Evidence map›Paper›PMID 39310216›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2024

Registration of Longitudinal Spine CTs for Monitoring Lesion Growth.

Malika Sanhinova, Nazim Haouchine, Steve D Pieper, William M Wells, Tracy A Balboni, Alexander Spektor, Mai Anh Huynh, Jeffrey P Guenette, Bryan Czajkowski, Sarah Caplan and 4 more

Abstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
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

14 authors.

Malika SanhinovaHarvard Medical School, Boston, MA, USA.
Nazim HaouchineHarvard Medical School, Boston, MA, USA.
Steve D PieperIsomics, Inc., Cambridge, MA, USA.
William M WellsHarvard Medical School, Boston, MA, USA.
Tracy A BalboniHarvard Medical School, Boston, MA, USA.
Alexander SpektorHarvard Medical School, Boston, MA, USA.
Mai Anh HuynhHarvard Medical School, Boston, MA, USA.
Jeffrey P GuenetteHarvard Medical School, Boston, MA, USA.
Bryan CzajkowskiHarvard Medical School, Boston, MA, USA.
Sarah CaplanHarvard Medical School, Boston, MA, USA.
Patrick DoyleHarvard Medical School, Boston, MA, USA.
Heejoo KangHarvard Medical School, Boston, MA, USA.
David B HackneyHarvard Medical School, Boston, MA, USA.
Ron N AlkalayHarvard Medical School, Boston, MA, USA.

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
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
NIAMS NIH HHS R01 AR075964NIAMS NIH HHS R56 AR075964
6 · The paper itself

Abstract

Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression. Our method follows a two-step pipeline where vertebrae are first automatically localized, labeled and 3D surfaces are generated using a deep learning model, then longitudinally aligned using a Gaussian mixture model surface registration. We tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3, 6, and 12 months follow-ups leading to 111 registrations. Our experiment showed accurate registration with an average Hausdorff distance of 0.65 mm and average Dice score of 0.92.

Identifiers

PMID39310216
PMCPMC11416858

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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.