Evidence map›Paper›PMID 32236847›Full record

ArticleMedical oncology (Northwood, London, England)2020

Delta-radiomics increases multicentre reproducibility: a phantom study.

Valerio Nardone, Alfonso Reginelli, Cesare Guida, Maria Paola Belfiore, Michelangelo Biondi, Maria Mormile, Fabrizio Banci Buonamici, Eugenio Di Giorgio, Marco Spadafora, Paolo Tini and 5 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Medical oncology (Northwood, London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
6.2field-weighted citation impact, top 3% 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

48 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.

  1. Delta radiomics: a systematic review.La Radiologia medica · 2021
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  18. Peritoneal Carcinosis: What the Radiologist Needs to Know.Diagnostics (Basel, Switzerland) · 2023
    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

15 authors at 3 institutions in 1 country.

Valerio NardoneUnit of Radiation Oncology, Ospedale del Mare, 80147, Naples, Italy.
Alfonso ReginelliDepartment of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy. alfonso.reginelli@hotmail.com.
Cesare GuidaUnit of Radiation Oncology, Ospedale del Mare, 80147, Naples, Italy.
Maria Paola BelfioreDepartment of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
Michelangelo BiondiUnit of Medical Physics, University Hospital of Siena, 53100, Siena, Italy.
Maria MormileUnit of Radiation Oncology, Ospedale del Mare, 80147, Naples, Italy.
Fabrizio Banci BuonamiciUnit of Medical Physics, University Hospital of Siena, 53100, Siena, Italy.
Eugenio Di GiorgioUnit of Nuclear Medicine, Ospedale del Mare, 80147, Naples, Italy.
Marco SpadaforaUnit of Nuclear Medicine, Ospedale del Mare, 80147, Naples, Italy.
Paolo TiniUnit of Radiation Oncology, University Hospital of Siena, 53100, Siena, Italy.
Roberta GrassiDepartment of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
Luigi PirtoliUnit of Radiation Oncology, University Hospital of Siena, 53100, Siena, Italy.
Pierpaolo CorrealeUnit of Medical Oncology, Grand Metropolitan Hospital "Bianchi Melacrino Morelli", 89124, Reggio Calabria, Italy.
Salvatore CappabiancaDepartment of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
Roberto GrassiDepartment of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
University of Campania "Luigi Vanvitelli" · ITUniversity of Siena · ITAzienda ospedaliera "Bianchi-Melacrino-Morelli" · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Texture analysis (TA) can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols. Delta-texture analysis (D-TA), conversely, consist in the analysis of TA feature variations at different acquisition times, usually before and after a therapy. Aim of this study was to investigate the influence of different CT scanners and acquisition parameters in the robustness of TA and D-TA. We scanned a commercial phantom (CIRS model 467, Gammex, Middleton, WI, USA), that is used for the calibration of electron density, two times by varying the disposition of plugs, using three different scanners. After the segmentation, we extracted TA features with LifeX and calculated TA features and D-TA features, defined as the variation of each TA parameters extracted from the same position by varying the plugs with the formula (Y-X)/X. The robustness of TA and D-TA features were then tested with intraclass coefficient correlation (ICC) analysis. The reliability of TA parameters across different scans, with different acquisition parameters and ROI positions has shown poor reliability in 12/37 and moderate reliability in the remaining 25/37, with no parameters showing good reliability. The reliability of D-TA, conversely, showed poor reliability in 10/37 parameters, moderate reliability in 10/37 parameters, and good reliability in 17/37 parameters. The comparison between TA and D-TA ICCs showed a significant difference for the whole group of parameters (p:0.004) and for the subclasses of GLCM parameters (p:0.033), whereas for the other subclasses of matrices (GLRLM, NGLDM, GLZLM, Histogram), the difference was not significant. D-TA features seem to be more robust than TA features. These findings reinforce the potentiality for using D-TA features for early assessment of treatment response and for developing tailored therapies. More work is needed in a clinical setting to confirm the results of the present study.

Indexed as

CalibrationHumansImage Processing, Computer-AssistedNeoplasmsPhantoms, ImagingPrognosisReproducibility of ResultsTomography, X-Ray ComputedTreatment OutcomeImagingLung cancerRadiomicsTexture analysis

Identifiers

PMID32236847
OpenAlexW3014792565

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.