Evidence map›Paper›PMID 31226518›Full record

ArticleTranslational oncology2019

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography.

Jason Fernandes, Lakshmanan Sannachi, William T Tran, Alexander Koven, Elyse Watkins, Farnoosh Hadizad, Sonal Gandhi, Frances Wright, Belinda Curpen, Ahmed El Kaffas and 3 more

Open access · goldAbstract read
In one paragraph

Article in Translational oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
5.0field-weighted citation impact, top 5% 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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.

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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

13 authors at 2 institutions in 2 countries.

Jason FernandesDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA.
Lakshmanan SannachiDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA; Physical Sciences, Sunnybrook Research Institute, Toronto, CA.
William T TranDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA; Department of Radiation Oncology, University of Toronto, Toronto, CA; Centre for Health and Social Care Research, Sheffield Hallam University, Sheffield, UK; Institute of Clinical Evaluative Sciences, Sunnybrook Research Institute, Toronto, CA.
Alexander KovenDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA.
Elyse WatkinsDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA.
Farnoosh HadizadDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA.
Sonal GandhiDivision of Medical Oncology, Sunnybrook Health Sciences Centre, Toronto, CA.
Frances WrightDivision of Surgical Oncology, Sunnybrook Health Sciences Centre, Toronto, CA.
Belinda CurpenDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, CA.
Ahmed El KaffasPhysical Sciences, Sunnybrook Research Institute, Toronto, CA.
Joanna FaltynPhysical Sciences, Sunnybrook Research Institute, Toronto, CA.
Ali Sadeghi-NainiDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA; Department of Radiation Oncology, University of Toronto, Toronto, CA; Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, CA; Physical Sciences, Sunnybrook Research Institute, Toronto, CA.
Gregory CzarnotaDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA; Department of Radiation Oncology, University of Toronto, Toronto, CA; Department of Medical Biophysics, University of Toronto, Toronto, CA; Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, CA; Physical Sciences, Sunnybrook Research Institute, Toronto, CA. Electronic address: gregory.czarnota@sunnybrook.ca.
Sunnybrook Health Science Centre · CASunnybrook Research Institute

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Strain elastography was used to monitor response to neoadjuvant chemotherapy (NAC) in 92 patients with biopsy-proven, locally advanced breast cancer. Strain elastography data were collected before, during, and after NAC. Relative changes in tumor strain ratio (SR) were calculated over time, and responder status was classified according to tumor size changes. Statistical analyses determined the significance of changes in SR over time and between response groups. Machine learning techniques, such as a naïve Bayes classifier, were used to evaluate the performance of the SR as a marker for Miller-Payne pathological endpoints. With pathological complete response (pCR) as an endpoint, a significant difference (P < .01) in the SR was observed between response groups as early as 2 weeks into NAC. Naïve Bayes classifiers predicted pCR with a sensitivity of 84%, specificity of 85%, and area under the curve of 81% at the preoperative scan. This study demonstrates that strain elastography may be predictive of NAC response in locally advanced breast cancer as early as 2 weeks into treatment, with high sensitivity and specificity, granting it the potential to be used for active monitoring of tumor response to chemotherapy.

Identifiers

PMID31226518
PMCPMC6586920
OpenAlexW2951468938

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.