Related Experiment Video
Updated: Jul 19, 2026
07:13
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Predicting sentinel node status in AJCC stage I/II primary cutaneous melanoma
Laura L Kruper1, Francis R Spitz, Brian J Czerniecki
1Melanoma Program of the Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. laura.kruper@uphs.upenn.edu
Cancer
|October 24, 2006
Summary
Identifying prognostic factors for sentinel lymph node (SLN) positivity in melanoma patients is crucial. This study found mitotic rate (MR) and tumor-infiltrating lymphocytes (TIL) predict SLN involvement, aiding risk stratification.
Area of Science:
- Oncology
- Dermatology
- Surgical Pathology
Background:
- Sentinel lymph node (SLN) status is a key prognostic indicator for cutaneous melanoma survival.
- Accurate prediction of SLN involvement is essential for patient risk stratification and management.
- Understanding melanoma progression aids in identifying predictive factors for SLN positivity.
Purpose of the Study:
- To identify prognostic factors predicting sentinel lymph node (SLN) involvement in primary cutaneous melanoma.
- To develop a model for stratifying patients into high-risk and low-risk groups for SLN positivity.
- To examine tumor and patient characteristics in the context of melanoma progression.
Main Methods:
- Analysis of 682 patients with vertical growth phase (VGP) melanoma undergoing SLN biopsy (1995-2003).
- Logistic regression and classification tree analyses were employed.
- Investigated associations between SLN positivity and Breslow thickness, Clark level, tumor infiltrating lymphocytes (TIL), ulceration, mitotic rate (MR), lesion site, gender, and age.
Main Results:
- 12.9% of patients (88/682) had positive SLNs.
- Multivariate analysis identified MR, TIL, and thickness as independent predictors of SLN positivity.
- A classification tree defined four risk groups (2.1% to 40.4% risk), with MR critical for lesions <2.0 mm and TIL for lesions >2.0 mm.
Conclusions:
- A prognostic model incorporating VGP, TIL, MR, and thickness can identify high- and minimal-risk patients for SLN positivity.
- Validated models can guide patient management, including decisions on SLN biopsy.
- This approach can refine patient selection for clinical trials.
