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

Simona Maria Fragomeni, Francesca Moro, Fernando Palluzzi, Floriana Mascilini, Vittoria Rufini, Angela Collarino, Frediano Inzani, Luciano Giacò, Giovanni Scambia, Antonia Carla Testa, Giorgia Garganese

Research output: Contribution to journalArticle

Abstract

Simple Summary Inguinal node status represents one of the key elements in defining prognosis and treatment strategies in vulvar cancer patients. Preoperative lymph node staging is still a challenging topic. Several imaging methods are currently recommended in the guidelines (CT, PET/CT, MRI, US) based on performance data that are still not conclusive in the literature. Recently, ultrasound is emerging as the method of choice for preoperative evaluation of the inguinofemoral LN, but only when performed by experienced operators, given the limited reliability of subjective evaluation by unskilled operators. The morphonode predictive model represents an artificial intelligence tool that aims to overcome this limitation by supporting the standard ultrasound in adequately predicting the presence of lymph node metastases for improving preoperative surgical planning. We plan to proceed with further multicenter prospective validation and further develop the actual model, including both clinical and biological data. 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.
Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalCancers
Volume15
DOIs
Publication statusPublished - 2023

Keywords

  • lymph nodes
  • vulvar cancer
  • ultrasound
  • machine learning

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