RESUMEN
INTRODUCTION: The aim of the present systematic review (SR) is to summarize Machine Learning (ML) models currently used to predict head and neck cancer (HNC) treatment-related toxicities, and to understand the impact of image biomarkers (IBMs) in prediction models (PMs). The present SR was conducted following the guidelines of the PRISMA 2022 and registered in PROSPERO database (CRD42020219304). METHODS: The acronym PICOS was used to develop the focused review question (Can PMs accurately predict HNC treatment toxicities?) and the eligibility criteria. The inclusion criteria enrolled Prediction Model Studies (PMSs) with patient cohorts that were treated for HNC and developed toxicities. Electronic database search encompassed PubMed, EMBASE, Scopus, Cochrane Library, Web of Science, LILACS, and Gray Literature (Google Scholar and ProQuest). Risk of Bias (RoB) was assessed through PROBAST and the results were synthesized based on the data format (with and without IBMs) to allow comparison. RESULTS: A total of 28 studies and 4,713 patients were included. Xerostomia was the most frequently investigated toxicity (17; 60.71 %). Sixteen (57.14 %) studies reported using radiomics features in combination with clinical or dosimetrics/dosiomics for modelling. High RoB was identified in 23 studies. Meta-analysis (MA) showed an area under the receiver operating characteristics curve (AUROC) of 0.82 for models with IBMs and 0.81 for models without IBMs (p value < 0.001), demonstrating no difference among IBM- and non-IBM-based models. DISCUSSION: The development of a PM based on sample-specific features represents patient selection bias and may affect a model's performance. Heterogeneity of the studies as well as non-standardized metrics prevent proper comparison of studies, and the absence of an independent/external test does not allow the evaluation of the model's generalization ability. CONCLUSION: IBM-featured PMs are not superior to PMs based on non-IBM predictors. The evidence was appraised as of low certainty.
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Neoplasias de Cabeza y Cuello , Xerostomía , Humanos , Neoplasias de Cabeza y Cuello/tratamiento farmacológico , Biomarcadores , Aprendizaje AutomáticoRESUMEN
BACKGROUND: Chronic myelogenous leukemia (CML) is a clonal stem cell disorder accounting for 15% of adult leukemias. We aimed to determine if machine learning models could predict CML using blood cell counts prior to diagnosis. METHODS: We identified patients with a diagnostic test for CML (BCR-ABL1) and at least 6 consecutive prior years of differential blood cell counts between 1999 and 2020 in the largest integrated health care system in the United States. Blood cell counts from different time periods prior to CML diagnostic testing were used to train, validate, and test machine learning models. RESULTS: The sample included 1,623 patients with BCR-ABL1 positivity rate 6.2%. The predictive ability of machine learning models improved when trained with blood cell counts closer to time of diagnosis: 2 to 5 years area under the curve (AUC), 0.59 to 0.67, 0.5 to 1 years AUC, 0.75 to 0.80, at diagnosis AUC, 0.87 to 0.92. CONCLUSIONS: Blood cell counts collected up to 5 years prior to diagnostic workup of CML successfully predicted the BCR-ABL1 test result. These findings suggest a machine learning model trained with blood cell counts could lead to diagnosis of CML earlier in the disease course compared to usual medical care.
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Pruebas Diagnósticas de Rutina , Leucemia Mielógena Crónica BCR-ABL Positiva , Registros Electrónicos de Salud , Proteínas de Fusión bcr-abl/genética , Humanos , Leucemia Mielógena Crónica BCR-ABL Positiva/diagnóstico , Aprendizaje Automático , Estudios RetrospectivosRESUMEN
This paper introduceds the tool named as "Prediction model Risk Of Bias ASsessment Tool" (PROBAST) to assess the risk of bias and applicability in prediction model studies and the relevant items and steps of assessment. PROBAST is organized into four domains including participants, predictors, outcome and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of risk of bias occurring in study design, conduct or analysis. Through comprehensive judgment, the risk of bias and applicability of original study is categorized as high, low or unclear. PROBAST enables a focused and transparent approach to assessing the risk of bias of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be also used more generally in critical appraisal of prediction model studies.