Lung CancerCASCADENCT06827132
Observational Study Evaluate Pathology Practice Use Artificial Intelligence in Patient Suspected Lung and Breast Cancer
An observational study of Lung Cancer and Breast Cancer, run by AstraZeneca at 2 sites in 2 countries.
Trial summary
A multinational observational study to evaluate the current pathology practices and the utilization of computational pathology plus artificial intelligence algorithms in patients with suspected lung and breast cancer. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Lung Cancer, Breast Cancer
- How many people
- 603 participants
- Where
- 2 sitesin 2 countries
- Study dates
- 25 Oct 2025to 19 Mar 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Data from samples that meet the following inclusion criterion will be analyzed. • Sample from adult patients (≥ 18 years) with suspected non-small cell lung cancer or invasive breast cancer or ductal carcinoma in situ.
Who may be able to join
None listed.
Who cannot join
- Samples with the inadequate technical quality of slides (pre-analytics quality) or images, e.g., broken slides, large out-of-focus areas, slides with fixation artefacts.
- Samples from cases that were included in the training or technical validation.
- Sample taken by fine needle aspiration.
- Sample sent for cytological evaluation.
Trial locations and contacts
2 sites in 2 countries.
2 sites in 2 countries
About this study
Official title: A Non-interventional Study Evaluating Samples From Patients With Suspected Non-small Lung Cancer or Breast Cancer to Describe Pathology Practices and to Evaluate Computational Pathology Plus Artificial Intelligence Algorithms.
A multinational observational study to evaluate the current pathology practices and the utilization of computational pathology plus artificial intelligence algorithms in patients with suspected lung and breast cancer.
Read the detailed description
A non-interventional study evaluating samples from patients with suspected non-small lung cancer or breast cancer to describe pathology practices and to evaluate computational pathology plus artificial intelligence algorithms in Australia, Brazil, Egypt, and Kenya. Use of digital and computational Artificial intelligence pathology in countries with low and high pathologist/population ratios is critical in developing a sustainable solution. The study has two parts, the first part will focus on breast cancer, and the second part will focus on lung cancer.
The laboratories have an active digital pathology setting and evaluate samples for cancer diagnosis. The centres of lung cancer part of the study will be selected at a later stage. The study will retrospectively evaluate samples from patients who have been preliminarily diagnosed with breast or lung cancer through clinical assessments and whose samples were evaluated only by using conventional workflow.
As part of the study, computational AI pathology algorithms will be implemented in each laboratory. Two AI pathology algorithms will be used in the breast cancer part of the study. Galen™ Breast application developed by Ibex Medical Analytics will be implemented in a laboratory in Australia. MindPeak Breast, developed by MindPeak GmbH will be implemented in laboratories in Brazil, Egypt, and Kenya. After implementing computational AI pathology algorithms, 150 samples evaluated for the primary objective from each laboratory for each cancer type will be evaluated using a conventional workflow plus an AI assisted workflow with human supervision and a conventional workflow plus an AI-assisted workflow without human supervision. These evaluations will be used to analyse secondary and exploratory objectives.
- Conditions
- Lung Cancer, Breast Cancer
- Keywords
- Artificial intelligence, Computational Pathology, Algorithm, Positive predictive value, Negative predictive value, Galen™ Breast application, MindPeak GmBH, Laboratories
- Medical subject headings
- Lung Neoplasms, Breast Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
primary objective
The duration between the biopsy-taken date/time and the biopsy-based pathological diagnosis date/time will be calculated based on the laboratory records retrospectively.
Measured: 2 years
Primary Objective
Reading time to assess section slides for pathological diagnosis will also be extracted from the laboratory records, if relevant information was kept in the records.
Measured: 2 years
exploratory objective
the total cost and fees related to training, for implementing digital pathology and computational AI pathology algorithms will be assessed as an endpoint.
Measured: 2 years
Secondary outcomes (2)
secondary objectives
agreement rate :PPV and NPV for computational AI pathology algorithms (with and without human supervision) when the conventional pathology workflow is the reference will also be evaluated.
Measured: 2 years
exploratory objective
the total cost and fees related to employees for implementing digital pathology and computational AI pathology algorithms will be assessed as an endpoint.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Feb 2025
- Study start25 Oct 2025
- Primary completion19 Mar 2026
- Study completion19 Mar 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by AstraZenecaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 603 participants (actual)
Sponsor and investigators
- Lead sponsor
- AstraZeneca
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06827132
- Sponsor's protocol ID
- D4191R00089
- Acronym
- CASCADE
- Status last verified
- Aug 2026
- First submitted
- 28 Jan 2025
- Last updated
- 3 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Qualified researchers can request access to anonymized individual patient-level data from AstraZeneca group of companies sponsored clinical trials via the request portal Vivli.org. All requests will be evaluated as per the AZ disclosure commitment: https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure. Yes, indicates that AZ are accepting requests for IPD, but this does not mean all requests will be shared.
Study at a glance
From the registry record, last updated by the sponsor on 3 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Non-interventional Study Evaluating Samples From Patients With Suspected Non-small Lung Cancer or Breast Cancer to Describe Pathology Practices and to Evaluate Computational Pathology Plus Artificial Intelligence Algorithms.
- ClinicalTrials.gov ID
- NCT06827132
- Acronym / sponsor ID
- CASCADE / D4191R00089
- Conditions
- Lung Cancer, Breast Cancer
- Keywords
- Artificial intelligence, Computational Pathology, Algorithm, Positive predictive value, Negative predictive value, Galen™ Breast application, MindPeak GmBH, Laboratories
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 603 participants (actual)
- Lead sponsor
- AstraZenecaIndustry (pharma, biotech, devices)
- Study start
- 25 Oct 2025 (actual)
- Primary completion
- 19 Mar 2026 (actual)
- Study completion
- 19 Mar 2026 (actual)
- First posted
- 14 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
A multinational observational study to evaluate the current pathology practices and the utilization of computational pathology plus artificial intelligence algorithms in patients with suspected lung and breast cancer.
Detailed description
A non-interventional study evaluating samples from patients with suspected non-small lung cancer or breast cancer to describe pathology practices and to evaluate computational pathology plus artificial intelligence algorithms in Australia, Brazil, Egypt, and Kenya. Use of digital and computational Artificial intelligence pathology in countries with low and high pathologist/population ratios is critical in developing a sustainable solution. The study has two parts, the first part will focus on breast cancer, and the second part will focus on lung cancer.
The laboratories have an active digital pathology setting and evaluate samples for cancer diagnosis. The centres of lung cancer part of the study will be selected at a later stage. The study will retrospectively evaluate samples from patients who have been preliminarily diagnosed with breast or lung cancer through clinical assessments and whose samples were evaluated only by using conventional workflow.
As part of the study, computational AI pathology algorithms will be implemented in each laboratory. Two AI pathology algorithms will be used in the breast cancer part of the study. Galen™ Breast application developed by Ibex Medical Analytics will be implemented in a laboratory in Australia. MindPeak Breast, developed by MindPeak GmbH will be implemented in laboratories in Brazil, Egypt, and Kenya. After implementing computational AI pathology algorithms, 150 samples evaluated for the primary objective from each laboratory for each cancer type will be evaluated using a conventional workflow plus an AI assisted workflow with human supervision and a conventional workflow plus an AI-assisted workflow without human supervision. These evaluations will be used to analyse secondary and exploratory objectives.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Data from samples that meet the following inclusion criterion will be analyzed. • Sample from adult patients (≥ 18 years) with suspected non-small cell lung cancer or invasive breast cancer or ductal carcinoma in situ.
Key inclusion criteria (0 listed)
Key exclusion criteria (4 listed)
- Samples with the inadequate technical quality of slides (pre-analytics quality) or images, e.g., broken slides, large out-of-focus areas, slides with fixation artefacts.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
primary objective
Time frame: 2 years
The duration between the biopsy-taken date/time and the biopsy-based pathological diagnosis date/time will be calculated based on the laboratory records retrospectively.
Primary Objective
Time frame: 2 years
Reading time to assess section slides for pathological diagnosis will also be extracted from the laboratory records, if relevant information was kept in the records.
exploratory objective
Time frame: 2 years
the total cost and fees related to training, for implementing digital pathology and computational AI pathology algorithms will be assessed as an endpoint.
2 secondary outcomes
secondary objectives
Time frame: 2 years
exploratory objective
Time frame: 2 years
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Sep 2026.
- Individual participant dataSharing planned: Yes. Qualified researchers can request access to anonymized individual patient-level data from AstraZeneca group of companies sponsored clinical trials via the request portal Vivli.org. All requests will be evaluated as per the AZ disclosure commitment: https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure. Yes, indicates that AZ are accepting requests for IPD, but this does not mean all requests will be shared.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Breast Cancer, run by AstraZeneca at 2 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 0 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 3 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Brazil, Kenya. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
primary objective (time frame: 2 years). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Sep 2026.