{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T20:10:00Z","timestamp":1784146200379,"version":"3.55.0"},"reference-count":29,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"3","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>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\u2013based approach for automated abdominal lymph node segmentation using vertebral landmarks as anatomic reference points to standardize the region of interest.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>MATERIALS AND METHODS<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      ConLymphNet achieved mean Dice coefficients of 0.696 \u00b1 0.179, 0.631 \u00b1 0.187, and 0.639 \u00b1 0.176 and low false-positive rates of 0.78 \u00b1 0.35, 0.81 \u00b1 0.33, 0.93 \u00b1 0.47\/volume on internal cross-validation, external GBC, and public lymph node data sets, respectively. Performance varied by lymph node location (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .0080) and size (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .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 \u00b1 12.4s\n                      <jats:italic toggle=\"yes\">v<\/jats:italic>\n                      165.3 \u00b1 47.8 s). AI assistance improved radiologists' performance significantly, particularly for less experienced readers (detection rate +20.2%, segmentation accuracy +13.0%,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001), while reducing false positives by 33.3%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>ConLymphNet demonstrates reasonable performance across diverse cancer types and imaging protocols, offering segmentation accuracy comparable with radiologists with substantially faster processing times.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-25-00299","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T20:00:11Z","timestamp":1784145611000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["ConLymphNet: A Generalizable Region-Constrained Deep Learning Architecture for Precise Abdominal Lymph Node Segmentation Across Cancer Types"],"prefix":"10.1200","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3914-3757","authenticated-orcid":true,"given":"Pankaj","family":"Gupta","sequence":"first","affiliation":[{"name":"Postgraduate Institute of Medical Education and Research, Chandigarh, India"},{"name":"Indian Institute of Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3282-0057","authenticated-orcid":true,"given":"Shubham","family":"Saini","sequence":"additional","affiliation":[{"name":"Postgraduate Institute 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