Three-dimensional histological electrophoresis with fluorescence labeling for detection of malignant tissue contours in precise conserving surgery.

Background: Breast-conserving surgery (BCS) is the primary surgical approach for patients with breast cancer. The accurate determination of surgical margins during BCS is critical for patient prognosis; however, time constraints and limitations in current pathological techniques often prevent pathologists from performing this assessment intraoperatively. The inability to reliably assess margins during surgery can lead to incomplete tumor removal and the need for additional surgeries. The challenge remains to develop a real-time, reliable method for margin assessment during surgery to improve patient outcomes and reduce healthcare costs.

Results: We developed a three-dimensional (3D) histological electrophoresis platform that could directly and accurately determine the margin status and distances in six directions for BCS samples during surgery. The platform incorporated a microporous array mold to hold frozen tissue slices, electrophoresis for isolating IR780-labeled proteins, gel imaging using ImageJ and AzureSpot, and a diagnostic algorithm developed using MATLAB. The method can rapidly label tumor-associated proteins within tissue sections by employing a tumor-selective dye (IR-780) conjugated with albumin and RAC-α serine/threonine-protein kinase (AKT1), both of which are highly expressed in breast cancer tissues. It autonomously distinguishes malignant regions from benign ones based on protein signaling, thereby reducing the reliance on visual diagnosis and accelerating intraoperative decision-making. The device was tested on various types of human breast tissues from 36 patients to demonstrate the accuracy and efficiency of the device for use in BCS therapy.

Significance: The platform leverages post-electrophoresis signal differences across various tissue types to predict the contours of malignant tissues. By generating margin distances in multiple directions, it addresses the gap in surgical margin calculation during clinical procedures. This saves time and reduces the costs associated with traditional pathological examinations, enhancing diagnostic detection efficiency. As a result, it improves the accuracy of tumor margin assessment, facilitating better surgical outcomes and treatment planning.