Can A Multivariate Model for Survival Estimation in Skeletal Metastases (PATHFx) Be Externally Validated Using Japanese Patients?

Life Expectancy After Treatment of Metastatic Bone Disease: An International Trend Analysis

INTRODUCTION

The decision to treat metastatic bone disease (MBD) surgically depends in part on patient life expectancy. We are unaware of an international analysis of how life expectancy among these patients has changed over time. Therefore, we asked (1) how has the life expectancy for patients treated for MBD changed over time, and (2) which, if any, of the common primary cancer types are associated with longer survival after treatment of MBD?

METHODS

We reviewed data collected from 2000 to 2022 in an international MBD database, as well as data used for survival model validation. We included 3,353 adults who underwent surgery and/or radiation. No patients were excluded. Patients were grouped by treatment date into period 1 (2000 to 2009), period 2 (2010 to 2019), and period 3 (2020 to 2022). Cumulative survival was portrayed using Kaplan-Meier curves; log-rank tests were used to determine significance at P < 0.05. Subgroup analyses by primary cancer diagnosis were performed.

RESULTS

Median survival in period 2 was longer than in period 1 ( P < 0.001). Median survival (at which point 50% of patients survived) had not been reached for period 3. Median survival was longer in period 2 for all cancer types ( P < 0.001) except thyroid. Only lung cancer reached median survival in period 3, which was longer compared with periods 1 and 2 ( P < 0.001). Slow-growth, moderate-growth, and rapid-growth tumors all demonstrated longer median survival from period 1 to period 2; only rapid-growth tumors reached median survival for period 3, which was longer compared with periods 1 and 2 ( P < 0.001).

DISCUSSION

Median duration of survival after treatment of MBD has increased, which was a consistent finding in nearly all cancer types. Longer survival is likely attributable to improvements in both medical and surgical treatments. As life expectancy for patients with MBD increases, surgical methods should be selected with this in mind.

LEVEL OF EVIDENCE

VI.

Artificial Intelligence in Bone Metastasis Imaging: Recent Progresses from Diagnosis to Treatment - A Narrative Review

The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be challenging but decisive, because it affects the diagnostic and therapeutic process, especially in case of metastases. Several studies have already demonstrated how the integration of AI-based tools in the current clinical workflow could bring benefits to patients and to healthcare workers. AI technologies could help radiologists in early bone metastases detection, increasing the diagnostic accuracy and reducing the overdiagnosis and the number of unnecessary deeper investigations. In addition, radiomics and radiogenomics approaches could go beyond the qualitative features, visible to the human eyes, extrapolating cancer genomic and behavior information from imaging, in order to plan a targeted and personalized treatment. In this article, we want to provide a comprehensive summary of the most promising AI applications in bone metastasis imaging and their role from diagnosis to treatment and prognosis, including the analysis of future challenges and new perspectives.

AAOS Clinical Practice Guideline Summary: Treatment of Metastatic Carcinoma and Myeloma of the Femur

The Musculoskeletal Tumor Society, in partnership with American Society of Clinical Oncology and American Society for Radiation Oncology, has developed a clinical practice guideline to assist providers with the care of patients with metastatic carcinoma and myeloma of the femur. The guideline was developed by an Expert Panel consisting of representatives of all three organizations by American Academy of Orthopaedic Surgeons (AAOS) methodologists using the AAOS standardized guideline development process. A systematic review of the available evidence was conducted, and the identified evidence was rated was rated for quality and potential for bias. Recommendations were developed based on this evidence in a standardized fashion. The guideline was approved by the guideline approval bodies of all three organizations. Thirteen recommendations were synthesized covering relevant subtopics such as imaging, use of bone-modifying agents, radiation therapy, and surgical reconstruction. The consensus of the expert panel was that bone-modifying agents may assist in reducing the incidence of femur fracture, regardless of tumor histology. The panel recommended the use of radiation therapy to decrease the rate of femur fractures for patients considered at increased risk. The panel recommended arthroplasty be considered to improve patient function and decrease the need of postoperative radiation therapy in patients with pathologic fractures in the femur.

Modification to Mirels scoring system location component improves fracture prediction for metastatic disease of the proximal femur

BACKGROUND

Correctly identifying patients at risk of femoral fracture due to metastatic bone disease remains a clinical challenge. Mirels criteria remains the most widely referenced method with the advantage of being easily calculated but it suffers from poor specificity. The purpose of this study was to develop and evaluate a modified Mirels scoring system through scoring modification of the original Mirels location component within the proximal femur.

METHODS

Computational (finite element) experiments were performed to quantify strength reduction in the proximal femur caused by simulated lytic lesions at defined locations. Virtual spherical defects representing lytic lesions were placed at 32 defined locations based on axial (4 axial positions: neck, intertrochanteric, subtrochanteric or diaphyseal) and circumferential (8 circumferential: 45-degree intervals) positions. Finite element meshes were created, material property assignment was based on CT mineral density, and femoral head/greater trochanter loading consistent with stair ascent was applied. The strength of each femur with a simulated lesion divided by the strength of the intact femur was used to calculate the Location-Based Strength Fraction (LBSF). A modified Mirels location score was next defined for each of the 32 lesion locations with an assignment of 1 (LBSF > 75%), 2 (LBSF: 51-75%), and 3 (LBSF: 0-50%). To test the new scoring system, data from 48 patients with metastatic disease to the femur, previously enrolled in a Musculoskeletal Tumor Society (MSTS) cross-sectional study was used. The lesion location was identified for each case based on axial and circumferential location from the CT images and assigned an original (2 or 3) and modified (1,2, or 3) Mirels location score. The total score for each was then calculated. Eight patients had a fracture of the femur and 40 did not over a 4-month follow-up period. Logistic regression and decision curve analysis were used to explore relationships between clinical outcome (Fracture/No Fracture) and the two Mirels scoring methods.

RESULTS

The location-based strength fraction (LBSF) was lowest for lesions in the subtrochanteric and diaphyseal regions on the lateral side of the femur; lesions in these regions would be at greatest risk of fracture. Neck lesions located at the anterior and antero-medial positions were at the lowest risk of fracture. When grouped, neck lesions had the highest LBSF (83%), followed by intertrochanteric (72%), with subtrochanteric (50%) and diaphyseal lesions (49%) having the lowest LBSF. There was a significant difference (p < 0.0001) in LBSF between each axial location, except subtrochanteric and diaphyseal which were not different from each other (p = 0.96). The area under the receiver operator characteristic (ROC) curve using logistic regression was greatest for modified Mirels Score using site specific location of the lesion (Modified Mirels-ss, AUC = 0.950), followed by a modified Mirels Score using axial location of lesion (Modified Mirels-ax, AUC = 0.941). Both were an improvement over the original Mirels score (AUC = 0.853). Decision curve analysis was used to quantify the relative risks of identifying patients that would fracture (TP, true positives) and those erroneously predicted to fracture (FP, false positives) for the original and modified Mirels scoring systems. The net benefit of the scoring system weighed the benefits (TP) and harms (FP) on the same scale. At a threshold probability of fracture of 10%, use of the modified Mirels scoring reduced the number of false positives by 17-20% compared to Mirels scoring.

CONCLUSIONS

A modified Mirels scoring system, informed by detailed analysis of the influence of lesion location, improved the ability to predict impending pathological fractures of the proximal femur for patients with metastatic bone disease. Decision curve analysis is a useful tool to weigh costs and benefits concerning fracture risk and could be combined with other patient/clinical factors that contribute to clinical decision making.

Can a Bayesian belief network for survival prediction in patients with extremity metastases (PATHFx) be externally validated in an Asian cohort of 356 surgically treated patients?

BACKGROUND AND PURPOSE

Predicted survival may influence the treatment decision for patients with skeletal extremity metastasis, and PATHFx was designed to predict the likelihood of a patient dying in the next 24 months. However, the performance of prediction models could have ethnogeographical variations. We asked if PATHFx generalized well to our Taiwanese cohort consisting of 356 surgically treated patients with extremity metastasis.

PATIENTS AND METHODS

We included 356 patients who underwent surgery for skeletal extremity metastasis in a tertiary center in Taiwan between 2014 and 2019 to validate PATHFx's survival predictions at 6 different time points. Model performance was assessed by concordance index (c-index), calibration analysis, decision curve analysis (DCA), Brier score, and model consistency (MC).

RESULTS

The c-indexes for the 1-, 3-, 6-, 12-, 18-, and 24-month survival estimations were 0.71, 0.66, 0.65, 0.69, 0.68, and 0.67, respectively. The calibration analysis demonstrated positive calibration intercepts for survival predictions at all 6 timepoints, indicating PATHFx tended to underestimate the actual survival. The Brier scores for the 6 models were all less than their respective null model's. DCA demonstrated that only the 6-, 12-, 18-, and 24-month predictions appeared useful for clinical decision-making across a wide range of threshold probabilities. The MC was < 0.9 when the 6- and 12-month models were compared with the 12-month and 18-month models, respectively.

INTERPRETATION

In this Asian cohort, PATHFx's performance was not as encouraging as those of prior validation studies. Clinicians should be cognizant of the potential decline in validity of any tools designed using data outside their particular patient population. Developers of survival prediction tools such as PATHFx might refine their algorithms using data from diverse, contemporary patients that is more reflective of the world's population.

The Prediction of Survival after Surgical Management of Bone Metastases of the Extremities—A Comparison of Prognostic Models

Individualized survival prognostic models for symptomatic patients with appendicular metastatic bone disease are key to guiding clinical decision-making for the orthopedic surgeon. Several prognostic models have been developed in recent years; however, most orthopedic surgeons have not incorporated these models into routine practice. This is possibly due to uncertainty concerning their accuracy and the lack of comparison publications and recommendations. Our aim was to conduct a review and quality assessment of these models. A computerized literature search in MEDLINE, EMBASE and PubMed up to February 2022 was done, using keywords: “Bone metastasis”, “survival”, “extremity” and “prognosis”. We evaluated each model’s performance, assessing the estimated discriminative power and calibration accuracy for the analyzed patients. We included 11 studies out of the 1779 citations initially retrieved. The 11 studies included seven different models for estimating survival. Among externally validated survival prediction scores, PATHFx 3.0, 2013-SPRING and potentially Optimodel were found to be the best models in terms of performance. Currently, it is still a challenge to recommend any of the models as the standard for predicting survival for these patients. However, some models show better performance status and other quality characteristics. We recommend future, large, multicenter, prospective studies to compare between PATHfx 3.0, SPRING 2013 and OptiModel using the same external validation dataset.

Machine learning algorithms to estimate 10-Year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool

Background

Prognostic indicators, treatments, and survival estimates vary by cancer type. Therefore, disease-specific models are needed to estimate patient survival. Our primary aim was to develop models to estimate survival duration after treatment for skeletal-related events (SREs) (symptomatic bone metastasis, including impending or actual pathologic fractures) in men with metastatic bone disease due to prostate cancer. Such disease-specific models could be added to the PATHFx clinical-decision support tool, which is available worldwide, free of charge. Our secondary aim was to determine disease-specific factors that should be included in an international cancer registry.

Methods

We analyzed records of 438 men with metastatic prostate cancer who sustained SREs that required treatment with radiotherapy or surgery from 1989–2017. We developed and validated 6 models for 1-, 2-, 3-, 4-, 5-, and 10-year survival after treatment. Model performance was evaluated using calibration analysis, Brier scores, area under the receiver operator characteristic curve (AUC), and decision curve analysis to determine the models’ clinical utility. We characterized the magnitude and direction of model features.

Results

The models exhibited acceptable calibration, accuracy (Brier scores < 0.20), and classification ability (AUCs > 0.73). Decision curve analysis determined that all 6 models were suitable for clinical use. The order of feature importance was distinct for each model. In all models, 3 factors were positively associated with survival duration: younger age at metastasis diagnosis, proximal prostate-specific antigen (PSA) < 10 ng/mL, and slow-rising alkaline phosphatase velocity (APV).

Conclusions

We developed models that estimate survival duration in patients with metastatic bone disease due to prostate cancer. These models require external validation but should meanwhile be included in the PATHFx tool. PSA and APV data should be recorded in an international cancer registry.

Prognosis by cancer type and incidence of zoledronic acid-related osteonecrosis of the jaw: a single-center retrospective study

PURPOSE

Survival time after bisphosphonate use has been increasingly recognized to be associated with the incidence of medication-related osteonecrosis of the jaw (MRONJ); however, this has not been elucidated sufficiently in the literature. This study aimed to clarify the incidence of MRONJ and the corresponding survival rate of patients treated with zoledronic acid (ZA) for each type of cancer and obtain useful information for the oral/dental supportive care of cancer patients.

METHODS

We evaluated 988 patients who were administered ZA at our hospital; among them, 862 patients with metastatic bone tumors or myeloma were included.

RESULTS

The median survival time (MST) after ZA initiation was 35, 34, 8, 41, 12, and 6 months for patients with breast, prostrate, lung, myeloma, renal, and other cancers, respectively. Patients with cancers that had a short survival time (lung and other cancers [MST = 8 and 6 months, respectively] and cancers with MST < 10 months) did not develop MRONJ; this could be attributed to the shorter duration of ZA administration. The cumulative incidence of MRONJ in breast cancer, prostate cancer, and multiple myeloma was related to the frequency of anti-resorptive drug use and the increased risk over time. In renal cancer, the cumulative incidence of MRONJ increased early, although the MST was 12 months.

CONCLUSION

For the dentists in charge of dental management, it is essential to be aware of prognosis-related factors, predict MRONJ risk for each cancer treatment, and use risk prediction in dental management planning, particularly for cancers with non-poor prognosis.

Comparison between different prognostic models to be used for metastatic bone disease on appendicular skeleton in a Chilean population

PURPOSE

Several preoperation prognosis models used on the treatment of metastatic bone disease on appendicular skeleton have been devised. The purpose of this study was to compare the performance of different survival prognostic models on patients with metastatic bone disease in long bones in a Chilean population.

METHODS

This is a multicentric retrospective study. We retrospectively reviewed the medical records of 136 patients who were confirmed with metastatic bone disease of the appendicular skeleton and who were treated surgically from 2016 to 2019. The minimum follow-up time was 12 months. All patients were assessed using four appendicular metastatic bone disease scoring systems. A preoperative predicted survival time for all 136 patients was retrospectively calculated making use of the revised Katagiri, PathFx, Optimodel and IOR score model.

RESULTS

The PathFx model demonstrated an accuracy at predicting 3 (area under the curve [AUC] = 0.61) and 6-month (AUC = 0.65) survival time after surgical management. IOR score model demonstrated an accuracy at predicting 12-month survival time (AUC = 0.64). The survival rate reached the 44% in a year. The median survival time to death or last follow-up time was 14.9 months (SD ± 15).

CONCLUSION

PathFx score model demonstrated the highest accuracy at predicting a survival time of 3 and 6 months. IOR score model was the most accurate measure at predicting a survival time of 12-months. To our knowledge, this is the first study reporting a comparative analysis of metastatic bone disease with predicting models in a country located in Latin America. PathFx's and IOR score models are the ones to be used in the Chilean population as the predictive models in metastatic bone disease of the appendicular skeleton.

Current Overview of Treatment for Metastatic Bone Disease

The number of patients with bone metastasis increases as medical management and surgery improve the overall survival of patients with cancer. Bone metastasis can cause skeletal complications, including bone pain, pathological fractures, spinal cord or nerve root compression, and hypercalcemia. Before initiation of treatment for bone metastasis, it is important to exclude primary bone malignancy, which would require a completely different therapeutic approach. It is essential to select surgical methods considering the patient’s prognosis, quality of life, postoperative function, and risk of postoperative complications. Therefore, bone metastasis treatment requires a multidisciplinary team approach, including radiologists, oncologists, and orthopedic surgeons. Recently, many novel palliative treatment options have emerged for bone metastases, such as stereotactic body radiation therapy, radiopharmaceuticals, vertebroplasty, minimally invasive spine stabilization with percutaneous pedicle screws, acetabuloplasty, embolization, thermal ablation techniques, electrochemotherapy, and high-intensity focused ultrasound. These techniques are beneficial for patients who may not benefit from surgery or radiotherapy.

Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review

Background and purpose - External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines.Material and methods - We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting.Results - We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43-89), with 6 items being reported in less than 4/18 of the studies.Interpretation - Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Validation of PATHFx 2.0: An open-source tool for estimating survival in patients undergoing pathologic fracture fixation

Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost-effective solution. Using open-source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open-source methods and externally validated in two unique patient populations. The training set of a well-characterized, database records of 189 patients and the bnlearn package within R Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18, and 24 months. Each was externally validated in both a Scandinavian (n = 815 patients) and a Japanese (n = 261 patients) data set. Brier scores and receiver operating characteristic curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian data set. For the 1-month time point, DCA of the Japanese data set suggested to expect better outcomes assuming all patients will survive greater than 1 month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance: we successfully transitioned to PATHFx 2.0 using open-source software and externally validated it in two unique patient populations, which can be used as a cost-effective option to guide surgical decisions in patients with metastatic bone disease.

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases

BACKGROUND

PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on successful external validation in unique patient populations but also on remaining relevant as more effective systemic treatments are introduced. With advancements in the treatment of metastatic disease, it is our responsibility to patients to ensure clinical support tools remain contemporary and accurate.

QUESTION/PURPOSES

Therefore, we sought to (1) generate updated PATHFx models using recent data from patients treated at one large, urban tertiary referral center and (2) externally validate the models using two contemporary patient populations treated either surgically or nonsurgically with external-beam radiotherapy alone for symptomatic skeletal metastases for symptomatic lesions.

METHODS

After obtaining institutional review board approval, we collected data on 208 patients undergoing surgical treatment for pathologic fractures at Memorial Sloan Kettering Cancer Center between 2015 and 2018. These data were combined with the original PATHFx training set (n = 189) to create the final training set (n = 397). We then created six Bayesian belief networks designed to estimate the likelihood of 1-month, 3-month, 6-month, 12-month, 18-month, and 24-month survival after treatment. Bayesian belief analysis is a statistical method that allows data-driven learning to arise from conditional probabilities by exploring relationships between variables to estimate the likelihood of an outcome using observed data. For external validation, we extracted the records of patients treated between 2016 and 2018 from the International Bone Metastasis Registry and records of patients treated nonoperatively with external-beam radiation therapy for symptomatic skeletal metastases from 2012 to 2016 using the Military Health System Data Repository (radiotherapy-only group). From each record, we collected the date of treatment, laboratory values at the time of treatment initiation, demographic data, details of diagnosis, and the date of death. All records reported sufficient follow-up to establish survival (yes/no) at 24-months after treatment. For external validation, we applied the data from each record to the new PATHFx models. We assessed calibration (calibration plots), accuracy (Brier score), discriminatory ability (area under the receiver operating characteristic curve [AUC]).

RESULTS

The updated PATHFx version 3.0 models successfully classified survival at each time interval in both external validation sets and demonstrated appropriate discriminatory ability and model calibration. The Bayesian models were reasonably calibrated to the Memorial Sloan Kettering Cancer Center training set. External validation with 197 records from the International Bone Metastasis Registry and 192 records from the Military Health System Data Repository for analysis found Brier scores that were all less than 0.20, with upper bounds of the 95% confidence intervals all less than 0.25, both for the radiotherapy-only and International Bone Metastasis Registry groups. Additionally, AUC estimates were all greater than 0.70, with lower bounds of the 95% CI all greater than 0.68, except for the 1-month radiotherapy-only group. To complete external validation, decision curve analysis demonstrated clinical utility. This means it was better to use the PATHFx models when compared to the default assumption that all or no patients would survive at all time periods except for the 1-month models. We believe the favorable Brier scores (< 0.20) as well as DCA indicate these models are suitable for clinical use.

CONCLUSIONS

We successfully updated PATHFx using contemporary data from patients undergoing either surgical or nonsurgical treatment for symptomatic skeletal metastases. These models have been incorporated for clinical use on PATHFx version 3.0 (https://www.pathfx.org). Clinically, external validation suggests it is better to use PATHFx version 3.0 for all time periods except when deciding whether to give radiotherapy to patients with the life expectancy of less than 1 month. This is partly because most patients survived 1-month after treatment. With the advancement of medical technology in treatment and diagnosis for patients with metastatic bone disease, part of our fiduciary responsibility is to the main current clinical support tools.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics

Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD-CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics. As these technologies take root and innovative applications continue to be incorporated into the main-stream orthopedics, as we know it today, it is imperative to look at and understand the basics of artificial intelligence and what work is being done in the field today. This article takes the form of a loosely structured narrative review and will introduce the reader to key concepts in the field of artificial intelligence as well as some of the directions in application of the same in orthopaedics. Some of the recent work has been summarised and we present our viewpoint at the conclusion as to why we must consider artificial intelligence as a disrupting positive influence on orthopaedic surgery.

Prediction of survival after surgical management of femoral metastatic bone disease - A comparison of prognostic models

Background

Operative fixation for femoral metastatic bone disease is based on the principles of reducing pain and restoring function. Recent literature has proposed a number of prognostic models for appendicular metastatic bone disease. The aim of this study was to compare the accuracy of proposed soring systems in the setting of femoral metastatic bone disease in order to provide surgeons with information to determine the most appropriate scoring system in this setting.

Methods

A retrospective cohort analysis of patients who underwent surgical management of femoral metastatic bone disease at a single institution were included. A pre-operative predicted survival for all 114 patients was retrospectively calculated utilising the revised Katagiri model, PathFx model, SSG score, Janssen nomogram, OPTModel and SPRING 13 nomogram. Univariate and multivariate Cox regression proportional hazard models were constructed to assess the role of prognostic variables in the patient group. Area under the receiver characteristics and Brier scores were calculated for each prognostic model from comparison of predicted survival and actual survival of patients to quantify the accuracy of each model.

Results

For the femoral metastatic bone disease patients treated with surgical fixation, multivariate analysis demonstrated a number of pre-operative factors associated with survival in femoral metastatic bone disease, consistent with established literature. The OPTIModel demonstrated the highest accuracy at predicting 12-month (Area Under the Curve [AUC] = 0.79) and 24-month (AUC = 0.77) survival after surgical management. PathFx model was the most accurate at predicting 3-month survival (AUC = 0.70) and 6-month (AUC = 0.70) survival. The PathFx model was successfully externally validated in the femoral patient dataset for all time periods.

Conclusions

Among six prognostic models assessed in the setting of femoral metastatic bone disease, the present study observed the most accurate model for 3-month, 6-month, 12-month and 24-month survival. The results of this study may be utilised by the treating surgical team to determine the most accurate model for the required time period and therefore improve decision-making in the care of patients with femoral metastatic bone disease.