Evidence map›Paper›PMID 38383799›Full record

ArticleEJNMMI physics2024

Fit of biokinetic data in molecular radiotherapy: a machine learning approach.

Davide Ciucci, Bartolomeo Cassano, Salvatore Donatiello, Federica Martire, Antonio Napolitano, Claudia Polito, Elena Solfaroli Camillocci, Gianluca Cervino, Ludovica Pungitore, Claudio Altini and 4 more

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

Article in EJNMMI physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Review
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

14 authors at 4 institutions in 2 countries.

Davide CiucciMedical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Bartolomeo CassanoMedical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy. bartolomeo.cassano@gmail.com.ORCID http://orcid.org/0000-0001-5747-8230
Salvatore DonatielloMedical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Federica MartireTor Vergata Postgraduate School of Medical Physics, University of Rome, Rome, Italy.
Antonio NapolitanoMedical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Claudia PolitoMedical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Elena Solfaroli CamillocciMedical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Gianluca CervinoRoma 3 University of Rome, Rome, Italy.
Ludovica PungitoreRoma 3 University of Rome, Rome, Italy.
Claudio AltiniNuclear Medicine Unit/Imaging Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Maria Felicia VillaniNuclear Medicine Unit/Imaging Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Milena PizzoferroNuclear Medicine Unit/Imaging Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Maria Carmen GarganeseNuclear Medicine Unit/Imaging Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Vittorio CannatàMedical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Bambino Gesù Children's Hospital · ITRoma Tre University · ITNational Cancer Institute · MYUniversity of Rome Tor Vergata · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks. The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (τ).

methodsTwo different ML systems have been developed for classifying the fit model and to predict the biokinetic parameters. The two systems were trained and tested with synthetic TACs simulating a whole-body Fraction Injected Activity for patients affected by metastatic Differentiated Thyroid Carcinoma, administered with [

resultsAs N varies, CA remains constant for ML (about 98%), while it improves for F-test (from 62 to 92%) and AICc (from 50 to 92%), as N increases. With AM, [Formula: see text] can reach down to - 67%, while using ML [Formula: see text] ranges within ± 25%. Using real TACs, there is a good agreement between τ obtained with ML system and AM.

conclusionsThe employing of ML systems may be feasible, having both a better classification and a better estimation of biokinetic parameters.

Indexed as

Akaike information criterionBiokinetic curvesFit functionF-testMachine learning

Identifiers

PMID38383799
PMCPMC10881934
OpenAlexW4392056665

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.