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Updated: Jan 22, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
A Clinical Nomogram for Predicting Node-positive Disease in Esophageal Cancer
Tara R Semenkovich1, Yan Yan2, Melanie Subramanian1
1Division of Cardiothoracic Surgery, Department of Surgery, Washington University, St Louis, MO.
Objective:
We developed and validated a nomogram predicting the likelihood of occult lymph node metastases in surgically resectable esophageal cancers.
Background:
Patients with esophageal cancer with positive lymph nodes benefit from neoadjuvant therapy, but limitations in current clinical staging techniques mean nodal metastases often go undetected preoperatively.
Methods:
The National Cancer Database was queried for patients with clinical T1-3N0M0 cancer undergoing upfront esophagectomy from 2004 to 2014. Multivariable logistic regression was used to develop the risk model using both statistical significance and clinical importance criteria for variable selection. Predictive accuracy was assessed and bootstrapping was used for validation. A nomogram was constructed for presentation of the final model.
Results:
Of 3186 patients, 688 (22%) had pathologic lymph node involvement (pN+) and 2498 (78%) had pN0 status. Variables associated with pN+ status included histology [adenocarcinoma vs squamous: odds ratio (OR) 1.75], tumor stage (T1: reference, T2: OR 1.90, T3: OR 2.17), tumor size (<1 cm: reference, 1-2 cm: OR 2.25, 2-3 cm: OR 3.82, 3-4 cm: OR 5.40, 4-5 cm: OR 5.66, ≥5 cm: OR 6.02), grade (1: reference, 2: OR 2.62, 3: OR 4.39, 4: OR 4.15, X: OR 1.87), and presence of lymphovascular invasion (absent: reference, present: OR 4.70, missing: OR 1.87), all P < 0.001. A nomogram with these variables had good predictive accuracy (Brier score: 0.14, calibration slope: 0.97, c-index: 0.77).
Conclusions:
We created a nomogram predicting the likelihood of pathologic lymph node involvement in patients with esophageal cancer who are clinically node negative using a generalizable dataset. Risk stratification with this nomogram could improve delivery of appropriate perioperative care.
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