Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

Objective.To develop an artificial intelligence method predicting lymph node metastasis(LNM)for patients with colorectal cancer(CRC).Impact Statement.A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers.Introduction.Preoperative diagnosis of LNM is essential in treatment planning for CRC patients.Existing radiology imaging and genomic tests approaches are either unreliable or too costly.Methods.A total of 1338 patients were recruited,where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort.We developed a Multimodal Multiple Instance Learning(MMIL)model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status.The heatmaps of the obtained MMIL model were generated for model interpretation.Results.The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve(AUCs)of 0.926,0.878,0.809,and 0.857 for patients with stage T1,T2,T3,and T4 CRC,on the discovery cohort.On the external cohort,it obtained AUCs of 0.855,0.832,0.691,and 0.792,respectively(T1-T4),which indicates its prediction accuracy and potential adaptability among multiple centres.Conclusion.The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers,which is easily accessed in different institutes.We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features.

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field.However,a persistent issue remains unsolved during experiments:the interferential technical noises caused by systematic errors(e.g.,temperature,reagent concentration,and well location)are always mixed up with the real biological signals,leading to misinterpretation of any conclusion drawn.Here,we reported a mean teacher-based deep learning model(Deep Noise)that can disentangle biological signals from the experimental noises.Specifically,we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images,which were totally unrecognizable by the human eye.We validated our model by participating in the Recursion Cellular Image Classification Challenge,and Deep Noise achieved an extremely high classification score(accuracy:99.596%),ranking the 2nd place among 866 participating groups.This promising result indicates the successful separation of biological and technical factors,which might help decrease the cost of treatment development and expedite the drug discovery process.The source code of Deep Noise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.