期刊
BREAST CANCER RESEARCH AND TREATMENT
卷 -, 期 -, 页码 -出版社
SPRINGER
DOI: 10.1007/s10549-023-07141-5
关键词
Breast Cancer; Oncotype DX; Clinico-pathological data; Machine Learning; Distributional Random Forest
类别
This study proposes a novel methodology based on distributional random forest to predict the Oncotype DX (ODX) score classes for breast cancer patients. The methodology achieved a high accuracy in predicting the risk of breast cancer recurrence.
PurposeThe Oncotype DX (ODX) test is a commercially available molecular test for breast cancer assay that provides prognostic and predictive breast cancer recurrence information for hormone positive, HER2-negative patients. The aim of this study is to propose a novel methodology to assist physicians in their decision-making.Methods A retrospective study between 2012 and 2020 with 333 cases that underwent an ODX assay from three hospitals in the Bourgogne Franche-Comt & eacute; region (France) was conducted. Clinical and pathological reports were used to collect the data. A methodology based on distributional random forest was developed to predict the ODX score classes (ODX <= 25 and ODX >25 ) using 9 clinico-pathological characteristics. This methodology can be used particularly to identify the patients of the training cohort that share similarities with the new patient and to predict an estimate of the distribution of the ODX score.ResultsThe mean age of participants is 56.9 years old. We have correctly classified 92% of patients in low risk and 40.2%of patients in high risk. The overall accuracy is 79.3% . The proportion of low risk correct predicted value (PPV) is 82% . The percentage of high risk correct predicted value (NPV) is approximately 62.3% . The F1-score and the Area Under Curve (AUC) are of 0.87 and 0.759, respectively.Conclusion The proposed methodology makes it possible to predict the distribution of the ODX score for a patient. This prediction is reinforced by the determination of a family of known patients with follow-up of identical scores. The use of this methodology with the pathologist's expertise on the different histological and immunohistochemical characteristics has a clinical impact to help oncologist in decision-making regarding breast cancer therapy
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