Decision tree algorithm in locally advanced rectal cancer: an example of over-interpretation and misuse of a machine learning approach

2019 ◽  
Vol 146 (3) ◽  
pp. 761-765 ◽  
Author(s):  
Francesca De Felice ◽  
D. Crocetti ◽  
M. Parisi ◽  
V. Maiuri ◽  
E. Moscarelli ◽  
...  
2019 ◽  
Vol 5 (suppl) ◽  
pp. 71-71
Author(s):  
Hyebin Lee

71 Background: Although many efforts to predict treatment response of concurrent chemoradiotherapy (CCRT) for locally advanced rectal cancer (LARC) have been made, no molecular has proved to be a robust biomarker. Methods: We performed mass spectrometry-based quantitative proteomic analysis of pretreatment Formalin-fixed, Paraffin-embedded (FFPE) biopsy samples of 13 patients with LARC, who were treated with CCRT followed by curative surgery. Based on pathologic report of surgical specimens, we divided thirteen patients as two response groups: complete response (CR) and non-complete response (nCR) groups. Results: A total of 3,637 proteins were identified and 498 proteins were confirmed as expressed at significantly different levels (DEPs; differently expressed proteins) between these two groups. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were also performed: the result showed that up-regulated DEPs enriched in biological processes (BP) were significantly different between two groups; immune response, cell migration & motility, protein transport in CR group; amide/peptide biosynthetic process, translation, posttranscriptional regulation of gene expression and detoxification in nCR group. To identify the best classifier to evaluate predictive power of signatures, we employed for different machine learning algorithms to classify samples between CR and nCR groups. As a result, we identified the predictive relevance of dual oxidase 2 (DUOX2) as the strongest predictive biomarker. Conclusions: This study identified a new biomarker, DUOX2, applicable to discrimination between CR and nCR after NACRT for LARC. To our knowledge, the present study provides the first identification of a clinical biomarker for response prediction based on in-depth proteomics and machine learning algorithms.


2021 ◽  
Vol 161 ◽  
pp. S1045
Author(s):  
F. De Felice ◽  
L. Belgioia ◽  
D. Musio ◽  
A. Bacigalupo ◽  
S. Vagge ◽  
...  

2021 ◽  
Vol 14 ◽  
pp. 175628482110421
Author(s):  
Francesca De Felice ◽  
Daniele Crocetti ◽  
Niccolò Petrucciani ◽  
Liliana Belgioia ◽  
Paolo Sapienza ◽  
...  

A bibliometric analysis was performed using a machine learning bibliometric methodology in order to evaluate the research trends in locally advanced rectal cancer treatment between 2000 and 2020. Information regarding publication outputs, countries, institutions, journals, keywords, funding, and citation counts was retrieved from Scopus database. During the search process, a total of 2370 publications were identified. The vast majority of papers originated from the United States of America, reflecting also its research drive in the collaboration network. Neoadjuvant treatment was the topic most studied in the highly cited studies. New keywords, including neoadjuvant chemotherapy, multiparametric magnetic resonance imaging, circulating tumor DNA, and genetic heterogeneity, appeared in the last 2 years. The quantity of publications on locally advanced rectal cancer treatment since 2000 showed an evolving research field. The ‘new’ keywords explain where research is presently heading.


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