Unsupervised versus Supervised Identification of Prognostic Factors in Patients with Localized Retroperitoneal Sarcoma: A Data Clustering and Mahalanobis Distance Approach

Abstract

The aim of this report is to unveil specific prognostic factors for retroperitoneal sarcoma (RPS) patients by univariate and multivariate statistical techniques. A phase I-II study on localized RPS treated with high-dose ifosfamide and radiotherapy followed by surgery (ISG-STS 0303 protocol) demonstrated that chemo/radiotherapy was safe and increased the 3-year relapse-free survival (RFS) with respect to historical controls. Of 70 patients, twenty-six developed local, 10 distant, and 5 combined relapse. Median disease-free interval (DFI) was 29.47 months. According to a discriminant function analysis, DFI, histology, relapse pattern, and the first treatment approach at relapse had a statistically significant prognostic impact. Based on scientific literature and clinical expertise, clinicopathological data were analyzed using both a supervised and an unsupervised classification method to predict the prognosis, with similar sample sizes (66 and 65, resp., in casewise approach and 70 in mean-substitution one). This is the first attempt to predict patients' prognosis by means of multivariate statistics, and in this light, it looks noticable that (i) some clinical data have a well-defined prognostic value, (ii) the unsupervised model produced comparable results with respect to the supervised one, and (iii) the appropriate combination of both models appears fruitful and easily extensible to different clinical contexts.