Now showing items 1-11 of 11

    • Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients. 

      Gurney-Champion, OJ; Klaassen, R; Froeling, M; Barbieri, S; Stoker, J; Engelbrecht, MRW; Wilmink, JW; Besselink, MG; Bel, A; van Laarhoven, HWM; Nederveen, AJ (2018-01)
      The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision ...
    • DESNT: A Poor Prognosis Category of Human Prostate Cancer. 

      Luca, B-A; Brewer, DS; Edwards, DR; Edwards, S; Whitaker, HC; Merson, S; Dennis, N; Cooper, RA; Hazell, S; Warren, AY; CancerMap Group; Eeles, R; Lynch, AG; Ross-Adams, H; Lamb, AD; Neal, DE; Sethia, K; Mills, RD; Ball, RY; Curley, H; Clark, J; Moulton, V; Cooper, CS (2018-12)
      <h4>Background</h4>A critical problem in the clinical management of prostate cancer is that it is highly heterogeneous. Accurate prediction of individual cancer behaviour is therefore not achievable at the time of diagnosis ...
    • Dicentric Dose Estimates for Patients Undergoing Radiotherapy in the RTGene Study to Assess Blood Dosimetric Models and the New Bayesian Method for Gradient Exposure. 

      Moquet, J; Higueras, M; Donovan, E; Boyle, S; Barnard, S; Bricknell, C; Sun, M; Gothard, L; O'Brien, G; Cruz-Garcia, L; Badie, C; Ainsbury, E; Somaiah, N (2018-12)
      The RTGene study was focused on the development and validation of new transcriptional biomarkers for prediction of individual radiotherapy patient responses to ionizing radiation. In parallel, for validation purposes, this ...
    • Evaluation of diffusion models in breast cancer. 

      Panek, R; Borri, M; Orton, M; O'Flynn, E; Morgan, V; Giles, SL; deSouza, N; Leach, MO; Schmidt, MA (2015-08)
      <h4>Purpose</h4>The purpose of this study is to investigate whether the microvascular pseudodiffusion effects resulting with non-monoexponential behavior are present in breast cancer, taking into account tumor spatial ...
    • Evaluation of dynamic contrast-enhanced MRI biomarkers for stratified cancer medicine: How do permeability and perfusion vary between human tumours? 

      Little, RA; Barjat, H; Hare, JI; Jenner, M; Watson, Y; Cheung, S; Holliday, K; Zhang, W; O'Connor, JPB; Barry, ST; Puri, S; Parker, GJM; Waterton, JC (2018-02)
      Solid tumours exhibit enhanced vessel permeability and fenestrated endothelium to varying degree, but it is unknown how this varies in patients between and within tumour types. Dynamic contrast-enhanced (DCE) MRI provides ...
    • Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. 

      Fachal, L; Aschard, H; Beesley, J; Barnes, DR; Allen, J; Kar, S; Pooley, KA; Dennis, J; Michailidou, K; Turman, C; Soucy, P; Lemaçon, A; Lush, M; Tyrer, JP; Ghoussaini, M; Moradi Marjaneh, M; Jiang, X; Agata, S; Aittomäki, K; Alonso, MR; Andrulis, IL; Anton-Culver, H; Antonenkova, NN; Arason, A; Arndt, V; Aronson, KJ; Arun, BK; Auber, B; Auer, PL; Azzollini, J; Balmaña, J; Barkardottir, RB; Barrowdale, D; Beeghly-Fadiel, A; Benitez, J; Bermisheva, M; Białkowska, K; Blanco, AM; Blomqvist, C; Blot, W; Bogdanova, NV; Bojesen, SE; Bolla, MK; Bonanni, B; Borg, A; Bosse, K; Brauch, H; Brenner, H; Briceno, I; Brock, IW; Brooks-Wilson, A; Brüning, T; Burwinkel, B; Buys, SS; Cai, Q; Caldés, T; Caligo, MA; Camp, NJ; Campbell, I; Canzian, F; Carroll, JS; Carter, BD; Castelao, JE; Chiquette, J; Christiansen, H; Chung, WK; Claes, KBM; Clarke, CL; GEMO Study Collaborators; EMBRACE Collaborators; Collée, JM; Cornelissen, S; Couch, FJ; Cox, A; Cross, SS; Cybulski, C; Czene, K; Daly, MB; de la Hoya, M; Devilee, P; Diez, O; Ding, YC; Dite, GS; Domchek, SM; Dörk, T; Dos-Santos-Silva, I; Droit, A; Dubois, S; Dumont, M; Duran, M; Durcan, L; Dwek, M; Eccles, DM; Engel, C; Eriksson, M; Evans, DG; Fasching, PA; Fletcher, O; Floris, G; Flyger, H; Foretova, L; Foulkes, WD; Friedman, E; Fritschi, L; Frost, D; Gabrielson, M; Gago-Dominguez, M; Gambino, G; Ganz, PA; Gapstur, SM; Garber, J; García-Sáenz, JA; Gaudet, MM; Georgoulias, V; Giles, GG; Glendon, G; Godwin, AK; Goldberg, MS; Goldgar, DE; González-Neira, A; Tibiletti, MG; Greene, MH; Grip, M; Gronwald, J; Grundy, A; Guénel, P; Hahnen, E; Haiman, CA; Håkansson, N; Hall, P; Hamann, U; Harrington, PA; Hartikainen, JM; Hartman, M; He, W; Healey, CS; Heemskerk-Gerritsen, BAM; Heyworth, J; Hillemanns, P; Hogervorst, FBL; Hollestelle, A; Hooning, MJ; Hopper, JL; Howell, A; Huang, G; Hulick, PJ; Imyanitov, EN; KConFab Investigators; HEBON Investigators; ABCTB Investigators; Isaacs, C; Iwasaki, M; Jager, A; Jakimovska, M; Jakubowska, A; James, PA; Janavicius, R; Jankowitz, RC; John, EM; Johnson, N; Jones, ME; Jukkola-Vuorinen, A; Jung, A; Kaaks, R; Kang, D; Kapoor, PM; Karlan, BY; Keeman, R; Kerin, MJ; Khusnutdinova, E; Kiiski, JI; Kirk, J; Kitahara, CM; Ko, Y-D; Konstantopoulou, I; Kosma, V-M; Koutros, S; Kubelka-Sabit, K; Kwong, A; Kyriacou, K; Laitman, Y; Lambrechts, D; Lee, E; Leslie, G; Lester, J; Lesueur, F; Lindblom, A; Lo, W-Y; Long, J; Lophatananon, A; Loud, JT; Lubiński, J; MacInnis, RJ; Maishman, T; Makalic, E; Mannermaa, A; Manoochehri, M; Manoukian, S; Margolin, S; Martinez, ME; Matsuo, K; Maurer, T; Mavroudis, D; Mayes, R; McGuffog, L; McLean, C; Mebirouk, N; Meindl, A; Miller, A; Miller, N; Montagna, M; Moreno, F; Muir, K; Mulligan, AM; Muñoz-Garzon, VM; Muranen, TA; Narod, SA; Nassir, R; Nathanson, KL; Neuhausen, SL; Nevanlinna, H; Neven, P; Nielsen, FC; Nikitina-Zake, L; Norman, A; Offit, K; Olah, E; Olopade, OI; Olsson, H; Orr, N; Osorio, A; Pankratz, VS; Papp, J; Park, SK; Park-Simon, T-W; Parsons, MT; Paul, J; Pedersen, IS; Peissel, B; Peshkin, B; Peterlongo, P; Peto, J; Plaseska-Karanfilska, D; Prajzendanc, K; Prentice, R; Presneau, N; Prokofyeva, D; Pujana, MA; Pylkäs, K; Radice, P; Ramus, SJ; Rantala, J; Rau-Murthy, R; Rennert, G; Risch, HA; Robson, M; Romero, A; Rossing, M; Saloustros, E; Sánchez-Herrero, E; Sandler, DP; Santamariña, M; Saunders, C; Sawyer, EJ; Scheuner, MT; Schmidt, DF; Schmutzler, RK; Schneeweiss, A; Schoemaker, MJ; Schöttker, B; Schürmann, P; Scott, C; Scott, RJ; Senter, L; Seynaeve, CM; Shah, M; Sharma, P; Shen, C-Y; Shu, X-O; Singer, CF; Slavin, TP; Smichkoska, S; Southey, MC; Spinelli, JJ; Spurdle, AB; Stone, J; Stoppa-Lyonnet, D; Sutter, C; Swerdlow, AJ; Tamimi, RM; Tan, YY; Tapper, WJ; Taylor, JA; Teixeira, MR; Tengström, M; Teo, SH; Terry, MB; Teulé, A; Thomassen, M; Thull, DL; Tischkowitz, M; Toland, AE; Tollenaar, RAEM; Tomlinson, I; Torres, D; Torres-Mejía, G; Troester, MA; Truong, T; Tung, N; Tzardi, M; Ulmer, H-U; Vachon, CM; van Asperen, CJ; van der Kolk, LE; van Rensburg, EJ; Vega, A; Viel, A; Vijai, J; Vogel, MJ; Wang, Q; Wappenschmidt, B; Weinberg, CR; Weitzel, JN; Wendt, C; Wildiers, H; Winqvist, R; Wolk, A; Wu, AH; Yannoukakos, D; Zhang, Y; Zheng, W; Hunter, D; Pharoah, PDP; Chang-Claude, J; García-Closas, M; Schmidt, MK; Milne, RL; Kristensen, VN; French, JD; Edwards, SL; Antoniou, AC; Chenevix-Trench, G; Simard, J; Easton, DF; Kraft, P; Dunning, AM (2020-01-07)
      Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association ...
    • Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants. 

      Dadaev, T; Saunders, EJ; Newcombe, PJ; Anokian, E; Leongamornlert, DA; Brook, MN; Cieza-Borrella, C; Mijuskovic, M; Wakerell, S; Olama, AAA; Schumacher, FR; Berndt, SI; Benlloch, S; Ahmed, M; Goh, C; Sheng, X; Zhang, Z; Muir, K; Govindasami, K; Lophatananon, A; Stevens, VL; Gapstur, SM; Carter, BD; Tangen, CM; Goodman, P; Thompson, IM; Batra, J; Chambers, S; Moya, L; Clements, J; Horvath, L; Tilley, W; Risbridger, G; Gronberg, H; Aly, M; Nordström, T; Pharoah, P; Pashayan, N; Schleutker, J; Tammela, TLJ; Sipeky, C; Auvinen, A; Albanes, D; Weinstein, S; Wolk, A; Hakansson, N; West, C; Dunning, AM; Burnet, N; Mucci, L; Giovannucci, E; Andriole, G; Cussenot, O; Cancel-Tassin, G; Koutros, S; Freeman, LEB; Sorensen, KD; Orntoft, TF; Borre, M; Maehle, L; Grindedal, EM; Neal, DE; Donovan, JL; Hamdy, FC; Martin, RM; Travis, RC; Key, TJ; Hamilton, RJ; Fleshner, NE; Finelli, A; Ingles, SA; Stern, MC; Rosenstein, B; Kerns, S; Ostrer, H; Lu, Y-J; Zhang, H-W; Feng, N; Mao, X; Guo, X; Wang, G; Sun, Z; Giles, GG; Southey, MC; MacInnis, RJ; FitzGerald, LM; Kibel, AS; Drake, BF; Vega, A; Gómez-Caamaño, A; Fachal, L; Szulkin, R; Eklund, M; Kogevinas, M; Llorca, J; Castaño-Vinyals, G; Penney, KL; Stampfer, M; Park, JY; Sellers, TA; Lin, H-Y; Stanford, JL; Cybulski, C; Wokolorczyk, D; Lubinski, J; Ostrander, EA; Geybels, MS; Nordestgaard, BG; Nielsen, SF; Weisher, M; Bisbjerg, R; Røder, MA; Iversen, P; Brenner, H; Cuk, K; Holleczek, B; Maier, C; Luedeke, M; Schnoeller, T; Kim, J; Logothetis, CJ; John, EM; Teixeira, MR; Paulo, P; Cardoso, M; Neuhausen, SL; Steele, L; Ding, YC; De Ruyck, K; De Meerleer, G; Ost, P; Razack, A; Lim, J; Teo, S-H; Lin, DW; Newcomb, LF; Lessel, D; Gamulin, M; Kulis, T; Kaneva, R; Usmani, N; Slavov, C; Mitev, V; Parliament, M; Singhal, S; Claessens, F; Joniau, S; Van den Broeck, T; Larkin, S; Townsend, PA; Aukim-Hastie, C; Gago-Dominguez, M; Castelao, JE; Martinez, ME; Roobol, MJ; Jenster, G; van Schaik, RHN; Menegaux, F; Truong, T; Koudou, YA; Xu, J; Khaw, K-T; Cannon-Albright, L; Pandha, H; Michael, A; Kierzek, A; Thibodeau, SN; McDonnell, SK; Schaid, DJ; Lindstrom, S; Turman, C; Ma, J; Hunter, DJ; Riboli, E; Siddiq, A; Canzian, F; Kolonel, LN; Le Marchand, L; Hoover, RN; Machiela, MJ; Kraft, P; PRACTICAL (Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome) Consortium; Freedman, M; Wiklund, F; Chanock, S; Henderson, BE; Easton, DF; Haiman, CA; Eeles, RA; Conti, DV; Kote-Jarai, Z (2018-06-11)
      Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable ...
    • Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma. 

      Went, M; Sud, A; Försti, A; Halvarsson, B-M; Weinhold, N; Kimber, S; van Duin, M; Thorleifsson, G; Holroyd, A; Johnson, DC; Li, N; Orlando, G; Law, PJ; Ali, M; Chen, B; Mitchell, JS; Gudbjartsson, DF; Kuiper, R; Stephens, OW; Bertsch, U; Broderick, P; Campo, C; Bandapalli, OR; Einsele, H; Gregory, WA; Gullberg, U; Hillengass, J; Hoffmann, P; Jackson, GH; Jöckel, K-H; Johnsson, E; Kristinsson, SY; Mellqvist, U-H; Nahi, H; Easton, D; Pharoah, P; Dunning, A; Peto, J; Canzian, F; Swerdlow, A; Eeles, RA; Kote-Jarai, Z; Muir, K; Pashayan, N; Nickel, J; Nöthen, MM; Rafnar, T; Ross, FM; da Silva Filho, MI; Thomsen, H; Turesson, I; Vangsted, A; Andersen, NF; Waage, A; Walker, BA; Wihlborg, A-K; Broyl, A; Davies, FE; Thorsteinsdottir, U; Langer, C; Hansson, M; Goldschmidt, H; Kaiser, M; Sonneveld, P; Stefansson, K; Morgan, GJ; Hemminki, K; Nilsson, B; Houlston, RS; PRACTICAL consortium (2018-09-13)
      Genome-wide association studies (GWAS) have transformed our understanding of susceptibility to multiple myeloma (MM), but much of the heritability remains unexplained. We report a new GWAS, a meta-analysis with previous ...
    • Performance of automatic image segmentation algorithms for calculating total lesion glycolysis for early response monitoring in non-small cell lung cancer patients during concomitant chemoradiotherapy. 

      Grootjans, W; Usmanij, EA; Oyen, WJG; van der Heijden, EHFM; Visser, EP; Visvikis, D; Hatt, M; Bussink, J; de Geus-Oei, L-F (2016-06)
      <h4>Background and purpose</h4>This study evaluated the use of total lesion glycolysis (TLG) determined by different automatic segmentation algorithms, for early response monitoring in non-small cell lung cancer (NSCLC) ...
    • Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies. 

      Larson, NB; McDonnell, S; Albright, LC; Teerlink, C; Stanford, J; Ostrander, EA; Isaacs, WB; Xu, J; Cooney, KA; Lange, E; Schleutker, J; Carpten, JD; Powell, I; Bailey-Wilson, J; Cussenot, O; Cancel-Tassin, G; Giles, G; MacInnis, R; Maier, C; Whittemore, AS; Hsieh, C-L; Wiklund, F; Catalona, WJ; Foulkes, W; Mandal, D; Eeles, R; Kote-Jarai, Z; Ackerman, MJ; Olson, TM; Klein, CJ; Thibodeau, SN; Schaid, DJ (2016-09)
      Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis ...
    • Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI. 

      Winfield, JM; Orton, MR; Collins, DJ; Ind, TEJ; Attygalle, A; Hazell, S; Morgan, VA; deSouza, NM (2017-02)
      <h4>Objectives</h4>Assessment of empirical diffusion-weighted MRI (DW-MRI) models in cervical tumours to investigate whether fitted parameters distinguish between types and grades of tumours.<h4>Methods</h4>Forty-two ...