Improved Detection of Bone Metastases in Children and Young Adults with Ferumoxytol-enhanced MRI.

Purpose To evaluate if ferumoxytol can improve the detection of bone marrow metastases at diffusion-weighted (DW) MRI in pediatric and young adult patients with cancer. Materials and Methods In this secondary analysis of a prospective institutional review board-approved study (ClinicalTrials.gov identifier NCT01542879), 26 children and young adults (age range: 2-25 years; 18 males) underwent unenhanced or ferumoxytol-enhanced whole-body DW MRI between 2015 and 2020. Two reviewers determined the presence of bone marrow metastases using a Likert scale. One additional reviewer measured signal-to-noise ratios (SNRs) and tumor-to-bone marrow contrast. Fluorine 18 (18F) fluorodeoxyglucose (FDG) PET and follow-up chest CT, abdominal and pelvic CT, and standard (non-ferumoxytol enhanced) MRI served as the reference standard. Results of different experimental groups were compared using generalized estimation equations, Wilcoxon rank sum test, and Wilcoxon signed rank test. Results The SNR of normal bone marrow was significantly lower at ferumoxytol-enhanced MRI compared with unenhanced MRI at baseline (21.380 ± 19.878 vs 102.621 ± 94.346, respectively; P = .03) and after chemotherapy (20.026 ± 7.664 vs 54.110 ± 48.022, respectively; P = .006). This led to an increased tumor-to-marrow contrast on ferumoxytol-enhanced MRI scans compared with unenhanced MRI scans at baseline (1397.474 ± 938.576 vs 665.364 ± 440.576, respectively; P = .07) and after chemotherapy (1099.205 ± 864.604 vs 500.758 ± 439.975, respectively; P = .007). Accordingly, the sensitivity and diagnostic accuracy for detecting bone marrow metastases were 96% (94 of 98) and 99% (293 of 297), respectively, with the use of ferumoxytol-enhanced MRI compared with 83% (106 of 127) and 95% (369 of 390) with the use of unenhanced MRI. Conclusion Use of ferumoxytol helped improve the detection of bone marrow metastases in children and young adults with cancer. Keywords: Pediatrics, Molecular Imaging-Cancer, Molecular Imaging-Nanoparticles, MR-Diffusion Weighted Imaging, MR Imaging, Skeletal-Appendicular, Skeletal-Axial, Bone Marrow, Comparative Studies, Cancer Imaging, Ferumoxytol, USPIO © RSNA, 2023 ClinicalTrials.gov registration no. NCT01542879 See also the commentary by Holter-Chakrabarty and Glover in this issue.

The transformational potential of molecular radiomics.

Conventional radiomics in nuclear medicine involve hand-crafted and computer-assisted regions of interest. Recent developments in artificial intelligence (AI) have seen the emergence of AI-augmented segmentation and extraction of lower order traditional radiomic features. Deep learning (DL) affords the opportunity to extract abstract radiomic features directly from input tensors (images) without the need for segmentation. These fourth-order, high dimensional radiomics produce deep radiomics and are well suited to the data density associated with the molecular nature of hybrid imaging. Molecular radiomics and deep molecular radiomics provide insights beyond images and quantitation typical of semantic reporting. While the application of molecular radiomics using hand-crafted and computer-generated features is integrated into decision-making in nuclear medicine, the acceptance of deep molecular radiomics is less universal. This manuscript aims to provide an understanding of the language and principles associated with radiomics and deep radiomics in nuclear medicine.

Flexible work arrangements and their impact on women in radiology: RSNA 2021 panel discussion summary sponsored by AAWR and more.

There is an ongoing trend in the direction of flexible work arrangements in which employees can decide where and when to work. Multiple studies have demonstrated a significant decrease in associated job-related stress, improved job satisfaction, job autonomy, and collaboration when flexible work arrangements exist. However, some have reported increased workload and home spillover to work.1 The American Association for Women in Radiology (AAWR) convened a panel of radiologist presenters with diverse backgrounds who shared their own experiences with flexible work arrangements at the Radiological Society of North America (RSNA) 2021 Scientific Assembly and Annual Meeting. This manuscript summarizes the discussion and reviews various aspects of workplace flexibility. The RSNA 2021 AAWR-sponsored panel on workplace flexibility reviewed the current state of different work arrangements available for radiologists and addressed future strategies for implementing workplace flexibility. The panelists addressed the imperatives and key factors for the availability of diverse opportunities and ways to foster future opportunities. Matters discussed included differences in the availability of flexible work arrangements in the healthcare system compared to other industries, normalizing flexible work arrangements at the organization level, underutilization of currently available flexible work arrangements, part-time positions and stigma associated with them, thriving in a part-time capacity, workplace flexibility options for radiology residents and fellows and successfully implementing workplace flexibility at institutions. The panel ended with a call to action to develop toolkits with effective resources to support implementing flexible workplace opportunities.

Diagnostic Accuracy of 2-[18F]FDG-PET and whole-body DW-MRI for the detection of bone marrow metastases in children and young adults.

OBJECTIVES: To compare the diagnostic accuracy of 2-[18F]fluoro-2-deoxy-D-glucose-enhanced positron emission tomography (2-[18F]FDG-PET) and diffusion-weighted magnetic resonance imaging (DW-MRI) for the detection of bone marrow metastases in children and young adults with solid malignancies. METHODS: In this cross-sectional single-center institutional review board-approved study, we investigated twenty-three children and young adults (mean age, 16.8 years ± 5.1 [standard deviation]; age range, 7-25 years; 16 males, 7 females) with 925 bone marrow metastases who underwent 66 simultaneous 2-[18F]FDG-PET and DW-MRI scans including 23 baseline scans and 43 follow-up scans after chemotherapy between May 2015 and July 2020. Four reviewers evaluated all foci of bone marrow metastasis on 2-[18F]FDG-PET and DW-MRI to assess concordance and measured the tumor-to-bone marrow contrast. Results were assessed with a one-sample Wilcoxon test and generalized estimation equation. Bone marrow biopsies and follow-up imaging served as the standard of reference. RESULTS: The reviewers detected 884 (884/925, 95.5%) bone marrow metastases on 2-[18F]FDG-PET and 893 (893/925, 96.5%) bone marrow metastases on DW-MRI. We found different "blind spots" for 2-[18F]FDG-PET and MRI: 2-[18F]FDG-PET missed subcentimeter lesions while DW-MRI missed lesions in small bones. Sensitivity and specificity were 91.0% and 100% for 18F-FDG-PET, 89.1% and 100.0% for DW-MRI, and 100.0% and 100.0% for combined modalities, respectively. The diagnostic accuracy of combined 2-[18F]FDG-PET/MRI (100.0%) was significantly higher compared to either 2-[18F]FDG-PET (96.9%, p < 0.001) or DW-MRI (96.3%, p < 0.001). CONCLUSIONS: Both 2-[18F]FDG-PET and DW-MRI can miss bone marrow metastases. The combination of both imaging techniques detected significantly more lesions than either technique alone. KEY POINTS: • DW-MRI and 2-[18F]FDG-PET have different strengths and limitations for the detection of bone marrow metastases in children and young adults with solid tumors. • Both modalities can miss bone marrow metastases, although the "blind spot" of each modality is different. • A combined PET/MR imaging approach will achieve maximum sensitivity and specificity for the detection of bone marrow metastases in children with solid tumors.

PET/MRI Improves Management of Children with Cancer.

Integrated PET/MRI has shown significant clinical value for staging and restaging of children with cancer by providing functional and anatomic tumor evaluation with a 1-stop imaging test and with up to 80% reduced radiation exposure compared with 18F-FDG PET/CT. This article reviews clinical applications of 18F-FDG PET/MRI that are relevant for pediatric oncology, with particular attention to the value of PET/MRI for patient management. Early adopters from 4 different institutions share their insights about specific advantages of PET/MRI technology for the assessment of young children with cancer. We discuss how whole-body PET/MRI can be of value in the evaluation of certain anatomic regions, such as soft tissues and bone marrow, as well as specific PET/MRI interpretation hallmarks in pediatric patients. We highlight how whole-body PET/MRI can improve the clinical management of children with lymphoma, sarcoma, and neurofibromatosis, by reducing the number of radiologic examinations needed (and consequently the radiation exposure), without losing diagnostic accuracy. We examine how PET/MRI can help in differentiating malignant tumors versus infectious or inflammatory diseases. Future research directions toward the use of PET/MRI for treatment evaluation of patients undergoing immunotherapy and assessment of different theranostic agents are also briefly explored. Lessons learned from applications in children might also be extended to evaluations of adult patients.

Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure.

PURPOSE: To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. METHODS: We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3-30 years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics. RESULTS: The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p < 0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers' diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa = 0.942) than 6.25% dose scans (kappa = 0.650). CONCLUSIONS: Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements.

Ethical and Legal Challenges of Artificial Intelligence in Nuclear Medicine.

Artificial intelligence (AI) in nuclear medicine has gained significant traction and promises to be a disruptive, but innovative, technology. Recent developments in artificial neural networks, machine learning, and deep learning have ignited debate with respect to ethical and legal challenges associated with the use of AI in healthcare and medicine. While AI in nuclear medicine has the potential to improve workflow and productivity, and enhance clinical and research capabilities, there remains a professional responsibility to the profession and to patients: ethical, social, and legal. Enthusiasm to embrace new technology should not displace responsibilities for the ethical, social, and legal application of technology. This is especially true in relation to data usage, the algorithms applied, and how algorithms are used in practice. Governance of software and algorithms used for detection (segmentation) and/or diagnosis (classification) of disease using medical images requires rigorous evidence-based regulation. A number of frameworks have been developed for ethical application of AI generally in society and in radiology. For nuclear medicine, consideration needs to be given to beneficence, nonmaleficence, fairness and justice, safety, reliability, data security, privacy and confidentiality, mitigation of bias, transparency, explainability, and autonomy. AI is merely a tool, how it is utilised is a human choice. There is potential for AI applications to enhance clinical and research practice in nuclear medicine and concurrently produce deeper, more meaningful interactions between the physicians and the patient. Nonetheless ethical, legal, and social challenges demand careful attention and formulation of standards/guidelines for nuclear medicine.

Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging.

Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI-based algorithms that enhance outcomes, quality, and efficiency. Moreover, a more holistic perspective of applications, opportunities, and challenges from a programmatic perspective contributes to ethical and sustainable implementation of AI solutions.