With transformative initiatives like the UK's 100,000 Genomes Project underway, vast amounts of data from genome sequencing are being generated. Genomic results are being actively returned to participants, although policies around their management remain inconsistent and a subject of debate. Secondary findings (SF) have been of particular concern - variants associated with health conditions other than the indication for sequencing, which may or may not be medically actionable. I have conducted a mixed methods study to explore the current transitional period and the issue of secondary findings, and inform future management. Following a narrative review of the literature around SF in genome sequencing and a focused systematic review of primary studies on stakeholder views towards the subject (Part I), gaps in the current literature were identified. These were, chiefly: (1) the need for diverse stakeholder views based on experience making actual decisions around SF; and, (2) empirical data - phenotypic, psychological, behavioural - on actual returned SF. Thus, taking advantage of the local programme of translational genome sequencing, I conducted qualitative studies involving genomic healthcare professionals and genome sequencing participants, to explore their views towards genomic medicine and SF (Part II). Following this, I detail a case study illustrating the process and challenges of returning an SF, as well as outline a study designed to collect empirical data on actual returned SF and present preliminary data to this end (Part III). I illustrate that secondary findings will be a part of tomorrow's genomic medicine: cautious optional screening of actionable SF (including treatable conditions and carrier status information) appears favourable. However, if SF are to be a part of the genomic medicine paradigm, several barriers must be considered: insufficient connectivity between specialties, variant interpretation, clinical interpretation and management, and overpromise and expectations (including recontact in light of new information). In order to overcome these challenges, individuals in unselected populations must be prospectively phenotyped to derive more accurate estimates of population-level penetrance and better understand the full phenotypic spectrum, and we must explore the downstream impact of disclosure. As genome sequencing is mainstreamed, clear evidence-based guidelines for SF in genome sequencing will be essential if harms are to be minimised and benefits are to be maximised, both for participants and the healthcare system at large. At this point, albeit cautiously, we must 'learn by doing'.

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第22889号 / 社医博第113号 / 新制||社医||11(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 川上 浩司, 教授 松田 文彦, 教授 中島 貴子 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

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O constante aumento do número de novos casos de câncer de mama vem despertando interesse na elaboração de módulos de esquemas CAD a fim de proporcionar um diagnóstico de maior precisão. Entretanto, a maioria das pesquisas está empenhada em detectar ou classificar fatores primários presentes em imagens mamográficas, como módulos e microcalcificações. Áreas assimétricas, retração de mamilo, linfonodos axilares, entre outros, são considerados como fatores secundários no diagnóstico do câncer de mama, apesar de poderem alertar para o surgimento não só dessa, mas de outras doenças no futuro. Por isso, essa pesquisa contempla a implementação de um sistema computacional capaz de auxiliar na detecção e classificação, conforme padrão BI-RADS®, de regiões que contenham sinais secundários capazes de levantar suspeitas da presença ou surgimento do câncer de mama, em imagens mamográficas digitais, utilizando técnicas inteligentes e automáticas de processamento de imagens e redes neurais artificiais. A acurácia alcançada em cada etapa foi: detecção de assimetria de 82,8%, retração de mamilo de 95% e Az = 0,93, detecção de linfonodos axilares = 74,9%. Objetiva-se que o resultado do trabalho seja inserido como um dos módulos de um protótipo de esquema CADx em mamografia, a fim de ampliar o conjunto de informações a serem usadas na classificação de cada caso sob análise, visando o aumento da precisão diagnóstica. / The increase in the number of cases of breast cancer have attracted interest in developing modules of CAD schemes to provider higher diagnostic accuracy. However, most researches are engaged in detect and classify primary factors present in mammographic images such as nodules and microcalcifications. Asymmetric areas, nipple retraction, axilary limph nodes, among other, are considered as secondary factors to diagnostic the breast cancer, although they may alert for the emergence not only of this but of other diseases in the future. Thus, this research includes the implementation of a computer system able to assist in the detection and classification, according to BI-RADS®, of regions that containing secondary signals able to arousing suspicion of the presence or appearance of breast cancer in digital mammographic images using intelligent and automatic techniques in the image processing and artificial neural networks. The accuracy obtained in each step was: detection of asymmetry of 82.8%, nipple retraction of 95% and Az = 0.93, detection of axilary lymph nodes = 74.9%. The purpose is that the result of the work is entered as one of the modules of a prototype of CADx schem in mammography in order to extend the range of information to be used in the classification of each case under analysis, aiming to increase diagnostic accuracy.

O constante aumento do número de novos casos de câncer de mama vem despertando interesse na elaboração de módulos de esquemas CAD a fim de proporcionar um diagnóstico de maior precisão. Entretanto, a maioria das pesquisas está empenhada em detectar ou classificar fatores primários presentes em imagens mamográficas, como módulos e microcalcificações. Áreas assimétricas, retração de mamilo, linfonodos axilares, entre outros, são considerados como fatores secundários no diagnóstico do câncer de mama, apesar de poderem alertar para o surgimento não só dessa, mas de outras doenças no futuro. Por isso, essa pesquisa contempla a implementação de um sistema computacional capaz de auxiliar na detecção e classificação, conforme padrão BI-RADS®, de regiões que contenham sinais secundários capazes de levantar suspeitas da presença ou surgimento do câncer de mama, em imagens mamográficas digitais, utilizando técnicas inteligentes e automáticas de processamento de imagens e redes neurais artificiais. A acurácia alcançada em cada etapa foi: detecção de assimetria de 82,8%, retração de mamilo de 95% e Az = 0,93, detecção de linfonodos axilares = 74,9%. Objetiva-se que o resultado do trabalho seja inserido como um dos módulos de um protótipo de esquema CADx em mamografia, a fim de ampliar o conjunto de informações a serem usadas na classificação de cada caso sob análise, visando o aumento da precisão diagnóstica. / The increase in the number of cases of breast cancer have attracted interest in developing modules of CAD schemes to provider higher diagnostic accuracy. However, most researches are engaged in detect and classify primary factors present in mammographic images such as nodules and microcalcifications. Asymmetric areas, nipple retraction, axilary limph nodes, among other, are considered as secondary factors to diagnostic the breast cancer, although they may alert for the emergence not only of this but of other diseases in the future. Thus, this research includes the implementation of a computer system able to assist in the detection and classification, according to BI-RADS®, of regions that containing secondary signals able to arousing suspicion of the presence or appearance of breast cancer in digital mammographic images using intelligent and automatic techniques in the image processing and artificial neural networks. The accuracy obtained in each step was: detection of asymmetry of 82.8%, nipple retraction of 95% and Az = 0.93, detection of axilary lymph nodes = 74.9%. The purpose is that the result of the work is entered as one of the modules of a prototype of CADx schem in mammography in order to extend the range of information to be used in the classification of each case under analysis, aiming to increase diagnostic accuracy.