Tag: nevin manimala

Int J Radiat Oncol Biol Phys. 2024 Apr 15:S0360-3016(24)00513-3. doi: 10.1016/j.ijrobp.2024.04.014. Online ahead of print.

ABSTRACT

PURPOSE: Regional nodal irradiation (RNI) to the axilla and supraclavicular area presents distinct toxicities, such as lymphedema and shoulder stiffness, compared to whole-breast irradiation. There is insufficient evidence on the safety of dose-escalation in hypofractionated RNI. We aimed to evaluate and compare toxicity rates in patients with breast cancer who received hypofractionated RNI with and without dose-escalation.

METHODS: We retrospectively analyzed 381 patients with breast cancer treated with hypofractionated RNI between March 2015 and February 2017. Patients received either the standard-dose to the regional nodal area (43.2 Gy/16 fx; 48.7 Gy_3.5 EQD2, 2 Gy equivalent dose with α/β=3.5 Gy) or dose-escalation with a median dose of 54.8 Gy_3.5 EQD2 (range, 51.7 – 60.9 Gy_3.5 EQD2) depending on clinical and pathologic nodal stage. Toxicity rates of lymphedema and shoulder stiffness were assessed, and statistical analyses were conducted to identify associated factors.

RESULTS: The median follow-up time was 32.3 months (5.7 – 47.0 months). After radiotherapy, 71 (18.6%) patients developed lymphedema, and 48 (12.6%) developed shoulder stiffness. Patients who received dose-escalation exhibited significantly higher rates of lymphedema (32.1% vs. 14.8%; Odds Ratio (OR): 2.72, p=0.0004) and shoulder stiffness (23.8% vs. 9.4%; OR: 2.01, p=0.0205) compared to the standard-dose group. Moreover, dose-escalation showed a tendency to increase the severity of lymphedema and shoulder stiffness.

CONCLUSIONS: Patients who received dose-escalation in hypofractionated RNI face a higher risk of developing lymphedema and shoulder stiffness compared to those who received standard-dose hypofractionated RNI. Therefore, it is crucial to implement close and frequent monitoring for early detection, along with timely rehabilitation interventions for these patients.

PMID:38631540 | DOI:10.1016/j.ijrobp.2024.04.014

Int J Radiat Oncol Biol Phys. 2024 Apr 15:S0360-3016(24)00481-4. doi: 10.1016/j.ijrobp.2024.04.001. Online ahead of print.

ABSTRACT

PURPOSE: To provide an understanding of current FLI techniques, and their potential to improve dosimetry and outcomes for lung cancer patients receiving radiation therapy (RT).

METHODS: EMBASE, PubMed, and Cochrane Library were searched from 1990 until April 2023. Articles were included if they reported on FLI in one of: techniques, incorporation into RT planning for lung cancer, quantification of RT-related outcomes for lung cancer patients. Studies involving all RT modalities, including stereotactic body radiotherapy and particle therapy, were included. Meta-analyses were conducted to investigate differences in dose-function parameters between anatomical and functional RT planning techniques, as well as to investigate correlations of dose-function parameters with grade 2+ radiation pneumonitis (RP).

RESULTS: 178 studies were included in the narrative synthesis. We report on FLI modalities, dose-response quantification, functional lung (FL) definitions, FL avoidance techniques, and correlations between FL irradiation and toxicity. Meta-analysis results show that FL avoidance planning gives statistically significant absolute reductions of 3.22% to the fraction of well-ventilated lung receiving 20 Gy or more (vent-fV20), 3.52% to the fraction of well-perfused lung receiving 20 Gy or more (perf-fV20), 1.3 Gy to the mean dose to the well-ventilated lung (vent-fMLD), and 2.41 Gy to the mean dose to the well-perfused lung (perf-fMLD). Increases in the threshold value for defining FL are associated with decreases in functional parameters. For intensity-modulated radiation therapy and volumetric modulated arc therapy, avoidance planning results in a 13% rate of grade 2+ RP, which seems reduced compared to results from conventional planning cohorts. A trend of increased predictive ability for grade 2+ RP was seen in models using FL information, but was not statistically significant.

CONCLUSIONS: FLI shows promise as a method to spare FL during thoracic RT, but interventional trials related to FL avoidance planning are sparse. Such trials are critical to understanding the impact of FL avoidance planning on toxicity reduction and patient outcomes.

PMID:38631538 | DOI:10.1016/j.ijrobp.2024.04.001

Magn Reson Imaging. 2024 Apr 15:S0730-725X(24)00131-0. doi: 10.1016/j.mri.2024.04.018. Online ahead of print.

ABSTRACT

PURPOSE: Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge.

METHODS: The effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin’s Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests.

RESULTS: The simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI).

CONCLUSION: ADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.

PMID:38631532 | DOI:10.1016/j.mri.2024.04.018

Med Phys. 2024 Apr 17. doi: 10.1002/mp.17081. Online ahead of print.

ABSTRACT

BACKGROUND: Statistical process control (SPC) is a powerful statistical tool for process monitoring that has been highly recommended in healthcare applications, including radiation therapy quality assurance (QA). The AAPM TG-218 report described the clinical implementation of SPC for Volumetric Modulated Arc Therapy (VMAT) pre-treatment verifications, pointing out the need to adjust tolerance limits based on plan complexity. However, the quantification of plan complexity and its integration into SPC remains an unresolved challenge.

PURPOSE: The primary aim of this study is to investigate the incorporation of plan complexity into the SPC framework for VMAT pre-treatment verifications. The study explores and evaluates various strategies for this incorporation, discussing their merits and limitations, and provides recommendations for clinical application.

METHODS: A retrospective analysis was conducted on 309 VMAT plans from diverse anatomical sites using the PTW OCTAVIUS 4D device for QA measurements. Gamma Passing Rates (GPR) were obtained, and lower control limits were computed using both the conventional Shewhart method and three heuristic methods (scaled weighted variance, weighted standard deviations, and skewness correction) to accommodate non-normal data distributions. The ‘Identify-Eliminate-Recalculate’ method was employed for robust analysis. Eight complexity metrics were analyzed and two distinct strategies for incorporating plan complexity into SPC were assessed. The first strategy focused on establishing control limits for different treatment sites, while the second was based on the determination of control limits as a function of individual plan complexity. The study extensively examines the correlation between control limits and plan complexity and assesses the impact of complexity metrics on the control process.

RESULTS: The control limits established using SPC were strongly influenced by the complexity of treatment plans. In the first strategy, a clear correlation was found between control limits and average plan complexity for each site. The second approach derived control limits based on individual plan complexity metrics, enabling tailored tolerance limits. In both strategies, tolerance limits inversely correlated with plan complexity, resulting in all highly complex plans being classified as in control. In contrast, when plans were collectively analyzed without considering complexity, all the out-of-control plans were highly complex.

CONCLUSIONS: Incorporating plan complexity into SPC for VMAT verifications requires meticulous and comprehensive analysis. To ensure overall process control, we advocate for stringent control and minimization of plan complexity during treatment planning, especially when control limits are adjusted based on plan complexity.

PMID:38630979 | DOI:10.1002/mp.17081

Neurology. 2024 May 14;102(9):e209271. doi: 10.1212/WNL.0000000000209271. Epub 2024 Apr 17.

ABSTRACT

BACKGROUND AND OBJECTIVES: Clinical heterogeneity of patients with Parkinson disease (PD) is well recognized. PD with REM sleep behavior disorder (RBD) is a more malignant phenotype with faster motor progression and higher nonmotor symptom burden. However, the neural mechanisms underlying this clinical divergence concerning imbalances in neurotransmitter systems remain elusive.

METHODS: Combining magnetic resonance (MR) spectroscopy and [¹¹C]ABP688 PET on a PET/MR hybrid system, we simultaneously investigated two different mechanisms of glutamate signaling in patients with PD. Patients were grouped according to their RBD status in overnight video-polysomnography and compared with age-matched and sex-matched healthy control (HC) participants. Total volumes of distribution (V_T) of [¹¹C]ABP688 were estimated with metabolite-corrected plasma concentrations during steady-state conditions between 45 and 60 minutes of the scan following a bolus-infusion protocol. Glutamate, glutamine, and glutathione levels were investigated with single-voxel stimulated echo acquisition mode MR spectroscopy of the left basal ganglia.

RESULTS: We measured globally elevated V_T of [¹¹C]ABP688 in 16 patients with PD and RBD compared with 17 patients without RBD and 15 HC participants (F(2,45) = 5.579, p = 0.007). Conversely, glutamatergic metabolites did not differ between groups and did not correlate with the regional V_T of [¹¹C]ABP688. V_T of [¹¹C]ABP688 correlated with the amount of REM sleep without atonia (F(1,42) = 5.600, p = 0.023) and with dopaminergic treatment response in patients with PD (F(1,30) = 5.823, p = 0.022).

DISCUSSION: Our results suggest that patients with PD and RBD exhibit altered glutamatergic signaling indicated by higher V_T of [¹¹C]ABP688 despite unaffected glutamate levels. The imbalance of glutamate receptors and MR spectroscopy glutamate metabolite levels indicates a novel mechanism contributing to the heterogeneity of PD and warrants further investigation of drugs targeting mGluR5.

PMID:38630966 | DOI:10.1212/WNL.0000000000209271

JCO Clin Cancer Inform. 2024 Apr;8:e2300239. doi: 10.1200/CCI.23.00239.

ABSTRACT

PURPOSE: The COVID-19 pandemic led to rapid expansion of telemedicine. The implications of telemedicine have not been rigorously studied in radiation oncology, a procedural specialty. This study aimed to evaluate the characteristics of in-person patients (IPPs) and virtual patients (VPs) who presented to a large cancer center before and during the pandemic and to understand variables affecting likelihood of receiving radiotherapy (yield) at our institution.

METHODS: A total of 17,915 patients presenting for new consultation between 2019 and 2021 were included, stratified by prepandemic and pandemic periods starting March 24, 2020. Telemedicine visits included video and telephone calls. Area deprivation indices (ADIs) were also compared.

RESULTS: The overall population was 56% male and 93% White with mean age of 63 years. During the pandemic, VPs accounted for 21% of visits, were on average younger than their in-person (IP) counterparts (63.3 years IP v 62.4 VP), and lived further away from clinic (215 miles IP v 402 VP). Among treated VPs, living closer to clinic was associated with higher yield (odds ratio [OR], 0.95; P < .001). This was also seen among IPPs who received treatment (OR, 0.96; P < .001); however, the average distance from clinic was significantly lower for IPPs than VPs (205 miles IP v 349 VP). Specialized radiotherapy (proton and brachytherapy) was used more in VPs. IPPs had higher ADI than VPs. Among VPs, those treated had higher ADI (P < .001).

CONCLUSION: Patient characteristics and yield were significantly different between IPPs and VPs. Telemedicine increased reach to patients further away from clinic, including from rural or health care-deprived areas, allowing access to specialized radiation oncology care. Telemedicine has the potential to increase the reach of other technical and procedural specialties.

PMID:38630957 | DOI:10.1200/CCI.23.00239

Health Rep. 2024 Apr 17;35(4):15-26. doi: 10.25318/82-003-x202400400002-eng.

ABSTRACT

BACKGROUND: This study investigates the association between dental insurance, income, and dental care access for Canadian children and youth aged 1 to 17 years. It contributes to a baseline understanding of oral health care use before the implementation of the Canadian Dental Care Plan (CDCP).

DATA AND METHODS: This study used data from the 2019 Canadian Health Survey on Children and Youth (n=47,347). Descriptive statistics and logistic regression models were employed to assess the association of dental insurance, adjusted family net income, and other sociodemographic factors on oral health care visits and cost-related avoidance of oral health care.

RESULTS: A large percentage of children under the age of 5 had never visited a dentist (79.8% of 1-year-olds to 16.4% of 4-year-olds). Overall, 89.6% of Canadian children and youth aged 5 to 17 had visited a dental professional within the past 12 months: 93.1% of those who were insured and 78.5% of those who were uninsured. Insured children and youth had a 4.5% cost-related avoidance of dental care, contrasting with 23.3% for uninsured children and youth. After adjustment for sociodemographic variables, children and youth with dental insurance were nearly three times more likely (odds ratio [OR]: 2.94; 95% confidence interval [CI]: 2.60 to 3.33) to have visited a dental professional in the past 12 months than uninsured children and youth. Having dental insurance (OR: 0.19; 95% CI: 0.16 to 0.21) was protective against barriers to seeing a dental professional because of cost. There was a strong income gradient for both dental service outcomes.

INTERPRETATION: The study emphasizes the significant association of dental insurance and access to oral health care for children and youth. It highlights a significant gap between insured and uninsured children and youth and points out the influence of sociodemographic and income factors on this disparity.

PMID:38630920 | DOI:10.25318/82-003-x202400400002-eng

Health Rep. 2024 Apr 17;35(4):3-14. doi: 10.25318/82-003-x202400400001-eng.

ABSTRACT

BACKGROUND: This study examines the association of dental insurance with oral health care access and utilization in Canada while accounting for income and sociodemographic factors. It contributes to a baseline of oral health care disparities before the implementation of the Canadian Dental Care Plan (CDCP).

DATA AND METHODS: This retrospective study of Canadians aged 18 to 64 years is based on data from the 2022 Canadian Community Health Survey. Multivariable logistic regression was employed to evaluate the association of dental insurance with the recency and frequency of dental visits, as well as avoidance of dental care because of cost.

RESULTS: Overall, 65.7% of Canadians reported visiting a dental professional in the previous year: 74.6% of those with private insurance, 62.8% with public insurance, and 49.8% uninsured. Cost-related avoidance of dental care was 16.0%, 20.9%, and 47.4% for the privately insured, publicly insured, and uninsured, respectively. After adjustment, adults with private (odds ratio [OR]=2.54; 95% confidence interval [CI]: 2.32 to 2.78) and public (OR=2.17; 95% CI: 1.75 to 2.68) insurance were more likely to have visited a dental professional in the last year compared with those without insurance. Similarly, both private (OR=0.22; 95% CI: 0.20 to 0.25) and public (OR=0.22; 95% CI: 0.17 to 0.29) insurance holders showed a significantly lower likelihood of avoiding dental visits because of cost when compared with uninsured individuals.

INTERPRETATION: This study showed the significant association of dental insurance with access to oral health care in Canada, contributing to setting a critical benchmark for assessments of the CDCP’s effectiveness in addressing oral health disparities.

PMID:38630919 | DOI:10.25318/82-003-x202400400001-eng

J Clin Psychol. 2024 Apr 17. doi: 10.1002/jclp.23695. Online ahead of print.

ABSTRACT

The Difficulties in Emotion Regulation Scale (DERS) is the most used self-report questionnaire to assess deficits in emotion regulation (ER), composed of 6 dimensions and 36 items. Many studies have evaluated its factor structure, not always confirming the original results, and proposed different factor models. A possible way to try to identify the dimensionality of the DERS could be through a meta-analysis with structural equation models (MASEM) of its factor structure. The MASEM indicated that a six-factor model with 32 items (DERS-32) was the most suitable to represent the dimensionality of the DERS (χ² = 2095.96, df = 449, p < .001; root mean square error of approximation [RMSEA] = 0.024, 95% confidence interval [CI]: 0.023-0.025; comparative fit index [CFI] = 0.97; Tucker Lewis index [TLI] = 0.96; standardized root mean squared residual [SRMR] = 0.04). This result was also confirmed by a confirmatory factor analysis (χ² = 3229.67, df = 449, p < 0.001; RMSEA = 0.075, 95% CI: 0.073-0.078; CFI = 0.94; TLI = 0.93; SRMR = 0.05) on a new sample (1092 participants; mean age: 28.28, SD = 5.82 years) recruited from the Italian population. Analyses and results from this sample are reported in the second study of this work. The DERS-32 showed satisfactory internal consistency (i.e., ordinal α, Molenaar Sijtsma statistic, and latent class reliability coefficient) for all its dimensions and correctly categorized individuals with probable borderline symptomatology. In conclusion, the DERS-32 has demonstrated to be the best model for the DERS among all the others considered in this work, as well as a reliable tool to assess deficits in ER.

PMID:38630901 | DOI:10.1002/jclp.23695

J Travel Med. 2024 Mar 17:taae062. doi: 10.1093/jtm/taae062. Online ahead of print.

ABSTRACT

BACKGROUND: The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics.

METHODS: We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected in outbreaks of MERS, Zika, and SARS-CoV-2 over the past decade. We then used a stochastic discrete time SEIR metapopulation model to simulate global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data.

RESULTS: We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruption occurred during the SARS-CoV-2 pandemic.For outbreak events that were accompanied by local, small, and short-term changes in air travel, epidemic models using historical flight data gave similar projections of timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2 in which there were durable and extensive levels of global travel disruption.

CONCLUSIONS: The use of historical flight data as a proxy in epidemic models is an acceptable practice except in rare, large epidemics that lead to substantial disruptions to international travel.

PMID:38630887 | DOI:10.1093/jtm/taae062