Evidence map›Paper›PMID 40445102›Full record

ArticleRadiology. Imaging cancer2025

Predicting Recurrence in Locally Advanced Rectal Cancer Using Multitask Deep Learning and Multimodal MRI.

Zonglin Liu, Runqi Meng, Qiong Ma, Zhen Guan, Rong Li, Caixia Fu, Yanfen Cui, Yiqun Sun, Tong Tong, Dinggang Shen

Abstract readMulticenter Study
In one paragraph

Article in Radiology. Imaging cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Zonglin Liu *Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Rd, 270, Xuhui District, Shanghai, 200032, China.
Runqi Meng *School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.ORCID 0009-0009-7192-970X
Qiong MaDepartment of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Rd, 270, Xuhui District, Shanghai, 200032, China.
Zhen GuanDepartment of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China.ORCID 0000-0002-6270-0614
Rong LiDepartment of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Rd, 270, Xuhui District, Shanghai, 200032, China.
Caixia FuMR Application Development, Siemens Shenzhen Magnetic Resonance, Shenzhen, China.
Yanfen CuiGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.ORCID 0000-0001-6631-5687
Yiqun SunDepartment of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Rd, 270, Xuhui District, Shanghai, 200032, China.ORCID 0000-0002-3375-3012
Tong Tong *Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Rd, 270, Xuhui District, Shanghai, 200032, China.ORCID 0000-0002-9180-8181
Dinggang Shen *School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose To develop and validate a deep multitask network, MultiRecNet, for fully automatic prediction of disease-free survival (DFS) in patients with neoadjuvant chemoradiotherapy (nCRT)-treated locally advanced rectal cancer (LARC). Materials and Methods This retrospective study collected clinical information and baseline multimodal MRI (T2, apparent diffusion coefficient [ADC],

Indexed as

Deep LearningMagnetic Resonance ImagingMultimodal ImagingNeoplasm Recurrence, LocalRectal NeoplasmsAdultAgedDisease-Free SurvivalFemaleHumansMaleMiddle AgedNeoadjuvant TherapyPredictive Value of TestsRetrospective StudiesAbdomen/GIMR-ImagingOncologyRectum

Identifiers

PMID40445102
PMCPMC12130701

What Socratic 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 Socratic graph.