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      Is Open Access

      survivalContour: visualizing predicted survival via colored contour plots

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          Abstract

          Summary

          Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time. Our approach is capable of supporting conventional models, including the Cox and Fine–Gray models. However, its capability shines when coupled with cutting-edge machine learning models such as random survival forests and deep neural networks.

          Availability and implementation

          We provide a Shiny app at https://biostatistics.mdanderson.org/shinyapps/survivalContour/ and an R package available at https://github.com/YushuShi/survivalContour as implementations of this tool.

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          Most cited references13

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          Tumor mutational load predicts survival after immunotherapy across multiple cancer types

          Immune checkpoint inhibitor (ICI) treatments benefit some patients with metastatic cancers, but predictive biomarkers are needed. Findings in select cancer types suggest that tumor mutational burden (TMB) may predict clinical response to ICI.To examine this association more broadly, we analyzed the clinical and genomic data of 1662 advanced cancer patients treated with ICI, and 5371 non-ICI treated patients, whose tumors underwent targeted next-generation sequencing (MSK-IMPACT). Among all patients, higher somatic TMB (highest 20% in each histology) was associated with better OS (HR 0.52; p=1.6 ×10 −6 ). For most cancer histologies, an association between higher TMB and improved survival was observed. The TMB cutpoints associated with improved survival varied markedly between cancer types. These data indicate that TMB is associated with improved survival in patients receiving ICI across a wide variety of cancer types, but that there may not be one universal definition of high TMB.
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            Random survival forests

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              The cost of dichotomising continuous variables.

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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Bioinform Adv
                Bioinform Adv
                bioadv
                Bioinformatics Advances
                Oxford University Press
                2635-0041
                2024
                25 July 2024
                25 July 2024
                : 4
                : 1
                : vbae105
                Affiliations
                Department of Population Health Sciences, Weill Cornell Medicine , New York, NY 10065, United States
                Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, OH 44106, United States
                Department of Biostatistic, The University of Texas MD Anderson Cancer Center , Houston, TX 77030, United States
                Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center , Houston, TX 77054, United States
                Department of Biostatistic, The University of Texas MD Anderson Cancer Center , Houston, TX 77030, United States
                Author notes
                Corresponding author. Department of Population Health Sciences, Weill Cornell Medicine, 402 E 67th Str, New York, NY 10065,  United States. E-mail: yus4011@ 123456med.cornell.edu
                Author information
                https://orcid.org/0000-0001-7575-9706
                https://orcid.org/0000-0001-5986-2260
                https://orcid.org/0000-0003-3316-0468
                Article
                vbae105
                10.1093/bioadv/vbae105
                11290613
                39086987
                212d5fc3-bd79-47ae-ac6d-122e9a6d94c6
                © The Author(s) 2024. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 February 2024
                : 31 May 2024
                : 09 July 2024
                : 24 July 2024
                : 31 July 2024
                Page count
                Pages: 5
                Funding
                Funded by: NIH, DOI 10.13039/100000002;
                Award ID: R01 HL158796
                Award ID: CCSG P30CA016672
                Award ID: SPORE P50CA140388
                Award ID: R01 HL124112
                Award ID: CCTS TR000371
                Funded by: Cancer Prevention and Research Institute of Texas, DOI 10.13039/100004917;
                Award ID: RP160693
                Funded by: NSF, DOI 10.13039/100000001;
                Award ID: 2310955
                Categories
                Application Note
                Data and Text Mining
                AcademicSubjects/SCI01060

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