Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer

Abstract

The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end-to-end deep learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population-based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67-0.758) and patients with any LNM with an AUROC of 0.711 (0.597-0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644-0.778) and 0.567 (0.542-0.597), respectively. Based on these findings, we used the deep learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and deep learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our deep learning algorithm is publicly available and can be used for biomarker discovery in any disease setting.

Artificial intelligence to identify genetic alterations in conventional histopathology

“…First, it can serve as a tool that helps clinicians to verify that the model has learned reasonable, previously known, morphological features [151]. Second, new features can be discovered, potentially yielding new mechanistic insight [152][153][154]. Both aspects can also help to reduce reservations and fears towards AI applications in medicine.…”

Digital Pathology and Artificial Intelligence Applications in Pathology

“…Studies have reported that AI-based algorithms can predict the expression of biomarkers and genetic mutations of tumors by evaluating only the morphological characteristics of hematoxylin and eosin (H&E)-stained slides that are basically produced for pathological diagnosis [ 21 22 26 27 28 ]. Studies have also shown that novel biomarkers can be discovered by utilizing AI models in various types of cancers [ 29 30 31 ]. Therefore, for the patient’s appropriate care, it is important to use AI in neurooncology to screen for pathological classification or to predict the characteristics of cancer.…”

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology

“…This type of visualization enables explainability of DL models, which is useful as a plausibility check as well as for discovery of new morphological biomarkers. 27 Another strength of the DeepMed is the subgroup training functionality, which enables users to apply the DL analysis on a subset of patients of the original dataset. The subsets are defined by users in the experiment script.In "train_and_deploy_subgroup_based_TMB.py" (Full Script 9), we show how to train a model for prediction of ER status on subgroups of patients based on their tumor mutational burde (TMB, low and high as binarized at the median).…”

“…Two recent large-scale assessments consistently demonstrated that this method predicts approximately one-third of all evaluated genetic changes in human cancer 4,6 -and two thirds of all tested molecular alterations are not predictable. However, although this approach is not ubiquitously applicable, it has been shown to provide clinically relevant performance 15,28 and can be used to discover previously unknown biological mechanisms 27,31 . Another restriction is that many non-computer-savvy researchers find obtaining, storing, preparing, and evaluating histology image data difficult.…”