Metastatic Spine Disease Alters Vertebral Load-To-Strength Ratios in Cancer Patients Compared to Healthy Individuals.

1
Introduction
Vertebral bone metastases affect up to 70% of patients with advanced cancer [1]. Pathologic vertebral fractures, a significant clinical complication in advanced stages of cancer, occur in 43% of multiple myeloma, 35% of breast, 19% of prostate, and 17% of lung cancer patients [2]. In addition to higher health costs [3, 4, 5], vertebral fractures cause pain and/or neurological deficits [6, 7], and severe impairment of quality of life [8], shortening patient survival [2, 9, 10]. Systemic therapy and radiation therapy are used to halt tumor progression in up to 50% of patients with metastatic spine disease. However, radiation therapy is increasingly recognized as a risk for vertebral fracture, with up to 39% of patients suffering vertebral fracture within 6 months posttreatment [11, 12, 13, 14]. Current clinical guidelines for predicting vertebral fracture risk are subjective and inaccurate [15], limiting clinicians' ability to offer treatment before vertebral fracture occurs [15, 16]. This reactive approach can be particularly tragic when treatment could have been offered before the development of Metastatic Epidural Spinal Cord Compression (MESCC) or cauda equina compression. The effect of metastatic lesions on vertebral anatomy and structure can be observed on computed tomography (CT) and MR imaging. However, the application of imaging data for the development of precise, individualized prediction of vertebral fracture risk has been limited by the lack of knowledge on the effects of patient‐specific biomechanics on vertebral fracture pathomechanism in the setting of metastatic bone spine disease.
The activity of the trunk and abdominal muscles required to balance the task‐specific external loads [17] and provide mechanical stability to maintain spinal posture [18, 19], is the main contributor to vertebral loading [20]. From a biomechanical perspective, a vertebral fracture may occur when bone metastasis has degraded the vertebral mechanical properties such that it cannot maintain its structural integrity under the loading resulting from daily tasks. This perspective is summarized by the metric of load‐to‐strength ratio (LSR) metric, indicating that the vertebra may initiate failure when this ratio is greater than a value of one [21]. Patients with osteoporotic vertebral fracture demonstrated lower thoracolumbar vertebral strength [22, 23, 24, 25], and higher LSRs for flexed or upright postures carrying weights [26, 27] than age and sex‐matched subjects without prevalent vertebral fracture. This finding suggests that LSR value and pattern are important considerations in osteoporosis‐related vertebral fracture risk. In cadaveric spines from cancer donors with metastatic spinal disease, a recent study demonstrated that bone metastasis modifies LSR at the vertebral level [28]. The study found that, for the same spines, cancer vertebrae with no observed radiographic evidence of metastatic lesions had higher LSRs than healthy normative values [28], suggesting cancer may have a systematic effect on the spinal column biomechanical properties. A better understanding of the role of patient‐ and task‐specific LSR on vertebral fracture risk is crucial for determining the potential clinical utility of this metric for a more comprehensible assessment of this risk in patients with metastatic spine disease.
This study aimed to evaluate the effects of cancer and vertebral bone metastases on in vivo vertebral loading, strength, and LSRs, by assessing these measures in a cohort of metastatic spine patients planned for radiation therapy and comparing them with a community‐based noncancer control cohort. We specifically investigated whether (1) vertebral strength is affected by cancer and bone metastasis classification (no CT‐observable lesion (NOL), osteolytic, osteosclerotic, and mixed lesions), and (2) estimated vertebral loading is affected by metastatic cancer and primary, and (3) LSRs in NOL vertebrae in the cancer patients differ from the norms. We hypothesized that:
Vertebrae in patients with metastatic spine disease have lower vertebral body strength overall, but levels with osteosclerotic and mixed lesions have higher strength than noncancer controls.

Vertebral loading is lower in patients with breast, lung, renal, or prostate cancer than in noncancer controls.

Vertebrae with osteosclerotic and mixed lesions will have lower LSRs than noncancer controls. In comparison, vertebrae with osteolytic lesions or no observed metastatic bone lesions on CT will have higher LSRs than healthy controls.

2
Methods
Figure 1 presents a graphical summary of the protocol for creating the musculoskeletal model and computing the vertebral LSR.
2.1
Study Subjects
2.1.1
Cancer Cohort
The study cohort comprised 135 patients with metastatic spinal cancer scheduled to receive radiation therapy at Dana Farber Cancer Institute or Brigham and Women's Hospital between September 2020 and July 2023. All of the patients had previously consented to the Broadband biorepository research project (MGB IRB 2016P001582). Study inclusion criteria were (1) histologically or cytologically documented Stage IV bone metastasis and radiographic (computed tomographic [CT] scan or bone scan) evidence of bone metastasis and (2) Karnofsky Performance Status [30] > 70, selected to enhance the likelihood of patient participation and follow‐up. Patients were excluded if they had: (1) diseases of abnormal bone metabolism (Paget disease and untreated hyperthyroidism, hyperprolactinemia, or Cushing disease); (2) received RT < 6 months before the current study radiation therapy at baseline, surgery, or vertebral augmentation at the site of radiation or adjacent levels.

2.1.2
Normative Dataset: Framingham Heart Study (FHS)
To provide a comparison of LSRs from patients with spine metastases to LSRs from healthy adults, we drew on the same normative dataset from our prior analysis of LSRs in cadaveric metastatic spines [27, 28]. The normative dataset includes a sample of 250 individuals from the FHS Multidetector CT Study [31]. Briefly, this is age‐ and sex‐stratified data, ranging from 41 to 90 years of age, included 25 males and 25 females within each of five age groups: 40–49, 50–59, 60–69, 70–79, and > 80 years of age [32].

2.2
CT Imaging Data Protocols
2.2.1
Cancer Cohort
Cancer patients planned for radiation therapy were simulated for treatment by our study clinical senior attending physicians (T.B., A.S., and M.A.H., 17, 10, and 8 years of experience, respectively) at the Radiation Oncology Department, Brigham and Women's Hospital (Appendix A.1 in the Supporting Information). Patients were simulated using the Siemens SOMATOM Confidence (Siemens Healthcare GmbH, Erlangen, Germany) or GE Lightspeed (General Electric Medical System, Waukesha, WI) CT scanners. Simulation scan parameters were tube voltage: 120 kVp; tube current: 240–300 mA; field of view: A (16 cm) or B (skin‐to‐skin); slice thickness: siemens (for stereotactic body radiotherapy [SBRT]: 0.5 mm, all others: 1.5 mm) GE lightspeed (SBRT, all others: 1.25 mm); in‐plane pixel size (mm), A: 0.31 × 0.31, B: 0.70–0.98; Gantry rotation: 1 s; gating: none; breath hold: none. For this study, we obtained the simulation CT data before treatment.

2.2.2
Normative Dataset (FHS)
All participants in this study underwent abdominal and thoracic scans on a GE Discovery VCT 64‐slice PET/CT scanner (GE Healthcare). Scan parameters: a tube voltage (120 kVp), tube current of 300/350 mA (≤ 220/> 220 lb. body weight), and gantry rotation of 350 ms. These acquisitions typically included vertebral levels in the range of T4–L4.

2.2.3
CT Imaging of Cadaveric Metastatic Spines
To allow evaluation of the CT‐strength estimation model (Section 2.2), we obtained diagnostic CT image data (Aquilion 64, Toshiba Medical, USA) previously acquired for 11 cancer donors' spines. Each spine was scanned using standard clinical spine CT acquisition parameters for living humans (125 kV, Matrix 512 × 512, field‐of‐view: 16 cm, slice thickness 0.5 mm, image voxel of (0.31 × 0.31 × 0.5) mm). A six‐chamber calcium hydroxyapatite (HA) phantom (0–1.5 g/cc3, CIRS, Norfolk, VR) provided bone density calibration.

2.3
CT Protocol for Estimating Vertebral Strength
CT data were imported to Analyze (12.0, AnalyzeDirect Inc., KS, USA), the axial CT image corresponding to sagittal, mid‐vertebral height was identified for each vertebra, and the vertebral body, including the proximal mid‐pedicle, was contoured. The resulting section was segmented using a threshold operation. For each vertebral segmentation, the vertebral cross‐sectional area (CSA) and volumetric integral bone mineral density (iBMD) were computed, with CT‐estimated vertebral strength (V
CT‐S) derived from Equation (1) [27]:

2.4
CT Versus Finite Element (FE)‐Estimation of Measured Strength in Metastatic Cadaveric Vertebrae
We compared the CT‐ and FE‐estimation of the measured compressive strength in 43 cadaveric vertebrae containing osteolytic, osteosclerotic, mixed, and NOL mechanically tested in a previous study [33]. For this purpose, each vertebra's micro‐CT data was reformatted at 0.31 mm voxel (matching the spine's clinical CT data in‐plane resolution; Section 2.1.1) and a machine‐learning registration [34] used to register each micro‐CT data to its corresponding CT clinical data. The registered CT image volumes were extracted from the clinical CT data, and the CT model was used to estimate vertebral strength from the clinical CT data (Equation 1).

2.5
Subject‐Specific Musculoskeletal Modeling for Computing Level‐Specific LSRs
Using established methods [27, 28], generic full‐body musculoskeletal models for men and women [35, 36, 37] were adjusted to match each cancer patient and noncancer control subject, resulting in individualized musculoskeletal spine models (Appendix A.1 in the Supporting Information). For this study, we simulated four static daily tasks in OpenSim version 4.3 [38] to estimate the resulting compressive loading applied to each vertebral level, termed V
CL, in the model [28]. The simulated tasks were (1) natural standing (NS), (2) standing + weight (each hand holding 5 kg), (3) forward flexion + weight, and (4) lateral bending + weight. Per task, level‐specific LSRs were defined as model‐estimated V
CL/V
CT‐S.

2.6
Metastatic Bone Lesion Classification
Vertebral bone lesion classification was performed by two investigators with years of experience in studies of spine imaging (D.B.H. and R.N.A.) using standard radiological criteria as defined by the Spinal Instability Neoplastic Score (SINS) protocol [39]. Specifically, vertebrae were classified as osteolytic, osteosclerotic, mixed, or NOL.

2.7
Statistical Analysis
2.7.1
CT‐ Versus FE‐Estimated Strength
Interclass correlation coefficient (ICC) was used to compare the degree of agreement between the CT‐ and FE‐estimation of the metastatic vertebrae experimental strength (n = 43). We used Corrected Akaike Information Criterion (AICc) to compare the CT‐ and FE‐model's performance for estimating the experimental strength for each bone metastasis class.

2.7.2
Load‐To‐Strength
This study's primary outcome variables of interest are vertebral body compressive loading (V
CL), strength (V
CT‐S), and LSR from T4 to L4. To highlight observable differences in LSR between the groups (noncancer controls and patients), we calculated standardized LSR at each vertebral level (Equation 2) separately for males and females:

With LSRFHS,NS denoting vertebra‐specific LSR measures from control subjects in NS. Body mass index was calculated using the standard formula [40]. Statistical analysis was performed in SAS (version 9.4 for Windows, SAS Institute, Cary, NC).
We computed descriptive statistics (mean and standard deviations for continuous variables; absolute and relative frequencies for nominal variables) to describe each cohort participant's characteristics (age, sex, weight, height). We used the chi‐square and the Mann–Whitney test to test for differences in age, sex, weight, and height between the cohorts. We applied linear mixed models (LMMs) to account for the correlated nature of the data (i.e., outcomes were calculated for multiple vertebral levels within each individual) and to test for differences in V
CL, V
CT‐S, and standardized LSRs between the cancer patients and noncancer controls for each task, grouped by spinal region (thoracic [T4–T10]; thoracolumbar [T11–L1]; and lumbar [L2–L4]) and stratified by sex. The models were implemented to estimate the effects of Group (cancer patients vs. noncancer controls), age, BMI, bone metastasis type (NOL, osteolytic, osteosclerotic, mixed) for V
CT‐S and cancer (breast, lung, other (< 3 patients per cancer), renal and prostate) for V
CL, and their interaction effects. Least squares means differences were calculated as linear combinations from the model results to investigate differences for each comparison of interest. The results of the models are presented in tabular form, and the summary of differences in least‐squares means in graphical form is created using the ggplot package in R [41]. The statistical significance level was set at 0.05, and the p‐values were adjusted using the false discovery rate approach [29].

3
Results
3.1
Spine Metastasis Patients and Normative Data Subjects' Characteristics
The study included 135 patients with spine metastatic disease and 246 participants from the FHS cohort. Table 1 details the demographics and anthropometric characteristics. For both groups, the average age was around 64 (p = 0.965) years old. The proportion of men was significantly higher (p = 0.001) in the patient cohort (66%) compared to controls (50%), with the cancer patients being taller (1.71 vs. 1.67 m; p < 0.001). Body weight was similar in the two groups (p = 0.715). In the cancer patients, the most common cancers were prostate (n = 56), lung (n = 23), renal (n = 14), and breast (n = 11). Among patients, 12% of vertebrae were classified as osteosclerotic, 6% as mixed, 8% as osteolytic, and 75% as NOL (Table 2).

3.2
CT‐ Versus FE‐Based Estimates of Metastatic Vertebral Strength
The ICC for agreement between the experimentally measured vertebral strength and the CT estimate of vertebral strength is 0.85 (95% CI: 0.74–0.92) and 0.87 (95% CI: 0.77–0.93) for the agreement between the experimentally measured vertebral strength and the FE‐based estimate of the vertebral strength. Figure 2 presents the linear regression models for the CT and FE estimation of the metastatic cadaveric vertebrae experimental strength stratified by bone metastasis classification. Stratified by lesion classification, the AICc criteria (Table 3) showed the FE model offered moderate improvements for estimating strength in NOL vertebrae and vertebrae with osteolytic and osteosclerotic lesions, with the highest difference in model performance observed for mixed lesions. Analysis of variance revealed none of the differences between the CT and FE‐derived vertebral estimated strength to be statistically significant at the 5% level. Based on these findings, we employed the CT model for estimating strength in the MSD patients.

3.3
Bone Metastasis Affects CT Estimated Vertebral Strength (
V
CT

‐S)
Overall vertebral strength (V
CT‐S) in male (thoracic: p = 0.008, thoracolumbar: p = 0.026, and lumbar: p = 0.005) and in female (thoracic: p = 0.003, thoracolumbar: p < 0.001, and lumbar: p < 0.001) cancer patients was higher than in corresponding controls, as LMMs that adjusted for age, BMI, spinal region, their interaction with group (cancer vs. control) and stratified by sex, see Appendix A.2 (Table A.1) in the Supporting Information. Figure 3 summarizes V
CT‐S values for the control and cancer (grouped by bone metastasis type), both cohorts stratified by sex and grouped by spinal region. For all three spinal regions and both male and female cancer patients, osteosclerotic and mixed bone metastasis levels had higher V
CT‐S values than the corresponding control levels (Figure 3). In osteolytic vertebrae, V
CT‐S values were similar between cancer patients and controls (Figure 3). In females, NOL vertebrae had significantly higher V
CT‐S than control subjects in all three regions, but no corresponding difference was found for males (Figure 3).

3.4
Primary Cancer Effects on Estimated Vertebral Compressive Loading
Independent of the task modeled or spinal region, younger age and higher BMI (p < 0.0001, all) were associated with higher vertebral compressive loading (V
CL) in both cohorts. Compared to controls, both male and female cancer patients predominantly showed higher model‐estimated V
CL, the difference statistically significant for male patients at the lumbar regions for all tasks, at the thoracolumbar region for NS, standing + weight, and lateral bending + weight, and the thoracic region for NS, Table 4, and in female patients at the lumbar region for NS and the thoracic region for forward flexion + weight.
Evaluating the effect of primary cancer on V
CL values in female patients showed that breast cancer patients had higher V
CL values than female controls; the difference was statistically significant in NS (lumbar) and in forward flexion + weight (thoracolumbar, lumbar) (Figure 4). Figure 4 summarizes, for each task simulated, the effect of primary cancer on the patients' V
CL values compared to the controls stratified by sex and grouped by spinal region. Detailed estimates for these comparisons, calculated from least squares means, are presented in Table 4. In male patients, prostate cancer patients showed significantly higher V
CL than male controls in the standing‐based tasks (NS, standing + weight) at the thoracolumbar and lumbar regions, a difference maintained only at the lumbar region under forward flexion + weight and lateral bending + weight tasks. In contrast to female cancer patients, male lung cancer patients showed higher V
CL than male controls in NS, for the thoracolumbar and lumbar regions.

3.5
Cancer Patients Show Significant Differences in LSRs From Age and Sex‐Comparable Normative Subjects
Spine LSRs were evaluated for 1508 patients' vertebrae (T4–L4) and 2990 in controls. The following results are presented for LMMs adjusted for age, BMI, and interaction terms.

Comparison of cancer cohort and controls: Table 5 details the LMMs results for comparing the cancer and FHS cohorts for each task simulated, stratified by sex and grouped by spinal region. Compared to female controls, female cancer patients had lower standardized LSR values in standing + weight and lateral bending + weight (all three regions), in forward flexion + weight (thoracolumbar and lumbar), and in NS (thoracic) simulated tasks (Table 5). Male cancer patients presented a less consistent pattern and only had a significant difference for the thoracolumbar region in NS, with a higher LSR than controls (p = 0.009) (Table 5).

Bone metastasis type significantly affects LSR in cancer patients: Figure 5 compares standardized LSR values for the cancer patients to controls for each task simulated, grouped by bone lesion type and spinal region. The LMM analysis was stratified by sex and adjusted for age, BMI, and their interaction with the cohort (cancer, controls). Full statistical analysis for the LMMs used to generate Figure 5 is presented in Tables A.2 and A.3 (Appendix A.3 in the Supporting Information).

Osteolytic bone metastasis: Compared to male controls, osteolytic vertebrae from male cancer patients had significantly higher LSR in NS and lateral bending + weight (thoracic and thoracolumbar) and standing + weight (thoracolumbar).

Osteosclerotic bone metastasis: Compared to female controls, osteosclerotic levels in female cancer patients had significantly lower LSRs at the thoracolumbar region, independent of the task modeled, the thoracic region for NS and LB + W tasks and the lumbar region for standing + weight and forward flexion + weight tasks. Compared to male controls, osteosclerotic levels in male cancer patients had significantly lower LSRs at FL + W (thoracolumbar and lumbar regions).

Mixed bone metastasis: For male and female patients, levels with mixed lesions had significantly lower LSRs than corresponding levels in controls, independent of the spinal region or task modeled (Figure 5).

In cancer patients, NOL vertebrae LSRs differ from their normative controls: In male cancer patients, NOL vertebrae in the thoracolumbar and lumbar regions showed higher LSRs in NS. In females, NOL vertebrae presented lower LSRs than female controls for NS (thoracolumbar), standing + weight (thoracic, thoracolumbar), forward flexion + weight (thoracolumbar, lumbar), and lateral bending + weight (thoracic, thoracolumbar) tasks (Figure 5).

4
Discussion
This study applies musculoskeletal models derived from clinical CT images to evaluate the biomechanical environment of the spine in metastatic spine disease patients planned for radiotherapy treatment, and demonstrates that patients with spinal bone metastasis exhibit significant task‐ and region‐specific differences in estimated LSR compared to normative controls. The effect depended on changes in vertebral strength mediated by the bone metastasis classification, the patient's sex, and the potential effect of primary cancer on the magnitude of applied vertebral loading. Compared to normative controls, the osteosclerotic and mixed vertebrae had higher vertebral strength, which resulted in lower LSR values. In men, higher computed applied vertebral loading coupled with lower vertebral strength resulted in osteolytic vertebrae showing higher LSRs. However, in women, we found no such differences in LSRs for osteolytic vertebrae, a result of both cohorts showing comparable applied vertebral loading and vertebral strength. Uniquely, we found vertebrae without CT‐identified lesions having LSRs lower than the normative cohort, suggesting that cancer and its treatment may have a systemic effect on the biomechanical environment of the spine in patients. Clinical evaluation of LSRs may provide an important metric for assessing the capacity of lesioned vertebrae to withstand daily loading, likely a key determinant of fracture risk. Our study highlights that multiple factors affect the metastatic vertebrae biomechanical environment, with important implications for patient management and risk reduction.
Our study estimated the compressive vertebral strength in 3000 levels (T4–L4) from normative subjects and 1508 (T4–L4) levels for spinal metastatic disease patients as part of the assessment of the cancer patients' and normative subjects' LSRs. For this purpose, we applied a CT‐based regression model, previously demonstrated in a sample of 139 noncancer subjects [27] to explain 90% of the variance of vertebral strength estimated using FE analysis [42] for 339 vertebrae of this normative cohort. The radiographic presentation of vertebrae with osteolytic, osteosclerotic, or mixed metastatic lesions is, however, remarkably varied. Preclinical studies showed osteolytic bone lesions to degrade the structure and composition of vertebral bone [43, 44, 45] and affect the accumulation of damage within the bone tissue [46], leading to degradation in vertebral strength [47, 48, 49]. Although these findings highlight bone metastasis to affect osteolytic bone quality, our nanoindentation study found no significant differences in bone tissue modulus in osteolytic cadaveric bone from lung and renal cancer patients from that of bone tissue from NOL vertebrae [33]. The effect of metastasis on the properties of vertebral bone, particularly, that of osteosclerotic and mixed bone lesions, remains unclear. In cadaveric vertebrae, we [47] and others [50, 51] found osteosclerotic, and mixed metastatic bone lesions to affect the vertebral bone architecture, composition, bone density, bone strain patterns and vertebral deformation [52, 53] and the mechanism of bone damage under applied loading [54]. The nanoindentation study found the osteosclerotic bone with a 5.8% decrease in bone modulus and lower ductility [33] likely a product of undermineralized bone [55]. Although these findings demonstrate that metastatic bone lesion type deferentially affect vertebral bone quality, the ability clinical CT to interrogate these changes beyond evaluation of bone density and architectural changes remains unclear [33].
Evaluated for 43 cadaveric metastatic vertebrae, mechanically tested in our previous study [33], the CT model's estimated vertebral strength was comparable to that derived from FE analysis when applied to the complete data, independent of bone lesion type. Stratified by lesion type, the FE‐models provided small improvement in predicting the measured vertebral strength in osteolytic, osteosclerotic, and NOL vertebrae (forming 94% of the vertebrae evaluated in our patient population). Our findings revealed a higher difference for mixed lesions, representing 6% of the vertebrae observed in our patient population. Presenting both osteolytic and osteosclerotic lesions, resulting in a high degree of bone tissue and architectural heterogeneity [47], mixed lesions affect a highly complex state of strain and stress [33, 54]. The FE's ability to better account for the spatial heterogeneity in bone tissue and architecture underlies its improved performance, highlighting the limitation of the CT‐model in this bone lesion type.
While our analysis did not find statistically significant differences in strength estimation between the CT and FE models, we recognize that this conclusion could be affected by the small sample size. Therefore, we suggest that a study with a larger sample size would be more appropriate to provide definite conclusions about the differences between FE‐ and CT‐models for estimating vertebral strength, stratified by lesion class. The application of FE to better evaluate the effect of bone metastasis on the strength and stiffness of human patients' vertebrae is an active area of research in our group [33, 54]. However, at present, preparing the FE model, running the simulation, validation, and interpretation of results required on average 1.5–2 h per vertebra, resulting in 323–430 days (@7 h/day) to complete the analysis in the cancer cohort in this study. Based on the performance of the CT‐model and the effort to perform the same analysis for the normative cohort, we elected to proceed with the CT‐based estimation for this study.
Previously, we have shown that the CT‐based estimate of vertebral compressive strength used here is well‐correlated with measured strength in cadaveric metastatic vertebrae [28] and that lower LSRs were due to higher estimated strengths in vertebrae with mixed and osteosclerotic lesions [28]. Our current analysis shows similar trends for strength and estimated LSRs, particularly, the reduced LSRs associated with osteosclerotic and mixed lesion types [28]. The prior study also indicated higher LSRs in osteolytic and NOL vertebrae. Here, we found this trend within the patients. Specifically, thoracic and thoracolumbar osteolytic vertebrae had higher LSRs in male patients than male controls, suggesting higher pathologic vertebral fracture risk in osteolytic vertebrae, a finding consistent with clinical observations [16]. In contrast, in female patients, osteolytic vertebrae had comparable LSRs to female controls independent of region or activity simulated, suggesting no increased risk of vertebral fracture, which is inconsistent with clinical experience [16]. Examination of LSRs by primary cancer revealed that breast and renal female cancer patients had higher LSRs than controls (Figure 5), indicating a higher risk of vertebral fracture, which is consistent with clinical experience [16]. By contrast, female lung patients had markedly lower model‐estimated vertebral compressive load, a mean difference of −11%, while differing by 0.5% in estimated strength, resulting in lower LSRs than the controls (Figure 5). With both groups being our study's largest groups of female patients, these differences led to our finding of lower LSRs in osteolytic vertebrae in female patients. Analyses of the factors that affect estimated vertebral compressive loading have highlighted that body weight is, not surprisingly, of primary importance [56]. Of note, lung patients had approximately 10% lower body weight than the breast and renal patients, explaining differences in estimated loading. Other factors, such as spinal muscle weakness, may play a more nuanced role. In cancer patients, low skeletal muscle mass (LSMM) measured on axial image at L3, most frequently on CT, is used as a surrogate marker of sarcopenia [57], and can predict major complications [58] and lower survival [59] in cancer patients. Although the prevalence of LSMM in the overall oncologic population and specifically in different tumors remains unclear [60], recent reviews highlighted the prevalence of sarcopenia in lung cancer patients, a mean (95% CI) of 51.5% (47.0%–56.1%) among the six cancer types with a prevalence > 50% [60, 61]. Lung cancer patients were found with a higher prevalence of sarcopenia than breast cancer patients, 41.3 (36.1–46.5) [60], and prostate patients were reported to have the highest prevalence of sarcopenia, 76.1% (72.2%–79.9%), likely due to old age (primary sarcopenia) and disease (secondary sarcopinea) [60, 61], we found prostate patients with higher estimated spine loading than male controls. It should be noted that sarcopenia, or low muscle strength in a musculoskeletal model, does not equate to low spine loads. In reality, it would likely lead to poor posture, instability, and thereby increases in spine loading. Directly assessing this would require measurements of posture and motion in patients with spine metastases, which are currently lacking. While examination by primary cancer was limited here to the few primary types present in sufficient numbers in the current study, our findings indicate the variations in biomechanical environment due to primary cancer provide motivation and support for future efforts to study this issue across different primary cancers. Based on the current study findings, we aim to expand our research to investigate the role of muscle quality evaluated from CT and MR imaging in affecting LSRs.
Spinal instability, which often occurs with neoplastic spinal disease [62], has largely been attributed solely to pathologic osseous changes within the vertebra that compromise its mechanical integrity, resulting in an increased vertebral fracture risk [63]. However, there remain uncertainties related to the association between cancer, bone metastasis, and, ultimately, the mechanisms of failure leading to vertebral fracture [54, 64], which is rarely a simple compression fracture. Paraspinal muscles play a critical role in the stabilization and mobility of the spine [65]. Model‐predicted spine loading is sensitive to individualized muscle parameters (CSA and location) and spinal posture in complex ways, which can alter loading estimates by up to 54% [36]. Myopathic changes [66] and sarcopenia, broadly characterized as the loss of muscle mass, strength, and functional decline, are a common sequela of cancer [67, 68]. The resulting lower CSA of the paraspinal muscles [67, 68] was reported to be associated with low back pain [69, 70] and disability [71]. Furthermore, spine posture parameters, shown to affect LSR in noncancer subjects [72], may not be normal in patients with cancer, further affecting the observed differences in LSRs. However, due to the patients being imaged supine while in a clinical scanner, we could not acquire reliable information about posture. This lack of data may explain our finding that LSRs do not increase uniformly in metastatic spines compared to noncancer controls, with complex variations related to sex, region, task, lesion type, and primary cancer. Whether these factors could affect musculoskeletal loading in cancer patients, manifesting as differences in LSRs, and how these vary with primary cancer and treatment has not previously been carefully investigated, to the best of our knowledge.
The study has several noted limitations. Our patient cohort was planned for treatment with radiotherapy, leading to patients with multiple primary cancers. Thus, we lacked the power to examine how the primary tumor affects spinal loading and vertebral strength outcomes reliably. Although the study patients and control subjects were not explicitly matched by age, sex, height, and weight, the control and patient groups were largely similar. However, due to the likelihood of survival, there was a higher proportion of men in the cancer patient group. Our analyses were stratified by sex, making this a minor limitation. We also considered adjusting the analyses for height and weight, but this had a minimal effect on the results and was not included in the reported results. Our study applied an established methodology for musculoskeletal model creation based on patient CT scans [27, 35, 36, 37]. However, the patient and control CT data were collected using different scanners and scan protocols. Although we employed a HA phantom to calibrate our measurement of BMD for vertebral strength estimation and muscle segmentation [35, 36, 37], differences in CT scanner properties and image acquisition parameters [73] could have affected segmentation and property estimation of the muscle and vertebrae, altering vertebral strength estimates.
Furthermore, existing musculoskeletal modeling methodologies likely do not fully capture the neuromuscular effects of cancer‐related cachexia or radiation treatment. Patients affected by cancer commonly develop cachexia with an increasing prevalence in the advanced disease stages [74]. Cachexia forms a complex multifactorial syndrome associated with loss of body weight, reduced adipose tissue, altered metabolism, low‐grade chronic inflammation, and muscle wasting caused by altered regulation of protein synthesis and degradation rates [75] and marked perturbations of the skeletal muscle energy, protein metabolism, and mitochondrial dysfunction, resulting in impaired oxidative capacity and the production of ATP [76]. Preclinical models demonstrate that cachexia affects the transition from slow to fast muscle fiber type, which could indicate an altered neuromuscular control essential for conferring contractile and metabolic characteristics to the muscle fiber [77]. Fat infiltration of skeletal muscle also occurs in cancer and should be noted as a factor that may affect muscle function. It has been widely studied in aging and is associated with clinical outcomes including fractures and mortality [78]. However, its relationship with muscle function is complex. Studies that compare specific skeletal muscle strength with muscle fat infiltration, assessed via CT‐based muscle density, have found low associations independent of muscle size [79, 80, 81]. Thus, its usefulness as an input for musculoskeletal modeling remains unclear.
Although cross‐sectional CT analysis and single‐slice measurements of paraspinal muscles are shown to correlate with the reduction in functional performance, muscle mass, and frailty in cancer patients [82], the CT image radiodensity does not show a commensurate reduction in value [83], nor did the CT data provide precise estimates of cancer‐related changes in protein content of skeletal muscles [84]. With the association of CT data with cancer‐related damage in muscle properties and activation remaining unclear, our study is unable to assess the degree to which cancer‐based damage to spinal muscles affects the model estimation of the applied vertebral loading. We assessed spine loading under standardized static poses, underestimating loading from comparable dynamic scenarios by approximately 16% [85], although this should not alter the overall findings. Increased LSRs have been associated with both prevalent [22, 23, 24, 25] and incident [86] vertebral fractures in older adults with osteoporosis. Importantly, the modeled activities are normal daily tasks not expected to overload the spine to failure. Thus, while the LSRs may be useful in predicting the fracture risk, they do not imply that these tasks will cause failure, nor do they suggest loading scenarios that might overload the spine. Despite these limitations, our study analyses of spine LSRs in patients with spine metastases in vivo are novel and highlight, for the first time, the effect of cancer on LSRs in patients versus healthy normative values.

5
Conclusion
Applying individualized spinal musculoskeletal modeling, our study introduced the metric of vertebral LSR to evaluate the effect of cancer and metastatic bone lesion classification on the biomechanical environment of human vertebrae from the patient's routine clinical CT. Our novel finding of task‐specific differences in the patients' estimated compressive vertebral loading and strength highlighted the effect of cancer, lesion type, and vertebral location on the patients' LSRs. Furthermore, our observation of lesion‐mediated differences in patients' LSRs suggests that different thresholds for such measurements might need to be established based on vertebral region and bone metastasis type. Uniquely, our finding of lower LSRs in vertebrae with no observed metastatic lesions on the patient CT when compared to age and sex‐similar controls from FHS poses the question of whether these vertebrae should be considered “normal,” with important implications for patient management and risk reduction. Although our study did not evaluate the role of LSRs in affecting the incidence of vertebral fracture in metastatic spine disease patients, our initial assessment supports further examination of whether vertebral load‐to‐strength measurements are associated with this risk and, if so, what threshold values indicate risk.

Author Contributions
D.E.A., T.B., D.B.H., and R.N.A. conceived and designed the study. R.N.A., D.E.A., M.K., and drafted the manuscript. D.K., P.F.D., H.K., S.K., S.C., T.B., A.S., and M.A.H. acquired and organized clinical data from patients. M.L.B. was responsible for the FHS study data and reviewed and edited the manuscript. D.E.A., J.J., and B.T.A. performed musculoskeletal modeling and associated programming and analysis for patients and the normative database. J.J. computed CT‐based vertebral strength. D.B.H. and R.N.A. performed the image‐based metastatic bone lesions classification. M.K. performed statistical analysis. D.E.A., M.K., and R.N.A. drafted and revised the manuscript. R.N.A. is responsible for all aspects of the study and manuscript. All authors have reviewed and approved the final manuscript.

Conflicts of Interest
The authors declare no conflicts of interest.

Supporting information

Appendix S1: jsp270111‐sup‐0001‐AppendixS1.docx.