Evidence map›Paper›PMID 36695966›Full record

ArticleMolecular imaging and biology2023

Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach.

Brandon K K Fields, Natalie L Demirjian, Steven Y Cen, Bino A Varghese, Darryl H Hwang, Xiaomeng Lei, Bhushan Desai, Vinay Duddalwar, George R Matcuk

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular imaging and biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
4.8field-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

20 citing papers in PubMed, 21 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

9 authors at 4 institutions in 1 country.

Brandon K K FieldsDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, 94143, USA.
Natalie L DemirjianCollege of Medicine - Tucson, University of Arizona, Tucson, AZ, 85724, USA.
Steven Y CenDepartment of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90033, USA.
Bino A VargheseDepartment of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90033, USA.
Darryl H HwangDepartment of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90033, USA.
Xiaomeng LeiDepartment of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90033, USA.
Bhushan DesaiDepartment of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90033, USA.
Vinay DuddalwarDepartment of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90033, USA.
George R MatcukDepartment of Radiology, S Mark Taper Foundation Imaging Center, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Ste M-335, CA, 90048, Los Angeles, USA. George.Matcuk@cshs.org.ORCID 0000-0002-2858-0794
University of Southern California · USCedars-Sinai Medical Center · USUniversity of Arizona · USUniversity of California, San Francisco · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the performance of machine learning-augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas.

methodsForty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning-augmented radiomics analyses.

resultsThough only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22-0.58) and 0.44 (95% CI 0.26-0.62) for RF and AdaBoost, respectively.

conclusionOverall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures.

Indexed as

Neoadjuvant TherapySarcomaHumansMachine LearningMagnetic Resonance ImagingRetrospective StudiesMachine learningMagnetic resonance imagingNeoadjuvant chemotherapyRadiomicsSarcomaSoft tissue

Identifiers

PMID36695966
PMCPMC10333397
OpenAlexW4317951874

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.