Evidence map›Paper›PMID 35480103›Full record

ArticleFrontiers in oncology2022

Dosimetric Impact of Inter-Fraction Variability in the Treatment of Breast Cancer: Towards New Criteria to Evaluate the Appropriateness of Online Adaptive Radiotherapy.

Martina Iezzi, Davide Cusumano, Danila Piccari, Sebastiano Menna, Francesco Catucci, Andrea D'Aviero, Alessia Re, Carmela Di Dio, Flaviovincenzo Quaranta, Althea Boschetti and 6 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Adaptive radiotherapy for breast cancer.Clinical and translational radiation oncology · 2023
    Review
  6. Article
  7. 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

16 authors.

Martina IezziUniversità Cattolica del Sacro Cuore, Rome, Italy.
Davide CusumanoDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Danila PiccariDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Sebastiano MennaDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Francesco CatucciUOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.
Andrea D'AvieroUOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.
Alessia ReUOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.
Carmela Di DioUOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.
Flaviovincenzo QuarantaUOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.
Althea BoschettiUOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.
Marco MarrasUOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.
Domenico PiroUOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.
Flavia TomeiUOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.
Claudio VottaDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Vincenzo ValentiniDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Gian Carlo MattiucciDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: As a discipline in its infancy, online adaptive RT (ART) needs new ontologies and Methods: The study was focused on right breast cancer patients treated using standard adjuvant RT on an artificial intelligence (AI)-based linear accelerator. Patients were treated with daily CBCT images and without online adaptation, prescribing 40.05 Gy in 15 fractions, with four IMRT tangential beams. ESTRO guidelines were followed for the delineation on planning CT (pCT) of organs at risk and targets. For each patient, all the CBCT images were rigidly aligned to pCT: CTV and PTV were manually re-contoured and the original treatment plan was recalculated. Various radiological parameters were measured on CBCT images, to quantify inter-fraction variability present in each RT fraction after the couch shifts compensation. The variation of these parameters was correlated with the variation of V95% of PTV (ΔV95%) using the Wilcoxon Mann-Whitney test. Fractions where ΔV95% > 2% were considered as adverse events. A logistic regression model was calculated considering the most significant parameter, and its performance was quantified with a receiver operating characteristic (ROC) curve. Results: A total of 75 fractions on 5 patients were analyzed. The body variation between daily CBCT and pCT along the beam axis with the highest MU was identified as the best predictor ( Conclusion: A novel strategy to identify treatment fractions that may benefit online ART was proposed. After image alignment, the measure of body difference between daily CBCT and pCT can be considered as an indirect estimator of V95% PTV variation: a difference larger than 3 mm will result in a V95% decrease larger than 2%. A larger number of observations is needed to confirm the results of this hypothesis-generating study.

Indexed as

AI radiotherapyCBCT radiotherapyinter-fraction dose variationonline adaptationpredictive modeling

Identifiers

PMID35480103
PMCPMC9035849

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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.