Department of Biotechnology, IIT Kharagpur, Kharagpur, West Bengal, 721302, India.
Sci Rep. 2022 Aug 26;12(1):14628. doi: 10.1038/s41598-022-18874-6.
Accurate classification of cancers into their types and subtypes holds the key for choosing the right treatment strategy and can greatly impact patient well-being. However, existence of large-scale variations in the molecular processes driving even a single type of cancer can make accurate classification a challenging problem. Therefore, improved and robust methods for classification are absolutely critical. Although deep learning-based methods for cancer classification have been proposed earlier, they all provide point estimates for predictions without any measure of confidence and thus, can fall short in real-world applications where key decisions are to be made based on the predictions of the classifier. Here we report a Bayesian neural network-based model for classification of cancer types as well as sub-types from transcriptomic data. This model reported a measure of confidence with each prediction through analysis of epistemic uncertainty. We incorporated an uncertainty correction step with the Bayesian network-based model to greatly enhance prediction accuracy of cancer types (> 97% accuracy) and sub-types (> 80%). Our work suggests that reporting uncertainty measure with each classification can enable more accurate and informed decision-making that can be highly valuable in clinical settings.
癌症的准确分类及其亚型是选择正确治疗策略的关键，这将极大地影响患者的健康状况。然而，即使是单一类型的癌症，其驱动的分子过程也存在大规模的变化，这使得准确分类成为一个具有挑战性的问题。因此，改进和稳健的分类方法是绝对关键的。虽然之前已经提出了基于深度学习的癌症分类方法，但它们都为预测提供了点估计，而没有任何置信度度量，因此，在需要根据分类器的预测做出关键决策的实际应用中可能会存在不足。在这里，我们报告了一种基于贝叶斯神经网络的模型，用于从转录组数据中分类癌症类型和亚型。该模型通过分析认识不确定性，为每个预测报告置信度度量。我们将不确定性校正步骤与基于贝叶斯网络的模型相结合，极大地提高了癌症类型（>97%的准确率）和亚型（>80%的准确率）的预测准确性。我们的工作表明，在每次分类中报告不确定性度量可以实现更准确和明智的决策，这在临床环境中可能具有极高的价值。
