Managing Catecholamine Release Syndrome During and Following Lu-177-DOTATATE in High-Risk Pheochromocytoma Patients

Pheochromocytomas and paragangliomas (PPGLs) are rare catecholamine-producing tumors that express somatostatin receptors (SSTR) that can be treated with lutetium-177 DOTATATE (Lu-177-TRT); however, treatment can be associated with life-threatening cardiovascular events. A patient case with management strategies for high-risk PPGL patients receiving Lu-177-TRT is described. The 78-year-old patient with metastatic paraganglioma was enrolled and treated under NCT03206060. Deemed to be at high risk, the patient was preemptively admitted to the intensive care unit (ICU) with central line access placed. Due to comorbidities, a reduced dose of 100 mCi x 4 cycles was used for this patient. Vital signs, blood work, and serum catecholamine levels were obtained at various time points. Despite reduced dosing, the patient still developed a severe hypertensive reaction with systolic blood pressure of 240 mmHg within minutes of Lu-177-TRT infusion, which was controlled with an intravenous nicardipine drip. The patient remained in the ICU for 24 hours post Lu-177-TRT before moving to an inpatient ward for an additional 24 hours. All subsequent infusions were performed using reduced doses with elective ICU admissions and were well-tolerated. Despite the increased risk, metastatic PPGL patients can be safely treated with proper staff training, monitoring, and preparation for intravenous medications, especially nicardipine.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study

RATIONALE AND OBJECTIVES

Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists.

MATERIALS AND METHODS

This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36).

RESULTS

In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%).

CONCLUSION

Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.

PI-RADS Version 2.0 Versus Version 2.1: Comparison of Prostate Cancer Gleason Grade Upgrade and Downgrade Rates From MRI-Targeted Biopsy to Radical Prostatectomy

BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL2). The v2.0 group (n = 177) and v2.1 group (n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP (n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy

BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models (p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.

Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI

OBJECTIVE

To evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history.

MATERIALS AND METHODS

This study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles.

RESULTS

Of the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0-2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively.

CONCLUSION

The reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation.

Deep Learning-Based T2-weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates

BACKGROUND

Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.

PURPOSE

To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm.

STUDY TYPE

Retrospective.

SUBJECTS

615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy.

FIELD STRENGTH/SEQUENCE

3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced.

ASSESSMENTS

Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category.

STATISTICAL TESTS

Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant.

RESULTS

385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09).

DATA CONCLUSION

Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Exploration of Imaging Biomarkers for Metabolically-Targeted Osteosarcoma Therapy in a Murine Xenograft Model

Background: Osteosarcoma (OS) is an aggressive pediatric cancer with unmet therapeutic needs. Glutaminase 1 (GLS1) inhibition, alone and in combination with metformin, disrupts the bioenergetic demands of tumor progression and metastasis, showing promise for clinical translation. Materials and Methods: Three positron emission tomography (PET) clinical imaging agents, [18F]fluoro-2-deoxy-2-D-glucose ([18F]FDG), 3'-[18F]fluoro-3'-deoxythymidine ([18F]FLT), and (2S, 4R)-4-[18F]fluoroglutamine ([18F]GLN), were evaluated in the MG63.3 human OS xenograft mouse model, as companion imaging biomarkers after treatment for 7 d with a selective GLS1 inhibitor (CB-839, telaglenastat) and metformin, alone and in combination. Imaging and biodistribution data were collected from tumors and reference tissues before and after treatment. Results: Drug treatment altered tumor uptake of all three PET agents. Relative [18F]FDG uptake decreased significantly after telaglenastat treatment, but not within control and metformin-only groups. [18F]FLT tumor uptake appears to be negatively affected by tumor size. Evidence of a flare effect was seen with [18F]FLT imaging after treatment. Telaglenastat had a broad influence on [18F]GLN uptake in tumor and normal tissues. Conclusions: Image-based tumor volume quantification is recommended for this paratibial tumor model. The performance of [18F]FLT and [18F]GLN was affected by tumor size. [18F]FDG may be useful in detecting telaglenastat's impact on glycolysis. Exploration of kinetic tracer uptake protocols is needed to define clinically relevant patterns of [18F]GLN uptake in patients receiving telaglenastat.

Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers

Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.1 categories and to identify MRI features that could improve csPCa detection. Materials and Methods This single-center prospective study included men with suspected or known prostate cancer who underwent multiparametric MRI and MRI/US-guided biopsy from April 2019 to December 2021. MRI scans were prospectively evaluated using PI-RADS version 2.1. Atypical transition zone (TZ) nodules were upgraded to category 3 if marked diffusion restriction was present. Lesions with an International Society of Urological Pathology (ISUP) grade of 2 or higher (range, 1-5) were considered csPCa. MRI features, including three-dimensional diameter, relative lesion volume (lesion volume divided by prostate volume), sphericity, and surface to volume ratio (SVR), were obtained from lesion contours delineated by the radiologist. Univariable and multivariable analyses were conducted at the lesion and participant levels to determine features associated with csPCa. Results In total, 454 men (median age, 67 years [IQR, 62-73 years]) with 838 lesions were included. The csPCa rates for lesions categorized as PI-RADS 1 (n = 3), 2 (n = 170), 3 (n = 197), 4 (n = 319), and 5 (n = 149) were 0%, 9%, 14%, 37%, and 77%, respectively. csPCa rates of PI-RADS 4 lesions were lower than PI-RADS 5 lesions (P < .001) but higher than PI-RADS 3 lesions (P < .001). Upgraded PI-RADS 3 TZ lesions were less likely to harbor csPCa compared with their nonupgraded counterparts (4% [one of 26] vs 20% [20 of 99], P = .02). Predictors of csPCa included relative lesion volume (odds ratio [OR], 1.6; P < .001), SVR (OR, 6.2; P = .02), and extraprostatic extension (EPE) scores of 2 (OR, 9.3; P < .001) and 3 (OR, 4.1; P = .02). Conclusion The rates of csPCa differed between consecutive PI-RADS categories of 3 and higher. MRI features, including lesion volume, shape, and EPE scores of 2 and 3, predicted csPCa. Upgrading of PI-RADS category 3 TZ lesions may result in unnecessary biopsies. ClinicalTrials.gov registration no. NCT03354416 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Goh in this issue.

Predicting Outcomes of Indeterminate Bone Lesions on 18F-DCFPyL PSMA PET/CT Scans in the Setting of High-Risk Primary or Recurrent Prostate Cancer

Indeterminate bone lesions (IBLs) on prostate-specific membrane antigen (PSMA) PET/CT are common. This study aimed to define variables that predict whether such lesions are likely malignant or benign using features on PSMA PET/CT. Methods: ¹⁸F-DCFPyL PET/CT imaging was performed on 243 consecutive patients with high-risk primary or biochemically recurrent prostate cancer. IBLs identified on PSMA PET/CT could not definitively be interpreted as benign or malignant. Medical records of patients with IBLs were reviewed to determine the ultimate status of each lesion. IBLs were deemed malignant or benign on the basis of evidence of progression or stability at follow-up, respectively, or by biopsy results; IBLs were deemed equivocal when insufficient or unclear evidence existed. Post hoc patient, lesion, and scan variables accounting for clustered data were evaluated using Wilcoxon rank-sum and χ² tests to determine features that favored benign or malignant interpretation. Results: Overall, 98 IBLs within 267 bone lesions (36.7%) were identified in 48 of 243 patients (19.8%). Thirty-seven of 98 IBLs were deemed benign, and 42 were deemed malignant, of which 8 had histologic verification; 19 remained equivocal. Location and SUV_max categorical variables were predictive of IBL interpretation (P = 0.0201 and P = 0.0230, respectively). For IBLs with new interpretations, 34 of 37 (91.9%) considered benign showed an SUV_max of less than 5 or exhibited focal uptake without coexisting bone metastases; 37 of 42 (88.1%) deemed malignant demonstrated an SUV_max of at least 5 or were present with coexisting bone metastases. Logistic regression predicted IBLs with a high SUV_max (univariable: odds ratio [OR], 9.29 [P = 0.0016]; multivariable: OR, 13.87 [P = 0.0089]) or present with other bone metastases (univariable: OR, 9.87 [P = 0.0112]; multivariable: OR, 11.35 [P = 0.003]) to be malignant. Conclusion: IBLs on PSMA PET/CT are concerning; however, characterizing their location, SUV, and additional scan findings can aid interpretation. IBLs displaying an SUV_max of at least 5 or present with other bone metastases favor malignancy. IBLs without accompanying bone metastases that exhibit an SUV_max of less than 5 and are observed only in atypical locations favor benign processes. These guidelines may assist in the interpretation of IBLs on PSMA PET/CT.

Ipsilateral hemigland prostate biopsy may underestimate cancer burden in patients with unilateral mpMRI-visible lesions

PURPOSE

To evaluate the cancer detection rates of reduced-core biopsy schemes in patients with unilateral mpMRI-visible intraprostatic lesions and to analyze the contribution of systematic biopsy cores in clinically significant prostate cancer (csPCa) detection.

METHODS

212 patients with mpMRI-visible unilateral intraprostatic lesions undergoing MRI/TRUS fusion-guided targeted biopsy (TBx) and systematic biopsy (SBx) were included. Cancer detection rates of TBx + SBx, as determined by highest Gleason Grade Group (GG), were compared to 3 reduced-core biopsy schemes: TBx alone, TBx + ipsilateral systematic biopsy (IBx; MRI-positive hemigland), and TBx + contralateral systematic biopsy (CBx; MRI-negative hemigland). Patient-level and biopsy core-level data were analyzed using descriptive statistics with confidence intervals. Univariable and multivariable logistic regression analysis was conducted to identify predictors of csPCa (≥ GG2) detected in MRI-negative hemiglands at p < 0.05.

RESULTS

Overall, 43.4% (92/212) of patients had csPCa and 66.0% (140/212) of patients had any PCa detected by TBx + SBx. Of patients with csPCa, 81.5% had exclusively ipsilateral involvement (MRI-positive), 7.6% had only contralateral involvement (MRI-negative), and 10.9% had bilateral involvement. The csPCa detection rates of reduced-core biopsy schemes were 35.4% (75/212), 40.1% (85/212), and 39.6% (84/212) for TBx alone, TBx + IBx, and TBx + CBx, respectively, with detection sensitivities of 81.5%, 92.4%, and 91.3% compared to TBx + SBx.

CONCLUSION

Reduced-core prostate biopsy strategies confined to the ipsilateral hemigland underestimate csPCa burden by at least 8% in patients with unilateral mpMRI-visible intraprostatic lesions. The combined TBx + SBx strategy maximizes csPCa detection.

The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms

OBJECTIVE

To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research.

MATERIALS AND METHODS

This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons.

RESULTS

In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation.

CONCLUSION

A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.

Recovery, recycling and re-irradiation of enriched 104Ru metal targets for cost effective production of 105Rh

Rhodium-105 (0.567 MeV β^-, 319 keV γ, 35.4 h half-life) was produced by neutron irradiation of enriched ¹⁰⁴Ru (>99%) over multiple decades. A method is reported to recover the previously irradiated ¹⁰⁴Ru (trapped in HCl as RuO₄^2-) as the metal. The ¹⁰⁴Ru was recovered in >93% yield and >98% enrichment. Neutron re-irradiation of the recycled ¹⁰⁴Ru produced ¹⁰⁵Rh, which was successfully radiolabeled with tetrathioethers in high yield. This recovery and recycling method for enriched ¹⁰⁴Ru makes ¹⁰⁵Rh production and utilization economical.

In Vitro and In Vivo Comparison of 3,2-HOPO Versus Deferoxamine-Based Chelation of Zirconium-89 to the Antimesothelin Antibody Anetumab

Introduction: [²²⁷Th]Th-3,2-HOPO-MSLN-mAb, a mesothelin (MSLN)-targeted thorium-227 therapeutic conjugate, is currently in phase I clinical trial; however, direct PET imaging using this conjugate is technically challenging. Thus, using the same MSLN antibody, we synthesized 3,2-HOPO and deferoxamine (DFO)-based zirconium-89 antibody conjugates, [⁸⁹Zr]Zr-3,2-HOPO-MSLN-mAb and [⁸⁹Zr]Zr-DFO-MSLN-mAb, respectively, and compared them in vitro and in vivo.

Methods: [⁸⁹Zr]Zr-3,2-HOPO-MSLN-mAb and [⁸⁹Zr]Zr-DFO-MSLN-mAb were evaluated in vitro to determine binding affinity and immunoreactivity in HT29-MSLN and PDX (NCI-Meso16, NCI-Meso21) cells. For both the zirconium-89 conjugates, in vivo studies (biodistribution/imaging) were performed at days 1, 3, and 6, from which tissue uptake was determined.

Results: Both the conjugates demonstrated a low nanomolar binding affinity for MSLN and >95% immunoreactivity. In all the three tumor types, biodistribution of [⁸⁹Zr]Zr-DFO-MSLN-mAb resulted in higher tumor uptake(15.88-28-33%ID/g) at all time points compared with [⁸⁹Zr]Zr-3,2-HOPO-MSLN-mAb(7–13.07%ID/g). [⁸⁹Zr]Zr-3,2-HOPO-MSLN-mAb femur uptake was always higher than [⁸⁹Zr]Zr-DFO-MSLN-mAb, and imaging results concurred with the biodistribution studies.

Conclusions: Even though the conjugates exhibited a high binding affinity for MSLN, [⁸⁹Zr]Zr-DFO-MSLN-mAb showed a higher tumor and lower femur uptake than [⁸⁹Zr]Zr-3,2-HOPO-MSLN-mAb. Nevertheless, [⁸⁹Zr]Zr-3,2-HOPO-MSLN-mAb could be used to study organ distribution and lesion uptake with the caveat of detecting MSLN-positive bone lesions. Clinical trial (NCT03507452).

Sodium Fluoride-18 and Radium-223 Dichloride Uptake Colocalize in Osteoblastic Mouse Xenograft Tumors

Background: Patients with osteoblastic bone metastases are candidates for radium-223 (²²³RaCl₂) therapy and may undergo sodium fluoride-18 (¹⁸F-NaF) positron emission tomography-computed tomography imaging to identify bone lesions. ¹⁸F-NaF has been shown to predict ²²³RaCl₂ uptake, but intratumor distributions of these two agents remain unclear. In this study, the authors evaluate the spatial distribution and relative uptakes of ¹⁸F-NaF and ²²³RaCl₂ in Hu09-H3 human osteosarcoma mouse xenograft tumors at macroscopic and microscopic levels to better quantify their correlation. Materials and Methods: ¹⁸F-NaF and ²²³RaCl₂ were co-injected into Hu09-H3 xenograft tumor severe combined immunodeficient mice. Tumor content was determined from in vivo biodistributions and visualized by PET, single photon emission computed tomography, and CT imaging. Intratumor distributions were visualized by quantitative autoradiography of tumor tissue sections and compared to histology of the same or adjacent sections. Results: ¹⁸F and ²²³Ra accumulated in proportional amounts in whole Hu09-H3 tumors (r² = 0.82) and in microcalcified regions within these tumors (r² = 0.87). Intratumor distributions of ¹⁸F and ²²³Ra were spatially congruent in these microcalcified regions. Conclusions: ¹⁸F-NaF and ²²³RaCl₂ uptake are strongly correlated in heterogeneously distributed microcalcified regions of Hu09-H3 xenograft tumors, and thus, tumor accumulation of ¹⁸F is predictive of ²²³Ra accumulation. Hu09-H3 xenograft tumors appear to possess certain histopathological features found in patients with metastatic bone disease and may be useful in clarifying the relationship between administered ²²³Ra dose and therapeutic effect.

Automated Synthesis of Fluorine-18 Labeled CXCR4 Ligand via the Conjugation with Nicotinic Acid N-Hydroxysuccinimide Ester (6-[18F]SFPy)

The C-X-C motif chemokine receptor 4 (CXCR4) is a seven-transmembrane G protein-coupled receptor that is overexpressed in numerous diseases, particularly in various cancers and is a powerful chemokine, attracting cells to the bone marrow niche. Therefore, CXCR4 is an attractive target for imaging and therapeutic purposes. The goal of this study is to develop an efficient, reproducible, and straightforward method to prepare a fluorine-18 labeled CXCR4 ligand. 6-[18F]Fluoronicotinic acid-2,3,5,6-tetrafluorophenyl ester (6-[18F]FPy-TFP) and nicotinic acid N-hydroxysuccinimide ester (6-[18F]SFPy) have been prepared using 'fluorination on the Sep-Pak' method. Conjugation of 6-[18F]SFPy or 6-[18F]FPy-TFP with the alpha-amino group at the N terminus of the protected T140 precursor followed by deprotection, yielded the final product 6-[18F]FPy-T140. The overall radiochemical yields were 6-17% (n = 15, decay-corrected) in a 90-min radiolabeling time with a radiochemical purity >99%. 6-[18F]FPy-T140 exhibited high specific binding and nanomolar affinity for CXCR4 in vitro, indicating that the biological activity of the peptide was preserved. For the first time, [18F]SFPy has been prepared using 'fluorination on the Sep-Pak' method that allows rapid automated synthesis of 6-[18F]FPy-T140. In addition to increased synthetic efficiency, this construct binds with CXCR4 in high affinity and may have potential as an in vivo positron emission tomography (PET) imaging agent. This radiosynthesis method should encourage wider use of this PET agent to quantify CXCR4 in both research and clinical settings.

Evaluation of 72Se/72As generator and production of 72Se for supplying 72As as a potential PET imaging radionuclide

Positron-emitting 72As is the PET imaging counterpart for beta-emitting 77As. Its parent, no carrier added (n.c.a.) 72Se, was produced for a 72Se/72As generator by irradiating an enriched 7°Ge metal-graphite target via the 70Ge(α, 2 n)72Se reaction. Target dissolution used a fast, environmentally friendly method with 93% radioactivity recovery. Chromatographic parameters of the 72Se/72As generator were evaluated, the eluted n.c.a. 72As was characterized with a phantom imaging study, and the previously reported trithiol and aryl-dithiol ligand systems were radiolabeled with the separated n.c.a. 72As in high yield.

Chemistry and radiochemistry of As, Re and Rh isotopes relevant to radiopharmaceutical applications: high specific activity radionuclides for imaging and treatment

Advances in production, separation, target recovery, and chelation chemistry of high specific activity radionuclides will promote new theranostic agent development.