AN AI-ENABLED STUDY ON OSTEOSARCOPENIA PROGRESSION IN PROSTATE CANCER PATIENTS USING MUSCULOSKELETAL IMAGING BIOMARKERS
Bibliographic record
Abstract
Osteosarcopenia is a musculoskeletal (MSK) condition that is a combination of osteopenia/osteoporosis (decreased bone mineral density - BMD) and sarcopenia (decreased skeletal muscle mass and strength). Both conditions are prevalent in prostate cancer cohorts. Advanced therapeutics extend survival in cancer patients, but the prolonged period of active disease and treatment have irreversible, deleterious impacts on MSK health due to the progressive nature of osteosarcopenia. Osteosarcopenia can lead to increased falls, fractures, decreases in quality of life and poor patient outcomes. Traditionally, osteopenia and sarcopenia were studied separately, leaving combined effects and interactions underexamined. Additionally, prior studies rely on single time point evaluations with Dual-Energy X-ray Absorptiometry (DEXA) and 2D Computed Tomography (CT) scans, neglecting temporal progression. To retrospectively quantify osteosarcopenia progression in advanced prostate cancer patients using imaging biomarkers. Sarcopenia will progress over time with decreases in muscle quality and quantity, while BMD may rise due to sclerotic metastatic lesions. MSK biomarkers were calculated for patients treated with systemic therapy at Sunnybrook Odette Cancer Centre from 2009 to 2021 using opportunistic prostate cancer surveillance images (REB#1862). Lumbar Spine 3D CT scans were reconstructed to a higher resolution of 1mm isotropic voxel size from diagnostic axial, sagittal, and coronal reconstructions using a custom iterative approach. The biomarker extraction pipeline segmented lumbar vertebral bodies (L1-L5) and psoas muscles using a combination of convolutional neural network architectures. The superior and inferior boundary of the psoas muscle were set from L2/L3 to L4/L5 disc mid-points based on vertebral segmentations. BMD of the lumbar vertebral bodies and the psoas muscle volume and density, quantified using Hounsfield units, were extracted. Our analysis included 142 male patients (age: mean=71 years, range=44-90 years) with 475 CT scans with average follow up of 1034 days (range=45-3284 days). Throughout this period, BMD increased (17.1%, STD=48%), psoas volume decreased (5.7%, STD=34.9%), and psoas density decreased (17.9% STD=46.7%). BMD was stable or decreased for 51.8% of subjects, while 25.9% of subjects had a >30% BMD increase. Psoas density and volume were decreased in two out of three subjects. Moderate to weak correlations were found for the temporal rate of change of the MSK biomarkers (psoas volume vs. BMD R2=-0.29, psoas density vs. BMD R2=-0.10, psoas volume vs. psoas density R2=-0.46, all p This study quantifies marked longitudinal changes in osteosarcopenia biomarkers in advanced prostate cancer patients. The majority of patients experienced sarcopenia progression despite prostate cancer treatments, highlighting the potential impacts of advanced therapeutics and disease progression on MSK health. The BMD increases were measured secondary to sclerotic metastatic lesions. The correlations between the rate of change of MSK biomarkers indicate a complex interaction between bone and muscle health in prostate cancer patients. This study showed greater deterioration of BMD at non-metastatic levels and of muscle quality and quantity in prostate cancer patients compared to previous reports, likely attributable to longer follow-up. The osteosarcopenia quantitative 3D imaging biomarkers are more sensitive to changes than 2D biomarkers for longitudinal assessment. These methods can track osteosarcopenia progression and monitor metastatic progression and other MSK health-impacting conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".