Evidence map›Paper›PMID 42396253›Full record

ArticlePhysics and imaging in radiation oncology2026

Computed tomography-based prediction of early recurrence risks with estimating individual times to recurrence for lung cancer patients prior to radiotherapy.

Takumi Kodama, Hidetaka Arimura, Yuko Shirakawa, Yagiz Yedekci, Hidetake Yabuuchi, Yu Jin, Noriyuki Nagami, Tadamasa Yoshitake, Yoshiyuki Shioyama, Pervin Hurmuz

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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.

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0citing papers in PubMed
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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

10 authors.

Takumi KodamaDivision of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Hidetaka ArimuraDivision of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Yuko ShirakawaRadiology Informatics and Network, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Yagiz YedekciDepartment of Radiation Oncology, Faculty of Medicine, Hacettepe University, Hacettepe Mahallesi, 06230 Sıhhiye, Ankara, Turkey.
Hidetake YabuuchiDivision of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Yu JinDivision of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Noriyuki NagamiDepartment of Radiology, Saga University Hospital, 5-1-1, Nabeshima, Saga City, Saga 849-8501, Japan.
Tadamasa YoshitakeRadiology Informatics and Network, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Yoshiyuki ShioyamaIon Beam Therapy Center, SAGA-HIMAT Foundation, 3049 Harakoga-machi, Tosu, Saga 841-0071, Japan.
Pervin HurmuzDepartment of Radiation Oncology, Faculty of Medicine, Hacettepe University, Hacettepe Mahallesi, 06230 Sıhhiye, Ankara, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Purpose: Current radiomics approaches have limitations in predicting recurrence with the time gap between radiomic features prior to treatment and individual recurrence risks following treatment. This study aimed to develop computed tomography (CT)-based prediction models of early recurrence risks by estimating individual times to recurrence (TTRs) in patients with non-small cell lung cancer (NSCLC) prior to stereotactic ablative radiotherapy (SABR). Materials and methods: A total of 143 patients with stage I-II NSCLC treated with SABR were enrolled. Planning CT images were used to construct internal ( Results: The best prediction models achieved AUCs of 0.926 and 0.763 for 1-year and 0.869 and 0.883 for 2-year recurrence in the internal and external tests, respectively. Conclusion: The proposed CT-based model demonstrated improved performance for predicting 1- and 2-year recurrences by directly predicting TTRs. This study could bridge the time gap between pretreatment radiomic features and individual recurrence risks.

Indexed as

Bayesian approachNon-small cell lung cancerRadiomicsRadiotherapyTime to recurrence

Identifiers

PMID42396253
PMCPMC13324663

What Socratic 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 Socratic graph.