Evidence map›Paper›PMID 42088604›Full record

ArticlePhysics and imaging in radiation oncology2026

A comparative assessment of deep learning and knowledge-based dose prediction models for advanced radiotherapy planning of prostate cancer with focal boosting.

Maria A Piliero, Antonio Angrisani, Davide G Bosetti, Margherita Casiraghi, Francesco Castronovo, Nicolas Vial, Salvatore Cozzi, Matteo Coppotelli, Klaudia Krzekotowska, Lisa Milan and 3 more

Abstract read
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In one paragraph

Article in Physics and imaging in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Maria A PilieroMedical Physics, Imaging Institute of Southern Switzerland, EOC, Bellinzona, Switzerland.
Antonio AngrisaniRadiation Oncology, Oncology Institute of Southern Switzerland (IOSI), EOC, Bellinzona, Switzerland.
Davide G BosettiRadiation Oncology, Oncology Institute of Southern Switzerland (IOSI), EOC, Bellinzona, Switzerland.
Margherita CasiraghiMedical Physics, Imaging Institute of Southern Switzerland, EOC, Bellinzona, Switzerland.
Francesco CastronovoRadiation Oncology, Oncology Institute of Southern Switzerland (IOSI), EOC, Bellinzona, Switzerland.
Nicolas VialRadiation Oncology, Oncology Institute of Southern Switzerland (IOSI), EOC, Bellinzona, Switzerland.
Salvatore CozziRadiation Oncology, Oncology Institute of Southern Switzerland (IOSI), EOC, Bellinzona, Switzerland.
Matteo CoppotelliMedical Physics, Imaging Institute of Southern Switzerland, EOC, Bellinzona, Switzerland.
Klaudia KrzekotowskaMedical Physics, Imaging Institute of Southern Switzerland, EOC, Bellinzona, Switzerland.
Lisa MilanMedical Physics, Imaging Institute of Southern Switzerland, EOC, Bellinzona, Switzerland.
Francesco PupilloMedical Physics, Imaging Institute of Southern Switzerland, EOC, Bellinzona, Switzerland.
Stefano PresillaMedical Physics, Imaging Institute of Southern Switzerland, EOC, Bellinzona, Switzerland.
Thomas ZilliRadiation Oncology, Oncology Institute of Southern Switzerland (IOSI), EOC, Bellinzona, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study compared knowledge-based and deep-learning dose prediction models with clinically approved plans for prostate radiotherapy with focal boosting. The knowledge-based model accurately reproduced clinical plans. Deep-learning predictions showed median variations of 5% (IQR < 5%) for bladder and rectum in high-dose regions and 3.8 Gy higher mean dose (IQR 1.2 Gy) for the femoral heads. However, pudendal artery mean doses were 15 Gy higher (IQR: 10-19 Gy) and urethral V

Indexed as

Deep learningDose predictionProstate cancerRadiotherapy planningVMAT

Identifiers

PMID42088604
PMCPMC13137215

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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.