Clausius-Mossotti Function for Restricted One-dimensional Operators

Deviations from constancy of the C_M function, are considered on the basis of a one-dimensional oscillator model in which the valence electrons are assumed restricted by infinite potentials, but interact with all others through dipolar forces. A computer calculation shows that the density-dependence of C_M is qualitatively in agreement with experiment, but the temperature-dependence is negligible. An interesting feature is the occurrence of negative polarizabilities for the excited states at modest densities -indicating an insulator-to-metal transition. This result is in conflict with the basic precepts of the model which does not permit fully delocalized electronic states. However, this analysis suggests a more promising three-dimensional model which admits of realistic atomic potentials, dynamical dipolar interaction and repulsive potentials which ensure the existence of the ionized state of the atom

Diagnostic performance of a streamlined 18 F-choline PET-CT protocol for the detection of prostate carcinoma recurrence in combination with appropriate-use criteria

Aim To evaluate the efficacy of single time-point half-body (skull base to thighs) fluorine-18 choline positron emission tomography-computed tomography (PET-CT) compared to a triple-phase acquisition protocol in the detection of prostate carcinoma recurrence. Materials and methods Consecutive choline PET-CT studies performed at a single tertiary referral centre in patients with biochemical recurrence of prostate carcinoma between September 2012 and March 2017 were reviewed retrospectively. The indication for the study, imaging protocol used, imaging findings, whether management was influenced by the PET-CT, and subsequent patient outcome were recorded. Results Ninety-one examinations were performed during the study period; 42 were carried out using a triple-phase protocol (dynamic pelvic imaging for 20 minutes after tracer injection, half-body acquisition at 60 minutes and delayed pelvic scan at 90 minutes) between 2012 and August 2015. Subsequently following interim review of diagnostic performance, a streamlined protocol and appropriate-use criteria were introduced. Forty-nine examinations were carried out using the single-phase protocol between 2015 and 2017. Twenty-nine (69%) of the triple-phase studies were positive for recurrence compared to 38 (78%) of the single-phase studies. Only one patient who had a single-phase study would have benefited from a dynamic acquisition, they have required no further treatment or imaging and are currently under prostate-specific antigen (PSA) surveillance. Conclusion Choline PET-CT remains a useful tool for the detection of prostate recurrence when used in combination with appropriate-use criteria. Removal of dynamic and delayed acquisition phases reduces study time without adversely affecting accuracy. Benefits include shorter imaging time which improves patient comfort, reduced cost, and improved scanner efficiency

Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT

Purpose: Incidence of anal squamous cell carcinoma (ASCC) is increasing, with curative chemoradiotherapy (CRT) as the primary treatment of non-metastatic disease. A significant proportion of patients have locoregional treatment failure (LRF), but distant relapse is uncommon. Accurate prognostication of progression-free survival (PFS) would help personalisation of CRT regimens. The study aim was to evaluate novel imaging pre-treatment features, to prognosticate for PFS in ASCC. Methods: Consecutive patients with ASCC treated with curative intent at a large tertiary referral centre who underwent pre-treatment FDG-PET/CT were included. Radiomic feature extraction was performed using LIFEx software on baseline FDG-PET/CT. Outcome data (PFS) was collated from electronic patient records. Elastic net regularisation and feature selection were used for logistic regression model generation on a randomly selected training cohort and applied to a validation cohort using TRIPOD guidelines. ROC-AUC analysis was used to compare performance of a regression model encompassing standard clinical prognostic factors (age, sex, tumour and nodal stage—model A), a radiomic feature model (model B) and a combined radiomic/clinical model (model C). Results: A total of 189 patients were included in the study, with 145 in the training cohort and 44 in the validation cohort. Median follow-up was 35.1 and 37. 9 months, respectively for each cohort, with 70.3% and 68.2% reaching this time-point with PFS. GLCM entropy (a measure of randomness of distribution of co-occurring pixel grey-levels), NGLDM busyness (a measure of spatial frequency of changes in intensity between nearby voxels of different grey-level), minimum CT value (lowest HU within the lesion) and SMTV (a standardized version of MTV) were selected for inclusion in the prognostic model, alongside tumour and nodal stage. AUCs for performance of model A (clinical), B (radiomic) and C (radiomic/clinical) were 0.6355, 0.7403, 0.7412 in the training cohort and 0.6024, 0.6595, 0.7381 in the validation cohort. Conclusion: Radiomic features extracted from pre-treatment FDG-PET/CT in patients with ASCC may provide better PFS prognosis than conventional staging parameters. With external validation, this might be useful to help personalise CRT regimens in the future

Comparative effectiveness of standard vs. AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging: a multi-institutional reader study evaluation

2024 Frood, Willaime, Miles, Chambers, Al-Chalabi, Ali, Hougham, Brooks, Petrides, Naylor, Ward, Sulkin, Chaytor, Strouhal, Patel and Scarsbrook.Background: Fluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) is widely used for staging high-grade lymphoma, with the time to evaluate such studies varying depending on the complexity of the case. Integrating artificial intelligence (AI) within the reporting workflow has the potential to improve quality and efficiency. The aims of the present study were to evaluate the influence of an integrated research prototype segmentation tool implemented within diagnostic PET/CT reading software on the speed and quality of reporting with variable levels of experience, and to assess the effect of the AI-assisted workflow on reader confidence and whether this tool influenced reporting behaviour. Methods: Nine blinded reporters (three trainees, three junior consultants and three senior consultants) from three UK centres participated in a two-part reader study. A total of 15 lymphoma staging PET/CT scans were evaluated twice: first, using a standard PET/CT reporting workflow; then, after a 6-week gap, with AI assistance incorporating pre-segmentation of disease sites within the reading software. An even split of PET/CT segmentations with gold standard (GS), false-positive (FP) over-contour or false-negative (FN) under-contour were provided. The read duration was calculated using file logs, while the report quality was independently assessed by two radiologists with >15 years of experience. Confidence in AI assistance and identification of disease was assessed via online questionnaires for each case. Results: There was a significant decrease in time between non-AI and AI-assisted reads (median 15.0 vs. 13.3 min, p < 0.001). Sub-analysis confirmed this was true for both junior (14.5 vs. 12.7 min, p = 0.03) and senior consultants (15.1 vs. 12.2 min, p = 0.03) but not for trainees (18.1 vs. 18.0 min, p = 0.2). There was no significant difference between report quality between reads. AI assistance provided a significant increase in confidence of disease identification (p < 0.001). This held true when splitting the data into FN, GS and FP. In 19/88 cases, participants did not identify either FP (31.8%) or FN (11.4%) segmentations. This was significantly greater for trainees (13/30, 43.3%) than for junior (3/28, 10.7%, p = 0.05) and senior consultants (3/30, 10.0%, p = 0.05). Conclusions: The study findings indicate that an AI-assisted workflow achieves comparable performance to humans, demonstrating a marginal enhancement in reporting speed. Less experienced readers were more influenced by segmentation errors. An AI-assisted PET/CT reading workflow has the potential to increase reporting efficiency without adversely affecting quality, which could reduce costs and report turnaround times. These preliminary findings need to be confirmed in larger studies

Coronary CT Angiography and 5-Year Risk of Myocardial Infarction.

BACKGROUND: Although coronary computed tomographic angiography (CTA) improves diagnostic certainty in the assessment of patients with stable chest pain, its effect on 5-year clinical outcomes is unknown. METHODS: In an open-label, multicenter, parallel-group trial, we randomly assigned 4146 patients with stable chest pain who had been referred to a cardiology clinic for evaluation to standard care plus CTA (2073 patients) or to standard care alone (2073 patients). Investigations, treatments, and clinical outcomes were assessed over 3 to 7 years of follow-up. The primary end point was death from coronary heart disease or nonfatal myocardial infarction at 5 years. RESULTS: The median duration of follow-up was 4.8 years, which yielded 20,254 patient-years of follow-up. The 5-year rate of the primary end point was lower in the CTA group than in the standard-care group (2.3% [48 patients] vs. 3.9% [81 patients]; hazard ratio, 0.59; 95% confidence interval [CI], 0.41 to 0.84; P=0.004). Although the rates of invasive coronary angiography and coronary revascularization were higher in the CTA group than in the standard-care group in the first few months of follow-up, overall rates were similar at 5 years: invasive coronary angiography was performed in 491 patients in the CTA group and in 502 patients in the standard-care group (hazard ratio, 1.00; 95% CI, 0.88 to 1.13), and coronary revascularization was performed in 279 patients in the CTA group and in 267 in the standard-care group (hazard ratio, 1.07; 95% CI, 0.91 to 1.27). However, more preventive therapies were initiated in patients in the CTA group (odds ratio, 1.40; 95% CI, 1.19 to 1.65), as were more antianginal therapies (odds ratio, 1.27; 95% CI, 1.05 to 1.54). There were no significant between-group differences in the rates of cardiovascular or noncardiovascular deaths or deaths from any cause. CONCLUSIONS: In this trial, the use of CTA in addition to standard care in patients with stable chest pain resulted in a significantly lower rate of death from coronary heart disease or nonfatal myocardial infarction at 5 years than standard care alone, without resulting in a significantly higher rate of coronary angiography or coronary revascularization. (Funded by the Scottish Government Chief Scientist Office and others; SCOT-HEART ClinicalTrials.gov number, NCT01149590 .)

Prediction of prostate tumour hypoxia using pre-treatment MRI-derived radiomics: preliminary findings

Purpose To develop a machine learning (ML) model based on radiomic features (RF) extracted from whole prostate gland magnetic resonance imaging (MRI) for prediction of tumour hypoxia pre-radiotherapy. Material and methods Consecutive patients with high-grade prostate cancer and pre-treatment MRI treated with radiotherapy between 01/12/2007 and 1/08/2013 at two cancer centres were included. Cancers were dichotomised as normoxic or hypoxic using a biopsy-based 32-gene hypoxia signature (Ragnum signature). Prostate segmentation was performed on axial T2-weighted (T2w) sequences using RayStation (v9.1). Histogram standardisation was applied prior to RF extraction. PyRadiomics (v3.0.1) was used to extract RFs for analysis. The cohort was split 80:20 into training and test sets. Six different ML classifiers for distinguishing hypoxia were trained and tuned using five different feature selection models and fivefold cross-validation with 20 repeats. The model with the highest mean validation area under the curve (AUC) receiver operating characteristic (ROC) curve was tested on the unseen set, and AUCs were compared via DeLong test with 95% confidence interval (CI). Results 195 patients were included with 97 (49.7%) having hypoxic tumours. The hypoxia prediction model with best performance was derived using ridge regression and had a test AUC of 0.69 (95% CI: 0.14). The test AUC for the clinical-only model was lower (0.57), but this was not statistically significant (p = 0.35). The five selected RFs included textural and wavelet-transformed features. Conclusion Whole prostate MRI-radiomics has the potential to non-invasively predict tumour hypoxia prior to radiotherapy which may be helpful for individualised treatment optimisation

AusTraits, a curated plant trait database for the Australian flora

We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge

Exploring the relationship between chronic undernutrition and asymptomatic malaria in Ghanaian children

<p>Abstract</p> <p>Background</p> <p>A moderate association has been found between asymptomatic parasitaemia and undernutrition. However, additional investigation using the gold standard for asymptomatic parasitaemia confirmation, polymerase chain reaction (PCR), is needed to validate this association. Anthropometric measurements and blood samples from children less than five years of age in a rural Ghanaian community were used to determine if an association exists between chronic undernutrition and PCR-confirmed cases of asymptomatic malaria.</p> <p>Methods</p> <p>This was a descriptive cross-sectional study of 214 children less than five years of age from a community near Kumasi, Ghana. Blood samples and anthropometric measurements from these children were collected during physical examinations conducted in January 2007 by partners of the Barekuma Collaborative Community Development Programme.</p> <p>Results</p> <p>Findings from the logistic model predicting the odds of asymptomatic malaria indicate that children who experienced mild, moderate or severe stunting were not more likely to have asymptomatic malaria than children who were not stunted. Children experiencing anaemia had an increased likelihood (OR = 4.15; 95% CI: 1.92, 8.98) of asymptomatic malaria. Similarly, increased spleen size, which was measured by ultrasound, was also associated with asymptomatic malaria (OR = 2.17; 95% CI: 1.44, 3.28). Fast breathing, sex of the child, and age of the child were not significantly associated with the asymptomatic malaria.</p> <p>Conclusions</p> <p>No significant association between chronic undernutrition and presence of asymptomatic malaria was found. Children who experience anaemia and children who have splenomegaly are more likely to present asymptomatic malaria. Programmes aimed at addressing malaria should continue to include nutritional components, especially components that address anaemia.</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing