Ignite Creation Date:
2025-12-24 @ 9:27 PM
Ignite Modification Date:
2026-01-01 @ 2:32 PM
Study NCT ID:
NCT06827132
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-02-14
First Post:
2025-01-28
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Observational Study Evaluate Pathology Practice Use Artificial Intelligence in Patient Suspected Lung and Breast Cancer
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D008175', 'term': 'Lung Neoplasms'}, {'id': 'D001943', 'term': 'Breast Neoplasms'}], 'ancestors': [{'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D001941', 'term': 'Breast Diseases'}, {'id': 'D012871', 'term': 'Skin Diseases'}, {'id': 'D017437', 'term': 'Skin and Connective Tissue Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 600}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-03-15', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-02', 'completionDateStruct': {'date': '2027-05-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-02-13', 'studyFirstSubmitDate': '2025-01-28', 'studyFirstSubmitQcDate': '2025-02-13', 'lastUpdatePostDateStruct': {'date': '2025-02-14', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-02-14', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-05-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Secondary Objective', 'timeFrame': '2 years', 'description': 'sensitivity: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.'}, {'measure': 'Secondary Objective', 'timeFrame': '2 years', 'description': 'specificity: 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.'}, {'measure': 'exploratory objective', 'timeFrame': '2 years', 'description': 'The total cost and fees related to hardware for implementing digital pathology and computational AI pathology algorithms will be assessed as an endpoint.'}, {'measure': 'Exploratory objective', 'timeFrame': '2 years', 'description': 'the total cost and fees related to software for implementing digital pathology and computational AI pathology algorithms will be assessed as an endpoint.'}], 'primaryOutcomes': [{'measure': 'primary objective', 'timeFrame': '2 years', 'description': '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.'}, {'measure': 'Primary Objective', 'timeFrame': '2 years', 'description': '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.'}, {'measure': 'exploratory objective', 'timeFrame': '2 years', 'description': 'the total cost and fees related to training, for implementing digital pathology and computational AI pathology algorithms will be assessed as an endpoint.'}], 'secondaryOutcomes': [{'measure': 'secondary objectives', 'timeFrame': '2 years', 'description': '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.'}, {'measure': 'exploratory objective', 'timeFrame': '2 years', 'description': 'the total cost and fees related to employees for implementing digital pathology and computational AI pathology algorithms will be assessed as an endpoint.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial intelligence', 'Computational Pathology', 'Algorithm', 'Positive predictive value', 'Negative predictive value', 'Galen™ Breast application', 'MindPeak GmBH', 'Laboratories'], 'conditions': ['Lung Cancer', 'Breast Cancer']}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.\n\nThe 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.\n\nAs 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Data from samples that meet the following inclusion criterion will be analyzed.\n\n• Sample from adult patients (≥ 18 years) with suspected non-small cell lung cancer or invasive breast cancer or ductal carcinoma in situ.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:Sample from adult patients (≥ 18 years) with suspected non-small cell lung cancer or invasive breast cancer or ductal carcinoma in situ.\n\n\\-\n\nExclusion Criteria:\n\n* 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.\n\n * Samples from cases that were included in the training or technical validation.\n * Sample taken by fine needle aspiration.\n * Sample sent for cytological evaluation.'}, 'identificationModule': {'nctId': 'NCT06827132', 'acronym': 'CASCADE', 'briefTitle': 'Observational Study Evaluate Pathology Practice Use Artificial Intelligence in Patient Suspected Lung and Breast Cancer', 'organization': {'class': 'INDUSTRY', 'fullName': 'AstraZeneca'}, 'officialTitle': '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.', 'orgStudyIdInfo': {'id': 'D4191R00089'}}, 'contactsLocationsModule': {'centralContacts': [{'name': 'AstraZeneca Clinical Study Information Center', 'role': 'CONTACT', 'email': 'information.center@astrazeneca.com', 'phone': '1-877-240-9479'}]}, 'ipdSharingStatementModule': {'url': 'https://vivli.org/', 'timeFrame': 'AstraZeneca will meet or exceed data availability as per the commitments made to the EFPIA PhRMA Data Sharing Principles. For details of our timelines, please rerefer to our disclosure commitment at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure.', 'ipdSharing': 'YES', 'description': '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.', 'accessCriteria': 'When a request has been approved AstraZeneca will provide access to the anonymized individual patient-level data via secure research environment Vivli.org.\n\nSigned Data Usage Agreement (non-negotiable contract for data accessors) must be in place before accessing requested information.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'AstraZeneca', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'SPONSOR'}}}}