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重建虚拟大切片可以提高新辅助治疗后乳腺癌肿瘤床评估的准确性和一致性。

Reconstructing virtual large slides can improve the accuracy and consistency of tumor bed evaluation for breast cancer after neoadjuvant therapy.

机构信息

Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China.

AI Lab, Tencent, Tencent Binhai Building, No. 33, Haitian Second Road, Nanshan District, Shenzhen, 518054, Guangdong, China.

出版信息

Diagn Pathol. 2022 Apr 28;17(1):40. doi: 10.1186/s13000-022-01219-2.

DOI:10.1186/s13000-022-01219-2
PMID:35484579
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9047297/
Abstract

BACKGROUND

To explore whether the "WSI Stitcher", a program we developed for reconstructing virtual large slide through whole slide imaging fragments stitching, can improve the efficiency and consistency of pathologists in evaluating the tumor bed after neoadjuvant treatment of breast cancer compared with the conventional methods through stack splicing of physical slides.

METHODS

This study analyzed the advantages of using software-assisted methods to evaluate the tumor bed after neoadjuvant treatment of breast cancer. This new method is to use "WSI Stitcher" to stitch all the WSI fragments together to reconstruct a virtual large slide and evaluate the tumor bed with the help of the built-in ruler and tumor proportion calculation functions.

RESULTS

Compared with the conventional method, the evaluation time of the software-assisted method was shortened by 35%(P < 0.001). In the process of tumor bed assessment after neoadjuvant treatment of breast cancer, the software-assisted method has higher intraclass correlation coefficient when measuring the length (0.994 versus 0.934), width (0.992 versus 0.927), percentage of residual tumor cells (0.947 versus 0.878), percentage of carcinoma in situ (0.983 versus 0.881) and RCB index(0.997 versus 0.772). The software-assisted method has higher kappa values when evaluating tumor staging(0.901 versus 0.687) and RCB grading (0.963 versus 0.857).

CONCLUSION

The "WSI Stitcher" is an effective tool to help pathologists with the assessment of breast cancer after neoadjuvant treatment.

摘要

背景

为了探索我们开发的用于通过全切片成像片段拼接重建虚拟大切片的“WSI 拼接器”是否可以通过物理切片的堆叠拼接来提高病理学家评估乳腺癌新辅助治疗后肿瘤床的效率和一致性，与传统方法相比。

方法

本研究分析了使用软件辅助方法评估乳腺癌新辅助治疗后肿瘤床的优势。这种新方法是使用“WSI 拼接器”将所有 WSI 片段拼接在一起，构建一个虚拟的大切片，并借助内置的标尺和肿瘤比例计算功能来评估肿瘤床。

结果

与传统方法相比，软件辅助方法的评估时间缩短了 35%（P < 0.001）。在乳腺癌新辅助治疗后肿瘤床评估过程中，软件辅助方法在测量长度（0.994 与 0.934）、宽度（0.992 与 0.927）、残留肿瘤细胞百分比（0.947 与 0.878）、原位癌百分比（0.983 与 0.881）和 RCB 指数（0.997 与 0.772）时具有更高的组内相关系数。软件辅助方法在评估肿瘤分期（0.901 与 0.687）和 RCB 分级（0.963 与 0.857）时具有更高的 Kappa 值。

结论

“WSI 拼接器”是帮助病理学家评估乳腺癌新辅助治疗后肿瘤床的有效工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/ba45ae9bc425/13000_2022_1219_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/74d887395483/13000_2022_1219_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/b900b8cdae0a/13000_2022_1219_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/3c0ca202b32d/13000_2022_1219_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/5ac39784b351/13000_2022_1219_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/1d09b7c6447b/13000_2022_1219_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/2bbe161481ad/13000_2022_1219_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/3488eee68e45/13000_2022_1219_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/ba45ae9bc425/13000_2022_1219_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/74d887395483/13000_2022_1219_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/b900b8cdae0a/13000_2022_1219_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/3c0ca202b32d/13000_2022_1219_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/5ac39784b351/13000_2022_1219_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/1d09b7c6447b/13000_2022_1219_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/2bbe161481ad/13000_2022_1219_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/3488eee68e45/13000_2022_1219_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a5/9047297/ba45ae9bc425/13000_2022_1219_Fig8_HTML.jpg

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