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Related Experiment Videos

ConLymphNet: A Generalizable Region-Constrained Deep Learning Architecture for Precise Abdominal Lymph Node

Pankaj Gupta1,2, Shubham Saini1, Anjali Aggarwal1

  • 1Postgraduate Institute of Medical Education and Research, Chandigarh, India.

JCO Clinical Cancer Informatics
|July 15, 2026
PubMed
Summary

Related Concept Videos

Detailed Structure and Function of Lymph Nodes01:23

Detailed Structure and Function of Lymph Nodes

Lymph nodes are bean-shaped structures that cluster along the lymphatic vessels in the inguinal, axillary, and cervical regions. Each node is divided into compartments by a capsule that extends trabeculae inward.
From a histological perspective, lymph nodes can be split into two main areas: the superficial cortex and the deep medulla. The outer cortex is populated by dendritic cells, macrophages, and B lymphocytes, which are densely packed into follicles. When these B-lymphocytes are presented...

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ConLymphNet, a deep learning tool, accurately segments abdominal lymph nodes on CT scans, comparable to radiologists but much faster. AI assistance significantly improves radiologist performance, aiding cancer staging and treatment planning.

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Accurate abdominal lymph node identification and segmentation on computed tomography (CT) are vital for cancer staging and treatment planning.
  • This task is challenging and time-consuming for radiologists.

Purpose of the Study:

  • To develop and validate ConLymphNet, a deep learning (DL) approach for automated abdominal lymph node segmentation.
  • To utilize vertebral landmarks for standardizing the region of interest in CT scans.
  • To assess the performance of ConLymphNet against radiologists and evaluate AI-assisted reading.

Main Methods:

  • Implemented a novel preprocessing pipeline using vertebral landmark-based region selection.
  • Employed a 3D nnUNet segmentation model trained on 481 contrast-enhanced CT scans from gallbladder cancer (GBC) patients.

Related Experiment Videos

  • Validated the model on diverse external datasets (GBC, public lymph node, multicancer) and compared its performance with radiologists.

    • Main Results:

    • ConLymphNet achieved competitive Dice coefficients (0.631–0.696) and low false-positive rates across datasets.
    • Detection rate (89.6%) was comparable to experienced residents (89.2%) with significantly reduced processing time (28.7s vs 165.3s).
    • AI assistance substantially improved radiologist performance (detection rate +20.2%, segmentation accuracy +13.0%) and reduced false positives by 33.3%.

    Conclusions:

    • ConLymphNet demonstrates effective performance in abdominal lymph node segmentation across various cancer types and imaging protocols.
    • The DL approach offers segmentation accuracy comparable to radiologists but with substantially faster processing times.
    • AI-assisted reading significantly enhances radiologist performance, particularly for less experienced individuals.