Metastatic Breast Cancer Clinical Trial
Official title:
Letrozole in the Treatment of 1st and 2nd Line Hormone Receptor Positive Breast Cancer: Pre-therapeutic Risk Assessment
The course of the disease in female patients with metastatic mammary carcinoma can vary
greatly. In this connection, the individual prognosis depends on a complex interaction of
tumor- and patient-related factors. To take account of such differences, it is necessary to
employ individual methods of treatment which are suited to the course of each patient's
disease. Prof. Possinger and Dr. Schmid (Charite Berlin) and Prof. Wischnewsky (University
of Bremen) have developed an approach that can help to achieve this goal with the aid of
computerized machine learning techniques (MLT).
The use of machine learning methods can be beneficial in oncology in two respects. On the
one hand, an attempt can be made to individually estimate clinically relevant parameters
like, for example, the recurrence probability or expected survival time as precisely as
possible based on the established prognostic factors. And on the other hand, it may be
possible with the aid of MLT to understand structural relationships between the clinical
result and measured or established tumor-/patient-related variables.
To analyze the possible benefits of machine learning techniques for patients with metastatic
breast cancer, the aim of study FEM-D-2 is to investigate whether it is possible to
characterize those patients who either do or do not respond to a specific treatment with a
precision of 90%, prospectively estimate the time until worsening of the disease under a
given treatment, and classify patients in groups with favorable and poor chances of
medium-term survival.
The use of inductive learning algorithms with machine learning also makes it possible to
very accurately estimate the time until progression of the tumor growth. In patients who
respond to letrozole therapy, the time until tumor progression depends on factors like pain,
age, body mass index, disease-free interval, main localization of metastatic spread, and
response to previous estrogen therapy. Only very minimal differences are found when
comparing the actual time until progression of the disease and that calculated by the system
(at least for survival times < 1 year). Furthermore, using machine learning techniques it
has become possible to use initial data assessed before a letrozole treatment to estimate
the survival time and distinguish patients with a high risk of dying soon from other
patients with a more favorable prognosis.
n/a
Allocation: Non-Randomized, Endpoint Classification: Safety/Efficacy Study, Intervention Model: Single Group Assignment, Masking: Open Label, Primary Purpose: Treatment
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