Two-year follow-up cohort study focused on gender-specific associations between socioeconomic status and body weight changes in overweight and obese middle-aged and older adults

OBJECTIVE

As overall spread of obesity in populations is generally acknowledged to result from unhealthy lifestyles rather than individual genetic makeup, this study aimed to gain specific insights into its determinants through assessing the prevalent associations between individual socioeconomic status (SES) and weight loss in overweight and obese men and women.

METHODS

A prospective, 2-year follow-up study covered 3362 (38.0% men) respondents, aged 43-64 years, body mass index ≥25 kg/m2. Changes in body weight were estimated as a percentage of initial weight. Three categories of changes were defined: gained ≥3%, stable (gained <3% or lost <3%), lost ≥3%. Body weight loss was determined against three categories: lost ≥3 to <5%, lost ≥5 to <10%, lost ≥10%. Select SES variables (ie, gender, age, education, marital status, occupational activity and income) were determined in line with the Health Status Questionnaire. The associations between SES and body weight changes were analysed with the aid of logistic regression models. The results were presented as ORs with 95% CIs.

RESULTS

Only 18% of the respondents had complied with the medical recommendations on weight loss. Significant differences were encountered between the gender, age and occupational activity variables and the weight loss one. Multifactorial models were used to determine the following gender-specific associations between SES and weight loss. Men with moderate income had significantly higher odds for weight loss (≈75%), as compared with the higher earners, whereas women with low income, occupationally inactive, had significantly higher odds (≈30% and ≈50%, respectively), as compared with the high earners and occupationally active ones.

CONCLUSIONS

Lower education, male gender, lower income per household, older age and unemployment status were the established factors predisposing to obesity. While aiming to ensure effectiveness of the measures specifically aimed at preventing obesity, population groups deemed most at risk of potential weight gain must prior be identified.

Evaluation of Complete Pathological Regression after Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Patients with BRCA1 Founder Mutation Aided Bayesian A/B Testing Approach

The aim of this study was to evaluate the probability of pathologic complete regression (pCR) by the BRCA1 gene mutation status in patients with triple-negative breast cancer (TNBC) treated with neoadjuvant chemotherapy. The study involved 143 women (mean age 55.4 ± 13.1 years) with TNBC. The BRCA1 mutation was observed in 17% of the subjects. The most commonly used (85.3%) chemotherapy regimen was four cycles of adriamycine and cyclophosphamide followed by 12 cycles of paclitaxel (4AC + 12T). The differences between clinico-pathological factors by BRCA1 status were estimated. Odds ratios and 95% confidence intervals for pCR vs. non-pCR were calculated using logistic regression. The probability distribution of pCR based on BRCA1 status was estimated using beta distributions. The presence of T3-T4 tumours, cancer in stages II and III, lymphovascular invasion, and the use of chemotherapy schedules other than 4AC + 12T significantly decreased the odds of pCR. It was established that there was a 20% chance that pCR in patients with the BRCA1 mutation was 50% or more times as frequent than in patients without the mutation. Thus, the BRCA1 mutation can be a predictive factor for pCR in patients with TNBC.

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization

Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localization and lower the radiation dose delivered to the surrounding healthy tissues. This study proposes a novel image registration method dedicated to breast tumor bed localization addressing the problem of missing data due to tumor resection that may be applied to real-time radiotherapy planning. We propose a deep learning-based nonrigid image registration method based on a modified U-Net architecture. The algorithm works simultaneously on several image resolutions to handle large deformations. Moreover, we propose a dedicated volume penalty that introduces the medical knowledge about tumor resection into the registration process. The proposed method may be useful for improving real-time radiation therapy planning after the tumor resection and, thus, lower the surrounding healthy tissues' irradiation. The data used in this study consist of 30 computed tomography scans acquired in patients with diagnosed breast cancer, before and after tumor surgery. The method is evaluated using the target registration error between manually annotated landmarks, the ratio of tumor volume, and the subjective visual assessment. We compare the proposed method to several other approaches and show that both the multilevel approach and the volume regularization improve the registration results. The mean target registration error is below 6.5 mm, and the relative volume ratio is close to zero. The registration time below 1 s enables the real-time processing. These results show improvements compared to the classical, iterative methods or other learning-based approaches that do not introduce the knowledge about tumor resection into the registration process. In future research, we plan to propose a method dedicated to automatic localization of missing regions that may be used to automatically segment tumors in the source image and scars in the target image.

Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms

Knowledge about tumor bed localization and its shape analysis is a crucial factor for preventing irradiation of healthy tissues during supportive radiotherapy and as a result, cancer recurrence. The localization process is especially hard for tumors placed nearby soft tissues, which undergo complex, nonrigid deformations. Among them, breast cancer can be considered as the most representative example. A natural approach to improving tumor bed localization is the use of image registration algorithms. However, this involves two unusual aspects which are not common in typical medical image registration: the real deformation field is discontinuous, and there is no direct correspondence between the cancer and its bed in the source and the target 3D images respectively. The tumor no longer exists during radiotherapy planning. Therefore, a traditional evaluation approach based on known, smooth deformations and target registration error are not directly applicable. In this work, we propose alternative artificial deformations which model the tumor bed creation process. We perform a comprehensive evaluation of the most commonly used deformable registration algorithms: B-Splines free form deformations (B-Splines FFD), different variants of the Demons and TV-L¹ optical flow. The evaluation procedure includes quantitative assessment of the dedicated artificial deformations, target registration error calculation, 3D contour propagation and medical experts visual judgment. The results demonstrate that the currently, practically applied image registration (rigid registration and B-Splines FFD) are not able to correctly reconstruct discontinuous deformation fields. We show that the symmetric Demons provide the most accurate soft tissues alignment in terms of the ability to reconstruct the deformation field, target registration error and relative tumor volume change, while B-Splines FFD and TV-L¹ optical flow are not an appropriate choice for the breast tumor bed localization problem, even though the visual alignment seems to be better than for the Demons algorithm. However, no algorithm could recover the deformation field with sufficient accuracy in terms of vector length and rotation angle differences.

Evaluation of set-up deviations during the irradiation of patients suffering from breast cancer treated with two different techniques

PURPOSE

To compare reproducibility of set-up for two different treatment techniques for external irradiation of the breast.

METHODS AND MATERIALS

In total, the analysis comprised 56 pairs of portal and simulator films for 14 consecutive patients treated following breast conserving therapy and 98 pairs of portal and simulator films for 20 consecutive patients treated after mastectomy. For the first group the tangential field technique (TF technique) was used, for the second the inverse hockey stick technique (IHS technique). Evaluation of the treatment reproducibility was performed in terms of systematic and random error calculated for the whole groups, comparison of set-up accuracy by means of comparison of cumulative distribution of the length of the displacement vector.

RESULTS

In the IHS and TF techniques for medial and lateral fields, displacement larger than 5 mm occurred in 28.3, 15.8 and 25.4%, respectively. For the IHS technique, the systematic errors for lateral and cranial-caudal direction were 1.9 and 1.7 mm, respectively (1 SD), the random errors for lateral and cranial-caudal direction were 2.0 and 2.5 mm. For the TF technique, the systematic errors for ventral-dorsal and cranial-caudal direction were 2.6 and 1.3 mm for medial field and 3.7 and 0.7 mm for lateral fields, respectively, the random errors for lateral and cranial-caudal direction were 2.2 and 1.0 mm for medial field and 2.9 and 1.1 for lateral field, respectively. Rotations were negligible in the IHS technique. For the TF technique the systematic and random components amounted to about 2.0 degrees (1 SD).

CONCLUSIONS

Both the inverse hockey stick and standard tangential techniques showed good reproducibility of patients' set-up with respect to cranial-caudal direction. For the TF technique, the accuracy should be improved for the medial field with respect to the ventral-dorsal direction.