bone (26.6%), and 12/94 into the prostate bed (12.7%). Detection rates were 58%, 81%, and 82% for serum PSA levels lower than 0.25 ng/mL, between 0.25 to ≤ 0.69 ng/mL and 0.70 ng/mL, respectively. As a result of the PSMA PET-CT, therapeutic management changed in 38/52 patients (73.1%). Patients had undetectable serum PSA levels after treatment guided by 68Ga-PSMA-11 PET-CT results in 10/52 (19.2%) cases and with a PSA decrease of over 60% in 18/52 (34.6%) patients. CONCLUSION: Whilst our patient population presented a very low PSA level, preliminary results of the 68Ga-PSMA PET-CT study showed recurrence localization in more than half of the patients and this had a major clinical impact, as it resulted in treatment change in more than half of the patients and a significant decrease in PSA levels in a third of patients.

Reoxygenation during radiotherapy in intermediate-risk prostate cancer.

Hypoxia is a major risk factor of prostate cancer radioresistance. We evaluated hypoxia non-invasively, using 18F-Misonidazole PET/CT prior to radiotherapy and after a dose of 20 Gy in intermediate-risk prostate cancer patients. Decreased hypoxic volumes were observed in all patients, suggesting that radiotherapy induces early prostate tumor reoxygenation.