Aim

Deep infection following endoprosthetic replacement (EPR) of long bones is a devastating complication occurring in 15% of musculoskeletal tumour patients. The recently published PARITY Trial demonstrated that extending antibiotic prophylaxis from 24 hours to 5 days does not reduce infection rates. However, questions remain about the optimal antibiotic choice and dose.

Functional outcomes are important for patients with bone tumors undergoing lower extremity endoprosthetic reconstruction; however, there is limited empirical evidence evaluating function longitudinally. The objective of this study was to determine the changes in function over time in patients undergoing endoprosthetic reconstructions of the proximal femur, distal femur and proximal tibia.

We conducted a secondary analysis of functional outcome data from the Prophylactic Antibiotic Regimens in Tumor Surgery (PARITY) trial. Patient function was assessed with the Musculoskeletal Tumor Society Score 93 (MSTS) and the Toronto Extremity Salvage Score (TESS), which were administered preoperatively and at 3, 6 and 12 months postoperatively. Both instruments are scored from 0-100, with higher scores indicated greater function. Mean functional scores were evaluated over time and we explored for differences among patients undergoing proximal femur reconstructions (PFR), distal femur reconstructions (DFR) and proximal tibia reconstructions (PTR). The patient-importance of statistically significant differences in function was evaluated utilizing the minimally important difference (MID) of 12 for the MSTS and 11 for the TESS. We explored for differences in change scores between each time interval with paired t-tests. Differences based on endoprosthetic reconstruction undertaken were evaluated by analysis of variance and post-hoc comparisons using the Tukey test.

A total of 573 patients were included. The overall mean MSTS and TESS scores were 77.1(SD±21) and 80.2(SD±20) respectively at 1-year post-surgery, demonstrating approximately a 20-point improvement from baseline for both instruments. When evaluating change scores over time by type of reconstruction, PFR patients experienced significant functional improvement during the 3-6 and 6-12 month follow-up intervals, DFR patients demonstrated significant improvements in function at each follow-up interval, and PTR patients reported a significant decrease in function from baseline to 3 months, and subsequent improvements during the 3-6 and 6-12 month intervals.

On average, patients undergoing endoprosthetic reconstruction of the lower extremity experience important improvements in function from baseline within the first year. Patterns of functional recovery varied significantly based on type of reconstruction performed. The results of this study will inform both clinicians and patients about the expected rehabilitation course and functional outcomes following endoprosthetic reconstruction of the lower extremity.

Radiation induced sarcoma of bone is a rare but challenging disease process associated with a poor prognosis. To date, series are limited by small patient numbers; data to inform prognosis and the optimal management for these patients is needed. We hypothesized that patients with radiation-induced pelvic bone sarcomas would have worse surgical, oncologic, and functional outcomes than patients diagnosed with primary pelvic bone sarcomas

This was a multi-institution, comparative cohort analysis. A retrospective chart review was performed of all patients diagnosed with a radiation-induced pelvic and sacral bone sarcoma between January 1st, 1985 and January 1st, 2020 (defined as a histologically confirmed bone sarcoma of the pelvis in a previously irradiated field with a minimum 3-year interval between radiation and sarcoma diagnosis). We also identified a comparison group including all patients diagnosed with a primary pelvic osteosarcoma/spindle cell sarcoma of bone (i.e. eligible for osteosarcoma-type chemotherapy) during the same time interval. The primary outcome measure was disease-free and overall survival.

We identified 85 patients with primary osteosarcoma of the pelvis (POP) and 39 patients with confirmed radiation induced sarcoma of the bony pelvis (RISB) undergoing surgical resection. Patients with RISB were older than patients with POP (50.5 years vs. 36.5 years, p67.7% of patients with POP underwent limb salvage as compared to 77% of patients with RISB; the type of surgery was not different between groups (p=.0.24). There was no difference in the rate of margin positive surgery for RISB vs. POP (21.1% vs. 14.1%, p=0.16). For patients undergoing surgical resection, the rate of surgical complications was high, with more RISB patients experiencing complications (79.5%) than POP patients (64.7%); this approached statistical significance (p=0.09).

15.4% of patients with RISB died perioperative period (within 90 days of surgery) as compared to 3.5% of patients with POP (p= 0.02). For patients undergoing surgical resection, 5-year OS was significantly worse for patients with RISB vs. POP (27.3% vs. 47.7%, p=0.02). When considering only patients without metastatic disease at presentation, a significant difference in 5-year survival remains for patients with RISB vs. POP (28.6% vs. 50%, p=0.03) was a trend towards poorer 5-year DFS for patients with RISB vs. POP (30% vs. 47.5%), though this did not achieve statistical significance (p=0.09).

POP and RISB represent challenging disease processes and the oncologic outcomes are similarly poor between the two; however, the disease course for patients with RISB appears to be worse overall. While surgery can result in a favorable outcome for a small subset of patients, surgical treatment is fraught with complications.

Functional outcomes are commonly reported in studies of musculoskeletal oncology patients undergoing limb salvage surgery; however, interpretation requires knowledge of the smallest amount of improvement that is important to patients – the minimally important difference (MID). We established the MIDs for the Musculoskeletal Tumor Society Rating Scale (MSTS) and Toronto Extremity Salvage Score (TESS) in patients with bone tumors undergoing lower limb salvage surgery.

This study was a secondary analysis of the recently completed PARITY (Prophylactic Antibiotic Regimens in Tumor Surgery) study. This data was used to calculate: (1) the anchor-based MIDs using an overall function scale and a receiver operating curve analysis, and (2) the distribution-based MIDs based on one-half of the standard deviation of the change scores from baseline to 12-month follow-up, for both the MSTS and TESS.

There were 591 patients available for analysis. The Pearson correlation coefficients for the association between changes in MSTS and TESS scores and changes in the external anchor scores were 0.71 and 0.57, indicating “high” and “moderate” correlation. Anchor-based MIDs were 12 points and 11 points for the MSTS and TESS, respectively. Distribution-based calculations yielded MIDs of 16-17 points for the MSTS and 14 points for the TESS.

The current study proposes MID scores for both the MSTS and TESS outcome measures based on 591 patients with bone tumors undergoing lower extremity endoprosthetic reconstruction. These thresholds will optimize interpretation of the magnitude of treatment effects, which will enable shared decision-making with patients in trading off desirable and undesirable outcomes of alternative management strategies. We recommend anchor-based MIDs as they are grounded in changes in functional status that are meaningful to patients.

Given the low prevalence of sarcoma, international cooperation is necessary to recruit sufficient numbers of patients for surgical trials. The PARITY (Prophylactic Antibiotic Regimens in Tumour Surgery) trial is the first international multicentre trial in orthopedic oncology and successfully achieved target enrollment of 600 patients across 12 countries. It is important to reflect upon the challenges encountered and experiences gained to inform future trials. The objective of this study is to describe recruitment patterns and examine the differences in enrollment across different PARITY sites and identify variables associated with varying levels of recruitment.

Data from this study was obtained from the PARITY trial Methods Centre and correspondence data. We performed descriptive statistics to demonstrate the recruitment patterns over time. We compared recruitment, time to set up, and time to enroll the first patient between North American and international sites, and sites that had dedicated research personnel. Two-tailed non-paired t-tests were performed to compare average monthly recruitment rates between groups with significance being set at alpha=0.05.

A total of 600 patients from 48 clinical sites and 12 countries were recruited from January 2013 through to October 2019. Average monthly enrollment increased every year of the study. There were 36 North American and 12 international sites. North American sites were able to set up significantly faster than international sites (19.3 vs. 28.3 months p=0.037). However, international sites had a significantly higher recruitment rate per month once active (0.2/month vs. 0.62/month, p=0.018). Of active sites, 40 (83%) had research support personnel and 8 (17%) sites did not. Sites with research personnel were able to reach ‘enrolment ready’ status significantly faster than sites without research support (19.6 vs. 30.7 months, p=0.032). However, there was no significant difference in recruitment rate per month once the sites began enrolling (0.28/month vs. 0.2/month, p=0.63). Trial sites that took longer than 1 year to recruit their first patient had 3x lower average recruitment rate compared to sites that were able to recruit their first patient within a year of being enrolment ready.

The PARITY trial is the first multicentre RCT in orthopaedic oncology. The PARITY investigators were able to increase the recruitment levels throughout the trial and generally avoid trial fatigue. This was a North American based trial which may explain the longer start up times internationally given the different regulatory bodies associated with drug-related trials. However, international sites should be considered critical as they were able to recruit significantly more patients per month once active. The absence of research support personnel should not preclude a site from inclusion. These sites took longer to setup but had no difference in monthly recruitment once active. This study will create a framework for identifying and targeting high yield sites for future randomized control trials within orthopaedic oncology to maximum recruitment and resource allocation. Data quality is another consideration that will be addressed in future analyses of the PARITY trial.

Lung cancer is the most common cancer diagnosed, the leading cause of cancer-related deaths, and bone metastases occurs in 20–40% of lung cancer patients. They often present symptomatically with pain or skeletal related events (SREs), which are independently associated with decreased survival. Bone modifying agents (BMAs) such as Denosumab or bisphosphonates are routinely used, however no specific guidelines exist from the National Comprehensive Cancer Center or the European Society of Medical Oncologists. Perhaps preventing the formation of guidelines is the lack of a high-quality quantitative synthesis of randomized controlled trial (RCT) data to determine the optimal treatment for the patient important outcomes of 1) Overall survival (OS), 2) Time to SRE, 3) SRE incidence, and 4) Pain Resolution. The objective of this study was to perform the first systematic review and network meta-analysis (NMA) to assess the best BMA for treatment of metastatic lung cancer to bone.

We conducted our study in accordance to the PRISMA protocol. We performed a librarian assisted search of MEDLINE, PubMed, EMBASE, and Cochrane Library and Chinese databases including CNKI and Wanfang Data. We included studies that are RCTs reporting outcomes specifically for lung cancer patients treated with a bisphosphonate or Denosumab. Screening, data extraction, risk of bias and GRADE were performed in duplicate. The NMA was performed using a Bayesian probability model with R. Results are reported as relative risks, odds ratios or mean differences, and the I2 value is reported for heterogeneity. We assessed all included articles for risk of bias and applied the novel GRADE framework for NMAs to rate the quality of evidence supporting each outcome.

We included 132 RCTs comprising 11,161 patients with skeletal metastases from lung cancer. For OS, denosumab was ranked above zoledronic acid (ZA) and estimated to confer an average of 3.7 months (95%CI: −0.5 – 7.6) increased survival compared to untreated patients. For time to SRE, denosumab was ranked first with an average of 9.1 additional SRE-free months (95%CI: 4.0 – 14.0) compared to untreated patients, while ZA conferred an additional 4.8 SRE-free months (2.4 – 7.0). Patients treated with the combination of Ibandronate and systemic therapy were 2.3 times (95%CI: 1.7 – 3.2) more likely to obtain successful pain resolution, compared to untreated. Meta-regression showed no effect of heterogeneity length of follow-up or pain scales on the observed treatment effects. Heterogeneity in the network was considered moderate for overall survival and time to SRE, mild for SRE incidence, and low for pain resolution. While a generally high risk of bias was observed across studies, whether they were from Western or Chinese databases. The overall GRADE for the evidence underlying our results is High for Pain control and SRE incidence, and Moderate for OS and time to SRE.

This study represents the most comprehensive synthesis of the best available evidence guiding pharmacological treatment of bone metastases from lung cancer. Denosumab is ranked above ZA for both overall survival and time to SRE, but both treatments are superior to no treatment. ZA was first among all bisphosphonates assessed for odds of reducing SRE incidence, while the combination of Ibandronate and radionuclide therapy was most effective at significantly reducing pain from metastases. Clinicians and policy makers may use this synthesis of all available RCT data as support for the use of a BMA in MBD for lung cancer.

Population-based studies from the United States have reported that sarcoma patients living in rural areas or belonging to lower socioeconomic classes experience worse overall survival; however, the evidence is not clear for universal healthcare systems where financial resources should theoretically not affect access to standard of care. The purpose of this study was to determine the survival outcomes of soft-tissue sarcoma (STS) patients treated in Ontario, Canada over 23 years and determine if the patient's geographic location or income quintile are associated with survival.

We performed a population-based cohort study using linked administrative databases of patients diagnosed with STS between 1993 – 2015. The Kaplan-Meier method was used to estimate 2, 5, 10, 15 and 20-year survival stratified by age, stage and location of tumor. We estimated survival outcomes based on the patient's geographic location and income quintile. The Log-Rank test was used to detect significant differences between groups. If groups were significantly different, a Cox proportional hazards model was used to test for interaction effects with other patient variables.

We identified 8,896 patients with biopsy-confirmed STS during the 23-year study period. Overall survival following STS diagnosis was 70% at 2 years, 59% at 5 years, 50% at 10 years, 43% at 15 years, and 38% at 20 years. Living in a rural location (p=0.0028) and belonging to the lowest income quintile (p<0.0001) were independently associated with lower overall survival following STS diagnosis. These findings were robust to tests of interaction with each other, age, gender, location of tumor and stage of disease.

This population-based cohort study of 8,896 STS patients treated in Ontario, Canada over 23 years reveals that patients living in a rural area and belonging to the lowest income quintile are at risk for decreased survival following STS diagnosis. We extend previous STS survival reporting by providing 15 and 20-year survival outcomes stratified by age, stage, and tumor location.

Advances in cancer therapy have prolonged patient survival even in the presence of disseminated disease and an increasing number of cancer patients are living with metastatic bone disease (MBD). The proximal femur is the most common long bone involved in MBD and pathologic fractures of the femur are associated with significant morbidity, mortality and loss of quality of life (QoL).

Successful prophylactic surgery for an impending fracture of the proximal femur has been shown in multiple cohort studies to result in longer survival, preserved mobility, lower transfusion rates and shorter post-operative hospital stays. However, there is currently no optimal method to predict a pathologic fracture. The most well-known tool is Mirel's criteria, established in 1989 and is limited from guiding clinical practice due to poor specificity and sensitivity. The ideal clinical decision support tool will be of the highest sensitivity and specificity, non-invasive, generalizable to all patients, and not a burden on hospital resources or the patient's time. Our research uses novel machine learning techniques to develop a model to fill this considerable gap in the treatment pathway of MBD of the femur. The goal of our study is to train a convolutional neural network (CNN) to predict fracture risk when metastatic bone disease is present in the proximal femur.

Our fracture risk prediction tool was developed by analysis of prospectively collected data of consecutive MBD patients presenting from 2009–2016. Patients with primary bone tumors, pathologic fractures at initial presentation, and hematologic malignancies were excluded. A total of 546 patients comprising 114 pathologic fractures were included. Every patient had at least one Anterior-Posterior X-ray and clinical data including patient demographics, Mirel's criteria, tumor biology, all previous radiation and chemotherapy received, multiple pain and function scores, medications and time to fracture or time to death.

We have trained a convolutional neural network (CNN) with AP X-ray images of 546 patients with metastatic bone disease of the proximal femur. The digital X-ray data is converted into a matrix representing the color information at each pixel. Our CNN contains five convolutional layers, a fully connected layers of 512 units and a final output layer. As the information passes through successive levels of the network, higher level features are abstracted from the data. The model converges on two fully connected deep neural network layers that output the risk of fracture. This prediction is compared to the true outcome, and any errors are back-propagated through the network to accordingly adjust the weights between connections, until overall prediction accuracy is optimized. Methods to improve learning included using stochastic gradient descent with a learning rate of 0.01 and a momentum rate of 0.9.

We used average classification accuracy and the average F1 score across five test sets to measure model performance. We compute F1 = 2 x (precision x recall)/(precision + recall). F1 is a measure of a model's accuracy in binary classification, in our case, whether a lesion would result in pathologic fracture or not. Our model achieved 88.2% accuracy in predicting fracture risk across five-fold cross validation testing. The F1 statistic is 0.87.

This is the first reported application of convolutional neural networks, a machine learning algorithm, to this important Orthopaedic problem. Our neural network model was able to achieve reasonable accuracy in classifying fracture risk of metastatic proximal femur lesions from analysis of X-rays and clinical information. Our future work will aim to externally validate this algorithm on an international cohort.

Advances in cancer therapy have prolonged cancer patient survival even in the presence of disseminated disease and an increasing number of cancer patients are living with metastatic bone disease (MBD). The proximal femur is the most common long bone involved in MBD and pathologic fractures of the femur are associated with significant morbidity, mortality and loss of quality of life (QoL).

Successful prophylactic surgery for an impending fracture of the proximal femur has been shown in multiple cohort studies to result in patients more likely to walk after surgery, longer survival, lower transfusion rates and shorter post-operative hospital stays. However, there is currently no optimal method to predict a pathologic fracture. The most well-known tool is Mirel's criteria, established in 1989 and is limited from guiding clinical practice due to poor specificity and sensitivity. The goal of our study is to train a convolutional neural network (CNN) to predict fracture risk when metastatic bone disease is present in the proximal femur.

Our fracture risk prediction tool was developed by analysis of prospectively collected data for MBD patients (2009–2016) in order to determine which features are most commonly associated with fracture. Patients with primary bone tumors, pathologic fractures at initial presentation, and hematologic malignancies were excluded. A total of 1146 patients comprising 224 pathologic fractures were included. Every patient had at least one Anterior-Posterior X-ray. The clinical data includes patient demographics, tumor biology, all previous radiation and chemotherapy received, multiple pain and function scores, medications and time to fracture or time to death. Each of Mirel's criteria has been further subdivided and recorded for each lesion.

We have trained a convolutional neural network (CNN) with X-ray images of 1146 patients with metastatic bone disease of the proximal femur. The digital X-ray data is converted into a matrix representing the color information at each pixel. Our CNN contains five convolutional layers, a fully connected layers of 512 units and a final output layer. As the information passes through successive levels of the network, higher level features are abstracted from the data. This model converges on two fully connected deep neural network layers that output the fracture risk. This prediction is compared to the true outcome, and any errors are back-propagated through the network to accordingly adjust the weights between connections. Methods to improve learning included using stochastic gradient descent with a learning rate of 0.01 and a momentum rate of 0.9.

We used average classification accuracy and the average F1 score across test sets to measure model performance. We compute F1 = 2 x (precision x recall)/(precision + recall). F1 is a measure of a test's accuracy in binary classification, in our case, whether a lesion would result in pathologic fracture or not. Five-fold cross validation testing of our fully trained model revealed accurate classification for 88.2% of patients with metastatic bone disease of the proximal femur. The F1 statistic is 0.87. This represents a 24% error reduction from using Mirel's criteria alone to classify the risk of fracture in this cohort.

This is the first reported application of convolutional neural networks, a machine learning algorithm, to an important Orthopaedic problem. Our neural network model was able to achieve impressive accuracy in classifying fracture risk of metastatic proximal femur lesions from analysis of X-rays and clinical information. Our future work will aim to validate this algorithm on an external cohort.

Level of evidence (LOE) determination is a reliable tool to assess the strength of research based on study design. Improvements in LOE are necessary for the advancement of evidence-based clinical care. The objectives of this study were to determine if the LOE presented at the Musculoskeletal Tumour Society (MSTS) annual meeting has improved over time and to determine how the LOE presented at MSTS annual meetings compares to that of the Orthopaedic Trauma Association (OTA) annual meetings.

We reviewed abstracts from the MSTS and OTA annual meeting podium presentations from 2005 to 2014. Three independent reviewers evaluated a total of 1222 abstracts for study type and LOE. Changes in the distributions of study type and LOE over time were evaluated by Pearson Chi-Squared test.

There were a total of 577 podium abstracts from the MSTS and 645 from the OTA. Of the MSTS therapeutic studies, 0.5% (2/376) were level I, while 75% (281/376) were level IV. There was a seven-fold higher proportion of level I studies (3.4% [14/409]) and less than half as many level IV studies (32% [130/409]) presented at OTA. There was no improvement in the MSTS LOE for all study types (p=0.13) and therapeutic study types (p=0.36) over the study decade. In contrast, the OTA LOE increased significantly over this time period for all study types (p<0.01). The proportion of controlled therapeutic studies (LOE I through III) versus uncontrolled studies (LOE IV) increased significantly over time at the OTA (p<0.021), but not at the MSTS (p=0.10).

Uncontrolled case series continue to dominate the MSTS scientific program, whereas over the past decade, higher-level studies and more modern study methodology has been employed by members of the OTA.