Although radioactive iodine imaging and therapy are one of the earliest applications of theranostics, there still remain a number of unresolved clinical questions as to the optimization of diagnostic techniques and dosimetry protocols. I-124 as a positron emission tomography (PET) radiotracer has the potential to improve the current clinical practice in the diagnosis and treatment of differentiated thyroid cancer. The higher sensitivity and spatial resolution of PET/computed tomography (CT) compared to standard gamma scintigraphy can aid in the detection of recurrent or metastatic disease and provide more accurate measurements of metabolic tumor volumes. However the complex decay schema of I-124 poses challenges to quantitative PET imaging. More prospective studies are needed to define optimal dosimetry protocols and to improve patient-specific treatment planning strategies, taking into account not only the absorbed dose to tumors but also methods to avoid toxicity to normal organs. A historical perspective of I-124 imaging and dosimetry as well as future concepts are discussed.

Accurate determination of the extrahepatic extent and intrahepatic distribution of disease is very important in patients with primary and metastatic liver disease for deciding whether a patient receives potentially curable surgery or palliative treatment. Our objective was to evaluate the efficacy of delayed phase FDG-PET/CT imaging in lesion detection and to define its clinical impact compared to triple-phase contrast enhanced CT (CECT).

Metabolic tumor volume (MTV) is a robust prognostic biomarker in diffuse large B-cell lymphoma (DLBCL). The available semiautomatic software for calculating MTV requires manual input limiting its routine application in clinical research. Our objective was to develop a fully automated method (AM) for calculating MTV and to validate the method by comparing its results with those from two nuclear medicine (NM) readers. The automated method designed for this study employed a deep convolutional neural network to segment normal physiologic structures from the computed tomography (CT) scans that demonstrate intense avidity on positron emission tomography (PET) scans. The study cohort consisted of 100 patients with newly diagnosed DLBCL who were randomly selected from the Alliance/CALGB 50,303 (NCT00118209) trial. We observed high concordance in MTV calculations between the AM and readers with Pearson’s correlation coefficients and interclass correlations comparing reader 1 to AM of 0.9814 (p < 0.0001) and 0.98 (p < 0.001; 95%CI = 0.96 to 0.99), respectively; and comparing reader 2 to AM of 0.9818 (p < 0.0001) and 0.98 (p < 0.0001; 95%CI = 0.96 to 0.99), respectively. The Bland–Altman plots showed only relatively small systematic errors between the proposed method and readers for both MTV and maximum standardized uptake value (SUVmax). This approach may possess the potential to integrate PET-based biomarkers in clinical trials.

Purpose: Significant progress has occurred in developing quantitative PET/CT biomarkers in diffuse large B-cell lymphoma (DLBCL). Total metabolic tumor volume (MTV) is the most extensively studied, enabling assessment of FDG-avid tumor burden associated with outcomes. However, prior studies evaluated the outcome of cytotoxic chemotherapy or chimeric antigen receptor T-cell therapy without data on recently approved FDA agents. Therefore, we aimed to assess the prognosis of PET/CT biomarkers in patients treated with loncastuximab tesirine. Experimental Design: We centrally reviewed screening PET/CT scans of patients with relapsed/refractory DLBCL enrolled in the LOTIS-2 (NCT03589469) study. MTV was obtained by computing individual volumes using the SUV ≥4.0 threshold. Other PET/CT metrics, clinical factors, and the International Metabolic Prognostic Index (IMPI) were evaluated. Logistic regression was used to assess the association between biomarkers and treatment response. Cox regression was used to determine the effect of biomarkers on time-to-event outcomes. We estimated biomarker prediction as continuous and binary variables defined by cutoff points. Results: Across 138 patients included in this study, MTV with a cutoff point of 96 mL was the biomarker associated with the highest predictive performance in univariable and multivariable models to predict failure to achieve complete metabolic response (OR, 5.42; P = 0.002), progression-free survival (HR, 2.68; P = 0.002), and overall survival (HR, 3.09; P < 0.0001). IMPI demonstrated an appropriate performance, however, not better than MTV alone. Conclusions: Pretreatment MTV demonstrated robust risk stratification, with those patients demonstrating high MTV achieving lower responses and survival to loncastuximab tesirine in relapsed/refractory DLBCL.

Thyroid cancer, specifically differentiated thyroid carcinoma (DTC), is one of the most prevalent endocrine malignancies worldwide. Radioactive iodine therapy (RAIT) using I-131 has been a standard-of-care approach for DTC due to its ability to ablate remnant thyroid disease following surgery, thus reducing the risk of recurrence. It is also used for the treatment of iodine-avid metastases. RAIT dosimetry can be employed to determine the optimal treatment dose of I-131 to effectively treat cancer cells while safeguarding against undesirable radiation effects such as bone marrow toxicity or radiation pneumonitis. Conventional dosimetry protocols for RAIT, however, are complex and time-consuming, involving multiple days of imaging and blood sampling. This study explores the use of Artificial Intelligence (AI) in simplifying and optimizing RAIT. A retrospective analysis was conducted on 83 adult patients with DTC who underwent RAIT dosimetry at our institution between 1996 and 2023. The conventional MIRD-based dosimetry protocol involved imaging and blood sampling at 4, 24, 48, 72, and 96 h post-administration of a tracer activity of I-131. An AI system based on a deep-learning neural network was developed to predict the maximum permissible activity (MPA) for RAIT using only the data obtained from the initial 4, 24, and 48 h time points. The AI system predicted the MPA values with high accuracy, showing no significant difference compared to the results obtained from conventional MIRD-based analysis utilizing a paired t-test (p = 0.351, 95% CI). The developed AI system offers the potential to streamline the dosimetry process, reducing the number of imaging and blood sampling sessions while also optimizing resource allocation. Additionally, the AI approach can uncover underlying relationships in data that were previously unknown. Our findings suggest that AI-based dosimetry may be a promising method for patient-specific treatment planning in differentiated thyroid carcinoma, representing a step towards applying precision medicine for thyroid cancer. Further validation and implementation studies are warranted to assess the clinical applicability of the AI system.