Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.

IF 5.6 1区 医学 Q1 Medicine
Xue Chao, Yu Wu, Xi Cai, Jiehua He, Chengyou Zheng, Mei Li, Rongzhen Luo, Lijuan Song, Xiaoqin Li, Wentai Feng, Shuoyu Xu, Peng Sun
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引用次数: 0

Abstract

Background: Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, highlight the need for improved diagnostic tools. This study aims to develop and validate a deep-learning model that transforms cryosectioned images into formalin-fixed paraffin-embedded (FFPE) images to enhance diagnostic performance in breast lesions.

Methods: We developed an unpaired image-to-image translation model (AI-FFPE) using the TCGA-BRCA dataset to convert FS images into FFPE-like images. The model employs a modified generative adversarial network (GAN) enhanced with an attention mechanism to correct artifacts and a self-regularization constraint to preserve clinically significant features. For validation, 132 FS whole slide images (WSIs) of breast lesions were collected from three cohorts (SYSUCC, GSPCH, and TCGA). These FS-WSIs were transformed into AI-FFPE-WSIs and independently evaluated by six pathologists for image quality, diagnostic concordance, and confidence in lesion properties and final diagnoses. Diagnostic performance was assessed using a diagnostic score (DS), calculated by multiplying the accuracy index by the confidence level. The dataset included 132 reference diagnoses and 1,584 pathologist reads.

Results: The AI-FFPE group showed a significant improvement in image quality compared to the FS group (p < 0.001). Concordance rates for lesion properties (79.9% vs. 79.9%) and final diagnoses (82.7% vs. 82.6%) were similar between two groups. In concordant cases, the AI-FFPE group demonstrated significantly higher diagnostic confidence than the FS group, with more diagnoses definitively categorized based on lesion properties (54.3% vs. 35.4%, p < 0.001) and final diagnoses (48.3% vs. 33.3%, p < 0.001). Paired t-tests revealed that the diagnostic scores in the AI-FFPE group were significantly higher than in the FS group (overall DS, 13.9 ± 6.6 vs. 12.0 ± 6.6, p < 0.001; DS for lesion property, 6.8 ± 3.8 vs. 5.8 ± 3.7, p < 0.001; DS for final diagnosis, 7.1 ± 3.2 vs. 6.2 ± 3.2, p < 0.001). Logistic regression showed that poorer image quality, atypical ductal hyperplasia/ ductal carcinoma in situ cases, and less experienced pathologists were associated with decreased diagnostic accuracy.

Conclusions: The AI-FFPE model improved perceived image quality and diagnostic confidence among pathologists in breast lesion evaluations. While diagnostic concordance remained comparable, the enhanced interpretability of AI-FFPE images may support more confident intraoperative decision-making.

探索性多队列、多读者研究深度学习模型在乳腺病变诊断中将冷冻切片转化为福尔马林固定石蜡包埋(FFPE)图像的临床应用。
背景:在术中评估时,冷冻切片组织经常会出现损害病理学家诊断准确性的伪影。这些不一致,加上各实验室冷冻切片(FS)生产的差异,突出了改进诊断工具的必要性。本研究旨在开发并验证一种深度学习模型,该模型将冷冻切片图像转换为福尔马林固定石蜡包埋(FFPE)图像,以提高乳腺病变的诊断性能。方法:利用TCGA-BRCA数据集开发了一种非配对图像到图像翻译模型(AI-FFPE),将FS图像转换为类似ffpe的图像。该模型采用改进的生成对抗网络(GAN),增强了注意机制来纠正伪像,并采用自正则化约束来保留临床重要特征。为了验证,我们从三个队列(SYSUCC、GSPCH和TCGA)中收集了132张乳腺病变的FS全切片图像(WSIs)。将这些fs - wsi转化为ai - ffpe - wsi,并由6名病理学家独立评估图像质量、诊断一致性、病变特性和最终诊断的可信度。诊断性能评估使用诊断评分(DS),通过准确性指数乘以置信水平计算。该数据集包括132个参考诊断和1584个病理读数。结果:与FS组相比,AI-FFPE组的图像质量有显著改善(p)。结论:AI-FFPE模型提高了病理学家对乳房病变评估的感知图像质量和诊断信心。虽然诊断一致性仍然具有可比性,但AI-FFPE图像的可解释性增强可能支持更自信的术中决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
76
审稿时长
12 weeks
期刊介绍: Breast Cancer Research, an international, peer-reviewed online journal, publishes original research, reviews, editorials, and reports. It features open-access research articles of exceptional interest across all areas of biology and medicine relevant to breast cancer. This includes normal mammary gland biology, with a special emphasis on the genetic, biochemical, and cellular basis of breast cancer. In addition to basic research, the journal covers preclinical, translational, and clinical studies with a biological basis, including Phase I and Phase II trials.
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