In Collaboration with: Dipartimento Scienze della salute della donna, del bambino e di sanità pubblica
Dr. Fernando Palluzzi, Dr. Luciano Giaco’, Ing. Tina Pasciuto, Dr.ssa Iolanda Mozzetta
The Morphonode Predictive Model (https://github.com/Morphonodepredictivemodel) is an ensemble methodology for the prediction of inguinal lymph node metastasis before surgery. The R-based package is composed by four modules, including: random forest classifiers (Morphonode-RFC) for malignancy prediction, robust binomial regression (Morphonode-RBM) for malignancy risk estimation, decision trees (Modphonode-DT) for the detection of signatures of malignancy risk and metastasis frequency, and a function for similarity profiling (Morphonode-SP) to search for patients with similar ultrasound characteristics, risk level and signature. This ensemble method revealed a higher performance than subjective assessment (93.3% vs. 76.4% of predictive accuracy, respectively) and high robustness to missing data, demonstrating the key importance of computational approaches in personalized medicine and surgery.
Garganese G, Fragomeni SM, Pasciuto T, Leombroni M, Moro F, Evangelista MT, Bove S, Gentileschi S, Tagliaferri L, Paris I, Inzani F, Fanfani F, Scambia G,Testa AC. Ultrasound morphometric and cytologic preoperative assessment of inguinal lymph-node status in women with vulvar cancer: MorphoNode study. Ultrasound Obstet Gynecol. 2020 Mar;55(3):401-410. doi: 10.1002/uog.20378. PMID:31237047.