Evidence map›Paper›PMID 38828071›Full record

ArticleJournal of medical physics

Deep Learning-based Lung dose Prediction Using Chest X-ray Images in Non-small Cell Lung Cancer Radiotherapy.

Takahiro Aoyama, Hidetoshi Shimizu, Yutaro Koide, Hidemi Kamezawa, Jun-Ichi Fukunaga, Tomoki Kitagawa, Hiroyuki Tachibana, Kojiro Suzuki, Takeshi Kodaira

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical physics. 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

9 authors.

Takahiro AoyamaDepartment of Radiation Oncology, Aichi Cancer Center, Nagoya, Japan.
Hidetoshi ShimizuDepartment of Radiation Oncology, Aichi Cancer Center, Nagoya, Japan.
Yutaro KoideDepartment of Radiation Oncology, Aichi Cancer Center, Nagoya, Japan.
Hidemi KamezawaDivision of Radiological Sciences, Graduate School of Health Sciences, Teikyo University, Fukuoka, Japan.
Jun-Ichi FukunagaDivision of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
Tomoki KitagawaDepartment of Radiation Oncology, Aichi Cancer Center, Nagoya, Japan.
Hiroyuki TachibanaDepartment of Radiation Oncology, Aichi Cancer Center, Nagoya, Japan.
Kojiro SuzukiDepartment of Radiology, Aichi Medical University, Nagakute, Aichi, Japan.
Takeshi KodairaDepartment of Radiation Oncology, Aichi Cancer Center, Nagoya, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to develop a deep learning model for the prediction of V Methods: The study utilized 91 chest X-ray images of patients with lung cancer acquired routinely during the admission workup. The prescription dose for the planning target volume was 60 Gy in 30 fractions. A convolutional neural network-based regression model was developed to predict V Results: The predictive results of the developed model for V Conclusions: The proposed deep learning chest X-ray model can predict V

Indexed as

Chest X-rayconvolutional neural networklung cancer radiotherapylung dose

Identifiers

PMID38828071
PMCPMC11141742

What OpenQuestion holds

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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.