“…CRLM-SPA was built upon our previously established auto-delineation framework CRC-SPA for primary colorectal cancer (CRC) using transfer learning. 25 More specifically, we employed the well-trained CRC tissue classification network to initialize the training of a classifier for CRLM tissue based on the Resnet50 CNN model ( Figure 1 ). The classifier was trained using a hand-annotated set of 143,718 histological tissue patches and tested in an independent set of 17,653 tissue patches from other patients ( Figure 1 ; Table S1 ).…”
Section: Resultsmentioning
confidence: 99%
“… Figure 1 A schematic figure illustrating the study design A deep convolutional neural network (CNN) CRLM-SPA was established for automated tissue classification of CRLM. The deep CNN was trained on a training set of 143,718 tissue patches after transfer learning from our CRC-SPA, 25 and subsequently validated in an independent testing set of 17,653 tissue patches. The H&E-stained WSIs in the in-house SYSUCC and BJCH cohorts were classified by the trained deep CNN after tessellation and normalization.…”
Section: Resultsmentioning
confidence: 99%
“…These two cohorts were used to train a CRC tissue classifier as described in our previous work. 25 Considering the difference in the tissue architecture between CRLM and CRC, we adjusted the classes of CRLM tissue to: background (BACK), debris (DEB), hepatocyte (HEP), lymphocytes (LYM), mucus (MUC), cancer-associated stroma (STR) and cancer epithelium (TUM). An experienced pathologist manually annotated 59 H&E slides of CRLM tissue in the SYSUCC cohort following the same criteria of the two public datasets.…”
“…CRLM-SPA was built upon our previously established auto-delineation framework CRC-SPA for primary colorectal cancer (CRC) using transfer learning. 25 More specifically, we employed the well-trained CRC tissue classification network to initialize the training of a classifier for CRLM tissue based on the Resnet50 CNN model ( Figure 1 ). The classifier was trained using a hand-annotated set of 143,718 histological tissue patches and tested in an independent set of 17,653 tissue patches from other patients ( Figure 1 ; Table S1 ).…”
Section: Resultsmentioning
confidence: 99%
“… Figure 1 A schematic figure illustrating the study design A deep convolutional neural network (CNN) CRLM-SPA was established for automated tissue classification of CRLM. The deep CNN was trained on a training set of 143,718 tissue patches after transfer learning from our CRC-SPA, 25 and subsequently validated in an independent testing set of 17,653 tissue patches. The H&E-stained WSIs in the in-house SYSUCC and BJCH cohorts were classified by the trained deep CNN after tessellation and normalization.…”
Section: Resultsmentioning
confidence: 99%
“…These two cohorts were used to train a CRC tissue classifier as described in our previous work. 25 Considering the difference in the tissue architecture between CRLM and CRC, we adjusted the classes of CRLM tissue to: background (BACK), debris (DEB), hepatocyte (HEP), lymphocytes (LYM), mucus (MUC), cancer-associated stroma (STR) and cancer epithelium (TUM). An experienced pathologist manually annotated 59 H&E slides of CRLM tissue in the SYSUCC cohort following the same criteria of the two public datasets.…”
“…Such factors are currently challenging to assess systematically because of costs and a lack of standardised methodology. Several studies have shown that spatial organisation features such as the stromal content/ratios, TIL morphology and even immune hot/cold phenotypes can be extracted from H&E patches and linked to prognosis 12,[18][19][20][21][22][23] .…”
Tumour immunity is key for the prognosis and treatment of colon adenocarcinoma, but its characterisation remains cumbersome and expensive, requiring sequencing or other complex assays. Detecting tumour-infiltrating lymphocytes in haematoxylin and eosin (H&E) slides of cancer tissue would provide a cost-effective alternative to support clinicians in treatment decisions, but inter- and intra-observer variability can arise even amongst experienced pathologists. Furthermore, the compounded effect of other cells in the tumour microenvironment is challenging to quantify but could yield useful additional biomarkers. We combined RNA sequencing, digital pathology and deep learning through the InceptionV3 architecture to develop a fully automated computer vision model that detects prognostic tumour immunity levels in H&E slides of colon adenocarcinoma with an area under the curve (AUC) of 82%. Amongst tumour infiltrating T cell subsets, we demonstrate that CD8+ effector memory T cell patterns are most recognisable algorithmically with an average AUC of 83%. We subsequently applied nuclear segmentation and classification via HoVer-Net to derive complex cell-cell interaction graphs, which we queried efficiently through a bespoke Neo4J graph database. This uncovered stromal barriers and lymphocyte triplets that could act as structural hallmarks of low immunity tumours with poor prognosis. Our integrated deep learning and graph-based workflow provides evidence for the feasibility of automated detection of complex immune cytotoxicity patterns within H&E-stained colon cancer slides, which could inform new cellular biomarkers and support treatment management of this disease in the future.
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