Overview

Schema Version

https://atlas.rsna.org/schemas/2025-11/dataset.json

Name

Dataset for Lymph Node Diagnosis in Rectal Cancer at MRI

Link

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10982819/

Indexing

Keywords: rectal cancer, lymph node, N staging, weakly supervised learning, multiple-instance learning, MRI, T2-weighted imaging, diffusion-weighted imaging, ADC, multicenter, radiologist assistance

Content: GI, MR

RadLex: RID12698, RID10312, RID39291, RID28879, RID39467, RID163, RID39536

Author(s)

Wei Xia

Dandan Li

Wenguang He

Perry J. Pickhardt

Junming Jian

Rui Zhang

Junjie Zhang

Ruirui Song

Tong Tong

Xiaotang Yang

Xin Gao

Yanfen Cui

Organization(s)

Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China

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, China

Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA

Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China

Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China

License

Text: CC BY 4.0

URL: https://creativecommons.org/licenses/by/4.0/

Contact

Corresponding author: Yanfen Cui, email: moc.621@012nefnay

Funding

National Natural Science Foundation of China (81871439, 82001789, 82171923, 81971687, 82271946); Key Research and Development Program of Shandong Province (2021SFGC0104); Key Research and Development Program of Jiangsu Province (BE2021663); Suzhou Science and Technology Plan Project (SJC2021014); China Postdoctoral Science Foundation (2021M700897); Applied Basic Research Projects of Shanxi Province, China, Outstanding Youth Foundation (202103021222014); Suzhou Association for Science and Technology Youth Science and Technology Talent Support Project; Taishan Industrial Experts Program (tscx202312131).

Ethical review

Approval from the institutional review board of participating hospitals and a waiver for informed consent were obtained for this retrospective study.

Comments

Retrospective multicenter MRI study of lymph node diagnosis in rectal cancer using weakly supervised learning (WISDOM). Data from three centers, with training/internal testing from center 1 and two external test cohorts from centers 2 and 3.

Date

Published: 2024-02-14

References

[1] Xia W, Li D, He W, Pickhardt PJ, Jian J, Zhang R, Zhang J, Song R, Tong T, Yang X, Gao X, Cui Y. "Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI". Radiology: Artificial Intelligence. 2024-03-01. doi:10.1148/ryai.230152. PMID: 38353633. PMCID: PMC10982819.

Dataset

Motivation

To build and evaluate a weakly supervised MRI-based model (WISDOM) for lymph node diagnosis and N staging in rectal cancer using patient-level postoperative pathology.

Sampling

Consecutive patients with rectal adenocarcinoma who underwent surgery and met inclusion criteria across three hospitals.

Partitioning scheme

Center 1 patients (Jan 2016–Nov 2017) split 4:1 chronologically into training (n=589) and internal test (n=146). External tests from center 2 (Apr–Jul 2017, n=117) and center 3 (Apr–Oct 2017, n=162).

Missing information

No node-by-node pathologic matching; imaging protocol details per center provided in supplemental appendices; per-cohort sex distribution not detailed in main text.

Relationships between instances

Each patient (bag) contains multiple lymph nodes (instances). Patient-level labels include presence/absence of lymph node metastasis and proportion of metastatic to resected nodes.

Sensitive data

Contains clinical MRI and postoperative pathology-derived labels; IRB approval and consent waiver obtained.