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Objectives: The objective of the study was to describe the occurrence and associations of oral self‐care by dependent older New Zealanders. Background: Dependent older adults who require some level of assisted daily care have been shown to have poorer oral health than their independent counterparts, yet national estimates are lacking. Materials and methods: A secondary analysis was conducted of data from New Zealand's 2012 Older People's Oral Health Survey, a national survey that interviewed and examined a representative sample of 2,218 dependent older adults living in aged residential care or receiving home‐based care. Survey data were weighted for statistical analyses, and modelling was carried out using a modified Poisson approach. Results: Overall, 59.5% (95% CI: 55.1, 63.7) of participants brushed their teeth twice a day, with this rate higher among women, Asians and those without cognitive or physical impairment. Cleaning interdentally at least once a week was less common than brushing, at 25.9% (95% CI: 21.5, 30.9), and that proportion was significantly lower among those with higher dependency or who were cognitively or physically impaired. Almost 9 in 10 (87.8%; 95% CI 83.1, 91.4) used fluoride toothpaste, and adults of high socio‐economic status had lower rates of fluoride toothpaste use. Only 15.0% (95% CI 12.4, 18.0) used mouthwash, with this being most prevalent in Pasifika. More than half of those who wore dentures also wore them at night. Conclusion: Oral self‐care was sub‐optimal among dependent older New Zealanders. Improving the situation should be a high priority. [ABSTRACT FROM AUTHOR]

Radiomics has the potential to aid prostate cancer (PC) diagnoses and prediction by analyzing and modeling quantitative features extracted from clinical imaging. However, its reliability has been a concern, possibly due to its high-dimensional nature. This study aims to quantitatively investigate the impact of randomly generated irrelevant features on MRI radiomics feature selection, modeling, and performance by progressively adding randomly generated features. Two multiparametric-MRI radiomics PC datasets were used (dataset 1 (n = 260), dataset 2 (n = 100)). The endpoint was to differentiate pathology-confirmed clinically significant (Gleason score (GS) ≥ 7) from insignificant (GS < 7) PC. Random features were generated at 12 levels with a 10% increment from 0% to 100% and an additional 5%. Three feature selection algorithms and two classifiers were used to build the models. The area under the curve and accuracy were used to evaluate the model's performance. Feature importance was calculated to assess features' contributions to the models. The metrics of each model were compared using an ANOVA test with a Bonferroni correction. A slight tendency to select more random features with the increasing number of random features introduced to the datasets was observed. However, the performance of the radiomics-built models was not significantly affected, which was partially due to the higher contribution of radiomics features toward the models compared to the random features. These reliability effects also vary among datasets. In conclusion, while the inclusion of additional random features may still slightly impact the performance of the feature selection, it may not have a substantial impact on the MRI radiomics model performance. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the potential value of MRI radiomics obtained from a 1.5 T MRI‐guided linear accelerator (MR‐LINAC) for D'Amico high‐risk prostate cancer (PC) classification in MR‐guided radiotherapy (MRgRT). Methods: One hundred seventy‐six consecutive PC patients underwent 1.5 T MRgRT treatment were retrospectively enrolled. Each patient received one or two pretreatment T2‐weighted MRI scans on a 1.5 T MR‐LINAC. The endpoint was to differentiate high‐risk from low/intermediate‐risk PC based on D'Amico criteria using MRI‐radiomics. Totally 1023 features were extracted from clinical target volume (CTV) and planning target volume (PTV). Intraclass correlation coefficient of scan–rescan repeatability, feature correlation, and recursive feature elimination were used for feature dimension reduction. Least absolute shrinkage and selection operator regression was employed for model construction. Receiver operating characteristic area under the curve (AUC) analysis was used for model performance assessment in both training and testing data. Results: One hundred and eleven patients fulfilled all criteria were finally included: 76 for training and 35 for testing. The constructed MRI‐radiomics models extracted from CTV and PTV achieved the AUC of 0.812 and 0.867 in the training data, without significant difference (P = 0.083). The model performances remained in the testing. The sensitivity, specificity, and accuracy were 85.71%, 64.29%, and 77.14% for the PTV‐based model; and 71.43%, 71.43%, and 71.43% for the CTV‐based model. The corresponding AUCs were 0.718 and 0.750 (P = 0.091) for CTV‐ and PTV‐based models. Conclusion: MRI‐radiomics obtained from a 1.5 T MR‐LINAC showed promising results in D'Amico high‐risk PC stratification, potentially helpful for the future PC MRgRT. Prospective studies with larger sample sizes and external validation are warranted for further verification. [ABSTRACT FROM AUTHOR]

Purpose: To prospectively investigate the impact of image reconstruction on MRI radiomics features. Methods: An anthropomorphic phantom was scanned at 1.5 T using a standardized sequence for MR‐guided radiotherapy under SENSE and compressed‐SENSE reconstruction settings. A total of 93 first‐order and texture radiomics features in 10 volumes of interest were assessed based on (1) accuracy measured by the percentage deviation from the reference, (2) robustness on reconstruction in all volumes of interest measured by the intraclass correlation coefficient, and (3) repeatability measured by the coefficient of variance over the repetitive acquisitions. Finally, reliable and unreliable radiomics features were comprehensively determined based on their accuracy, robustness, and repeatability. Results: Better accuracy and robustness of the radiomics features were achieved under SENSE than compressed‐SENSE reconstruction. The feature accuracy under SENSE reconstruction was more affected by acceleration factor than direction, whereas under compressed‐SENSE reconstruction, accuracy was substantially impacted by the increasing denoising levels. Feature repeatability was dependent more on feature types than on reconstruction. A total of 45 reliable features and 13 unreliable features were finally determined for SENSE, compared with 22 reliable and 26 unreliable features for compressed SENSE. First‐order and gray‐level co‐occurrence matrix features were generally more reliable than other features. Conclusion: Radiomics features could be substantially affected by MRI reconstruction, so precautions need to be taken regarding their reliability for clinical use. This study helps the guidance of the preselection of reliable radiomics features and the preclusion of unreliable features in MR‐guided radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: Determination of reliable change of radiomics feature over time is essential and vital in delta-radiomics, but has not yet been rigorously examined. This study attempts to propose a methodological approach using reliable change index (RCI), a statistical metric to determine the reliability of quantitative biomarker changes by accounting for the baseline measurement standard error, in delta-radiomics. The use of RCI was demonstrated with the MRI data acquired from a group of prostate cancer (PCa) patients treated by 1.5 T MRI-guided radiotherapy (MRgRT)., Methods: Fifty consecutive PCa patients who underwent five-fractionated MRgRT were retrospectively included, and 1023 radiomics features were extracted from the clinical target volume (CTV) and planning target volume (PTV). The two MRI datasets acquired at the first fraction (MRI11 and MRI21) were used to calculate the baseline feature reliability against image acquisition using intraclass correlation coefficient (ICC). The RCI was constructed based on the baseline feature measurement standard deviation, ICC, and feature value differences at two time points between the fifth (MRI51) and the first fraction MRI (MRI11). The reliable change of features was determined in each patient only if the calculated RCI was over 1.96 or smaller than -1.96. The feature changes between MRI51 and MRI11 were correlated to two patient-reported quality-of-life clinical endpoints of urinary domain summary score (UDSS) and bowel domain summary score (BDSS) in 35 patients using the Spearman correlation test. Only the significant correlations between a feature that was reliably changed in ≥7 patients (20%) by RCI and an endpoint were considered as true significant correlations., Results: The 352 (34.4%) and 386 (37.7%) features among all 1023 features were determined by RCI to be reliably changed in more than five (10%) patients in the CTV and PTV, respectively. Nineteen features were found reliably changed in the CTV and 31 features in the PTV, respectively, in 10 (20%) or more patients. These features were not necessarily associated with significantly different longitudinal feature values (group p-value < 0.05). Most reliably changed features in more than 10 patients had excellent or good baseline test-retest reliability ICC, while none showed poor reliability. The RCI method ruled out the features to be reliably changed when substantial feature measurement bias was presented. After applying the RCI criterion, only four and five true significant correlations were confirmed with UDSS and BDSS in the CTV, respectively, with low true significance correlation rates of 10.8% (4/37) and 17.9% (5/28). No true significant correlations were found in the PTV., Conclusions: The RCI method was proposed for delta-radiomics and demonstrated using PCa MRgRT data. The RCI has advantages over some other statistical metrics commonly used in the previous delta-radiomics studies, and is useful to reliably identify the longitudinal radiomics feature change on an individual basis. This proposed RCI method should be helpful for the development of essential feature selection methodology in delta-radiomics., (© 2022 American Association of Physicists in Medicine.)