There are few studies detailing the incidence, patient outcomes and prognostic factors for chondrosarcomas (CS). Those that do exist have small sample sizes and/or use older datasets. The purpose of this study was to determine the incidence, overall survival (OS) and prognostic factors for OS of CS patients, as well as investigate the efficacy of curettage. We analyzed data of 2186 patients diagnosed with chondrosarcomas between '89-'13 from the Netherlands Cancer Registry. The effect of risk factors on OS was assessed with a multivariate Cox regression. Median Follow-up was determined with reversed Kaplan-Meier. OS was estimated using Kaplan-Meier method. The relative incidence of CS was 2.88 per million citizens between '89-'96, 4.15 between '96-'04 and 8.78 between '05-'13. Most of the increase in incidence came from atypical cartilaginous tumours/grade I chondrosarcoma (ACT/CS I). The 3-, 5- and 10-years survival were, respectively, 96%, 93% and 88% for ACT/CS I, 82%, 74% and 62% for grade II CS and 38%, 31% and 26% for grade III CS. Prognostics factors significantly associated with OS were age, histological grade, year of diagnosis, tumour location and size. The incidence of CS, and especially ACT/CS I, has increased over time, which could be driven by both an ageing population and increased diagnostic imaging. With the increased number of diagnosed ACT/CS I, the number of preventative curettages of this tumour has also increased. Despite the supposed preventative character of this treatment, the incidence of high-grade CS did not decrease.

Orthopedic oncologic surgery requires preservation of a functioning limb at the essence of achieving safe margins. With most bone sarcomas arising from the metaphyseal region, in close proximity to joints, joint-salvage surgery can be challenging. Intraoperative guidance techniques like computer-assisted surgery (CAS) and patient-specific instrumentation (PSI) could assist in achieving higher surgical accuracy. This study investigates the surgical accuracy of freehand, CAS- and PSI-assisted joint-preserving tumor resections and tests whether integration of CAS with PSI (CAS + PSI) can further improve accuracy. CT scans of 16 simulated tumors around the knee in four human cadavers were performed and imported into engineering software (MIMICS) for 3D planning of multiplanar joint-preserving resections. The planned resections were transferred to the navigation system and/or used for PSI design. Location accuracy (LA), entry and exit points of all 56 planes, and resection time were measured by postprocedural CT. Both CAS + PSI- and PSI-assisted techniques could reproduce planned resections with a mean LA of less than 2 mm. There was no statistical difference in LA between CAS + PSI and PSI resections (p=0.92), but both CAS + PSI and PSI showed a significantly higher LA compared to CAS (p=0.042 and p=0.034, respectively). PSI-assisted resections were faster compared to CAS + PSI (p<0.001) and CAS (p<0.001). Adding CAS to PSI did improve the exit points, however not significantly. In conclusion, PSI showed the best overall surgical accuracy and is fastest and easy to use. CAS could be used as an intraoperative quality control tool for PSI, and integration of CAS with PSI is possible but did not improve surgical accuracy. Both CAS and PSI seem complementary in improving surgical accuracy and are not mutually exclusive. Image-based techniques like CAS and PSI are superior over freehand resection. Surgeons should choose the technique most suitable based on the patient and tumor specifics.

Background: There is mounting evidence that objective measures of pathophysiology do not correlate well with symptom intensity. A growing line of inquiry identifies statistical combinations (so-called “phenotypes”) of various levels of distress and unhelpful thoughts that are associated with distinct levels of symptom intensity and magnitude of incapability. As a next step, it would be helpful to understand how distress and unhelpful thoughts interact with objective measures of pathologic conditions such as the radiologic severity of osteoarthritis. The ability to identify phenotypes of these factors that are associated with distinct levels of illness could contribute to improved personalized musculoskeletal care in a comprehensive, patient-centered model. Questions/purposes: (1) When measures of mental health are paired with radiologic osteoarthritis severity, are there distinct phenotypes among adult patients with hip and knee osteoarthritis? (2) Is there a difference in the degree of capability and pain self-efficacy among the identified mental health and radiologic phenotypes? (3) When capability (Patient-reported Outcomes Measurement Information System Physical Function [PROMIS PF]) is paired with radiographic osteoarthritis severity, are there distinct phenotypes among patients with hip and knee osteoarthritis? (4) Is there a difference in mental health among patients with the identified capability and radiologic phenotypes? Methods: We performed a secondary analysis of data from a study of 119 patients who presented for musculoskeletal specialty care for hip or knee osteoarthritis. Sixty-seven percent (80 of 119) of patients were women, with a mean age of 62 ± 10 years. Seventy-six percent (91 of 119) of patients had knee osteoarthritis, and 59% (70 of 119) had an advanced radiographic grade of osteoarthritis (Kellgren-Lawrence grade 3 or higher). This dataset is well-suited for our current experiment because the initial study had broad enrollment criteria, making these data applicable to a diverse population and because patients had sufficient variability in radiographic severity of osteoarthritis. All new and returning patients were screened for eligibility. We do not record the percentage of eligible patients who do not participate in cross-sectional surveys, but the rate is typically high (more than 80%). One hundred forty-eight eligible patients started the questionnaires, and 20% (29 of 148) of patients did not complete at least 60% of the questionnaires and were excluded, leaving 119 patients available for analysis. We measured psychologic distress (Patient Health Questionnaire-2 [PHQ-2] and Generalized Anxiety Disorder-2 questionnaire [GAD-2]), unhelpful thoughts about pain (Pain Catastrophizing Scale-4 [PCS-4]), self-efficacy when in pain (Pain Self-Efficacy Questionnaire-2), and capability (PROMIS PF). One of two arthroplasty fellowship–trained surgeons assigned the Kellgren-Lawrence grade of osteoarthritis based on radiographs in the original study. We used a cluster analysis to generate two sets of phenotypes: (1) measures of mental health (PHQ-2, GAD-2, PCS-4) paired with the Kellgren-Lawrence grade and (2) capability (PROMIS PF) paired with the Kellgren-Lawrence grade. We used one-way ANOVA and Kruskal-Wallis H tests to assess differences in capability and self-efficacy and mental health, respectively. Results: When pairing measures of psychologic distress (PHQ-2 and GAD-2) and unhelpful thoughts (catastrophic thinking) with the grade of radiographic osteoarthritis, six distinct phenotypes arose. These groups differed in terms of capability and pain self-efficacy (for example, mild pathology/low distress versus average pathology/high distress [PROMIS PF, mean ± standard deviation]: 43 ± 6.3 versus 33 ± 4.8; p = 0.003). When pairing the degree of capability (PROMIS PF) with the Kellgren-Lawrence grade, four distinct phenotypes arose. Patients in three of these did not differ in terms of disease severity but had notable variation in the degree of limitations. Patients with these radiologic and capability phenotypes differed in terms of distress and unhelpful thoughts (for example, moderate pathology/low capability versus mild pathology/high capability [PHQ-2, median and interquartile range]: 3 [1 to 5] versus 0 [0 to 0]; p < 0.001). Conclusion: Statistical groupings (“phenotypes”) that include both measures of pathology and mental health are associated with differences in symptom intensity and magnitude of incapability and have the potential to help musculoskeletal specialists discern mental and social health priorities. Future investigations may test whether illness phenotype-specific comprehensive biopsychosocial treatment strategies are more effective than treatment of pathology alone. Level of Evidence: Level III, prognostic study.

Aims: The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs? Methods: The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS). Results: Out of 1,349 studies, 36 reported development of a CNN for fracture detection and/or classification. Of these, only four (11%) reported a form of EV. One study used temporal EV, one conducted both temporal and geographical EV, and two used geographical EV. When comparing the CNN’s performance on the IV set versus the EV set, the following were found: AUCs of 0.967 (IV) versus 0.975 (EV), 0.976 (IV) versus 0.985 to 0.992 (EV), 0.93 to 0.96 (IV) versus 0.80 to 0.89 (EV), and F1-scores of 0.856 to 0.863 (IV) versus 0.757 to 0.840 (EV). Conclusion: The number of externally validated CNNs in orthopaedic trauma for fracture recognition is still scarce. This greatly limits the potential for transfer of these CNNs from the developing institute to another hospital to achieve similar diagnostic performance. We recommend the use of geographical EV and statements such as the Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI), the Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis–Machine Learning (TRIPOD-ML) to critically appraise performance of CNNs and improve methodological rigor, quality of future models, and facilitate eventual implementation in clinical practice. Cite this article: Bone Jt Open 2021;2(10):879–885.

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.