Evidence map›Paper›PMID 38067368›Full record

ArticleCancers2023

Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Patients Using Harmonized Radiomics of Multcenter

Hye-Min Ju, Jingyu Yang, Jung-Mi Park, Joon-Ho Choi, Hyejin Song, Byung-Il Kim, Ui-Sup Shin, Sun Mi Moon, Sangsik Cho, Sang-Keun Woo

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2023. 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
1.2field-weighted citation impact, top 17% of its field
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, 5 citations in OpenAlex.

  1. Review
  2. Article
  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

10 authors at 2 institutions in 1 country.

Hye-Min JuRadiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Republic of Korea.
Jingyu YangDivision of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea.
Jung-Mi ParkDepartment of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea.
Joon-Ho ChoiDepartment of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea.ORCID 0000-0002-7735-761X
Hyejin SongDivision of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea.
Byung-Il KimDepartment of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea.
Ui-Sup ShinDepartment of Surgery, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea.
Sun Mi MoonDepartment of Surgery, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea.
Sangsik ChoDepartment of Surgery, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea.
Sang-Keun WooRadiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Republic of Korea.ORCID 0000-0002-6728-8876
Korea Institute of Radiological and Medical Sciences · KRSoonchunhyang University · KR

Funding

National Research Foundation of Korea 2020M2D9A1094070
6 · The paper itself

Abstract

We developed machine and deep learning models to predict chemoradiotherapy in rectal cancer using

Indexed as

18F-FDG PETdeep learningharmonized radiomicsmachine learningradiochemotherapy

Identifiers

PMID38067368
PMCPMC10705093
OpenAlexW4389206477

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.