Precise Insights

Advanced Data and AI for Breast Cancer

On-demand access to the largest and most sophisticated oncology data for accelerated RWE and improved patient outcomes.

See some of the 1000s of data elements we offer:

Structured Fields

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Results from variety of patient tests


Oncology practice visits


Tumor-related observations plus vitals, biometrics, pain, and more


Surgeries, radiation, biopsies, imaging, chemotherapy, etc


Start and end dates, brand vs. generic codes, dosage, duration, and cycles


Disease state, status, severity, and metastasis

Expert Abstraction

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Biomarker testing, results, and timing

Disease Progression

Directly-observed measures of critical endpoints

Adverse Events

Different types of adverse responses


Classification into multiple categories

Enriched Data

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Cost & Utilization

Adjudicated costs with linked claims data

Safety, Comorbidities

Pre-cancer and claims charge events

Specialty Pharmacy & Hub

Rx acquisition status details

Payer & Formulary

Drug tiers and coverage

Social Determinants

Social and physical environment factors

AI & Model-Based Insights

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HER2 Status

Positive or negative biomarker imputation

HR Status

Positive or negative biomarker imputation

Triple Negative

Positive or negative biomarker imputation

Date of Metastasis

Rules-based imputation of date of metastasis

Metastatic Status

Imputation of missing data from unstructured notes

Date of Initial Dx

Imputation of index event

Line of Therapy

Regimen or progression-based drug classes

Patient Adherence

Identify root cause of product switching

Patient Acquisition

Predict factors driving patients’ brand decision

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Explore ConcertAI datasets to see how many patients are in your disease area and meet study criteria.

Breast cancer

Research Studies

Our scientists regularly publish leading RWE studies in the fields of clinical development and health economics and outcomes research.

breast cancer research study

Predict Metastatic Recurrence

Current models predict risk of distant mBC recurrence. Dynamic understanding of this risk can help guide patient care and surveillance decisions. ConcertAI tested multiple dynamic machine learning models to understand risk of recurrence at any point in the patient journey.

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5 Machine Learning Models Were Tested


Patient Features

  • Lab Tests
  • Age and gender
  • Biomarker status
  • Surgery
  • Radiation
  • Tumor grade
  • Stage
  • Lab tests
  • Care plan
  • Prior regimen
  • Menopausal diagnosis


Patient records enriched by nurse abstractors

Within 4 Years of Diagnosis:


Patients had metastatic recurrence

The model improved accuracy:

Extremely Random Forest 
had best overall accuracy




Average age at diagnosis


Years of follow-up


AI can be used to dynamically predict risk of metastatic recurrence with promising accuracy to enable new ways of managing patient care and understand evolving risk profiles.

Research for ASCO, 2020

breast cancer research study

Healthcare Cost and Resource Utilization Study

The available economic evidence base for early-stage Triple Negative Breast Cancer (ESTNBC) is limited. This study evaluated costs and HCRU for patients receiving neoadjuvant treatment 
for ESTNBC.​

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Retrospective Observational Study

Patient Criteria
  • Adult females
  • Stage II-IIIB ESTNBC between 3/2008-3/2016
  • Surgery following neoadjuvant therapy, with or without adjuvant therapy


Eligible Patients

primary cost drivers:

Infused or injected care

Systemic anticancer therapy

Between treatment initiation to surgery:

Monthly cost for neo



Monthly cost for neo+adj 



Patients received neoadjuvant (neo) but not adjuvant (adj) treatment


Patients received neo and adj treatment


The study demonstrates the economic and resource burden of ESTNBC, particularly during the time from neoadjuvant treatment initiation until surgery.

Research for ESMO Breast Cancer, 2020