A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1/PD-L1 immunotherapy in advanced stage non-small-cell lung cancer.
Yi Yang, Jiancheng Yang, Lan Shen, Jiajun Chen, Liliang Xia, Bingbing Ni, Liang Ge, Ying Wang, Shun Lü
Abstract
=0.01) than high-risk patients. In conclusion, the SimTA-based multi-omics serial deep learning provides a promising methodology for predicting response of advanced NSCLC patients to anti-PD-1/PD-L1 monotherapy. Moreover, our model could better differentiate survival benefit among SD patients than the traditional RECIST evaluation method.
Topics & Concepts
MedicineImmunotherapyStage (stratigraphy)OncologyLung cancerInternal medicineCancerProgression-free survivalLog-rank testSurvival analysisOverall survivalPaleontologyBiologyRadiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and TreatmentEsophageal Cancer Research and Treatment