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Letrozole in the Treatment of 1st and 2nd Line Hormone Receptor Positive Breast Cancer: Pre-therapeutic Risk Assessment

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT00241046
Recruitment Status : Terminated
First Posted : October 18, 2005
Last Update Posted : April 19, 2012
Information provided by (Responsible Party):
Novartis ( Novartis Pharmaceuticals )

Brief Summary:

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.

Condition or disease Intervention/treatment Phase
Metastatic Breast Cancer Drug: Letrozole Phase 4

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Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 13 participants
Allocation: Non-Randomized
Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Treatment
Official Title: Letrozole in the Treatment of 1st and 2nd Line Hormone Receptor Positive Breast Cancer: Pre-therapeutic Risk Assessment
Study Start Date : April 2002
Actual Primary Completion Date : March 2005

Resource links provided by the National Library of Medicine

Drug Information available for: Letrozole

Primary Outcome Measures :
  1. Comparison of the the individual pretherapeutic predictions from the computer or doctor with the patient data obtained in a first- or second-line treatment of metastatic breast cancer after progression of the disease

Secondary Outcome Measures :
  1. Determination of the individual response at 3 monthly assessments

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   No

Inclusion criteria for the second-line therapy:

Patients can only take part in the study if they meet all of the following criteria:

  • Histologically established mammary carcinoma
  • Estrogen receptor (ER+) positive and/or progesterone receptor (PgR+) positive or ER/PgR unknown
  • Postmenopausal women; defined by (at least one criterion applicable):
  • Amenorrhea ≥ 5 years
  • Age ≥ 60
  • Age ≥ 45 with amenorrhea ≥ 12 months
  • Postmenopausal LH / FSH values (according to the respective institution)
  • Bilateral oophorectomy
  • Patients with a primary or recurrent local advanced mammary carcinoma which cannot be curatively treated with surgical procedures or radiation therapy, or patients with a metastatic mammary carcinoma after antiestrogen pretreatment.
  • Patients with a recurrence under adjuvant antiestrogen therapy (e.g. tamoxifen; with or without adjuvant chemotherapy) which was administered for at least 6 months or a recurrence within the last 12 months after adjuvant antiestrogen therapy (e.g. tamoxifen, with or without adjuvant chemotherapy) which was administered for at least 6 months or progression under palliative first-line antiestrogen therapy can be included.
  • At most, a previous palliative cytostatic treatment is possible
  • Measurable or assessable metastases in at least one organ system with objective proof of progression; that is, evidence of newly occurring lesions or an increase in size of preexisting lesions by more than 25% with measurable metastases or worsening with assessable changes within the last 3 months before inclusion in the study
  • In the case of bone metastases, imaging methods should verify that at least one preexisting osteolysis or the lytic part of an assessable mixed lesion has increased in size, or that new measurable or assessable bone metastases have developed. In assessable mixed lesions, the measurable part must constitute at least 50% of the size of the metastasis.

If no previous images are available, the increase in bone pain in connection with the detectable, measurable osteolyses or assessable mixed metastases in the pretreatment image are regarded as progression.

  • Previous radiotherapy is permitted as long as the irradiated area is not the only measurable lesion
  • Estimated life expectancy of at least 12 weeks
  • Performance status of 50 or higher on the Karnofsky scale or WHO grade 0-2.
  • Age ≥18 years
  • Written informed consent of the patient

Exclusion criteria for the second-line therapy:

Patients are not allowed to take part in the study if they meet at least one of the following criteria:

  • Cerebral metastasis
  • Lymphangitis carcinomatosa of the lung (> 50% of the lung affected)
  • Very extensive liver metastasis (in ultrasound or CT > 33% of the liver replaced by metastases)
  • Inflammatory mammary carcinoma
  • Other primary malignant diseases (except in situ carcinoma of the cervix or adequately treated basal cell carcinoma of the skin)
  • Patients with concomitant serious, unstable cardiac diseases (angina pectoris, arrhythmia, myocardial infarction within the last six months) or uncontrolled diabetes mellitus
  • Known hypersensitivity to components of the study medication
  • Exclusively osteoblastic or mixed bone metastases, with a lytic percentage < 50%, in so far as no other measurable or assessable lesions are present
  • Antihormonal pretreatment with aromatase inhibitors, megestrol acetate, medroxyprogesterone, or GnRH analogues
  • Treatment with a hormone replacement therapy
  • Taking of non-approved substances within the past 30 days and concomitant treatment with non-approved drugs

Patients with the following pretreatments should not be included in the study (selection):

  • Previous endocrine treatment of a metastatic mammary carcinoma
  • Patients with adjuvant or neoadjuvant endocrine treatment with or without chemotherapy within the past 12 months
  • Patients with adjuvant or neo-adjuvant endocrine antiestrogen treatment for whom a recurrence occurred during or within 12 months after the end of treatment
  • Patients who received more than one regimen of a systemic chemotherapy against their advanced breast cancer

Other protocol-related inclusion / exclusion criteria may apply

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT00241046

Sponsors and Collaborators
Novartis Pharmaceuticals
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Study Chair: Novartis Novartis

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Responsible Party: Novartis Pharmaceuticals Identifier: NCT00241046    
Other Study ID Numbers: CFEM345ADE02
First Posted: October 18, 2005    Key Record Dates
Last Update Posted: April 19, 2012
Last Verified: April 2012
Keywords provided by Novartis ( Novartis Pharmaceuticals ):
Breast cancer
Machine learning techniques
Aromatase inhibitors
1st line treatment
2nd line treatment
Additional relevant MeSH terms:
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Breast Neoplasms
Neoplasms by Site
Breast Diseases
Skin Diseases
Hormones, Hormone Substitutes, and Hormone Antagonists
Physiological Effects of Drugs
Antineoplastic Agents
Aromatase Inhibitors
Steroid Synthesis Inhibitors
Enzyme Inhibitors
Molecular Mechanisms of Pharmacological Action
Estrogen Antagonists
Hormone Antagonists