Evidence map›Paper›PMID 38673063›Full record

ArticleJournal of personalized medicine2024

Computational Approach for Spatially Fractionated Radiation Therapy (SFRT) and Immunological Response in Precision Radiation Therapy.

Paolo Castorina, Filippo Castiglione, Gianluca Ferini, Stefano Forte, Emanuele Martorana

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
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

5 authors.

Paolo CastorinaIstituto Oncologico del Mediterraneo, Via Penninazzo, 7, 95029 Viagrande, Italy.ORCID 0000-0002-5617-8102
Filippo CastiglioneBiotechnology Research Center, Technology Innovation Institute, Abu Dhabi P.O. Box 9639, United Arab Emirates.
Gianluca FeriniREM Radioterapia, Via Penninazzo, 11, 95029 Viagrande, Italy.ORCID 0000-0002-2305-5544
Stefano ForteIstituto Oncologico del Mediterraneo, Via Penninazzo, 7, 95029 Viagrande, Italy.ORCID 0000-0001-5746-7451
Emanuele MartoranaIstituto Oncologico del Mediterraneo, Via Penninazzo, 7, 95029 Viagrande, Italy.ORCID 0000-0001-9760-887X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of precision radiation therapy has seen remarkable advancements in both experimental and computational methods. Recent literature has introduced various approaches such as Spatially Fractionated Radiation Therapy (SFRT). This unconventional treatment, demanding high-precision radiotherapy, has shown promising clinical outcomes. A comprehensive computational scheme for SFRT, extrapolated from a case report, is proposed. This framework exhibits exceptional flexibility, accommodating diverse initial conditions (shape, inhomogeneity, etc.) and enabling specific choices for sub-volume selection with administrated higher radiation doses. The approach integrates the standard linear quadratic model and, significantly, considers the activation of the immune system due to radiotherapy. This activation enhances the immune response in comparison to the untreated case. We delve into the distinct roles of the native immune system, immune activation by radiation, and post-radiotherapy immunotherapy, discussing their implications for either complete recovery or disease regrowth.

Indexed as

immunotherapyin-silico modelmathematical frameworkradiotherapySpatially Fractionated Radiation Therapy

Identifiers

PMID38673063
PMCPMC11051362

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.