Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Diederick DeJong"'
Autor:
Konstantinos Pitsikakis MD, Diederick DeJong MBBCh, PhD, MSc, Konstantinos Kitsos-Kalyvianakis MD, Marios Evangelos Mamalis BA, MSc, Michela Quaranta MBChB, MRCOG, MD, Aishath Shavee MBChB, Alina Wahab MBChB, Amudha Thangavelu MBChB, MRCOG, MD, Timothy Broadhead MBChB, MRCOG, MD, David Nugent MBChB, MRCOG, MD, Evangelos Kalampokis BA, PhD, Alexandros Laios MD, PhD, Postdoc(Ox)
Publikováno v:
Cancer Control, Vol 31 (2024)
Background Cytoreductive surgery is critical for optimal tumor clearance in advanced epithelial ovarian cancer (EOC). Despite best efforts, some patients may experience R2 (>1 cm) resection, while others may not undergo surgery at all. We aimed to co
Externí odkaz:
https://doaj.org/article/18d2a09d9006480f9a0ce507d17a1dc6
Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models
Autor:
Alexandros Laios, Alexandros Gryparis, Diederick DeJong, Richard Hutson, Georgios Theophilou, Chris Leach
Publikováno v:
Journal of Ovarian Research, Vol 13, Iss 1, Pp 1-8 (2020)
Abstract Background The foundation of modern ovarian cancer care is cytoreductive surgery to remove all macroscopic disease (R0). Identification of R0 resection patients may help individualise treatment. Machine learning and AI have been shown to be
Externí odkaz:
https://doaj.org/article/cd5afab14dda4c77ba58f7483849be2c
Autor:
Alexandros Laios, Evangelos Kalampokis, Racheal Johnson, Sarika Munot, Richard Hutson, Amudha Thangavelu, Tim Broadhead, Georgios Theophilou, David Nugent, Diederick DeJong
Publikováno v:
Ovarian cancer.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::17a28f49bf0b32feaeeeaac7e95ea139
https://doi.org/10.1136/ijgc-2022-esgo.773
https://doi.org/10.1136/ijgc-2022-esgo.773
Autor:
Alexandros Laios, Evangelos Kalampokis, Racheal Johnson, Sarika Munot, Richard Hutson, Amudha Thangavelu, Tim Broadhead, Georgios Theophilou, David Nugent, Diederick DeJong
Publikováno v:
Ovarian cancer.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c815f5b977b03a9b1ec893d4066af34
https://doi.org/10.1136/ijgc-2022-esgo.767
https://doi.org/10.1136/ijgc-2022-esgo.767
Autor:
David Nugent, Angeliki V. Katsenou, G Theophilou, Alexandros Laios, Amudha Thangavelu, T Broadhead, Yong Sheng Tan, Richard Hutson, Mohamed Otify, Diederick Dejong
Publikováno v:
Ovarian cancer.
Introduction/Background* The prognosis of advanced stage high grade serous ovarian cancer patients (HGSOC) is multifactorial, and could be accurately predicted by using Machine Learning (ML) algorithms. We designed a study to support the feature sele
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34d59ca69d21aff0281ccb526aa63832
https://doi.org/10.1136/ijgc-2021-esgo.376
https://doi.org/10.1136/ijgc-2021-esgo.376
Autor:
Gwendolyn Saalmink, Richard Hutson, A Zubayraeva, Amudha Thangavelu, KM Gomes de Lima, D Lucas, Alexandros Laios, T Broadhead, David Nugent, Yong Sheng Tan, RV De Oliveira, Diederick Dejong, G Theophilou
Publikováno v:
Organization of gynaecological cancer care.
Introduction/Background* In advanced stage high grade serous ovarian cancer (HGSOC), the introduction of maximal surgical effort without compromising peri-operative management and subsequent recovery to achieve complete cytoreduction, requires Critic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a998637df16054a6ca561e4d5207d7f
https://doi.org/10.1136/ijgc-2021-esgo.304
https://doi.org/10.1136/ijgc-2021-esgo.304
Autor:
Amudha Thangavelu, George Theophilou, Richard Hutson, Diederick Dejong, Mohamed Otify, Alexandros Laios, David Nugent, Yong Tan, Angelika Kaufmann
Publikováno v:
Ovarian cancer.
Introduction/Background Clarity and precision about the anatomical extent of disease in cancer is essential for prognostication, research, and cancer-control activities. To select effective therapeutic approaches for advanced high-grade serous ovaria
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30bdf62a3cea001aa64ba7340e312b1e
https://doi.org/10.1136/ijgc-2020-esgo.107
https://doi.org/10.1136/ijgc-2020-esgo.107
Autor:
Angelika Kaufmann, Angeliki V. Katsenou, Yong Tan, Amudha Thangavelu, Diederick Dejong, David Nugent, Mohamed Otify, Alexandros Laios
Publikováno v:
Ovarian cancer.
Introduction/Background Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning (ML) can serve this purpose by making predictions based upon generalizable clinical patterns emb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4afbb40192c47d67e376b25be41319c
https://doi.org/10.1136/ijgc-2020-esgo.121
https://doi.org/10.1136/ijgc-2020-esgo.121
Publikováno v:
Ovarian cancer.
Introduction/Background The foundation of modern ovarian cancer care is cytoreductive surgery to remove all macroscopic disease (R0). Identification of R0 resection patients may help individualise treatment. Machine learning and AI have been shown to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8fc9cfc367f854d0873dbaa702f0fa64
https://doi.org/10.1136/ijgc-2020-esgo.106
https://doi.org/10.1136/ijgc-2020-esgo.106
Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models
Autor:
Richard Hutson, Chris Leach, G Theophilou, Alexandros Laios, Diederick Dejong, Alexandros Gryparis
Publikováno v:
Journal of Ovarian Research, Vol 13, Iss 1, Pp 1-8 (2020)
Journal of Ovarian Research
Journal of Ovarian Research
Background The foundation of modern ovarian cancer care is cytoreductive surgery to remove all macroscopic disease (R0). Identification of R0 resection patients may help individualise treatment. Machine learning and AI have been shown to be effective
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::462a1aeb255c8343426af5d05d5d0bde
https://doi.org/10.1186/s13048-020-00700-0
https://doi.org/10.1186/s13048-020-00700-0