Assessment of body composition in breast cancer patients: concordance between transverse computed tomography analysis at the fourth thoracic and third lumbar vertebrae.

Introduction: Specific body composition markers derived from L3 axial computed tomography (CT) images predict clinical cancer outcomes, including chemotherapy toxicity and survival. However, this method is only applicable to those undergoing lumbar (L3) CT scanning, which is not universally conducted in early breast cancer cases. This study aimed to evaluate CT analysis at T4 as a feasible alternative marker of body composition in breast cancer. Method: All patients participated in the Investigating Outcomes from Breast Cancer: Correlating Genetic, Immunological, and Nutritional (BeGIN) Predictors observational cohort study (REC reference number: 14/EE/1297). Staging chest-abdomen-pelvic CT scan images from 24 women diagnosed with early breast cancer at University Hospital Southampton were analysed. Adipose tissue, skeletal muscle, and muscle attenuation were measured from the transverse CT slices' cross-sectional area (CSA) at T4 and L3. Adipose tissue and skeletal muscle area measurements were adjusted for height. Spearman's rank correlation coefficient analysis was used to determine concordance between body composition measurements using CT analysis at L3 and T4 regions. Results: Derived estimates for total adipose tissue, subcutaneous adipose tissue, and intramuscular adipose tissue mass following adjustment for height were highly concordant when determined from CSAs of CT slices at T4 and L3 (Rs = 0.821, p < 0.001; Rs = 0.816, p < 0.001; and Rs = 0.830, p < 0.001). In this cohort, visceral adipose tissue (VAT) and skeletal muscle estimates following height adjustment were less concordant when measured by CT at T4 and L3 (Rs = 0.477, p = 0.039 and Rs = 0.578, p = 0.003). The assessment of muscle attenuation was also highly concordant when measured by CT at T4 and L3 (Rs = 0.840, p < 0.001). Discussion: These results suggest that the CT analysis at T4 and L3 provides highly concordant markers for total adipose, subcutaneous adipose, and intramuscular adipose estimation, but not VAT, in this breast cancer population. High concordance between T4 and L3 was also found when assessing skeletal muscle attenuation. Lower concordance was observed for the estimates of skeletal muscle area, potentially explained by differences in the quantity and proportions of axial and appendicular muscle between the thorax and abdomen. Future studies will determine the value of T4 metrics as predictive tools for clinical outcomes in breast cancer.

Occupational models from 42 million unstructured job postings.

Structuring jobs into occupations is the first step for analysis tasks in many fields of research, including economics and public health, as well as for practical applications like matching job seekers to available jobs. We present a data resource, derived with natural language processing techniques from over 42 million unstructured job postings in the National Labor Exchange, that empirically models the associations between occupation codes (estimated initially by the Standardized Occupation Coding for Computer-assisted Epidemiological Research method), skill keywords, job titles, and full-text job descriptions in the United States during the years 2019 and 2021. We model the probability that a job title is associated with an occupation code and that a job description is associated with skill keywords and occupation codes. Our models are openly available in the sockit python package, which can assign occupation codes to job titles, parse skills from and assign occupation codes to job postings and resumes, and estimate occupational similarity among job postings, resumes, and occupation codes.

The First 48 Consecutive Patients with 3-Year Symptom Score Follow-Up Post-Prostate Artery Embolization (PAE) at a Single-Centre University Hospital.

INTRODUCTION: Few studies on prostate artery embolization (PAE) follow patients up after 12 months. We aimed to evaluate the symptomatic efficacy of PAE in our patient cohort at 3 years. METHOD: A total of 48 consecutive patients undergoing PAE from June 2012 to August 2014 were included in this retrospective study. All patients underwent formal urodynamics to confirm bladder outflow obstruction prior to PAE. International Prostate Symptom Score (IPSS) was performed at baseline, 3 months, 12 months and 3 years post-PAE. RESULTS: Mean patient age was 65.6 ± 7.4, prostate volume 99.1 ± 56.6 cm3, IPSS 23.5 ± 6.0, quality-of-life score 4.6 ± 0.9, Qmax 8.4 ± 2.8 ml/s, post-void residual volume 185.8 ± 55.6 ml. Technical success (bilateral embolization) was achieved in 43 out of 48 cases (89.6%). 11/39 bilateral PAE patients completing follow-up (2 died, 2 lost to follow-up) underwent surgery, indicating a 71.8% clinical success rate at 3 years. No significant change was demonstrated in IPSS or QOL between 1 and 3 years for patients free from surgical intervention (IPSS 8.3 vs 10.0, p = 0.09 and QOL 1.3 vs 1.5, p = 0.23). 3/11 patients undergoing surgery had a prominent 'ball-valve' median lobe, and 1/11 patients had a high bladder neck elevation contributing to symptoms. CONCLUSION: Clinical success post-PAE remains high with few patients opting for surgery or experiencing a worsening of symptoms after 12 months.

Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging.

BACKGROUND: While cognition is clearly affected by aging, it is unclear whether the process of brain aging is driven solely by accumulation of environmental damage, or involves biological pathways. METHODS: We applied quantitative image analysis to profile the alteration of brain tissues during aging. A dataset of 463 brain MRI images taken from a cohort of 416 subjects was analyzed using a large set of low-level numerical image content descriptors computed from the entire brain MRI images. The correlation between the numerical image content descriptors and the age was computed, and the alterations of the brain tissues during aging were quantified and profiled using machine learning. RESULTS: The comprehensive set of global image content descriptors provides high Pearson correlation of ~0.9822 with the chronological age, indicating that the machine learning analysis of global features is sensitive to the age of the subjects. Profiling of the predicted age shows several periods of mild changes, separated by shorter periods of more rapid alterations. The periods with the most rapid changes were around the age of 55, and around the age of 65. CONCLUSION: The results show that the process of brain aging of is not linear, and exhibit short periods of rapid aging separated by periods of milder change. These results are in agreement with patterns observed in cognitive decline, mental health status, and general human aging, suggesting that brain aging might not be driven solely by accumulation of environmental damage. Code and data used in the experiments are publicly available.