Evidence map›Paper›PMID 42298800›Full record

ArticleJournal of applied clinical medical physics2026

Design and validation of a deep learning GAN for predicting

Otieno Erick Kapis, Muchai Ian Kaniu, Iaccarino Giuseppe, Hudson Kalambuka Angeyo

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied clinical medical physics, 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

4 authors.

Otieno Erick KapisDepartment of Physics, University of Nairobi, Nairobi, Kenya.
Muchai Ian KaniuDepartment of Physics, University of Nairobi, Nairobi, Kenya.
Iaccarino GiuseppeLaboratory of Medical Physics, IRCCS Istituto Nazionale Tumori Regina Elena, Rome, Italy.
Hudson Kalambuka AngeyoDepartment of Physics, University of Nairobi, Nairobi, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe rapid evolution of deep learning (DL), particularly the emergence of generative adversarial networks (GANs) has demonstrated strong potential for generating high-resolution, voxel-level dosimetric data. However, GAN-based approaches have not yet been broadly applied to the generation of tissue-specific dose voxel kernels (DVKs) in internal dosimetry. This gap underscores a critical unmet need for methods that can deliver Monte Carlo-level accuracy while achieving substantially improved computational efficiency. PURPOSE: This study aimed to design a GAN architecture to train models that can generate MATERIALS AND

methodsA GAN model was trained with paired CT-derived density kernel maps (DKs; voxel size 0.24

resultsThe GAN model predicted

conclusionGAN-generated

Indexed as

Deep LearningImage Processing, Computer-AssistedLutetiumMonte Carlo MethodPhantoms, ImagingRadioisotopesRadiometryRadiotherapy Planning, Computer-AssistedGenerative Adversarial NetworksGenerative Artificial IntelligenceHumansRadiotherapy DosageLutetiumLutetium-177Radioisotopes3D‐GAN DVK ModelGATE Monte CarloLu‐177 DVK Dosimetry

Identifiers

PMID42298800
PMCPMC13269658

What OpenQuestion holds

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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.