Keith Humphreys

Keith Humphreys

Professor
Telephone: +46852486887
Visiting address: Nobels väg 12a, 17165 Solna
Postal address: C8 Medicinsk epidemiologi och biostatistik, C8 MEB Humphreys, 171 77 Stockholm
Part of:

About me

  • I obtained my doctoral degree in Statistics at Southampton University (U.K.) in 1996. In 2005, I was appointed Senior Lecturer in Biostatistics at Karolinska Institutet at the Department of Medical Epidemiology and Biostatistics. In 2019, I was appointed as Professor in Biostatistics in the same department.

    Education:
    1996, PhD (Social Statistics), Southampton University, U.K.
    1997, Postdoc (Statistics/Psychometrics), Stockholm University
    1999, Postdoc (Statistics), Glasgow University, U.K.
    2002, Docent (Biostatistics), Karolinska Institutet

Research

  • I am involved in a number of research projects which have a general aim to contribute to the understanding of breast cancer tumour progression and to the identification of women with high risk for breast cancer, in particular that which is aggressive/has poor prognosis. We develop novel statistical approaches and apply them to large, and detailed, studies of breast cancer/mammography screening. My research builds on previous work on the identification of genetic variants for breast cancer, the development of statistical models of breast cancer risk and tumour growth and the development of novel approaches for measuring mammographic density.

    Current supervision of PhD students and Postdoctoral researcher

    Letizia Orsini (PhD student)
    Evripidis Kapanidis (PhD student)

    Jonas Gjesvik (PhD student)
    Veronica Vinattieri (Postdoc)

Teaching

  • I am course director for the Biostatistics course on the master program for Molecular Techniques in the Life Sciences.

Selected publications

Articles

All other publications

Grants

  • Swedish Research Council
    1 January 2024 - 31 December 2027
    We will develop and use novel, biologically-motivated statistical models of tumor progression to elucidate mechanisms of breast cancer progression and to predict the risk of (in particular, aggressive) breast cancer. The models will be estimated using data from Swedish and Norwegian studies which represent some of the world’s most detailed population-based studies of breast cancer and breast cancer screening, combining longitudinal register, questionnaire, image and molecular data. The models are extensions of prior methods that we have developed that separate out the roles of factors in screening and symptomatic detection of breast cancer, and tumor onset, growth and spread. The information we create is vital for planning and evaluating approaches to (secondary) prevention of breast cancer. Our risk prediction models are novel, and rigorously incorporate screening information.We will also study in detail the background of false-positive mammography results – i.e. recalls that do not lead to a diagnosis, but can be a considerable psychological burden for women. Moreover, we will use simulation-based approaches to evaluate the performance of new (personalised) screening strategies.Finally, we will use our novel statistical approaches with detailed prescription data to study the efficacy of hormone therapy in estrogen receptor-positive patients.
  • Swedish Research Council
    1 January 2023 - 31 December 2025
  • Swedish Research Council
    1 January 2022 - 31 December 2024
  • Swedish Research Council
    1 January 2021 - 31 December 2023
  • Swedish Research Council
    1 December 2019 - 31 December 2023
  • Swedish Research Council for Health Working Life and Welfare
    1 January 2019 - 31 December 2023
  • Modeling of tumor growth, lymph node spread, presence of metastases and survival in breast cancer patients
    Swedish Cancer Society
    1 January 2018
    Breast cancer is the most common form of cancer in women and in 2015 accounted for 13.3% of all female deaths from cancer. One possible way to reduce mortality is personal prevention and mammography screening. These strategies will only be effective if women at high risk of aggressive cancer can be identified. Understanding molecular subtypes and the significance of genetic variation and mammographic density of tumor progression and effectiveness of screening is important to improve prediction of breast cancer and mortality. The project includes and integrates two research lines: modeling of tumor growth and survival, and development of measurement methods for mammographic density. More specifically, the intention is to better understand which women have a high risk of breast cancer, especially the breast cancer that is aggressive (has poor prognosis). New large-scale studies in Sweden collect huge amounts of mammographic images and genetic data. We will utilize these unique resources to study in detail the role of mammographic density as well as inherited genetic changes in breast cancer risk, tumor progression and prognosis. The project aims to increase knowledge of the causes of aggressive and fatal breast cancer. This is made possible by using material from large breast cancer studies where large amounts of mammographic images have been collected and the information on risk factors is updated and current. The knowledge can be used to learn more about personalized screening when it comes to bringing down both mortality and the number of people who are ill in breast cancer.
  • Swedish Research Council
    1 January 2018 - 31 December 2021
  • Modeling of tumor growth, lymph node spread, presence of metastases and survival in breast cancer patients
    Swedish Cancer Society
    1 January 2017
    Breast cancer is the most common form of cancer in women and in 2015 accounted for 13.3% of all female deaths from cancer. One possible way to reduce mortality is personal prevention and mammography screening. These strategies will only be effective if women at high risk of aggressive cancer can be identified. Understanding molecular subtypes and the significance of genetic variation and mammographic density of tumor progression and effectiveness of screening is important to improve prediction of breast cancer and mortality. The project includes and integrates two research lines: modeling of tumor growth and survival, and development of measurement methods for mammographic density. More specifically, the intention is to better understand which women have a high risk of breast cancer, especially the breast cancer that is aggressive (has poor prognosis). New large-scale studies in Sweden collect huge amounts of mammographic images and genetic data. We will utilize these unique resources to study in detail the role of mammographic density as well as inherited genetic changes in breast cancer risk, tumor progression and prognosis. The project aims to increase knowledge of the causes of aggressive and fatal breast cancer. This is made possible by using material from large breast cancer studies where large amounts of mammographic images have been collected and the information on risk factors is updated and current. The knowledge can be used to learn more about personalized screening when it comes to bringing down both mortality and the number of people who are ill in breast cancer.
  • Swedish Research Council for Health Working Life and Welfare
    1 January 2017 - 31 December 2019
  • Swedish Research Council
    1 January 2017 - 31 December 2020
  • Improvement of prediction of risk for breast cancer by modeling of tumor growth and new measurement methods of mammographic density.
    Swedish Cancer Society
    1 January 2016
    Breast cancer is the most common cancer diagnosed in women and accounts for about 14% of all female deaths from cancer. One possible way to reduce mortality is personal prevention and mammography screening. These strategies will only be effective if women at high risk can be identified, especially those at high risk of aggressive cancer. Understanding the importance of genetic variation and mammographic density is considered important for improving prediction of breast cancer risk and mortality. The project is about understanding more about which women have a high risk of breast cancer, especially the breast cancer that is aggressive / has poor prognosis. Two new large-scale studies in Sweden collect huge amounts of mammographic images and genetic data. We will make use of these unique resources to study in detail the role of mammographic density and inherited genetic changes in breast cancer risk, tumor progression and prognosis. In order to achieve our goals, we will develop and apply new statistical methods for measuring breast tissue composition and for studying tumor progression. The project aims to increase knowledge of the causes of aggressive and fatal breast cancer. This is made possible by using material from large breast cancer studies where large amounts of mammographic images have been collected and the information on risk factors is updated and current. The knowledge can be used to learn more about personalized screening when it comes to bringing down both mortality and the number of people who are ill in breast cancer.
  • Improvement of prediction of risk for breast cancer by modeling of tumor growth and new measurement methods of mammographic density.
    Swedish Cancer Society
    1 January 2015
    Breast cancer is the most common cancer diagnosed in women and accounts for about 14% of all female deaths from cancer. One possible way to reduce mortality is personal prevention and mammography screening. These strategies will only be effective if women at high risk can be identified, especially those at high risk of aggressive cancer. Understanding the importance of genetic variation and mammographic density is considered important for improving prediction of breast cancer risk and mortality. The project is about understanding more about which women have a high risk of breast cancer, especially the breast cancer that is aggressive / has poor prognosis. Two new large-scale studies in Sweden collect huge amounts of mammographic images and genetic data. We will make use of these unique resources to study in detail the role of mammographic density and inherited genetic changes in breast cancer risk, tumor progression and prognosis. In order to achieve our goals, we will develop and apply new statistical methods for measuring breast tissue composition and for studying tumor progression. The project aims to increase knowledge of the causes of aggressive and fatal breast cancer. This is made possible by using material from large breast cancer studies where large amounts of mammographic images have been collected and the information on risk factors is updated and current. The knowledge can be used to learn more about personalized screening when it comes to bringing down both mortality and the number of people who are ill in breast cancer.
  • Swedish Research Council
    1 January 2015 - 31 December 2018
  • Improvement of prediction of risk for breast cancer by modeling of tumor growth and new measurement methods of mammographic density.
    Swedish Cancer Society
    1 January 2014
    Breast cancer is the most common cancer diagnosed in women and accounts for about 14% of all female deaths from cancer. One possible way to reduce mortality is personal prevention and mammography screening. These strategies will only be effective if women at high risk can be identified, especially those at high risk of aggressive cancer. Understanding the importance of genetic variation and mammographic density is considered important for improving prediction of breast cancer risk and mortality. The project is about understanding more about which women have a high risk of breast cancer, especially the breast cancer that is aggressive / has poor prognosis. Two new large-scale studies in Sweden collect huge amounts of mammographic images and genetic data. We will make use of these unique resources to study in detail the role of mammographic density and inherited genetic changes in breast cancer risk, tumor progression and prognosis. In order to achieve our goals, we will develop and apply new statistical methods for measuring breast tissue composition and for studying tumor progression. The project aims to increase knowledge of the causes of aggressive and fatal breast cancer. This is made possible by using material from large breast cancer studies where large amounts of mammographic images have been collected and the information on risk factors is updated and current. The knowledge can be used to learn more about personalized screening when it comes to bringing down both mortality and the number of people who are ill in breast cancer.
  • Swedish Research Council
    1 January 2012 - 31 December 2015
  • Swedish Research Council
    1 January 2012 - 31 December 2014
  • Swedish Research Council
    1 January 2010 - 31 December 2012
  • Swedish Research Council
    1 January 2009 - 31 December 2011
  • Swedish Research Council
    1 January 2009 - 31 December 2011

Employments

  • Professor, Biostatistics, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 2019-

Degrees and Education

  • Docent, Karolinska Institutet, 2002

Supervision

  • Supervision to doctoral degree

    • Letizia Orsini, 2026
    • Jonas Gjesvik, 2025-
    • Jonas Gjesvik, 2025-
    • Leo Gkekos, The role of reproductive and socioeconomic factors for risk and survival of breast cancer subtypes - application of advanced statistical methods to registry data, 2023-
    • Evripidis Kapanidis, 2023-
    • Johanna Holm, Aggressive breast cancer: epidemiological studies addressing disease heterogeneity, 2023
    • Xinhe Mao, Epidemiological studies on breast cancer risk factors and screening, 2023
    • Ziyan Ma, 2022-
    • Rickard Strandberg, Breast cancer natural history models and risk prediction in mammography screening cohorts, 2022
    • Emilio Ugalde Morales, Molecular epidemiology studies on risk factors for breast cancer and disease aggressiveness, 2020
    • Gabriel Isheden, Statistical models of breast cancer tumour growth and spread., 2019
    • Linda Abrahamsson, Statistical models of breast cancer tumour progression for mammography screening data, 2018
    • Fredrik Strand, Determinants of interval cancer and tumor size among breast cancer screening participants, 2016
    • Hatef Darabi, Genetic association and risk prediction of breast cancer from an epidemiological and biostatistical perspective, 2012
    • Louise Eriksson, Mammographic density and breast cancer phenotypes, 2012
    • Jingmei Li, Genetic determinants of breast cancer risk, 2011
    • Kristjana Einarsdottir, Genetic determinants of postmenopausal breast and endometrial cancer, 2007

News from KI

Events from KI