Uma equipe de pesquisadores da Universidade de Oxford, liderada pelo Departamento de Ciências da Saúde da Atenção Primária de Nuffield , desenvolveu um novo modelo que prevê de forma confiável a probabilidade de uma mulher...
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)
Novo modelo prevê risco de câncer de mama em dez anos - Crédito da imagem: Shutterstock
Uma equipe de pesquisadores da Universidade de Oxford, liderada pelo Departamento de Ciências da Saúde da Atenção Primária de Nuffield , desenvolveu um novo modelo que prevê de forma confiável a probabilidade de uma mulher desenvolver e morrer de câncer de mama dentro de uma década.
O estudo, publicado hoje no The Lancet Digital Health , analisou dados anonimizados de 11,6 milhões de mulheres com idades entre 20 e 90 anos, entre 2000 e 2020. Todas essas mulheres não tinham histórico prévio de câncer de mama ou da condição pré-cancerosa chamada “carcinoma ductal in situ”. ou DCIS.
O rastreio do cancro da mama é vital, mas apresenta desafios. Embora reduza as mortes por cancro da mama, por vezes detecta tumores que não são prejudiciais («sobrediagnóstico»), o que leva a tratamentos desnecessários. Isto não só prejudica algumas mulheres, mas também causa custos desnecessários para o SNS. Por cada 10.000 mulheres do Reino Unido com 50 anos de idade convidadas para o rastreio da mama durante os próximos 20 anos, 43 mortes por cancro da mama serão evitadas pelo rastreio, mas 129 mulheres serão “sobrediagnosticadas” .
O “rastreio baseado no risco” visa personalizar o rastreio com base no risco de um indivíduo, para maximizar os benefícios e minimizar as desvantagens desse rastreio. A adaptação de programas de rastreio com base em riscos individuais foi recentemente destacada como um caminho para melhorias adicionais na estratégia de rastreio pelo Prof Chris Whitty (https://www.gresham.ac.uk/watch-now/medical-screening). Atualmente, no rastreio da mama baseado no risco, a maioria dos modelos de risco funcionam estimando o risco de uma mulher ter um diagnóstico de cancro da mama. No entanto, nem todos os cancros da mama são fatais e sabemos que o risco de ser diagnosticado nem sempre se alinha bem com o risco de morrer de cancro da mama uma vez diagnosticado.
O novo modelo desenvolvido pela equipe trabalha para prever o risco combinado de uma mulher desenvolver e depois morrer de câncer de mama em 10 anos. Identificar as mulheres com maior risco de cancros mortais poderia melhorar o rastreio. Estas mulheres poderiam ser convidadas a iniciar o rastreio mais cedo, a fazer rastreios mais frequentes ou a fazer rastreios com diferentes tipos de imagiologia. Uma abordagem tão personalizada poderia reduzir ainda mais as mortes por cancro da mama, evitando ao mesmo tempo rastreios desnecessários para mulheres de menor risco. Mulheres com maior risco de desenvolver um câncer mortal também poderiam ser consideradas para tratamentos que tentam prevenir o desenvolvimento de câncer de mama.
A professora Julia Hippisley-Cox , professora de clínica geral e epidemiologia e autora sênior do Departamento de Ciências da Saúde de Atenção Primária de Nuffield da Universidade de Oxford, disse: “Este é um novo estudo importante que potencialmente oferece uma nova abordagem para a triagem. As estratégias baseadas no risco poderiam oferecer um melhor equilíbrio entre benefícios e danos no rastreio do cancro da mama, permitindo informações mais personalizadas às mulheres para ajudar a melhorar a tomada de decisões. As abordagens baseadas no risco também podem ajudar a fazer uma utilização mais eficiente dos recursos dos serviços de saúde, direccionando as intervenções para aqueles com maior probabilidade de beneficiarem. Agradecemos aos muitos milhares de GPs que contribuíram com dados anónimos para a base de dados QResearch, sem os quais esta investigação não teria sido possível.'
Os pesquisadores testaram quatro técnicas de modelagem diferentes para prever o risco de mortalidade por câncer de mama. Dois eram modelos mais tradicionais baseados em estatística e dois usavam aprendizado de máquina, uma forma de inteligência artificial. Todos os modelos incluíram os mesmos tipos de dados, como idade da mulher, peso, histórico de tabagismo, histórico familiar de câncer de mama e uso de terapia hormonal (TRH).
Os modelos foram avaliados pela sua capacidade de prever o risco com precisão em geral e numa gama diversificada de grupos de mulheres, tais como de diferentes origens étnicas e faixas etárias. Foi utilizada uma técnica chamada 'validação cruzada interna-externa'. Isto envolve dividir o conjunto de dados em partes estruturalmente diferentes, neste caso, por região e período de tempo, para compreender até que ponto o modelo pode ser transportado para diferentes ambientes.
Os resultados mostraram que um modelo estatístico, desenvolvido utilizando a “regressão de riscos concorrentes”, teve o melhor desempenho geral. Previu com mais precisão quais mulheres desenvolverão e morrerão de câncer de mama dentro de 10 anos. Os modelos de aprendizagem automática eram menos precisos, especialmente para diferentes grupos étnicos de mulheres.
Ashley Kieran Clift , primeira autora e pesquisadora clínica do Departamento de Ciências da Saúde da Atenção Primária de Nuffield, Universidade de Oxford, disse: 'Financiado pela Cancer Research UK e aproveitando o tamanho e a riqueza do banco de dados QResearch com suas fontes de dados vinculadas na Universidade de Oxford, pudemos explorar diferentes abordagens para desenvolver uma ferramenta que possa ser útil para novas estratégias de saúde pública baseadas no risco.
“Se mais estudos confirmarem a precisão deste novo modelo, ele poderá ser usado para identificar mulheres com alto risco de cancro da mama mortal, que poderão beneficiar de um melhor rastreio e de tratamentos preventivos”.
O professor Stavros Petrou , co-autor e líder de Economia da Saúde no Departamento de Ciências da Saúde de Cuidados Primários de Nuffield, Universidade de Oxford, disse: 'Este artigo adotou uma nova abordagem e perguntou: “podemos prever quais mulheres correm maior risco de desenvolver uma doença? câncer que irá matá-los?” Poderíamos utilizar essas informações para orientar melhor o rastreio ou mesmo para estratégias de prevenção para aqueles que mais beneficiariam.
«Uma avaliação mais aprofundada do modelo de riscos concorrentes deve incluir a avaliação dos modelos num ambiente diferente, como outro conjunto de dados do Reino Unido ou do estrangeiro.»
O artigo completo, ‘ Predição do risco de mortalidade por câncer de mama em 10 anos na população feminina em geral na Inglaterra: um estudo de desenvolvimento e validação de modelo ’, pode ser lido no The Lancet Digital Health.