Evidence map›Paper›PMID 40453356›Full record

ArticleiRadiology2024

Artificial Intelligence and Radiomics Applied to Prostate Cancer Bone Metastasis Imaging: A Review.

S J Pawan, Joseph Rich, Jonathan Le, Ethan Yi, Timothy Triche, Amir Goldkorn, Vinay Duddalwar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iRadiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Individualized radiotherapy of bone metastases from prostate cancer: time trends and 5-year survival results.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026
    Article
  2. Radiomics and Back Pain.Global spine journal · 2026
    Review
  3. 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

7 authors.

S J PawanDepartment of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.
Joseph RichBiology and Bioengineering, California Institute of Technology, Pasadena, California, USA.
Jonathan LeKeck School of Medicine, University of Southern California, Los Angeles, California, USA.
Ethan YiKeck School of Medicine, University of Southern California, Los Angeles, California, USA.
Timothy TricheCenter for Personalized Medicine, Dept of Pathology and Laboratory medicine, Children's Hospital, Los Angeles, California, USA.
Amir GoldkornDepartment of Medicine and Biochemistry & Molecular Medicine, USC Norris Comprehensive Cancer Center & Keck School of Medicine, Los Angeles, California, USA.
Vinay DuddalwarDepartment of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.

Funding

Integrated radiomic and liquid biopsy monitoring in SWOG S1802: A phase 3 therapeutic trial for metastatic prostate cancerR01CA257610 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI DUDDALWAR, VINAY, GOLDKORN, AMIR · 2021 to 2025
$3.4M
NCI NIH HHS R01 CA257610
6 · The paper itself

Abstract

The skeletal system is the most common site of metastatic prostate cancer, and these lesions are associated with poor outcomes. Diagnosing these osseous metastatic lesions relies on radiologic imaging, making early detection, diagnosis, and monitoring crucial for clinical management. However, the literature lacks a detailed analysis of various approaches and future directions. To address this gap, we present a scoping review of quantitative methods from diverse domains, including radiomics, machine learning, and deep learning, applied to imaging analysis of prostate cancer with clinical insights. Our findings highlight the need for developing clinically significant methods to aid in the battle against prostate bone metastasis.

Indexed as

bone metastasismachine learningprostate cancerradiomics

Identifiers

PMID40453356
PMCPMC12124838

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.