ArticleRadiology. Artificial intelligence2024
Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI.
Open access · hybridAbstract readMulticenter Study
In one paragraph
Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
6.5field-weighted citation impact, top 3% of its field
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.
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.
Who cites it
12 citing papers in PubMed, 16 citations in OpenAlex.
- Radiological staging clinical decision support model for rectal cancer lymph node metastasis detection on MRI.BMC cancer · 2026Article
- Diagnostic Performance of a Deep Learning-Based Tool for the Detection and Staging of Rectal Cancers on Endoscopic Ultrasound: Prospective Study.Diagnostics (Basel, Switzerland) · 2026Article
- Medical image pretraining-based transfer learning for generalizable and robust diagnosis of bone tumors on radiographs: a multi-center study.Insights into imaging · 2026Article
- Lymph nodes in MRI-based early-stage rectal cancer: can MRI accurately predict lymph node involvement?European radiology · 2026Article
- Development and Evaluation of a Hallucination Awareness Scale for Healthcare Professionals and its impact on diagnostic confidence.Frontiers in digital health · 2026Article
- Artificial intelligence in the surgical management for colorectal cancer: current applications and future directions.Frontiers in oncology · 2026Review
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- Non-invasive multi-phase CT artificial intelligence for predicting pre-treatment enlarged lymph node status in colorectal cancer: a prospective validation study.European radiology · 2025Article
- Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction.European radiology · 2025Review
- Article
- Review
- Role of Artificial Intelligence in MRI-Based Rectal Cancer Staging: A Systematic Review.Cureus · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 2 countries.
Wei Xia *From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0003-3368-4050
Dandan Li *From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).
Wenguang HeFrom the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0009-0001-2609-4995
Perry J PickhardtFrom the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0002-5534-8202
Junming JianFrom the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0003-1683-0546
Rui ZhangFrom the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0002-6371-9050
Junjie ZhangFrom the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0001-9102-6588
Ruirui SongFrom the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0002-3393-2985
Tong TongFrom the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0002-9180-8181
Xiaotang YangFrom the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0002-2233-1404
Xin Gao *From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0001-9906-0596
Yanfen Cui *From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.).ORCID 0000-0001-6631-5687
Shanxi Medical University · CNChinese Academy of Sciences · CNCancer Hospital of Chinese Academy of Medical Sciences · CNShanghai Medical College of Fudan University · CNUniversity of Wisconsin–Madison · US
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rectal cancer (RC) that uses preoperative MRI data coupled with postoperative patient-level pathologic information. Materials and Methods In this retrospective study, the WISDOM model was built using MRI (T2-weighted and diffusion-weighted imaging) and patient-level pathologic information (the number of postoperatively confirmed metastatic LNs and resected LNs) based on the data of patients with RC between January 2016 and November 2017. The incremental value of the model in assisting radiologists was investigated. The performances in binary and ternary N staging were evaluated using area under the receiver operating characteristic curve (AUC) and the concordance index (C index), respectively. Results A total of 1014 patients (median age, 62 years; IQR, 54-68 years; 590 male) were analyzed, including the training cohort (
Indexed as
Deep LearningRectal NeoplasmsHumansLymph NodesMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesAbdomen/GIComputer Applications-Detection/DiagnosisMR ImagingRectum
Identifiers
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.