Evidence map›Paper›PMID 30361937›Full record

ReviewCurrent treatment options in oncology2018

Big Data in Head and Neck Cancer.

Carlo Resteghini, Annalisa Trama, Elio Borgonovi, Hykel Hosni, Giovanni Corrao, Ester Orlandi, Giuseppina Calareso, Loris De Cecco, Cesare Piazza, Luca Mainardi and 1 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current treatment options in oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Carlo ResteghiniHead and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. carlo.resteghini@istitutotumori.mi.it.
Annalisa TramaEvaluative Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Elio BorgonoviDepartment of Policy Analysis and Public Management, Research Center on Health and Social Care Management, CERGAS, SDA Bocconi School of Management, Bocconi University, Milan, Italy.
Hykel HosniDepartment of Philosophy, University of Milan, Milan, Italy.
Giovanni CorraoDepartment of Statistics and Quantitative Methods, Division of Biostatistics, Epidemiology and Public Health, University of Milano-Bicocca, Milan, Italy.
Ester OrlandiRadiotherapy 2 Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133, Milan, MI, Italy.
Giuseppina CalaresoDepartment of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Loris De CeccoIntegrated Biology Platform, Department of Applied Research and Technology Development, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Cesare PiazzaDepartment of Otorhinolaryngology, Maxillofacial, and Thyroid Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Luca MainardiDepartment of Electronic, Information, and Bioengineering, Politecnico di Milano, Milan, Italy.
Lisa LicitraHead and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

opinion statementHead and neck cancers can be used as a paradigm for exploring "big data" applications in oncology. Computational strategies derived from big data science hold the promise of shedding new light on the molecular mechanisms driving head and neck cancer pathogenesis, identifying new prognostic and predictive factors, and discovering potential therapeutics against this highly complex disease. Big data strategies integrate robust data input, from radiomics, genomics, and clinical-epidemiological data to deeply describe head and neck cancer characteristics. Thus, big data may advance research generating new knowledge and improve head and neck cancer prognosis supporting clinical decision-making and development of treatment recommendations.

Indexed as

Big DataDecision Support Systems, ClinicalHead and Neck NeoplasmsHumansMachine LearningPrognosisSupport Vector MachineSurveys and QuestionnairesBig dataDecision support systemEvidence based medicineForecastingGenomicsGuidelinesHead and neck cancerMachine learningOncologyRadiomicsRadiotherapySupport vector machineSurgery

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.