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Evaluation of Recurrence Risk Factors in Locally Advanced Breast Cancer Patients Underwent Neoadjuvant Chemotherapy.

Recruiting
Conditions
Breast Cancer
Recurrence, Local Neoplasm
Chemotherapy Effect
Triple Negative Breast Cancer
Risk Factors
Interventions
Diagnostic Test: MRI
Registration Number
NCT06441240
Lead Sponsor
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Brief Summary

INTRODUCTION Breast cancer (BC) is the leading cause of cancer-related death in women. Since the early 1980s, the implementation of screening programs has reduced the number of patients diagnosed with locally advanced breast cancer. Currently, the treatment for these patients involves initial neoadjuvant chemotherapy (NACT) followed by surgical treatment. In recent years, NACT has also been used for highly chemoresponsive tumors such as triple-negative (TN) and HER2-positive (HER2+) breast cancer.

The widespread use of NACT has led to additional benefits, including downstaging of breast and axillary neoplasms, resulting in reduced morbidity; improved cosmetic outcomes due to increased use of conservative interventions; and personalized adjuvant chemotherapy treatment. Several studies have shown that response to chemotherapy predicts better systemic outcomes. Complete pathological response (pCR), defined as the absence of invasive neoplastic residue in the surgical specimen, has been predictive of better distant outcomes. Limited evidence exists regarding other predictive factors for distant outcomes.

Given the significant impact of disease recurrence on patient prognosis, efforts have been made to understand the factors contributing to recurrence and to predict which patients are more prone to relapse. In this context, the term "Early Disease Recurrence" (EDR) has been coined to define the occurrence of disease recurrence, both locally and distantly, within 3 years after completing treatment.

In recent years, the potential of radiomic analysis in aiding diagnostic and therapeutic decision-making processes in BC has been demonstrated. Specifically, radiomic features obtained from Magnetic Resonance Imaging (MRI) images appear capable of predicting tumor receptor status, differentiating tumor subtypes, and predicting response to NACT.

Although the role of radiomics in predicting recurrence has been investigated, research is still in its early stages, and there are variations in technology and methodology for extracting radiomic features. Additionally, to date, no studies have evaluated the feasibility and reliability of using radiomic models combined with clinical and radiological variables to predict disease recurrence in BC patients undergoing NACT.

Detailed Description

Not available

Recruitment & Eligibility

Status
RECRUITING
Sex
Female
Target Recruitment
933
Inclusion Criteria
  • Histological diagnosis of locally advanced Luminal or HER2+ or Triple-negative breast cancer (cT2, T3, T4 N0 or any T N1, N2, N3, M0), clinical stage of disease from I to III.
  • Patient undergoing neoadjuvant chemotherapy treatment from January 1, 2014, to June 30, 2021.
  • Age > 18 years
  • Availability of clinical data, staging MRI diagnostic images (for the radiomic sub-study), and biomolecular data.
Exclusion Criteria
  • Previous or synchronous history of systemic malignancies.
  • History of ipsilateral or contralateral breast neoplasia.
  • Evidence of metastatic disease (Stage IV).
  • Neoadjuvant treatment with hormone therapy.
  • Patients with unavailable or low-quality MRI images that did not allow lesion identification (for the radiomic study only).

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
Breast cancer patients underwent neoadjuvant chemotherapyMRIPatients undergoing neoadjuvant chemotherapy and subsequent surgical treatment. Patients must have undergone radiological evaluation by MRI at the beginning and end of chemotherapy treatment
Primary Outcome Measures
NameTimeMethod
Association between radiomic features and risk of recurrence7 years

Association between radiomic features extracted from pre-operative MRI and the onset of disease recurrence within 3 years from the end of neoadjuvant treatment

Description of molecular subtypes7 years

Frequency of molecular subtypes (Her2 positive, hormone receptor-positive/Her2 negative, Triple Negative) in the considered cases

Secondary Outcome Measures
NameTimeMethod
Evaluation of radiological response7 years

Frequency of radiological response according to molecular subtype, treatment, type of imaging examination used, and initial staging.

Assessment of risk of recurrence using models7 years

Accuracy of pure models (radiomic/clinical/radiological) and combined models in predicting disease recurrence within 3 years from the end of neoadjuvant treatment

Association between chemotherapy and neoplastic characteristics7 years

Evaluate whether clinical features (divided into: age assessed in years, menopausal status divided into menopausal or fertile age), radiological features such as initial extent of disease and lymph node involvement at diagnosis) and biomolecular features (such as histotype: divided into ductal, lobular or nonspecial type, grading, hormone receptor status) influence response to neoadjuvant chemotherapy

Translated with DeepL.com (free version)

Evaluation of oncological outcomes7 years

Disease-free survival (DFS) measured from the start of neoadjuvant therapy to the first evidence of disease recurrence or death, whichever occurs first.

Description of surgical treatment according to the cancer characteristics7 years

Frequency of surgical procedures based on radiological response, molecular subtypes, and initial staging.

Frequency of complete pathological response7 years

Frequency of complete pathological response according to molecular subtype, treatment, and initial staging.

Description of adjuvant treatments7 years

Frequency of adjuvant therapies based on neoadjuvant treatment, molecular subtypes, and initial staging.

Trial Locations

Locations (1)

Fondazione Policlinico Universitario A. Gemelli - IRCCS

🇮🇹

Roma, Italy

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