Prediction of heart transplant rejection from routine pathology slides with self-supervised Deep Learning

Seraphin, Tobias Paul; Luedde, Mark; Roderburg, Christoph; Treeck, Marko van; Schneider, Pascal; Bülow, Roman David; Loosen, Sven H.; Provazník, Zdeněk; Mendelsohn, Daniel; Berisha, Filip; Magnussen, Christina; Westermann, Dirk; Luedde, Tom; Brochhausen, Christoph; Sossalla, Samuel; Kather, Jakob Nikolas

doi:10.1101/2022.09.29.22279995

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…8). We then used our in-house open-source DL pipeline (https:// github.com/KatherLab/marugoto) which uses the SSL-trained model RetCCL 32 to obtain 2048 features per tile and uses attMIL to make patient-level predictions 33,34 .…”

Section: Data Acquisition and Experimental Designmentioning

confidence: 99%

Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology

et al. 2023

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The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types. We show that an analysis pipeline that integrates self-supervised feature extraction and attention-based multiple instance learning achieves a robust predictability and generalizability.

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Section: Data Acquisition and Experimental Designmentioning

confidence: 99%

Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology

et al. 2023

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“…Attention-based multiple instance learning (attMIL) 47,48 models were used to predict ERα and PR status in both female and male breast cancer patient samples. Models were trained on FBC H&E-stained WSIs from the TCGA-BRCA cohort (n = 1085) using biomarker-strati ed ve-fold cross-validation.…”

Section: Methodsmentioning

confidence: 99%

Deep learning models predicting hormone receptor status in breast cancer trained on females do not generalize to males: further evidence of sex-based disparity in breast cancer

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et al. 2023

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Breast cancer prognosis and management for both men and women are reliant upon estrogen receptor alpha (ERα) and progesterone receptor (PR) expression to inform therapy. Previous studies have shown that there are sex-specific binding characteristics of ERα and PR in breast cancer and, counterintuitively, ERα expression is more common in male than female breast cancer. We hypothesized that these differences could have morphological manifestations that are undetectable to human observers but could be elucidated computationally. To investigate this, we trained attention-based multiple instance learning prediction models for ERα and PR using H&E-stained images of female breast cancer from the Cancer Genome Atlas (TCGA) (n = 1085), and deployed them on external female (n = 192) and male breast cancer images (n = 245). Both targets were predicted in the internal (AUROC for ERα prediction: 0.86 ± 0.02, p < 0.001; AUROC for PR prediction = 0.76 ± 0.03, p < 0.001) and external female cohorts (AUROC for ERα prediction: 0.78 ± 0.03, p < 0.001; AUROC for PR prediction = 0.80 ± 0.04, p < 0.001) but not the male cohort (AUROC for ERα prediction: 0.66 ± 0.14, p = 0.43; AUROC for PR prediction = 0.63 ± 0.04, p = 0.05). This suggests that subtle morphological differences invisible upon visual inspection may exist between the sexes, supporting previous immunohistochemical, genomic, and transcriptomic analyses.

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Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study

et al. 2023

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Introduction The Laurén classification is widely used for Gastric Cancer (GC) histology subtyping. However, this classification is prone to interobserver variability and its prognostic value remains controversial. Deep Learning (DL)-based assessment of hematoxylin and eosin (H&E) stained slides is a potentially useful tool to provide an additional layer of clinically relevant information, but has not been systematically assessed in GC. Objective We aimed to train, test and externally validate a deep learning-based classifier for GC histology subtyping using routine H&E stained tissue sections from gastric adenocarcinomas and to assess its potential prognostic utility. Methods We trained a binary classifier on intestinal and diffuse type GC whole slide images for a subset of the TCGA cohort (N = 166) using attention-based multiple instance learning. The ground truth of 166 GC was obtained by two expert pathologists. We deployed the model on two external GC patient cohorts, one from Europe (N = 322) and one from Japan (N = 243). We assessed classification performance using the Area Under the Receiver Operating Characteristic Curve (AUROC) and prognostic value (overall, cancer specific and disease free survival) of the DL-based classifier with uni- and multivariate Cox proportional hazard models and Kaplan–Meier curves with log-rank test statistics. Results Internal validation using the TCGA GC cohort using five-fold cross-validation achieved a mean AUROC of 0.93 ± 0.07. External validation showed that the DL-based classifier can better stratify GC patients' 5-year survival compared to pathologist-based Laurén classification for all survival endpoints, despite frequently divergent model-pathologist classifications. Univariate overall survival Hazard Ratios (HRs) of pathologist-based Laurén classification (diffuse type versus intestinal type) were 1.14 (95% Confidence Interval (CI) 0.66–1.44, p-value = 0.51) and 1.23 (95% CI 0.96–1.43, p-value = 0.09) in the Japanese and European cohorts, respectively. DL-based histology classification resulted in HR of 1.46 (95% CI 1.18–1.65, p-value < 0.005) and 1.41 (95% CI 1.20–1.57, p-value < 0.005), in the Japanese and European cohorts, respectively. In diffuse type GC (as defined by the pathologist), classifying patients using the DL diffuse and intestinal classifications provided a superior survival stratification, and demonstrated statistically significant survival stratification when combined with pathologist classification for both the Asian (overall survival log-rank test p-value < 0.005, HR 1.43 (95% CI 1.05–1.66, p-value = 0.03) and European cohorts (overall survival log-rank test p-value < 0.005, HR 1.56 (95% CI 1.16–1.76, p-value < 0.005)). Conclusion Our study shows that gastric adenocarcinoma subtyping using pathologist’s Laurén classification as ground truth can be performed using current state of the art DL techniques. Patient survival stratification seems to be better by DL-based histology typing compared with expert pathologist histology typing. DL-based GC histology typing has potential as an aid in subtyping. Further investigations are warranted to fully understand the underlying biological mechanisms for the improved survival stratification despite apparent imperfect classification by the DL algorithm.

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Prediction of heart transplant rejection from routine pathology slides with self-supervised Deep Learning

Cited by 3 publications

References 34 publications

Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology

Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology

Deep learning models predicting hormone receptor status in breast cancer trained on females do not generalize to males: further evidence of sex-based disparity in breast cancer

Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study

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