2D-based indoor mobile laser scanning for construction digital mapping application

A common issue which occurs often in construction projects is how to determine the discrepancies between as-built or existing constructions and initial design. Physical manual measurement usually brings many of problems such as long measuring time, high labor consumption, and measurement error accumulation and in some cases lower accuracy. Therefore, more advanced technologies such as laser scanning and total station, which are used in geospatial mapping and surveying have been adopted in order to provide much more reliable and accurate measurements. However, technical and financial issues still constrain the widespread applications of well-known 3-dimensional (3D) terrestrial and aerial laser scanning, such as high equipment cost, complex pre-preparation, inconvenience of use and spatial limitation. This paper aims to introduce an innovative laser scanning method for indoor construction mapping. This method integrates an IMU-GPS positioning approach with a more convenient, more time saving and lower costed 2-dimensional (2D) laser scanner to realize indoor mobile 3D mapping for construction model creation, which can be integrated with Building Information Modelling (BIM) design in order to realize the applications, such as quality control of as-built construction or indoor mapping of existing building. Although compared with traditional 3D laser scanning, its accuracy and reliability cannot reach such a high level currently, experimental results still indicate feasibility, reliability and potential capability of this indoor mobile laser scanning method. It is hoped that this method will be further improved to substitute the stationary 3D laser scanning for narrow and limited construction spatial mapping in the near future

Adverse health effects after breast cancer up to 14 years after diagnosis

BACKGROUND: The number of breast cancer survivors increases, but information about long-term adverse health effects in breast cancer survivors is sparse. We aimed to get an overview of the health effects for which survivors visit their general practitioner up to 14 years after diagnosis. METHODS: We retrieved data on 11,671 women diagnosed with breast cancer in 2000–2016 and 23,242 age and sex matched controls from the PSCCR-Breast Cancer, a database containing data about cancer diagnosis, treatment and primary healthcare. We built Cox regression models for 685 health effects, with time until the health effect as the outcome and survivor/control and cancer treatment as predictors. Models were built separately for four age groups (aged 18/44, 45/59, 60/74 and 75/89) and two follow-up periods (1/4 and 5/14 years after diagnosis). RESULTS: 229 health effects occurred statistically significantly more often in survivors than in controls (p < 0.05). Health effects varied by age, time since diagnosis and treatment, but coughing, respiratory and urinary infections, fatigue, sleep problems, osteoporosis and lymphedema were statistically significantly increased in breast cancer survivors. Osteoporosis and chest symptoms were associated with hormone therapy; respiratory and skin infections with chemotherapy and lymphedema and skin infections with axillary dissection. CONCLUSIONS: Breast cancer survivors may experience numerous adverse health effects up to 14 years after diagnosis. Insight in individual risks may assist healthcare professionals in managing patient expectations and improve monitoring, detection and treatment of adverse health effects

Deflection characterisation of rotary systems using a ground-based radar

In the last two decades, an increase in large rotary machines/systems has been witnessed. To ensure safe operation of these systems especially due to extreme stress caused by centrifugal forces as well as the wind or water loadings, regular structural health monitoring (SHM) of the unbalanced parameters, particularly at the blade tips is necessary. For this, the use of non-contact sensors provides the most appropriate approach; however, millimetric out-of-plane deflection monitoring using non-contact sensors at distances &gt;1 m has not been comprehensively addressed for rotary systems, like wind turbines. This study presents a modelling environment to simulate radar returns to analyse rotary systems. Employing Sammon mapping as a dimensionality reduction procedure in conjunction with 2D visualisation, the study demonstrates the characterisation of dynamic deflection parameters using a fast, portable ground-based interferometric radar (GBR). Comparisons between the GBR results with those of a Leica AR20 GPS indicate a divergence ±12.79 mm. The study utilises SHM framework to acquire, normalise, extract, and validate GBR signals within an SHM framework for structures under test or for deflection validation of the new system. Further, it contributes to the non-contact structural fatigue damage detection during design, testing, and operating stages of rotary structures blade tips

Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry:A network analysis

BACKGROUND: Research into the clustering of symptoms may improve the understanding of the underlying mechanisms that affect survivors' symptom burden. This study applied network analyses in a balanced sample of cancer survivors to 1) explore the clustering of symptoms and 2) assess differences in symptom clustering between cancer types, treatment regimens, and short‐term and long‐term survivors. METHODS: This study used cross‐sectional survey data, collected between 2008 and 2018, from the population‐based Patient Reported Outcomes Following Initial Treatment and Long Term Evaluation of Survivorship registry, which included survivors of 7 cancer types (colorectal cancer, breast cancer, ovarian cancer, thyroid cancer, chronic lymphocytic leukemia, Hodgkin lymphoma, and non‐Hodgkin lymphoma). Regularized partial correlation network analysis was used to explore and visualize the associations between self‐reported symptoms (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire) and the centrality of these symptoms in the network (ie, how strongly a symptom was connected to other symptoms) for the total sample and for subgroups separately. RESULTS: In the total sample (n = 1330), fatigue was the most central symptom in the network with moderate direct relationships with emotional symptoms, cognitive symptoms, appetite loss, dyspnea, and pain. These relationships persisted after adjustments for sociodemographic and clinical characteristics. Connections between fatigue and emotional symptoms, appetite loss, dyspnea, and pain were consistently found across all cancer types (190 for each), treatment regimens, and short‐term and long‐term survivors. CONCLUSIONS: In a heterogenous sample of cancer survivors, fatigue was consistently the most central symptom in all networks. Although longitudinal data are needed to build a case for the causal nature of these symptoms, cancer survivorship rehabilitation programs could focus on fatigue to reduce the overall symptom burden

Health state utility and health-related quality of life measures in patients with advanced ovarian cancer

Purpose: Measuring health-related quality of life (HRQoL) in ovarian cancer patients is critical to understand the impact of disease and treatment. Preference-based HRQoL measures, called health state utilities, are used specifically in health economic evaluations. Real-world patient-reported data on HRQoL and health state utilities over the long-term course of ovarian cancer are limited. This study aims to determine HRQoL and health state utilities in different health states of ovarian cancer. Methods: This cross-sectional, multicenter study included patients with stage III-IV ovarian cancer in six health states: at diagnosis, during chemotherapy, after cytoreductive surgery (CRS), after chemotherapy, in remission, and at first recurrence. HRQoL was measured using the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire C30, and the ovarian cancer-specific module OV28. Health state utilities were assessed using the EuroQol five-dimension five-level (EQ-5D-5L) questionnaire. Descriptive analyses were performed for each health state. Results: Two hundred thirty-two patients participated, resulting in 319 questionnaires. Median age was 66 years. The lowest HRQoL was observed during chemotherapy and shortly after CRS. Physical and role functioning were most affected and the highest symptom prevalence was observed in the fatigue, nausea, pain, dyspnea, gastrointestinal, neuropathy, attitude, and sexuality domains. Patients in remission had the best HRQoL. Mean utility values ranged from 0.709 (±0.253) at diagnosis to 0.804 (±0.185) after chemotherapy.Conclusions: This study provides clinicians with a valuable resource to aid in patient counseling and clinical decision-making. The utilities, in particular, are crucial for researchers conducting economic analyses to inform policy decisions.</p

The added value of immediate breast reconstruction to health-related quality of life of breast cancer patients

Background: Postmastectomy immediate breast reconstruction (IBR) may improve the quality of life (QoL) of breast cancer patients. Guidelines recommend to discuss the option IBR with all patients undergoing mastectomy. However, substantial hospital variation in IBR-rates was previously observed in the Netherlands, influenced by patient, tumour and hospital factors and clinicians’ believes. Information provision about IBR may have a positive effect on receiving IBR and therefore QoL. This study investigated patient-reported QoL of patients treated with mastectomy with and without IBR. Methods: An online survey, encompassing the validated BREAST-Q questionnaire, was distributed to a representative sample of 1218 breast cancer patients treated with mastectomy. BREAST-Q scores were compared between patients who had undergone mastectomy either with or without IBR. Results: A total of 445 patients were included for analyses: 281 patients with and 164 without IBR. Patients who had received IBR showed significantly higher BREAST-Q scores on “psychosocial well-being” (75 versus 67, p < 0.001), “sexual well-being” (62 versus 52, p < 0.001) and “physical well-being” (77 versus 74, p = 0.021) compared to patients without IBR. No statistically significant difference was found for “satisfaction with breasts” (64 versus 62, p = 0.21). Similar results were found after multivariate regression analyses, revealing IBR to be an independent factor for a better patient-reported QoL. Conclusions: Patients diagnosed with breast cancer with IBR following mastectomy report a better QoL on important psychosocial, sexual and physical well-being domains. This further supports the recommendation to discuss the option of IBR with all patients with an indication for mastectomy and to enable shared decision-making

Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis

Purpose: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim was to predict individual risks for developing CRF.Methods: Two pre-existing datasets were used. The Nivel-Primary Care Database and the Netherlands Cancer Registry (NCR) formed the Primary Secondary Cancer Care Registry (PSCCR). NCR data with Patient Reported Outcomes Following Initial treatment and Long-term Evaluation of Survivorship (PROFILES) data resulted in the PSCCR-PROFILES dataset. Predictors were patient, tumor and treatment characteristics, and pre-diagnosis health. Fatigue was GP-reported (PSCCR) or patient-reported (PSCCR-PROFILES). Machine learning models were developed, and performances compared using the C-statistic.Results: In PSCCR, 2224/12813 (17%) experienced fatigue up to 7.6 ± 4.4 years after diagnosis. In PSCCR-PROFILES, 254 (65%) of 390 patients reported fatigue 3.4 ± 1.4 years after diagnosis. For both, models predicted fatigue poorly with best C-statistics of 0.561 ± 0.006 (PSCCR) and 0.669 ± 0.040 (PSCCR-PROFILES).Conclusion: Fatigue (GP-reported or patient-reported) could not be predicted accurately using available data of the PSCCR and PSCCR-PROFILES datasets.Implications for Cancer Survivors: CRF is a common but underreported problem after breast cancer. We aimed to develop a model that could identify individuals with a high risk of developing CRF, ideally to help them prevent (chronic) CRF. As our models had poor predictive abilities, they cannot be used for this purpose yet. Adding patient-reported data as predictor could lead to improved results. Until then, awareness for CRF stays crucial