Evidence map›Paper›PMID 31759204›Full record

ReviewBone2020

The effects of metastatic lesion on the structural determinants of bone: Current clinical and experimental approaches.

Stacyann Bailey, David Hackney, Deepak Vashishth, Ron N Alkalay

Open access · greenAbstract readReview
In one paragraph

Review in Bone, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
0.9field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Clinical and geriatric factors associated with fracture risk in older adults with bone metastases: A prospective observational study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Observational
  5. Review
  6. Article
  7. Article
  8. Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026
    Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Stacyann BaileyCenter for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States of America.
David HackneyDepartment of Radiology, Beth Israel Deaconess Medical Center, Boston, MA 02215, United States of America.
Deepak VashishthCenter for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States of America.
Ron N AlkalayCenter for Advanced Orthopedic Studies, Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States of America. Electronic address: ralkalay@bidmc.harvard.edu.
Beth Israel Deaconess Medical Center · USRensselaer Polytechnic Institute · US

Funding

Predicting Fracture Risk in Patients Treated with Radiotherapy for Spinal Metastatic DiseaseR01AR075964 · NIAMS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI ALKALAY, RON N, BALBONI, TRACY A. · 2020 to 2024
$3.2M
Training Program in Cancer Prevention and Control for Priority PopulationsT32CA225617 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Guy H Montgomery, JUAN P WISNIVESKY · 2018 to 2026
$2.6M
Predicting Fracture Risk in Patients Treated with Radiotherapy for Spinal Metastatic DiseaseR56AR075964 · NIAMS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI ALKALAY, RON N, BALBONI, TRACY A. · 2019 to 2019
$662k
Lesion-specific QCT Improves Prediction of Pathologic Vertebrae Fracture LoadR21AR066916 · NIAMS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI ALKALAY, RON N · 2015 to 2016
$425k
NCI NIH HHS T32 CA225617NIAMS NIH HHS R01 AR075964NIAMS NIH HHS R21 AR066916NIAMS NIH HHS R56 AR075964
6 · The paper itself

Abstract

Metastatic bone disease is incurable with an associated increase in skeletal-related events, particularly a 17-50% risk of pathologic fractures. Current surgical and oncological treatments are palliative, do not reduce overall mortality, and therefore optimal management of adults at risk of pathologic fractures presents an unmet medical need. Plain radiography lacks specificity and may result in unnecessary prophylactic fixation. Radionuclide imaging techniques primarily supply information on the metabolic activity of the tumor or the bone itself. Magnetic resonance imaging and computed tomography provide excellent anatomical and structural information but do not quantitatively assess bone matrix. Research has now shifted to developing unbiased data-driven tools that can predict risk of impending fractures and guide individualized treatment decisions. This review discusses the state-of-the-art in clinical and experimental approaches for prediction of pathologic fractures with bone metastases. Alterations in bone matrix quality are associated with an age-related increase in skeletal fragility but the impact of metastases on the intrinsic material properties of bone is unclear. Engineering-based analyses are non-invasive with the capability to evaluate oncological treatments and predict failure due to the progression of metastasis. The combination of these approaches may improve our understanding of the underlying deterioration in mechanical performance.

Indexed as

Bone NeoplasmsFractures, BoneFractures, SpontaneousAdultHumansRadionuclide ImagingTomography, X-Ray ComputedBone qualityEngineering analysisFracture predictionImage-based

Identifiers

PMID31759204
PMCPMC7531290
OpenAlexW2989894985

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.