Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Departments of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
BJS Open. 2024 Oct 29;8(6). doi: 10.1093/bjsopen/zrae127.
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.
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.
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).
The neural image compression approach is suitable for pathology-based classification tasks of colorectal liver metastases.
在接受结直肠肝转移切除术的患者中，组织病理学生长模式是最强的预后因素之一。开发一种高效、客观且理想的自动化组织病理学生长模式评分方法，可以极大地帮助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，以区分结直肠肝转移的纤维组织增生型和非纤维组织增生型组织病理学生长模式，基于数字化苏木精和伊红染色切片。
该算法是使用 2000 年 1 月至 2019 年 2 月期间在一家单中心（荷兰伊拉斯谟医学中心）接受首次治愈性意向切除术治疗的患者的数字化全切片图像开发的。外部验证是在另一家机构（荷兰拉德堡德大学医学中心）于 2004 年 10 月至 2017 年 12 月期间切除的患者的全切片图像上进行的。主要结局是通过深度学习模型对二分类肝生长模式进行自动分类，区分纤维组织增生型肝生长模式和非纤维组织增生型生长模式；次要结局是这些分类与组织病理学手动评估的组织病理学生长模式以及使用神经图像压缩评估的组织病理学生长模式的总体生存率之间的相关性。
共回顾了 932 例患者，对应 3641 张全切片图像，用于开发算法，870 张全切片图像用于外部验证。发展队列和验证队列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开发队列中达到了显著的区分能力，以 100%的准确率对 100%的纤维组织增生型组织病理学生长模式进行分类，曲线下面积为 0.93，在外部验证中达到了 0.95。组织病理学手动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发展队列（组织病理学手动评分：0.63 与神经图像压缩：0.64）和验证队列（组织病理学手动评分：0.40 与神经图像压缩：0.48）中均具有类似的多变量风险比，用于纤维组织增生型与非纤维组织增生型生长模式的比较。
神经图像压缩方法适用于结直肠肝转移的基于病理学的分类任务。
