Evidence map›Paper›PMID 35814259›Full record

ArticlePhysics and imaging in radiation oncology2022

Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy.

Pier Giorgio Esposito, Roberta Castriconi, Paola Mangili, Sara Broggi, Andrei Fodor, Marcella Pasetti, Alessia Tudda, Nadia Gisella Di Muzio, Antonella Del Vecchio, Claudio Fiorino

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Physics and imaging in radiation oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06317948 (Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models), which is not on this map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.2field-weighted citation impact, top 22% 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.

NCT06317948 unknown statusnot on this map

Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models

TypeobservationalSponsorIRCCS Ospedale San RaffaeleRan2022 to 2025Enrolled1,000ConditionsBreast Cancer, Prostate CancerArmstreatment plan comparison
3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
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  3. The smoothing factor in breast intensity-modulated radiotherapy: dosimetric impact and a predictive model of plan acceptability.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026
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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

10 authors at 2 institutions in 1 country.

Pier Giorgio EspositoMedical Physics, San Raffaele Scientific Institute, Milano, Italy.
Roberta CastriconiMedical Physics, San Raffaele Scientific Institute, Milano, Italy.
Paola MangiliMedical Physics, San Raffaele Scientific Institute, Milano, Italy.
Sara BroggiMedical Physics, San Raffaele Scientific Institute, Milano, Italy.
Andrei FodorRadiotherapy, San Raffaele Scientific Institute, Milano, Italy.
Marcella PasettiRadiotherapy, San Raffaele Scientific Institute, Milano, Italy.
Alessia TuddaMedical Physics, San Raffaele Scientific Institute, Milano, Italy.
Nadia Gisella Di MuzioRadiotherapy, San Raffaele Scientific Institute, Milano, Italy.
Antonella Del VecchioMedical Physics, San Raffaele Scientific Institute, Milano, Italy.
Claudio FiorinoMedical Physics, San Raffaele Scientific Institute, Milano, Italy.
San Raffaele University of Rome · ITVita-Salute San Raffaele University · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/Purpose: Tomotherapy may deliver high-quality whole breast irradiation at static angles. The aim of this study was to implement Knowledge-Based (KB) automatic planning for left-sided whole breast using this modality. Materials/Methods: Virtual volumetric plans were associated to the dose distributions of 69 Tomotherapy (TT) clinical plans of previously treated patients, aiming to train a KB-model using a commercial tool completely implemented in our treatment planning system. An individually optimized template based on the resulting KB-model was generated for automatic plan optimization. Thirty patients of the training set and ten new patients were considered for internal/external validation. Fully-automatic plans (KB-TT) were generated and compared using the same geometry/number of fields of the corresponding clinical plans. Results: KB-TT plans were successfully generated in 26/30 and 10/10 patients of the internal/external validation sets; for 4 patients whose original plans used only two fields, the manual insertion of one/two fields before running the automatic template was sufficient to obtain acceptable plans. Concerning internal validation, planning target volume V Conclusion: Automatic TT left-sided breast KB-plans are comparable to or slightly better than clinical plans and can be obtained with limited planner time. The approach is currently under clinical implementation.

Indexed as

AI in Radiation OncologyBreast cancerKnowledge-based modelsRadiotherapy planning optimizationTomotherapy

Identifiers

PMID35814259
PMCPMC9256826
OpenAlexW4283317178

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.