Beyond statistical significance

Walsh, Michelle

doi:10.1016/s0897-1897(88)80015-6

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Several studies have tried to identify predictive molecular features derived from genetic sequencing, but these molecular features are based on alterations in a single gene or gene family. [17][18][19][20] For example, somatic BAP1 alterations, first identified in 2011, 21 have been associated with prolonged survival, 18 whereas CDKN2A deletion is associated with poor survival. 22 In modern analyses, many of these factors have been reconfirmed 6,23,24 and other factors have been identified.…”

Section: Introductionmentioning

confidence: 99%

The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study

Zauderer

Martin

Egger

et al. 2021

The Lancet Digital Health

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Background Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progress rapidly; for others with advanced sarcomatoid cancers, a poor prognosis can progress slowly. Therefore, we aimed to develop and validate a machine-learning tool-known as OncoCast-MPM-that could create a model for patient prognosis.Methods We did a retrospective study looking at malignant pleural mesothelioma tumours using next-generation sequencing from the Memorial Sloan Kettering Cancer Center-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). We collected clinical, pathological, and routine next-generation sequencing data from consecutive patients with malignant pleural mesothelioma treated at the Memorial Sloan Kettering Cancer Center (New York, NY, USA), as well as the MSK-IMPACT data. Together, these data comprised the MSK-IMPACT cohort. Using OncoCast-MPM, an open-source, web-accessible, machine-learning risk-prediction model, we integrated available data to create risk scores that stratified patients into low-risk and high-risk groups. Risk stratification of the MSK-IMPACT cohort was then validated using publicly available malignant pleural mesothelioma data from The Cancer Genome Atlas (ie, the TCGA cohort).

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