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

AstraZeneca

Status: Not yet enrolling

Conditions: Breast Cancer; Lung Cancer

Study type: Observational

Funder types: Industry

Identifiers

NCT06827132

D4191R00089

Details and patient eligibility

About

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.

Full 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.

Enrollment: 600 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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.