Background: Accurate orientation of resected breast specimens is essential for proper pathological evaluation and margin assessment. Misorientation may compromise analysis, lead to imprecise re-excisions, and increase the risk of local recurrence. This study aims to evaluate a novel specimen plate designed to maintain consistent tissue orientation and compares its effectiveness to traditional suture marking. Methods: In a single-center, prospective, randomized two-arm trial, 56 specimens were oriented with the new plate and 54 with conventional sutures. Outcomes included intraoperative imaging interpretation, specimen handling, and pathological assessment, with a focus on orientation accuracy and margin evaluation. Results: The specimen plate significantly reduced misorientation (p < 0.01) and improved interpretation during intraoperative imaging. Pathologists reported greater ease in identifying direction and tumor-free zones, leading to a more accurate margin assessment. Non-R0 resections requiring re-excision were fewer with the specimen plate (8.9%) compared to suture marking (22.2%). Conclusions: The newly developed specimen plate can offer a reliable solution for improving specimen orientation in breast cancer surgery; however, further validation in multicenter studies is needed to confirm its applicability across diverse surgical settings. By ensuring consistent orientation and enhancing diagnostic interpretation, it may help reduce re-excisions and improve patient safety.

Background/Aims: We have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the hyperplastic or neoplastic histology of polyps. Our aim was to analyze the accuracy of AIPHP and narrow-band imaging international colorectal endoscopic (NICE) classification based histology predictions and also to compare the results of the two methods. Methods: We studied 373 colorectal polyp samples taken by polypectomy from 279 patients. The documented NBI still images were analyzed by the AIPHP method and by the NICE classification parallel. The AIPHP software was created by machine learning method. The software measures five geometrical and color features on the endoscopic image. Results: The accuracy of AIPHP was 86.6% (323/373) in total of polyps. We compared the AIPHP accuracy results for diminutive and non-diminutive polyps (82.1% vs. 92.2%; p=0.0032). The accuracy of the hyperplastic histology prediction was significantly better by NICE compared to AIPHP method both in the diminutive polyps (n=207) (95.2% vs. 82.1%) (p<0.001) and also in all evaluated polyps (n=373) (97.1% vs. 86.6%) (p<0.001) Conclusions: Our artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup.