Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer

PurposeThe study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP).MethodsThe DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed.ResultsThere was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and ‘dead’ group in their Kaplan-Meyer curves (p = 0.019).ConclusionDeep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT.SummaryWhile current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients

Genomic diversity of bacteriophages infecting Microbacterium spp

The bacteriophage population is vast, dynamic, old, and genetically diverse. The genomics of phages that infect bacterial hosts in the phylum Actinobacteria show them to not only be diverse but also pervasively mosaic, and replete with genes of unknown function. To further explore this broad group of bacteriophages, we describe here the isolation and genomic characterization of 116 phages that infect Microbacterium spp. Most of the phages are lytic, and can be grouped into twelve clusters according to their overall relatedness; seven of the phages are singletons with no close relatives. Genome sizes vary from 17.3 kbp to 97.7 kbp, and their G+C% content ranges from 51.4% to 71.4%, compared to ~67% for their Microbacterium hosts. The phages were isolated on five different Microbacterium species, but typically do not efficiently infect strains beyond the one on which they were isolated. These Microbacterium phages contain many novel features, including very large viral genes (13.5 kbp) and unusual fusions of structural proteins, including a fusion of VIP2 toxin and a MuF-like protein into a single gene. These phages and their genetic components such as integration systems, recombineering tools, and phage-mediated delivery systems, will be useful resources for advancing Microbacterium genetics

Genomic diversity of bacteriophages infecting Microbacterium spp.

The bacteriophage population is vast, dynamic, old, and genetically diverse. The genomics of phages that infect bacterial hosts in the phylum Actinobacteria show them to not only be diverse but also pervasively mosaic, and replete with genes of unknown function. To further explore this broad group of bacteriophages, we describe here the isolation and genomic characterization of 116 phages that infect Microbacterium spp. Most of the phages are lytic, and can be grouped into twelve clusters according to their overall relatedness; seven of the phages are singletons with no close relatives. Genome sizes vary from 17.3 kbp to 97.7 kbp, and their G+C% content ranges from 51.4% to 71.4%, compared to ~67% for their Microbacterium hosts. The phages were isolated on five different Microbacterium species, but typically do not efficiently infect strains beyond the one on which they were isolated. These Microbacterium phages contain many novel features, including very large viral genes (13.5 kbp) and unusual fusions of structural proteins, including a fusion of VIP2 toxin and a MuF-like protein into a single gene. These phages and their genetic components such as integration systems, recombineering tools, and phage-mediated delivery systems, will be useful resources for advancing Microbacterium genetics