Machine Learning Based Assessment of Inguinal Lymph Node Metastasis in Patients with Squamous Cell Carcinoma of the Vulva - Research paper

Aug 5, 2026

Despite efforts in clinical and pathology work to characterize the extent of inguinal lymph node metastasis in vulvar cancer, many studies still focus on new imaging parameters or tissue diagnostics rather than alternative statistical approaches. This study develops a supervised machine learning model to predict groin metastasis in vulvar squamous cell carcinoma using classical histomorphological features. A retrospective cohort of 157 patients is trained with internal cross-validation and evaluated by external holdout testing using sensitivity, PPV, and AUROC metrics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Risk factors and prognostic significance

Rationale for machine learning

Materials and Methods

Patient cohort and study design

Predictor variables and model training

Validation strategy and metrics

Article Machine Learning Based Assessment of Inguinal Lymph Node Metastasis in Patients with Squamous Cell Carcinoma of the Vulva Gilbert Georg Klamminger 1,2, *, Meletios P. Nigdelis 3, Annick Bitterlich 1, Bashar Haj Hamoud 3, Erich-Franz Solomayer 3, Annette Hasenburg 2 and Mathias Wagner 1 Academic Editor: Marco D’Indinosante Received: 21 March 2025 Revised: 25 April 2025 Accepted: 10 May 2025 Published: 17 May 2025 Citation: Klamminger, G.G.; Nigdelis, M.P.; Bitterlich, A.; Haj Hamoud, B.; Solomayer, E.-F.; Hasenburg, A.; Wagner, M. Machine Learning Based Assessment of Inguinal Lymph Node Metastasis in Patients with Squamous Cell Carcinoma of the Vulva. J. Clin. Med. 2025, 14, 3510 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)jcm14103510 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Department of General and Special Pathology, Saarland University (USAAR) and Saarland University Medical Center (UKS), 66424 Homburg, Germany 2 Department of Obstetrics and Gynecology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany 3 Department of Gynecology, Obstetrics and Reproductive Medicine, Saarland University Medical Center (UKS), 66424 Homburg, Germany * Correspondence: gilbert.klamminger@unimedizin-mainz.de; Tel.: +49-6131-17-2456 Abstract: Background/Objectives: Despite great efforts from both clinical and pathological sides to address the extent of metastatic inguinal lymph node involvement in patients with vulvar cancer, current research attempts are still mostly aimed at identifying new imaging parameters or superior tissue diagnostic workflows rather than alternative ways of statistical data analysis. In the present study, we therefore establish a supervised machine learning algorithm to predict groin metastasis in patients with squamous cell carcinoma of the vulva (VSCC) based on classical histomorphological features. Methods: In total, 157 patients with VSCC were included in this retrospective study. After initial exploration of valuable clinicopathological predictor variables by means of Spearman correlation, a decision tree was trained and internally validated (5-fold cross-validation) using a training data set (n = 126) and afterwards externally validated employing a holdout validation data set (n = 31) using standard metrices such sensitivity, positive predictive value, and AUROC curve. Results: Our established classifier can predict inguinal lymph node status with an internal accuracy of 79.4%(AUROC value = 0.64) . Reaching similar performances and an overall accuracy of 83.9% on an unknown data input (external validation set), our classifier demonstrates robustness. Conclusions: The presented results suggest that machine learning can predict groin lymph node status in VSCC based on histological findings of the primary tumor. Such research attempts may be useful in the future for an additional assessment of inguinal lymph nodes, aiming to maximize oncological safety when targeting the most accurate diagnosis of lymph node involvement. Keywords: vulvar cancer; lymph node metastasis; machine learning; artificial intelligence; cancer 1. Introduction Several risk factors, such as depth of invasion and lymphovascular space invasion (LVSI), have already been identified in the rare gyneco-oncological disease of squamous cell carcinoma of the vulva (VSCC), which is the most common tumor entity of all vulvar cancers (VC) . However, from a histopathological point of view, other factors such as ulceration and infiltration into small veins remain of uncertain potential [1–3], not to mention emerging histological biomarkers such as tu

FAQs

What clinical problem does the study address?

The study targets predicting inguinal lymph node metastasis extent in patients with vulvar squamous cell carcinoma, aiming to improve assessment beyond current diagnostic workflows.

How was the machine learning model built and validated?

A decision tree was trained on a retrospective cohort with internal 5-fold cross-validation, then externally validated using a holdout dataset.

Which histological inputs are used for prediction?

The model predicts groin metastasis using classical histomorphological features from the primary tumor.

Aug 5, 2026

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