Evidence map›Paper›PMID 34291325›Full record

ArticleSkeletal radiology2022

Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.

Connie Y Chang, Colleen Buckless, Kaitlyn J Yeh, Martin Torriani

Abstract read
PubMed Publisher
In one paragraph

Article in Skeletal radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 18% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

  1. Pooled it
  2. Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026
    Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. 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

4 authors at 1 institution in 1 country.

Connie Y ChangDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6, Boston, MA, 02114, USA. cychang@mgh.harvard.edu.ORCID http://orcid.org/0000-0003-1304-1576
Colleen BucklessDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6, Boston, MA, 02114, USA.
Kaitlyn J YehDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6, Boston, MA, 02114, USA.
Martin TorrianiDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6, Boston, MA, 02114, USA.
Harvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop a deep convolutional neural network capable of detecting spinal sclerotic metastases on body CTs. MATERIALS AND

methodsOur study was IRB-approved and HIPAA-compliant. Cases of confirmed sclerotic bone metastases in chest, abdomen, and pelvis CTs were identified. Images were manually segmented for 3 classes: background, normal bone, and sclerotic lesion(s). If multiple lesions were present on a slice, all lesions were segmented. A total of 600 images were obtained, with a 90/10 training/testing split. Images were stored as 128 × 128 pixel grayscale and the training dataset underwent a processing pipeline of histogram equalization and data augmentation. We trained our model from scratch on Keras/TensorFlow using an 80/20 training/validation split and a U-Net architecture (64 batch size, 100 epochs, dropout 0.25, initial learning rate 0.0001, sigmoid activation). We also tested our model's true negative and false positive rate with 1104 non-pathologic images. Global sensitivity measured model detection of any lesion on a single image, local sensitivity and positive predictive value (PPV) measured model detection of each lesion on a given image, and local specificity measured the false positive rate in non-pathologic bone.

resultsDice scores were 0.83 for lesion, 0.96 for non-pathologic bone, and 0.99 for background. Global sensitivity was 95% (57/60), local sensitivity was 92% (89/97), local PPV was 97% (89/92), and local specificity was 87% (958/1104).

conclusionA deep convolutional neural network has the potential to assist in detecting sclerotic spinal metastases.

Indexed as

Neural Networks, ComputerPelvisHumansImage Processing, Computer-AssistedArtificial intelligenceBone lesionsDeep convolutional neural networkSclerotic

Identifiers

PMID34291325
OpenAlexW3185059906

What OpenQuestion holds

Textmetadata
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.