Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.

IF 3.5 3区 医学 Q1 SURGERY
BJS Open Pub Date : 2024-10-29 DOI:10.1093/bjsopen/zrae127
Diederik J Höppener, Witali Aswolinskiy, Zhen Qian, David Tellez, Pieter M H Nierop, Martijn Starmans, Iris D Nagtegaal, Michail Doukas, Johannes H W de Wilt, Dirk J Grünhagen, Jeroen A W M van der Laak, Peter Vermeulen, Francesco Ciompi, Cornelis Verhoef
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引用次数: 0

Abstract

Background: Histopathological growth patterns are one of the strongest prognostic factors in patients with resected colorectal liver metastases. Development of an efficient, objective and ideally automated histopathological growth pattern scoring method can substantially help the implementation of histopathological growth pattern assessment in daily practice and research. This study aimed to develop and validate a deep-learning algorithm, namely neural image compression, to distinguish desmoplastic from non-desmoplastic histopathological growth patterns of colorectal liver metastases based on digital haematoxylin and eosin-stained slides.

Methods: The algorithm was developed using digitalized whole-slide images obtained in a single-centre (Erasmus MC Cancer Institute, the Netherlands) cohort of patients who underwent first curative intent resection for colorectal liver metastases between January 2000 and February 2019. External validation was performed on whole-slide images of patients resected between October 2004 and December 2017 in another institution (Radboud University Medical Center, the Netherlands). The outcomes of interest were the automated classification of dichotomous hepatic growth patterns, distinguishing between desmoplastic hepatic growth pattern and non-desmoplatic growth pattern by a deep-learning model; secondary outcome was the correlation of these classifications with overall survival in the histopathology manual-assessed histopathological growth pattern and those assessed using neural image compression.

Results: Nine hundred and thirty-two patients, corresponding to 3.641 whole-slide images, were reviewed to develop the algorithm and 870 whole-slide images were used for external validation. Median follow-up for the development and the validation cohorts was 43 and 29 months respectively. The neural image compression approach achieved significant discriminatory power to classify 100% desmoplastic histopathological growth pattern with an area under the curve of 0.93 in the development cohort and 0.95 upon external validation. Both the histopathology manual-scored histopathological growth pattern and neural image compression-classified histopathological growth pattern achieved a similar multivariable hazard ratio for desmoplastic versus non-desmoplastic growth pattern in the development cohort (histopathology manual score: 0.63 versus neural image compression: 0.64) and in the validation cohort (histopathology manual score: 0.40 versus neural image compression: 0.48).

Conclusions: The neural image compression approach is suitable for pathology-based classification tasks of colorectal liver metastases.

利用深度学习分析对切除的结直肠肝转移组织病理学生长模式进行分类。
背景:组织病理学生长模式是切除的结直肠肝转移患者最有力的预后因素之一。开发一种高效、客观和理想的自动组织病理学生长模式评分方法,可大大有助于在日常实践和研究中实施组织病理学生长模式评估。本研究旨在开发和验证一种深度学习算法,即神经图像压缩算法,以基于数字化血红素和伊红染色切片区分结直肠肝转移瘤的去瘤组织病理学生长模式和非去瘤组织病理学生长模式:该算法是利用 2000 年 1 月至 2019 年 2 月期间在单中心(荷兰伊拉斯谟 MC 癌症研究所)队列中获得的数字化全切片图像开发的,该队列中的患者均因结直肠肝转移而接受了首次根治性切除术。外部验证是在另一家机构(荷兰拉德布德大学医学中心)对2004年10月至2017年12月期间切除患者的全切片图像进行的。相关结果是对二分法肝脏生长模式的自动分类,通过深度学习模型区分去瘤细胞肝脏生长模式和非去瘤细胞生长模式;次要结果是这些分类与组织病理学人工评估的组织病理学生长模式和使用神经图像压缩评估的组织病理学生长模式的总生存率的相关性:为开发算法,对 932 例患者(对应 3.641 张整张病理切片图像)进行了审查,并使用 870 张整张病理切片图像进行了外部验证。开发组和验证组的中位随访时间分别为 43 个月和 29 个月。神经图像压缩方法在对 100% 脱鳞组织病理学生长模式进行分类方面具有显著的鉴别力,开发组的曲线下面积为 0.93,外部验证的曲线下面积为 0.95。组织病理学人工评分的组织病理学生长模式和神经图像压缩分类的组织病理学生长模式在开发队列(组织病理学人工评分:0.63,神经图像压缩:0.64)和验证队列(组织病理学人工评分:0.40,神经图像压缩:0.48)中的去瘤组织病理学生长模式与非去瘤组织病理学生长模式的多变量危险比相似:结论:神经图像压缩方法适用于基于病理学的结直肠肝转移分类任务。
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
BJS Open
BJS Open SURGERY-
CiteScore
6.00
自引率
3.20%
发文量
144
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