# Curating musculoskeletal CT data to enable the development of AI/ML approaches for analysis of clinical CT in patients with metastatic spinal disease

> **NIH NIH R01** · BETH ISRAEL DEACONESS MEDICAL CENTER · 2022 · $352,266

## Abstract

Project Summary/Abstract
Vertebral bone metastases, widespread in patients with cancer, destroy vertebral anatomy, bone architecture,
and mechanical strength, exposing patients to a high risk of pathologic vertebral fracture (PVF). PVFs cause
neurological deficits in up to 50% of patients with further complications that may be fatal. Our parent grant
(AR075964) develops and validates a novel computational musculoskeletal approach for patient-specific,
precise prediction of PVF risk from clinical CT. Segmentation of vertebral anatomy, bone properties, and
individual spinal musculature cross-sectional area from clinical CT imaging, a fundamental step for assembling
the computational musculoskeletal models, faces unique challenges due to the cancer-mediated alteration in
skeletal tissues radiological appearance. Application of deep learning (DL) methods will speed and standardize
the critical segmentation step, permitting analysis of larger datasets promoting new DL analysis for improved
insight into the drivers of PVF risk in patients with metastatic spine disease. This development is hindered by the
lack of publicly available, clinically annotated image data specific to metastatic human spines.
This proposal aims to establish a curated, publicly accessible, 4D CT imaging dataset of human metastatic
spines and associated radiological delineations of lesions and vertebral features, to enable the advancement of
DL methods to analyze PVF risk. We thus propose three specific aims: 1) prepare the CT dataset for the
application of deep learning: Our data is based on our parent grant (AR075964) de-identified spinal column and
thoracoabdominal muscles CT image datasets obtained from 140 patients at radiation planning and additional
CT diagnostic data at 3, 6, 9, and 12 months follow up (1400 image data sets overall). We will assemble and
curate the data for public distribution by a) preprocessing the CT images for machine learning applications, b)
delineating the vertebrae, lesions, and muscles in the images, c) assembling metadata, including limited subject
demographic and disease status into JSON files, and d) extract SIFT features for computer-vision style analyses.
2) Testbed Deep Learning (DL) Segmentation: To ensure that the curated data set is suitable for training artificial
intelligence and machine learning (AI/ML) systems, we will develop and train testbed DL segmentation networks
to segment bones, lesions, and muscles in baseline and follow-up clinical CT. We will use the networks to control
the quality the curated CT images and delineations, 3) Disseminate 4D dataset following best practices: Upon
completion of tasks 1 and 2, we will make the data available to the research community via the Cancer Image
Archive (TCIA) following their established methods for de-identifying DICOM scans and annotating and encoding
clinical data and analysis results. Integrating DL systems within our approach will change the patient
management paradigm from reactiv...

## Key facts

- **NIH application ID:** 10593799
- **Project number:** 3R01AR075964-03S1
- **Recipient organization:** BETH ISRAEL DEACONESS MEDICAL CENTER
- **Principal Investigator:** RON N ALKALAY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $352,266
- **Award type:** 3
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10593799

## Citation

> US National Institutes of Health, RePORTER application 10593799, Curating musculoskeletal CT data to enable the development of AI/ML approaches for analysis of clinical CT in patients with metastatic spinal disease (3R01AR075964-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10593799. Licensed CC0.

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