학술논문
Multicancer screening test based on the detection of circulating non haematological proliferating atypical cells.
Document Type
Editorial & Opinion
Author
Malara N; Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy. nataliamalara@unicz.it.; Coluccio ML; Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, IT, Italy.; Grillo F; Department of Chemistry, University of Leicester, Leicester, UK.; Ferrazzo T; Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy.; Garo NC; Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy.; Donato G; Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy.; Lavecchia A; Pathology Unit Pugliese Ciaccio Hospital, Catanzaro, IT, Italy.; Fulciniti F; Unilabs Pathology, Lugano, CH, Switzerland.; Sapino A; Candiolo Cancer Institute, FPO-IRCCS, Candiolo (TO), Turin, Italy.; Cascardi E; Candiolo Cancer Institute, FPO-IRCCS, Candiolo (TO), Turin, Italy.; Pellegrini A; Società Italiana di Citologia (SICi), AO S.Giovanni-Addolorata, President, Roma, IT, Italy.; Foxi P; Cytodiagnostic Pistoia-Pescia Unit, USL Toscana Centro, Pistoia, IT, 51100, Italy.; Furlanello C; LIGHT Center, Brescia, IT, 25123, Italy.; Negri G; Pathology Unit, Central Hospital Bolzano, via Boehler 5, Bolzano, IT, 39100, Italy.; Fadda G; Human Pathology Department, Gaetano Barresi University, Messina, IT, Italy.; Capitanio A; Linköping University Hospital SE , Linköping University, Linköping, Sweden.; Pullano S; Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy.; Garo VM; Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy.; Ferrazzo F; Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy.; Lowe A; Department of Pathology, Stanford University Hospital, Stanford, CA, USA.; Torsello A; UOC Oncology, AO San Giovanni, Roma, IT, Italy.; Candeloro P; Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, IT, Italy.; Gentile F; Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, IT, Italy.
Source
Publisher: BioMed Central Country of Publication: England NLM ID: 101147698 Publication Model: Electronic Cited Medium: Internet ISSN: 1476-4598 (Electronic) Linking ISSN: 14764598 NLM ISO Abbreviation: Mol Cancer Subsets: MEDLINE
Subject
Language
English
Abstract
Background: the problem in early diagnosis of sporadic cancer is understanding the individual's risk to develop disease. In response to this need, global scientific research is focusing on developing predictive models based on non-invasive screening tests. A tentative solution to the problem may be a cancer screening blood-based test able to discover those cell requirements triggering subclinical and clinical onset latency, at the stage when the cell disorder, i.e. atypical epithelial hyperplasia, is still in a subclinical stage of proliferative dysregulation.
Methods: a well-established procedure to identify proliferating circulating tumor cells was deployed to measure the cell proliferation of circulating non-haematological cells which may suggest tumor pathology. Moreover, the data collected were processed by a supervised machine learning model to make the prediction.
Results: the developed test combining circulating non-haematological cell proliferation data and artificial intelligence shows 98.8% of accuracy, 100% sensitivity, and 95% specificity.
Conclusion: this proof of concept study demonstrates that integration of innovative non invasive methods and predictive-models can be decisive in assessing the health status of an individual, and achieve cutting-edge results in cancer prevention and management.
(© 2024. The Author(s).)
Methods: a well-established procedure to identify proliferating circulating tumor cells was deployed to measure the cell proliferation of circulating non-haematological cells which may suggest tumor pathology. Moreover, the data collected were processed by a supervised machine learning model to make the prediction.
Results: the developed test combining circulating non-haematological cell proliferation data and artificial intelligence shows 98.8% of accuracy, 100% sensitivity, and 95% specificity.
Conclusion: this proof of concept study demonstrates that integration of innovative non invasive methods and predictive-models can be decisive in assessing the health status of an individual, and achieve cutting-edge results in cancer prevention and management.
(© 2024. The Author(s).)