创作:Lexi 审核:Lexi 2021年11月08日
  • 开发一个弱监管深度学习框架,涉及三个独立卷积神经网络模型;
  • 迭代绘制和秩抽样方法可产生平均接收机工作特性曲线凸包下面积(AUROC),用于预测高突变、微卫星不稳性、染色体不稳定、BRAF突变和TP53突变;
  • 该AUROC高于已发表的预测方法,对KRAS突变预测与之前的方法相当,预测CpG岛甲基化表型高状态的平均AUROC为0.79;
  • 高比例肿瘤浸润淋巴细胞(TIL)和坏死癌细胞与微卫星不稳定性有关,高比例TIL和低比例坏死癌细胞与高突变相关。
确定大肠癌中分子通路和关键突变的状态对最佳治疗决策至关重要。最新发表在Lancet Digital Health的研究,经过大规模验证,提出用于预测临床重要突变和分子通路的算法(如微卫星不稳定性),可在结直肠癌中对患者分层,进行靶向治疗。相比基于测序或基于免疫组化的方法,该方法可能成本更低,周转时间更快。

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study



2021-10-19, Article

Abstract & Authors:展开

Background: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as an alternative to current tests.
Methods: In this retrospective study, we used 502 diagnostic slides of primary colorectal tumours from 499 patients in The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) cohort and developed a weakly supervised deep learning framework involving three separate convolutional neural network models. Whole-slide images were divided into equally sized tiles and model 1 (ResNet18) extracted tumour tiles from non-tumour tiles. These tumour tiles were inputted into model 2 (adapted ResNet34), trained by iterative draw and rank sampling to calculate a prediction score for each tile that represented the likelihood of a tile belonging to the molecular labels of high mutation density (vs low mutation density), microsatellite instability (vs microsatellite stability), chromosomal instability (vs genomic stability), CpG island methylator phenotype (CIMP)-high (vs CIMP-low), BRAFmut (vs BRAFWT), TP53mut (vs TP53WT), and KRASWT (vs KRASmut). These scores were used to identify the top-ranked titles from each slide, and model 3 (HoVer-Net) segmented and classified the different types of cell nuclei in these tiles. We calculated the area under the convex hull of the receiver operating characteristic curve (AUROC) as a model performance measure and compared our results with those of previously published methods.
Findings: Our iterative draw and rank sampling method yielded mean AUROCs for the prediction of hypermutation (0·81 [SD 0·03] vs 0·71), microsatellite instability (0·86 [0·04] vs 0·74), chromosomal instability (0·83 [0·02] vs 0·73), BRAFmut (0·79 [0·01] vs 0·66), and TP53mut (0·73 [0·02] vs 0·64) in the TCGA-CRC-DX cohort that were higher than those from previously published methods, and an AUROC for KRASmut that was similar to previously reported methods (0·60 [SD 0·04] vs 0·60). Mean AUROC for predicting CIMP-high status was 0·79 (SD 0·05). We found high proportions of tumour-infiltrating lymphocytes and necrotic tumour cells to be associated with microsatellite instability, and high proportions of tumour-infiltrating lymphocytes and a low proportion of necrotic tumour cells to be associated with hypermutation.
Interpretation: After large-scale validation, our proposed algorithm for predicting clinically important mutations and molecular pathways, such as microsatellite instability, in colorectal cancer could be used to stratify patients for targeted therapies with potentially lower costs and quicker turnaround times than sequencing-based or immunohistochemistry-based approaches.
Funding: The UK Medical Research Council.

First Authors:
Mohsin Bilal

Correspondence Authors:
Nasir M Rajpoot

All Authors:
Mohsin Bilal,Shan E Ahmed Raza,Ayesha Azam,Simon Graham,Mohammad Ilyas,Ian A Cree,David Snead,Fayyaz Minhas,Nasir M Rajpoot