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.
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 Type
OBSERVATIONAL
Enrollment
600
Research Site
Nairobi, Kenya
RECRUITINGprimary 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.
Time frame: 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.
Time frame: 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.
Time frame: 2 years
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.
Time frame: 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.
Time frame: 2 years
AstraZeneca Clinical Study Information Center
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