ArticleBJS open2024
Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.
Article in BJS open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The prognostic significance of histopathological growth patterns in liver metastases undergoing surgery: a systematic review and meta-analysis.Clinical & experimental metastasis · 2025Pooled it
- Prognostic value of desmoplastic growth patterns in resected hepatocellular carcinoma.Scientific reports · 2026Article
- Beyond mismatch repair in colorectal cancer liver metastases: rethinking immunotherapy through the hepatic metastatic niche.Frontiers in immunology · 2026Article
- Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification.World journal of clinical oncology · 2025Review
- Factors affecting survival prognosis of patients with rectal cancer after neoadjuvant chemoradiotherapy.Frontiers in oncology · 2025Article
- Examination of histopathological growth patterns of liver metastases in a retrospective, consecutive, single-center, cohort study.Pathology oncology research : POR · 2025Article
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14 authors.
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Abstract
backgroundHistopathological 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.
methodsThe 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.
resultsNine 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).
conclusionsThe neural image compression approach is suitable for pathology-based classification tasks of colorectal liver metastases.
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