Anja J. Rueten-Budde

A digital twin (DT), originally defined as a virtual representation of a physical asset, system, or process, is a new concept in health care. A DT in health care is not a single technology but a domain-adapted multimodal modeling approach incorporating the acquisition, management, analysis, prediction, and interpretation of data, aiming to improve medical decision-making. However, there are many challenges and barriers that must be overcome before a DT can be used in health care. In this viewpoint paper, we build on the current literature, address these challenges, and describe a dynamic DT in health care for optimizing individual patient health care journeys, specifically for women at risk for cardiovascular complications in the preconception and pregnancy periods and across the life course. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods that will guide the development of the dynamic DT and implementation strategies in health care.

UNSTRUCTURED A Digital Twin (DT), which is defined originally as a virtual representation of a physical asset, system or process, is a new concept in healthcare. DT in healthcare cannot be a single technology, but a domain adapted multi-modal modelling approach, which incorporates the acquisition, management, analysis, prediction, and interpretation of the data, aiming to improve medical decision making. However, there are many challenges and barriers that has to be overcome before a DT can be used in healthcare. In this viewpoint paper, we address these challenges, and envision a dynamic DT in healthcare for optimizing individual patient health care journeys. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods, which guide the development of the dynamic DT and the implementation strategies in healthcare.

Age is a known prognostic factor for many sarcoma subtypes, however in the literature there are limited data on the different risk profiles of different age groups for osteosarcoma survival. This study aims to provide an overview of survival in patients with high-grade osteosarcoma in different age groups and prognostic variables for survival and local control among the entire cohort. In this single center retrospective cohort study, 402 patients with skeletal high-grade osteosarcoma were diagnosed and treated with curative intent between 1978 and 2017 at the Leiden University Medical Center (LUMC). Prognostic factors for survival were analyzed using a Cox proportional hazard model. In this study poor overall survival (OS) and event-free survival (EFS) were associated with increasing age. Age groups, tumor size, poor histopathological response, distant metastasis (DM) at presentation and local recurrence (LR) were important independent prognostic factors influencing OS and EFS. Differences in outcome among different age groups can be partially explained by patient and treatment characteristics.

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.

Background: Osteofibrous dysplasia-like adamantinoma (OFD-AD) and classic adamantinoma (AD) are rare, neoplastic diseases with only limited data supporting current treatment protocols. We believe that our retrospective multicenter cohort study is the largest analysis of patients with adamantinoma to date. The primary purpose of this study was to describe the disease characteristics and evaluate the oncological outcomes. The secondary purpose was to identify risk factors for local recurrence after surgical treatment and propose treatment guidelines. Methods: Three hundred and eighteen confirmed cases of OFD-AD and AD for which primary treatment was carried out between 1985 and 2015 were submitted by 22 tertiary bone tumor centers. Proposed clinical risk factors for local recurrence such as size, type, and margins were analyzed using univariable and multivariate Cox regression analysis. Results: Of the 318 cases, 128 were OFD-AD and 190 were AD. The mean age at diagnosis was 17 years (median, 14.5 years) for OFD-AD and 32 years (median, 28 years) for AD; 53% of the patients were female. The mean tumor size in the OFD-AD and AD groups combined was 7.8 cm, measured histologically. Sixteen percent of the patients sustained a pathological fracture prior to treatment. Local recurrence was recorded in 22% of the OFD-AD cases and 24% of the AD cases. None of the recurrences in the OFD-AD group progressed to AD. Metastatic disease was found in 18% of the AD cases and fatal disease, in 11% of the AD cases. No metastatic or fatal disease was reported in the OFD-AD group. Multivariate Cox regression analysis demonstrated that uncontaminated resection margins (hazard ratio [HR] = 0.164, 95% confidence interval [CI] = 0.092 to 0.290, p < 0.001), pathological fracture (HR = 1.968, 95% CI = 1.076 to 3.600, p = 0.028), and sex (female versus male: HR = 0.535, 95% CI = 0.300 to 0.952, p = 0.033) impacted the risk of local recurrence. Conclusions: OFD-AD and AD are parts of a disease spectrum but should be regarded as different entities. Our results support reclassification of OFD-AD into the intermediate locally aggressive category, based on the local recurrence rate of 22% and absence of metastases. In our study, metastatic disease was restricted to the AD group (an 18% rate). We advocate wide resection with uncontaminated margins including bone and involved periosteum for both OFD-AD and AD. Level of Evidence: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Update: This article was updated on April 9, 2020, because of a previous error. On page 1309, in the list of Tenosynovial Giant Cell Tumors (TGCT) Study Group members, the contributor name that had read “M. Wook Joo” should have read “M.W. Joo.” An erratum has been published: J Bone Joint Surg Am. 2020 May 20;102(10):e49. Background: Localized-type tenosynovial giant cell tumor (TGCT) is a rare, neoplastic disease with only limited data supporting treatment protocols. We describe treatment protocols and evaluate their oncological outcome, complications, and functional results in a large multicenter cohort of patients. A secondary study aim was to identify factors associated with local recurrence after surgical treatment. Methods: Patients with histologically proven localized TGCT of a large joint were included if they had been treated between 1990 and 2017 in 1 of 31 tertiary sarcoma centers. Of 941 patients with localized TGCT, 62% were female. The median age at initial treatment was 39 years, and the median duration of follow-up was 34 months. Sixty-seven percent of the tumors affected the knee, and the primary treatment at the tertiary center was 1-stage open resection in 73% of the patients. Proposed factors for predicting a first local recurrence after treatment in the tertiary center were tested in a univariate analysis, and those that demonstrated significance were subsequently included in a multivariate analysis. Results: The localized TGCT recurred in 12% of all cases, with local-recurrence-free rates at 3, 5, and 10 years of 88%, 83%, and 79%, respectively. The strongest factor for predicting recurrent disease was a prior recurrence (p < 0.001). Surgical treatment decreased pain and swelling in 71% and 85% of the patients, respectively, and such treatment was associated with complications in 4% of the patients. Univariate and multivariate analyses of the patients who had not undergone therapy previously yielded positive associations between local recurrence and a tumor size of ≥5 cm versus <5 cm (hazard ratio [HR] = 2.50; 95% confidence interval [CI] = 1.32 to 4.74; p = 0.005). Arthroscopy (versus open surgery) was significantly associated with tumor recurrence in the univariate analysis (p = 0.04) but not in the multivariate analysis (p = 0.056). Conclusions: Factors associated with recurrence after resection of localized-type TGCT were larger tumor size and initial treatment with arthroscopy. Relatively low complication rates and good functional outcomes warrant an open approach with complete resection when possible to reduce recurrence rates in high-risk patients. Level of Evidence: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Survival analysis is used in the medical field to identify the effect of predictive variables on time to a specific event. Generally, not all variation of survival time can be explained by observed covariates. The effect of unobserved variables on the risk of a patient is called frailty. In multicenter studies, the unobserved center effect can induce frailty on its patients, which can lead to selection bias over time when ignored. For this reason, it is common practice in multicenter studies to include a random frailty term modeling center effect. In a more complex event structure, more than one type of event is possible. Independent frailty variables representing center effect can be incorporated in the model for each competing event. However, in the medical context, events representing disease progression are likely related and correlation is missed when assuming frailties to be independent. In this work, an additive gamma frailty model to account for correlation between frailties in a competing risks model is proposed, to model frailties at center level. Correlation indicates a common center effect on both events and measures how closely the risks are related. Estimation of the model using the expectation‐maximization algorithm is illustrated. The model is applied to a data set from a multicenter clinical trial on breast cancer from the European Organisation for Research and Treatment of Cancer (EORTC trial 10854). Hospitals are compared by employing empirical Bayes estimates methodology together with corresponding confidence intervals.