ConLymphNet: A Generalizable Region-Constrained Deep Learning Architecture for Precise Abdominal Lymph Node Segmentation Across Cancer Types.

Authors

Pankaj Gupta, Shubham Saini, Anjali Aggarwal, Ajay Gulati, Shraddha Patkar, Niharika Dutta, Sakshi Jeswani, Lovleen Kakkar, Kartik Bose, Gaurav Prakash, Thakur Deen Yadav, Lileswar Kaman, Santosh Irrinki, Harjeet Singh, Divya Khosla, Niket Shah, Amit Choudhari, Mahesh Goel, Rakesh Kapoor, Rajesh Gupta, Usha Dutta, Chetan Arora

Published in

JCO clinical cancer informatics. Volume 10. Issue 3. Pages e2500299. Epub Jul 15, 2026.

Abstract

Accurate identification and segmentation of abdominal lymph nodes on computed tomography (CT) is crucial for cancer staging and treatment planning but remains a challenging and time-consuming radiologic task. We developed and validated ConLymphNet, a deep learning-based approach for automated abdominal lymph node segmentation using vertebral landmarks as anatomic reference points to standardize the region of interest.
We implemented a novel preprocessing pipeline incorporating vertebral landmark-based region selection followed by a 3D nnUNet segmentation model. The model was trained on 481 contrast-enhanced CT scans from patients with gallbladder cancer (GBC) and validated on external data sets including GBC (n = 54), public lymph node (n = 81), and multicancer data sets (n = 606). Performance was compared with three radiologists of varying experience levels. Interobserver agreement among radiologists and the incremental value of artificial intelligence (AI)-assisted reading were also assessed. To support reproducibility and future research, we also contribute 45 expert-verified lymph node segmentations for cases from public multicancer data sets.
ConLymphNet achieved mean Dice coefficients of 0.696 ± 0.179, 0.631 ± 0.187, and 0.639 ± 0.176 and low false-positive rates of 0.78 ± 0.35, 0.81 ± 0.33, 0.93 ± 0.47/volume on internal cross-validation, external GBC, and public lymph node data sets, respectively. Performance varied by lymph node location (P = .0080) and size (P = .0444), with larger nodes yielding better results. The model's detection rate (89.6%) was comparable with experienced residents (89.2%) but with reduced processing time (28.7 ± 12.4s v 165.3 ± 47.8 s). AI assistance improved radiologists' performance significantly, particularly for less experienced readers (detection rate +20.2%, segmentation accuracy +13.0%, P < .001), while reducing false positives by 33.3%.
ConLymphNet demonstrates reasonable performance across diverse cancer types and imaging protocols, offering segmentation accuracy comparable with radiologists with substantially faster processing times.

PMID:
42456080
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.

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