Skip to content
DraconiticResearch
Search Sources API
203,952 studies31 sourcesUpdated 22 min ago
Peer-reviewedCC BY-NC-NDGland surgery

Deep learning radiomics model for lymph node metastases prediction: a diagnostic study.

  • Yongbin Li1,2
  • Shangdi Wu1,3
  • Jiajun Qiu4
  • Lingwei Meng1,2
  • Man Zhang2
  • He Cai1,2
  • Jun Xu1,2
  • Wei Zhang2
  • Yaying Zhu5
  • Bing Peng1,2,3
5 affiliations
  1. 1Division of Pancreatic Surgery, Department of General Surgery, West China Hospital of Sichuan University, Chengdu, China.
  2. 2Department of Hepatopancreatobiliary Minimal Invasive Surgery, Chengdu Shang Jin Nan Fu Hospital, Chengdu, China.
  3. 3West China School of Medicine, West China Hospital of Sichuan University, Chengdu, China.
  4. 4Biomedical Big Data Center, West China Hospital of Sichuan University, Chengdu, China.
  5. 5Department of Radiology, Chengdu Shang Jin Nan Fu Hospital, Sichuan University, Chengdu, China.
Read full textPDFDOICite

Abstract

Pancreatic ductal adenocarcinoma (PDAC) frequently exhibits lymph node (LN) metastasis, making early identification of high-risk patients crucial for optimizing treatment strategies. However, robust predictive models and external validation remain limited. This study aimed to develop and validate an artificial intelligence (AI) model for preoperative detection of LN status in PDAC patients. We retrospectively enrolled 233 pathologically confirmed PDAC patients from two high-volume pancreatic surgery centers between July 2015 and July 2022. All patients underwent contrast-enhanced computed tomography (CT) scans and met strict inclusion and exclusion criteria. Two experienced radiologists independently reviewed imaging. Radiomics analysis was performed using MATLAB and Python-based PyRadiomics. Following feature selection, 14 radiomic features were retained to construct a radiomics score. Three predictive models were developed and compared. From 1,652 extracted radiomic features, 14 were selected to build a radiomics score significantly associated with LN metastasis (P<0.001). Multivariate logistic regression identified tumor location, vascular invasion, white blood cell (WBC) count, platelet count, and radiomics score as independent predictors of LN metastasis, and a nomogram was generated. Various machine learning algorithms were evaluated to identify the optimal model combining radiomic and clinical data. Decision curve analysis demonstrated that the extreme gradient boosting (XGBoost) model achieved superior accuracy and stability, with an area under the curve (AUC) of 0.86 in the training set and 0.81 in the validation set. We successfully developed a LN metastasis prediction model integrating preoperative CT radiomics and clinical variables. The XGBoost-based model exhibited excellent accuracy, robustness, and generalizability. Future studies should focus on constructing more reliable models through standardized pathological sampling and prospective multicenter trials, alongside advancing imaging techniques to improve identification of small LNs.

Related

  1. Development and Patient-Level Internal Validation of an MRI-Based Radiomics Machine Learning Model for Preoperative Prediction of Cervical Lymph Node Metastasis in Oral Squamous Cell Carcinoma: A Pilot Study.
    İrem Öztürk, Ferhat Erişir, Kaan Orhan +1
    Diagnostics (Basel, Switzerland)Peer-reviewedCC BYFull textMedical imaging & radiologyEurope PMC
  2. Ultrasound-Based Radiomics and Deep Learning for Prediction of Lateral and Central Lymph Node Metastasis and BRAF Gene Mutation in Papillary Thyroid Carcinoma
    Qiao Zheng, Huihui Li, Tongbin Zhang +7
    Technology in Cancer Research & TreatmentPeer-reviewedCC BY-NC 4.0Full textMedical imaging & radiologyPMC
  3. CT-based multi-station lymph-node radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a proof-of-concept comparison with primary-tumor radiomics
    Zejin Lin, Jue Wang, Hairong Chen +6
    European radiology experimentalPeer-reviewedCC BYFull textMedical imaging & radiologyEurope PMC
  4. Habitat-based ultrasound radiomics for preoperative prediction of axillary lymph node metastasis in breast cancer: a two-center study
    Zimeng Fang, Xiaoyu Yan, Yunyun Zhan +6
    Gland SurgeryPeer-reviewedCC BY-NC-NDFull textGeneral medicineEurope PMC
  5. Interpretable deep learning and radiomics for predicting the occurrence of brain metastases following prophylactic brain irradiation in small cell lung cancer
    Xingtong Zhang, Lei Zhao, Jie Wang +5
    Translational Lung Cancer ResearchPeer-reviewedCC BY-NC-NDFull textOncologyEurope PMC

Cite

BibTeXRIS
BibTeX
@article{li2026deep,
  title = {{Deep learning radiomics model for lymph node metastases prediction: a diagnostic study.}},
  author = {Yongbin Li and Shangdi Wu and Jiajun Qiu and Lingwei Meng and Man Zhang and He Cai and Jun Xu and Wei Zhang and Yaying Zhu and Bing Peng},
  journal = {Gland surgery},
  year = {2026},
  month = oct,
  doi = {10.21037/gs-2026-0378},
  url = {https://europepmc.org/article/MED/42996563},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Deep learning radiomics model for lymph node metastases prediction: a diagnostic study.
AU  - Yongbin Li
AU  - Shangdi Wu
AU  - Jiajun Qiu
AU  - Lingwei Meng
AU  - Man Zhang
AU  - He Cai
AU  - Jun Xu
AU  - Wei Zhang
AU  - Yaying Zhu
AU  - Bing Peng
PY  - 2026
DA  - 2026/10/21
JO  - Gland surgery
DO  - 10.21037/gs-2026-0378
UR  - https://europepmc.org/article/MED/42996563
AB  - Pancreatic ductal adenocarcinoma (PDAC) frequently exhibits lymph node (LN) metastasis, making early identification of high-risk patients crucial for optimizing treatment strategies. However, robust predictive models and external validation remain limited. This study aimed to develop and validate an artificial intelligence (AI) model for preoperative detection of LN status in PDAC patients. We retrospectively enrolled 233 pathologically confirmed PDAC patients from two high-volume pancreatic surgery centers between July 2015 and July 2022. All patients underwent contrast-enhanced computed tomography (CT) scans and met strict inclusion and exclusion criteria. Two experienced radiologists independently reviewed imaging. Radiomics analysis was performed using MATLAB and Python-based PyRadiomics. Following feature selection, 14 radiomic features were retained to construct a radiomics score. Three predictive models were developed and compared. From 1,652 extracted radiomic features, 14 were selected to build a radiomics score significantly associated with LN metastasis (P<0.001). Multivariate logistic regression identified tumor location, vascular invasion, white blood cell (WBC) count, platelet count, and radiomics score as independent predictors of LN metastasis, and a nomogram was generated. Various machine learning algorithms were evaluated to identify the optimal model combining radiomic and clinical data. Decision curve analysis demonstrated that the extreme gradient boosting (XGBoost) model achieved superior accuracy and stability, with an area under the curve (AUC) of 0.86 in the training set and 0.81 in the validation set. We successfully developed a LN metastasis prediction model integrating preoperative CT radiomics and clinical variables. The XGBoost-based model exhibited excellent accuracy, robustness, and generalizability. Future studies should focus on constructing more reliable models through standardized pathological sampling and prospective multicenter trials, alongside advancing imaging techniques to improve identification of small LNs.
SN  - 2227-684X
SN  - 2227-8575
ER  -