Evidence map›Paper›PMID 40856068›Full record

ArticleJournal of Korean medical science2025

Novel Bone Scan Features for Predicting Prognosis in Men With Bone Metastatic Prostate Cancer: A Retrospective Study.

Byung Woo Kim, Jang Hee Han, Sang Hyun Yoo, Minh-Tung Do, Minho Kang, Seung-Bo Lee, Dongkyu Oh, Gi Jeong Cheon, Ja Hyeon Ku, Cheol Kwak and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of Korean medical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Byung Woo Kim *Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0009-0003-0829-3573
Jang Hee Han *Department of Urology, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0000-0003-4942-4632
Sang Hyun YooDepartment of Urology, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0000-0001-9820-5634
Minh-Tung DoDepartment of Urology, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0000-0003-4936-5660
Minho KangDepartment of Urology, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0009-0005-2228-5398
Seung-Bo LeeDepartment of Medical Informatics, Keimyung University School of Medicine, Daegu, Korea.ORCID https://orcid.org/0000-0002-6817-5793
Dongkyu OhDepartment of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-7935-3479
Gi Jeong CheonDepartment of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-1360-5186
Ja Hyeon KuDepartment of Urology, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0000-0002-0391-2342
Cheol KwakDepartment of Urology, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0000-0002-1987-2111
Young-Gon KimDepartment of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0000-0001-6840-6830
Chang Wook JeongDepartment of Urology, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0000-0002-2200-5019

Funding

Korea Health Industry Development Institute HI22C1322Korea Health Industry Development Institute RS-2022-KH129321
6 · The paper itself

Abstract

backgroundBone metastasis frequently occurs in patients with prostate cancer, however, a consensus has not been reached regarding bone scan image analysis. We aimed to analyse various bone scan imaging features of metastatic prostate cancer and to assess their impact on prognosis.

methodsOne thousand five hundred sixty-three paired sets of bone scan images (anterior and posterior) were obtained from patients with metastatic prostate cancer at Seoul National University Hospital. U-Net architecture was used for the segmentation of metastatic bone lesions. Imaging features describing the overall metastatic burden (n = 18) and largest metastatic burden (n = 32) were extracted using computer vision techniques. Kaplan-Meier survival analysis and Cox proportional risk model were used to analyse the prognostic impact of each feature.

resultsThe correlation coefficient between the actual number of lesions and that predicted by the deep learning model was 0.87, indicating a strong correlation. Multivariate Cox regression showed that metastasis intensity difference (hazard ratio [HR], 0.53;

conclusionAlthough the number of bone metastases is a standardised prognostic factor, additional consideration of morphological or intensity-related novel features may be useful to more accurately predict the prognosis of patients with metastatic prostate cancer.

Indexed as

Bone and BonesBone NeoplasmsProstatic NeoplasmsAgedAged, 80 and overDeep LearningHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisProportional Hazards ModelsRetrospective StudiesBone MetastasisBone Scan ImageDeep LearningPrognosisProstate Cancer

Identifiers

PMID40856068
PMCPMC12378022

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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.