Evidence map›Paper›PMID 37065427›Full record

ArticleSovremennye tekhnologii v meditsine2022

Radiomics in Breast Cancer: In-Depth Machine Analysis of MR Images of Metastatic Spine Lesion.

V Steinhauer, N I Sergeev

Abstract read
In one paragraph

Article in Sovremennye tekhnologii v meditsine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
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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

2 authors.

V SteinhauerSystem Architect; Devoteam GmbH, 14D Wiesenstrasse, Weiterstadt, Hessen, 64331, Germany.
N I SergeevLeading Researcher, Department of Complex Disease Diagnosing; Russian Research Center of Roentgenology and Radiology of the Ministry of Health of the Russian Federation, 86 Profsoyuznaya St., Moscow, 117997, Russia; Professor, Department of FDPE Roentgenology and Radiology; Pirogov Russian National Research Medical University, 1 Ostrovityanova St., Moscow, 117997, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using mathematic criteria for image processing (radiomics) makes it possible to more accurately assess the nature of therapy-associated changes and determine the sites of maximal response. Comparison of the acquired quantitative and clinical data may assist radiologists in making the optimal decision. Materials and Methods: MRI data of three patients with breast cancer T Results: The structure of the metastatically modified spine was analysed segment by segment in the sagittal and axial projections using machine image analysis operators. Rapid changes in the "complexity" of vertebrae images have been found, allowing one to suggest the efficacy of treatment in one of the three options - stabilization, improvement, progression. Changes in the vertebrae structure with a positive response to the treatment in the form of the formation of bone objects, calderas, reduction of the contrast agent circulation at the microlevel, confirmed by mathematical analysis, have been monitored. A correlation was obtained between the established changes and the level of the CA 15-3 cancer marker. Conclusion: The study has shown a high effectiveness of machine image analysis algorithms, high correlation of the obtained results with the radiologist's report and clinical and laboratory data in 9 cases out of 12. The Pearson correlation coefficient between the classical marker and matrix filter curve was 0.8.

Indexed as

Breast NeoplasmsAlgorithmsFemaleHumansMagnetic Resonance ImagingSoftwareSpinebone metastasesbreast cancerin-depth machine analysisradiomicstherapy of spine metastases

Identifiers

PMID37065427
PMCPMC10090925

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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.