Evaluating the Risk of Inguinal Lymph Node Metastases before Surgery Using the Morphonode Predictive Model: A Prospective Diagnostic Study in Vulvar Cancer Patients

Author:

Fragomeni Simona Maria1,Moro Francesca1,Palluzzi Fernando2ORCID,Mascilini Floriana1,Rufini Vittoria34,Collarino Angela4,Inzani Frediano5,Giacò Luciano2ORCID,Scambia Giovanni16,Testa Antonia Carla16,Garganese Giorgia16

Affiliation:

1. Unità di Ginecologia Oncologica, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy

2. Bioinformatics Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy

3. Section of Nuclear Medicine, Department of Radiological Sciences and Haematology, Università Cattolica del Sacro Cuore, Largo A. Gemelli, 8, 00168 Rome, Italy

4. Unit of Nuclear Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy

5. Anatomic Pathology Unit, Department of Molecular Medicine, University of Pavia, Viale C. Golgi 19, 27100 Pavia, Italy

6. Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Sezione di Ginecologia ed Ostetricia, Università Cattolica del Sacro Cuore, Largo A. Gemelli, 8, 00168 Rome, Italy

Abstract

Ultrasound examination is an accurate method in the preoperative evaluation of the inguinofemoral lymph nodes when performed by experienced operators. The purpose of the study was to build a robust, multi-modular model based on machine learning to discriminate between metastatic and non-metastatic inguinal lymph nodes in patients with vulvar cancer. One hundred and twenty-seven women were selected at our center from March 2017 to April 2020, and 237 inguinal regions were analyzed (75 were metastatic and 162 were non-metastatic at histology). Ultrasound was performed before surgery by experienced examiners. Ultrasound features were defined according to previous studies and collected prospectively. Fourteen informative features were used to train and test the machine to obtain a diagnostic model (Morphonode Predictive Model). The following data classifiers were integrated: (I) random forest classifiers (RCF), (II) regression binomial model (RBM), (III) decisional tree (DT), and (IV) similarity profiling (SP). RFC predicted metastatic/non-metastatic lymph nodes with an accuracy of 93.3% and a negative predictive value of 97.1%. DT identified four specific signatures correlated with the risk of metastases and the point risk of each signature was 100%, 81%, 16% and 4%, respectively. The Morphonode Predictive Model could be easily integrated into the clinical routine for preoperative stratification of vulvar cancer patients.

Publisher

MDPI AG

Subject

Cancer Research,Oncology

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