177Lu-DOTATATE peptide receptor radionuclide therapy (PRRT) is an established therapeutic option for patients with neuroendocrine tumors (NETs). Although radiomics has been increasingly applied in NET research, it was developed almost exclusively using 68Ga-DOTATOC PET imaging, whereas PRRT-related scintigraphic acquisitions remain largely unexplored. This study evaluates the feasibility of extracting hepatic radiomic features from posttreatment whole-body planar scintigraphic (WBS) and tomographic (SPECT) scans, enabling the optimization of preprocessing strategies and a direct comparison of radiomic profiles between imaging modalities. Methods: We conducted a retrospective analysis of data for 49 patients with well-differentiated, progressive metastatic gastroenteropancreatic NETs from 2 centers. All patients underwent posttreatment imaging within 2 d after each 177Lu-DOTATATE PRRT cycle, for a total of 183 WBS and 108 SPECT scans. Preprocessing operations included image-intensity normalization, dividing each image by the mean counts of a region of interest positioned on the spleen (S-norm) and the third lumbar vertebra (V-norm). Radiomic features were extracted with no spatial resampling, using 64-bin discretization and relative intensity rescaling. Results: Texture extraction was successfully performed on all planar and SPECT images from both centers. Correlations between intensity-based features measured using S-norm and V-norm images showed weak yet statistically significant r values. Conversely, there was a robust correlation between histogram-based and second-order features measured on S-norm and V-norm images, reflected in high r values. V-norm intensity-based features exhibited significantly higher values and lower SDs when compared with S-norm intensity-based variables. When comparing WBS and SPECT scans, a strong correlation was observed between intensity-based variables, whereas histogram-based and second-order features showed no significant correlation. Conclusion: Radiomic analysis on 177Lu-DOTATATE posttreatment scans was feasible, with proportional scaling to third-lumbar-vertebra activity resulting in the most effective intensity normalization approach, given the decreased variability compared with scaling to spleen activity. Correlations between features assessed using the 2 different scaling methods were weak for intensity-based features and strong for histogram-based and second-order features. Except for intensity-based features, radiomic features derived from WBS images were not comparable with those obtained from SPECT scans.

Amyloid PET has become a pivotal imaging biomarker for Alzheimer disease (AD), enabling invivo detection and quantification of β-amyloid deposition. However, variability in quantitative measurements across tracers, scanners, and analysis methods has considerably limited direct comparison of amyloid burden between studies and centers. To address this issue, the Centiloid Project has been developed to provide a standardized quantitative scale for amyloid PET, harmonizing results across tracers and institutions. In this literature review, we aim to summarize current evidence on the development, validation, and clinical application of the Centiloid scale in amyloid PET imaging, emphasizing its methodologic foundations, tracer-specific conversions, and diagnostic thresholds. Methods: A literature review was conducted using PubMed, Scopus, and Embase databases from January 2015 through October 2025. Search terms included "amyloid PET," "Centiloid," "SUVR," and "Alzheimer disease." We reviewed articles with quantitative amyloid PET analyses and Centiloid conversion and studies which compared multiple tracers using MIMneuro software Centiloid calibration. Results: The Centiloid method demonstrates strong intertracer and interscanner harmonization when standardized to the 11C-Pittsburgh compound B reference, with high correlation (r 2 > 0.9) across 18F-labeled tracers, including florbetapir, flutemetamol, and florbetaben. Most articles identified a Centiloid threshold between 20 and 25 Centiloids as indicative of significant amyloid pathology. Implementation of the Centiloid framework improved comparability across clinical trials and longitudinal studies, facilitating integration into the AT(N) research framework. Conclusion: The Centiloid scale represents a critical advancement in quantitative amyloid PET imaging, providing a universal reference that enhances data reproducibility, cross-trial comparisons, and clinical decision-making. Continued work is needed to expand standardization for novel tracers and hybrid imaging systems, ensuring full clinical translation of this metric in dementia imaging.

Lung perfusion scintigraphy is a cornerstone for evaluating pulmonary embolism but may reveal unexpected vascular anomalies. Persistent left superior vena cava, a congenital anomaly, can cause a right-to-left shunt when draining into the left atrium, bypassing pulmonary circulation. We report the incidental detection of persistent left superior vena cava during lung perfusion scintigraphy in a patient with post-COVID-19 hypoxia. Dual-site radiotracer injections and SPECT/CT confirmed the diagnosis, highlighting the need for adaptive imaging techniques to accurately interpret atypical findings in nuclear medicine.

Today's common practice of empiric activity prescription in radiopharmaceutical therapy is inconsistent with the modern philosophy of personalized medicine. We investigate the radiobiologic relationship between tumor mean absorbed dose (D mean) and progression-free survival (PFS) in 177Lu-PSMA I&T therapy guided by personalized predictive dosimetry in metastatic castration-resistant prostate cancer. Methods: We conducted a single-center retrospective study of the first 20 patients treated with 177Lu-PSMA I&T at our institution. PFS was calculated from the day a patient was assessed by a nuclear medicine physician in the clinic until treatment cessation (due to progression or toxicity) or death. D mean was estimated by predictive dosimetry. Correlation with PFS was performed on the following baseline characteristics: age, Gleason score, Eastern Cooperative Oncology Group performance status, estimated glomerular filtration rate, prostate-specific antigen (PSA) doubling time, injected activity per treatment, D mean and D mean per gigabecquerel. Results: Twenty patients naïve to 177Lu-PSMA were treated over a 2-y period, totaling 65 infusions. The median PSA doubling time was 1.6 mo at baseline. The median follow-up time was 8.4 mo (interquartile range [IQR], 4.6-14.5 mo). Each patient received 3 ± 1 treatments (range, 1-5), with a mean injected activity of 7.74 ± 0.66 GBq per treatment. The mean injected activity for the first treatment was 7.85 ± 0.71 GBq. The median D mean for the first treatment was 23 Gy (IQR, 15-36 Gy). The median PFS was 10.6 mo for a D mean of 23 Gy or greater and was 3.1 mo for a D mean of less than 23 Gy (hazard ratio, 0.39; 95% CI, 0.22-0.68; P < 0.01). There were statistically significant correlations between PFS, PSA doubling time, and D mean (P < 0.05). However, there was no statistically significant correlation between the injected activity per treatment versus PFS or D mean (P > 0.05). Conclusion: We found a strong direct correlation between D mean and PFS in patients with metastatic castration-resistant prostate cancer treated with 177Lu-PSMA in a real-world setting. We identified a D mean of 23 Gy as a possible threshold for significantly better PFS in our cohort with median PSA doubling time of 1.6 mo. Our findings support radiobiologically sound prescription based on the radiation absorbed dose by personalized predictive dosimetry.

Isolated skeletal lymphoma is a rare subtype of lymphoma, and isolated mandibular involvement is extremely uncommon. We describe a case of isolated mandibular lymphoma detected on 18F-FDG PET/CT, presenting as an 18F-FDG-avid lytic expansile lesion in the left hemimandible, with no other pathologic findings in the body.

![Figure][1]</img> Kathy S. Thomas, MHA, CNMT, PET, FSNMMI-TS Let’s face it. Writing a scientific manuscript is not easy! It begins with an idea that may require data collection or a review of available literature on the topic. The next step is to organize that information or data into a

Theranostics, the combination of diagnostic and therapeutic nuclear medicine, has revolutionized the field by offering patient-specific, targeted treatment options. Although the concept dates back to the early use of radioiodine to treat thyroid disorders, modern theranostics encompasses a wide range of diseases and options and is becoming an integral component of precision medicine. Despite rapid advancement, significant barriers, such as geographic disparities, workforce shortages, regulatory burden, inadequate infrastructure, high costs, and low awareness, among providers and patients limit equitable patient access to theranostic services. Overcoming these barriers will require coordinated action among health care institutions, policymakers, educators, and professional societies to harmonize standards, support workforce expansion, and develop flexible, scalable models of care and training. By uniting stakeholder efforts, embracing innovation, and prioritizing equity, the field can fulfill the promise of theranostics and ensure that these therapies reach all patients.

The recent reinvigoration of theranostics comes with advances in computing technology, radiochemistry, and instrumentation that synergize with developments in artificial intelligence (AI). There is a wide array of applications of AI in nuclear medicine that have translational benefits to theranostics, including attenuation correction, artifact and noise reduction, enhanced workflow, and lesion characterization, and segmentation and quantitation, among many others. For theranostics, there are potentially significant applications that could move closer to precision medicine. Perhaps the most important application is predictive dosimetry from diagnostic images to optimize therapeutic dose. There are also valuable benefits from AI-augmented radioligand design and development, preclinical imaging, and practice sustainability. Generative AI has also emerged as a powerful tool to support decision-making, information dissemination, and medical image analysis. There are, however, several ongoing challenges that must be considered pertaining to the development and application of AI tools in theranostics.