![]() ![]() size, nodal status, ER-status and grade), but they perform well at the population level, but exhibit a high degree of discordance in the intermediate and poor prognosis groups 9, 10. The online tools provide personalized 10-year overall survival estimates for the adjuvant treatment setting by basing their predictions on patient data (e.g. Indeed, in clinical practice, medical oncologists are increasingly using prediction tools available online, such as PREDICT, Adjuvant!, and CancerMath to guide systemic adjuvant treatment 8. In general, new interdisciplinary approaches are emerging in survival analysis, which aim to analyze data commonly collected in the clinical practice and drive the therapeutic choices. This issue is currently driving the studies aimed the achievement of the same information by means of less expensive procedure. Nevertheless, the adoption in the clinical practice of these decisional support tools requires a careful analysis of their cost-effectiveness, because genomics tests have an important cost and not all centers are provided with laboratories performing this type of analyses. In particular, the use of gene signatures has provided a standardized reproducible and quantitative tool able to define the risk of distant recurrent for ER-positive, HER2-negative early BC. Recent years have witnessed the availability of several molecular tests which have received long-standing recommendations in clinical guidelines 7. Such studies range from genomic tests 4, 5 to sophisticated artificial intelligence models 6, with the purpose of describing the benefit gained by each patient undergoing a specific therapy. The use of adjuvant chemotherapy for estrogen receptor (ER)-positive, HER2-negative BC patients has been investigated by an impressive number of studies aimed at measuring its efficiency in a predictive manner 3. These patients are at risk for being undertreated or overtreated with endocrine therapy and chemotherapy, and tests are required to save an important number of patients from the potentially harmful side effects of chemotherapy in particular, several studies have showed that a non-negligible proportion of BC patients, especially those with a hormone receptor-positive and lymph node-negative disease, could only be effectively treated with hormone therapy alone 1, 2. ![]() ![]() The integrated use of data already collected in clinical practice for routine diagnostic investigations, if properly analyzed, can reduce time and costs of the genomic tests.Īlthough there are detailed guidelines on the use of adjuvant chemotherapy, not all patients with endocrine-positive Her2 negative breast cancer at an early stage have real benefit from adding chemotherapy to adjuvant endocrine therapy. The preliminary results obtained by including only clinical determinants are encouraging. Moreover, machine learning survival models have accurately discriminated low- and high-risk patients, and so a large group which can be spared additional chemotherapy to hormone therapy. The c-index at 10 years obtained by random survival forest, gradient boosting, and component-wise gradient boosting is stabled with or without feature selection at approximately 0.68 in average respect to 0.57 obtained to Cox model. Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics evaluated in cross-validation. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori “Giovanni Paolo II”. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. Several genomic tests are available on the market but are very expensive. For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is not still confirmed. ![]()
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