PURPOSE: To characterize the association between neurocognitive outcomes (memory and processing speed) and radiation (RT) dose to the hippocampus, corpus callosum (CC), and frontal white matter (WM) in children with medulloblastoma treated on a prospective study, SJMB03. PATIENTS AND METHODS: Patients age 3-21 years with medulloblastoma were treated at a single institution on a phase III study. The craniospinal RT dose was 23.4 Gy for average-risk patients and 36-39.6 Gy for high-risk patients. The boost dose was 55.8 Gy to the tumor bed. Patients underwent cognitive testing at baseline and once yearly for 5 years. Performance on tests of memory (associative memory and working memory) and processing speed (composite processing speed and perceptual speed) was analyzed. Mixed-effects models were used to estimate longitudinal trends in neurocognitive outcomes. Reliable change index and logistic regression were used to define clinically meaningful neurocognitive decline and identify variables associated with decline. RESULTS: One hundred and twenty-four patients were eligible for inclusion, with a median neurocognitive follow-up of 5 years. Mean right and left hippocampal doses were significantly associated with decline in associative memory in patients without posterior fossa syndrome (all P < .05). Mean CC and frontal WM doses were significantly associated with decline in both measures of processing speed (all P < .05). Median brain substructure dose–volume histograms were shifted to the right for patients with a decline in associative memory or processing speed. The odds of decline in associative memory and composite processing speed increased by 23%-26% and by 10%-15% for every 1-Gy increase in mean hippocampal dose and mean CC or frontal WM dose, respectively. CONCLUSION: Increasing RT dose to the CC or frontal WM and hippocampus is associated with worse performance on tests of processing speed and associative memory, respectively. Brain substructure–informed RT planning may mitigate neurocognitive impairment.

Image-based data mining (IBDM) is a novel voxel-based method for analyzing radiation dose responses that has been successfully applied in adult data. Because anatomic variability and side effects of interest differ for children compared to adults, we investigated the feasibility of IBDM for pediatric analyses. We tested IBDM with CT images and dose distributions collected from 167 children (aged 10 months to 20 years) who received proton radiotherapy for primary brain tumors. We used data from four reference patients to assess IBDM sensitivity to reference selection. We quantified spatial-normalization accuracy via contour distances and deviations of the centers-of-mass of brain substructures. We performed dose comparisons with simplified and modified clinical dose distributions with a simulated effect, assessing their accuracy via sensitivity, positive predictive value (PPV) and Dice similarity coefficient (DSC). Spatial normalizations and dose comparisons were insensitive to reference selection. Normalization discrepancies were small (average contour distance < 2.5 mm, average center-of-mass deviation < 6 mm). Dose comparisons identified differences (p < 0.01) in 81% of simplified and all modified clinical dose distributions. The DSCs for simplified doses were high (peak frequency magnitudes of 0.9–1.0). However, the PPVs and DSCs were low (maximum 0.3 and 0.4, respectively) in the modified clinical tests. IBDM is feasible for childhood late-effects research. Our findings may inform cohort selection in future studies of pediatric radiotherapy dose responses and facilitate treatment planning to reduce treatment-related toxicities and improve quality of life among childhood cancer survivors.

Targeted radionuclide therapy (TRT) for internal pathway-directed treatment is a game changer for precision medicine. TRT improves tumor control while minimizing damage to healthy tissue and extends the survival for patients with cancer. The application of theranostic-paired TRT along with cellular phenotype and genotype correlative analysis has the potential for malignant disease management. Chelation chemistry is essential for the development of theranostic-paired radiopharmaceuticals for TRT. Among image-guided TRT, ⁶⁸Ga and ^99mTc are the current standards for diagnostic radionuclides, while ¹⁷⁷Lu and ²²⁵Ac have shown great promise for β- and α-TRT, respectively. Their long half-lives, potent radiobiology, favorable decay schemes, and ability to form stable chelation conjugates make them ideal for both manufacturing and clinical use. The current challenges include optimizing radionuclide production processes, coordinating chelation chemistry stability of theranostic-paired isotopes to reduce free daughters [this pertains to ²²⁵Ac daughters ²²¹Fr and ²¹³Bi]-induced tissue toxicity, and improving the modeling of micro dosimetry to refine dose–response evaluation. The empirical approach to TRT delivery is based on standard radionuclide administered activity levels, although clinical trials have revealed inconsistent outcomes and normal-tissue toxicities despite equivalent administered activities. This review presents the latest optimization methods for chelation-based theranostic radiopharmaceuticals, advancements in micro-dosimetry, and SPECT/CT technologies for quantifying whole-body uptake and monitoring therapeutic response as well as cytogenetic correlative analyses.

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67^th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.