Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

Artificial Intelligence for Neuroimaging in Pediatric Cancer

BACKGROUND/OBJECTIVES

Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer neuroimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pediatric population.

METHODS

A comprehensive literature review was conducted, focusing on AI's impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. Challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models were analyzed.

RESULTS

AI has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging, resulting in improved accuracy in tumor segmentation and outcome prediction for treatment. However, progress is hindered by the scarcity of pediatric datasets, issues with data sharing, and the ethical implications of applying AI in vulnerable populations.

CONCLUSIONS

To overcome current limitations, future research should focus on building robust pediatric datasets, fostering multi-institutional collaborations for data sharing, and developing interpretable AI models that align with clinical practice and ethical standards. These efforts are essential in harnessing the full potential of AI in pediatric neuroimaging and improving outcomes for children with cancer.

Workplace violence in medical radiation science: A systematic review

INTRODUCTION

According to World Health Organization (WHO), workplace violence (WPV) is a significant issue in healthcare. However, no systematic review on WPV in medical radiation science (MRS) has been published yet. The purpose of this paper is to systematically review prevalence of WPV in MRS and its risk factors.

METHODS

Electronic scholarly publication databases, namely EBSCOhost/Cumulative Index of Nursing and Allied Health Literature Ultimate, PubMed/Medline, ScienceDirect, Scopus, and Wiley Online Library were used for literature search to identify articles about WPV in MRS published over last 10 years as per preferred reporting items for systematic reviews and meta-analyses guidelines. To facilitate comparisons of the WPV prevalence and relative importance of individual risk factors across the included studies, their reported absolute figures of findings were used to synthesize respective percentages (if not stated).

RESULTS

Twelve papers met the selection criteria and were included. This review shows that the WPV prevalence were 69.2-100 % (whole career) and 46.1-83.0 % (last 12 months) in diagnostic radiography, 63.0-84.0 % (whole career) in radiation therapy, 57.6 % in medical sonography (last 12 months), and 46.8 % (last 6 months) in nuclear medicine. The identified WPV risk factors included intoxicated patients, staff stress, feeling of inadequacy resulting in self-protection, more vulnerable practitioners (female, <40 years old and <5-year experience), working in radiation therapy treatment room, emergency department, examination room, general radiography, public hospital, and non-examination and waiting areas, long patient waiting time, night shift, overcrowding environment, unable to meet patients'/family members' expectations, miscommunication, patient handling, inadequate staff and security measures, interaction with colleagues, and lone working.

CONCLUSION

The WPV risk in diagnostic radiography and radiation therapy appears extremely high as a result of the aforementioned risk factors. Nevertheless, these study findings should be used with caution due to potential non-response bias.

IMPLICATIONS FOR PRACTICE

A WPV policy should be developed in every clinical workplace. Even if such policy is available, its enforcement including policy awareness boosting, and encouraging incident reporting and support seeking will be essential for reducing WPV. More survey studies based on WHO WPV questionnaire should be conducted for strengthening evidence base.

Comparative Study of Eclipse and RayStation Multi-Criteria Optimization-Based Prostate Radiotherapy Treatment Planning Quality

Multi-criteria optimization (MCO) function has been available on commercial radiotherapy (RT) treatment planning systems to improve plan quality; however, no study has compared Eclipse and RayStation MCO functions for prostate RT planning. The purpose of this study was to compare prostate RT MCO plan qualities in terms of discrepancies between Pareto optimal and final deliverable plans, and dosimetric impact of final deliverable plans. In total, 25 computed tomography datasets of prostate cancer patients were used for Eclipse (version 16.1) and RayStation (version 12A) MCO-based plannings with doses received by 98% of planning target volume having 76 Gy prescription (PTV76D98%) and 50% of rectum (rectum D50%) selected as trade-off criteria. Pareto optimal and final deliverable plan discrepancies were determined based on PTV76D98% and rectum D50% percentage differences. Their final deliverable plans were compared in terms of doses received by PTV76 and other structures including rectum, and PTV76 homogeneity index (HI) and conformity index (CI), using a t-test. Both systems showed discrepancies between Pareto optimal and final deliverable plans (Eclipse: -0.89% (PTV76D98%) and -2.49% (Rectum D50%); RayStation: 3.56% (PTV76D98%) and -1.96% (Rectum D50%)). Statistically significantly different average values of PTV76D98%,HI and CI, and mean dose received by rectum (Eclipse: 76.07 Gy, 0.06, 1.05 and 39.36 Gy; RayStation: 70.43 Gy, 0.11, 0.87 and 51.65 Gy) are noted, respectively (p < 0.001). Eclipse MCO-based prostate RT plan quality appears better than that of RayStation.

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy

Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study's results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa.