Observational Study Evaluate Pathology Practice Use Artificial Intelligence in Patient Suspected Lung and Breast Cancer

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.

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06827132

Acronym

CASCADE

Enrollment

603

Registered

2025-02-14

Start date

2025-10-25

Completion date

2026-03-19

Last updated

2026-09-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast Cancer, Lung Cancer

Keywords

Artificial intelligence, Computational Pathology, Algorithm, Positive predictive value, Negative predictive value, Galen™ Breast application, MindPeak GmBH, Laboratories

Brief 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.

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

Inclusion criteria

Sample from adult patients (≥ 18 years) with suspected non-small cell lung cancer or invasive breast cancer or ductal carcinoma in situ. \-

Exclusion criteria

* 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.

Design outcomes

Primary

Measure	Time frame	Description
primary objective	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	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	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.

Secondary

Measure	Time frame	Description
secondary objectives	2 years	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.
exploratory objective	2 years	the total cost and fees related to employees for implementing digital pathology and computational AI pathology algorithms will be assessed as an endpoint.

Countries

Brazil, Kenya

Outcome results