Spectral Cancer Tissue Recognition - I

SPCTR100 enrolled

Overview

This analytical performance study aims to validate the SPCTRone system for use in breast conserving surgery of breast cancer patients. By collecting spectral biomarkers and correlating these with the golden standard of histopathological assessment by a pathologist, we aim to train and optimize an AI model that is able to achieve the following outcomes with classifying tissues: * Sensitivity (percentage of classified positive margins of actual positive margins): ≥ 96% CI 95.5-97.5% * Specificity (percentage of classified free margins of actual free margins): 96% CI 95.5-97.5% * Accuracy (total correctly classified margins): ≥ 96% CI 95.5-97.5% * Negative predictive value (amount of true negative - free margins - among the classified negative margins): ≥ 95% CI 94.5-96.5%

Study Type

OBSERVATIONAL

Enrollment

100

Conditions

Margin Assessment Breast Cancer

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Age ≥18 years * Confirmed diagnosis breast cancer for which surgical procedure is needed * Planned for breast conserving surgery Exclusion Criteria: * Person that undergoes breast amputation without confirmed cancer diagnosis

Locations (2)

Martini Ziekenhuis

Groningen, Provincie Groningen, Netherlands

RECRUITING

UMC Utrecht

Utrecht, Utrecht, Netherlands

NOT_YET_RECRUITING

Outcomes

Primary Outcomes

Diagnostic performance of SPCTRone system to assess margin status of breast cancer specimens

Diagnostic performance indicators are: Sensitivity, Specificity, Negative Predictive Value, and Accuracy.

Time frame: Through study completion, an average of 6 months