RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness

Extraskeletal osteosarcoma: A European Musculoskeletal Oncology Society study on 266 patients

PURPOSE: Prognosis of extraskeletal osteosarcoma (ESOS) is reported to be poorer than that of skeletal osteosarcoma. This multicenter retrospective study aimed to evaluate factors influencing ESOS prognosis. PATIENTS AND METHODS: Members of the European Musculoskeletal Oncology Society (EMSOS) submitted institutional data on patients with ESOS. RESULTS: Data from 274 patients treated from 1981 to 2014 were collected from 16 EMSOS centres; 266 patients were eligible. Fifty (18.7%) had metastases at diagnosis. Of 216 patients with localised disease, 211 (98%) underwent surgery (R0 = 70.6%, R1 = 27%). Five-year overall survival (OS) for all 266 patients was 47% (95% CI 40-54%). Five-year OS for metastatic patients was 27% (95% CI 13-41%). In the analysis restricted to the 211 localised patients who achieved complete remission after surgery 5-year OS was 51.4% (95% CI 44-59%) and 5-year disease-free survival (DFS) was 43% (95% CI 35-51%). One hundred twenty-one patients (57.3%) received adjuvant or neoadjuvant chemotherapy and 80 patients (37.9%) received radiotherapy. A favourable trend was seen for osteosarcoma-type chemotherapy versus soft tissue sarcoma-type (doxorubicin ± ifosfamide) regimens. For the 211 patients in complete remission after surgery, patient age, tumour size, margins and chemotherapy were positive prognostic factors for DFS and OS by univariate analysis. At multivariate analysis, patient age (≤40 years versus >40 years) (P = 0.05), tumour size (P = 0.0001) and receipt of chemotherapy (P = 0.006) were statistically significant prognostic factors for survival. CONCLUSION: Patient age and tumour size are factors influencing ESOS prognosis. Higher survival was observed in patients who received perioperative chemotherapy with a trend in favour of multiagent osteosarcoma-type regimen which included doxorubicin, ifosfamide and cisplatin.info:eu-repo/semantics/publishedVersio

External validation and adaptation of a dynamic prediction model for patients with high‐grade extremity soft tissue sarcoma

Background and Objectives: A dynamic prediction model for patients with soft tissue sarcoma of the extremities was previously developed to predict updated overall survival probabilities throughout patient follow‐up. This study updates and externally validates the dynamic model. Methods: Data from 3826 patients with high‐grade extremity soft tissue sarcoma, treated surgically with curative intent were used to update the dynamic PERsonalised SARcoma Care (PERSARC) model. Patients were added to the model development cohort and grade was included in the model. External validation was performed with data from 1111 patients treated at a single tertiary center. Results: Calibration plots show good model calibration. Dynamic C‐indices suggest that the model can discriminate between high‐ and low‐risk patients. The dynamic C‐indices at 0, 1, 2, 3, 4, and 5 years after surgery were equal to 0.697, 0.790, 0.822, 0.818, 0.812, and 0.827, respectively. Conclusion: Results from the external validation show that the dynamic PERSARC model is reliable in predicting the probability of surviving an additional 5 years from a specific prediction time point during follow‐up. The model combines patient‐, treatment‐specific and time‐dependent variables such as local recurrence and distant metastasis to provide accurate survival predictions throughout follow‐up and is available through the PERSARC app.Peer reviewe

Age-related differences of oncological outcomes in primary extremity soft tissue sarcoma: a multistate model including 6260 patients

Purpose: No studies extensively compared the young adults (YA, 18-39 years), middle-aged (40-69 years), and elderly (≥70 years) population with primary high-grade extremity soft tissue sarcoma (eSTS). This study aimed to determine whether the known effect of age on overall survival (OS) and disease progression can be explained by differences in tumour characteristics and treatment protocol among the YA, middle-aged and elderly population in patients with primary high-grade eSTS treated with curative intent. Methods: In this retrospective multicentre study, inclusion criteria were patients with primary high-grade eSTS of 18 years and older, surgically treated with curative intent between 2000 and 2016. Cox proportional hazard models and a multistate model were used to determine the association of age on OS and disease progression. Results: A total of 6260 patients were included in this study. YA presented more often after 'whoops'-surgery or for reresection due to residual disease, and with more deep-seated tumours. Elderly patients presented more often with grade III and larger (≥10 cm) tumours. After adjustment for the imbalance in tumour and treatment characteristics the hazard ratio for OS of the middle-aged population is 1.47 (95% confidence interval [CI]: 1.23-1.76) and 3.13 (95% CI: 2.59-3.78) in the elderly population, compared with YA. Discussion: The effect of age on OS could only partially be explained by the imbalance in the tumour characteristics and treatment variables. The threefold higher risk of elderly could, at least partially, be explained by a higher other-cause mortality. The results might also be explained by a different tumour behaviour or suboptimal treatment in elderly compared with the younger population. Keywords: Adolescents and young adults; Elderly; Extremities; Metastasis; Middle-aged; Recurrence; Soft tissue sarcoma; Survival.Peer reviewe

Age-related differences of oncological outcomes in primary extremity soft tissue sarcoma: a multistate model including 6260 patients

Abstract Purpose: No studies extensively compared the young adults (YA, 18e39 years), middle-aged (40e69 year