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A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care.

Abstract：

:Background uPA and PAI-1 are breast cancer biomarkers that evaluate the benefit of chemotherapy (CT) for HER2-negative, estrogen receptor-positive, low or intermediate grade patients. Our objectives were to observe clinical routine use of uPA/PAI-1 and to build a new therapeutic decision tree integrating uPA/PAI-1. Methods We observed the concordance between CT indications proposed by a canonical decision tree representative of French practices (not including uPA/PAI-1) and actual CT prescriptions decided by a medical board which included uPA/PAI-1. We used a method of machine learning for the analysis of concordant and non-concordant CT prescriptions to generate a novel scheme for CT indications. Results We observed a concordance rate of 71% between indications proposed by the canonical decision tree and actual prescriptions. Discrepancies were due to CT contraindications, high tumor grade and uPA/PAI-1 level. Altogether, uPA/PAI-1 were a decisive factor for the final decision in 17% of cases by avoiding CT prescription in two-thirds of cases and inducing CT in other cases. Remarkably, we noted that in routine practice, elevated uPA/PAI-1 levels seem not to be considered as a sufficient indication for CT for N≤3, Ki 67≤30% tumors, but are considered in association with at least one additional marker such as Ki 67>14%, vascular invasion and ER-H score <150. Conclusions This study highlights that in the routine clinical practice uPA/PAI-1 are never used as the sole indication for CT. Combined with other routinely used biomarkers, uPA/PAI-1 present an added value to orientate the therapeutic choice.

journal_name

Clin Chem Lab Med

journal_title

Clinical chemistry and laboratory medicine

authors

Reix N,Lodi M,Jankowski S,Molière S,Luporsi E,Leblanc S,Scheer L,Ibnouhsein I,Benabu JC,Gabriele V,Guggiola A,Lessinger JM,Chenard MP,Alpy F,Bellocq JP,Neuberger K,Tomasetto C,Mathelin C

doi

10.1515/cclm-2018-1065

subject

Has Abstract

pub_date

2019-05-27 00:00:00

pages

901-910

issue

eissn

1434-6621

issn

1437-4331

pii

/j/cclm.ahead-of-print/cclm-2018-1065/cclm-2018-10

journal_volume

pub_type

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A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care.