BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Talkington A, Durrett R. Estimating Tumor Growth Rates In Vivo. Bull Math Biol. 2015;77:1934-1954. [PMID: 26481497 DOI: 10.1007/s11538-015-0110-8] [Cited by in Crossref: 59] [Cited by in F6Publishing: 51] [Article Influence: 9.8] [Reference Citation Analysis]
Number Citing Articles
1 Wodarz D, Komarova NL. Mutant Evolution in Spatially Structured and Fragmented Expanding Populations. Genetics 2020;216:191-203. [PMID: 32661138 DOI: 10.1534/genetics.120.303422] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
2 Goswami M. Deep learning models for benign and malign ocular tumor growth estimation. Comput Med Imaging Graph 2021;93:101986. [PMID: 34509705 DOI: 10.1016/j.compmedimag.2021.101986] [Reference Citation Analysis]
3 Xiong H, Wang C, Wang Z, Lu H, Yao J. Self-assembled nano-activator constructed ferroptosis-immunotherapy through hijacking endogenous iron to intracellular positive feedback loop. Journal of Controlled Release 2021;332:539-52. [DOI: 10.1016/j.jconrel.2021.03.007] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
4 Murphy H, McCarthy G, Dobrovolny HM. Understanding the effect of measurement time on drug characterization. PLoS One 2020;15:e0233031. [PMID: 32407356 DOI: 10.1371/journal.pone.0233031] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
5 Syriopoulou E, Gasparini A, Humphreys K, Andersson TM. Assessing lead time bias due to mammography screening on estimates of loss in life expectancy. Breast Cancer Res 2022;24:15. [PMID: 35197123 DOI: 10.1186/s13058-022-01505-3] [Reference Citation Analysis]
6 Isheden G, Humphreys K. Modelling breast cancer tumour growth for a stable disease population. Stat Methods Med Res 2019;28:681-702. [DOI: 10.1177/0962280217734583] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 1.2] [Reference Citation Analysis]
7 Tang J, Malachowska B, Wu X, Guha C. Repurposing Radiation Therapy for Immuno-oncology. Clin Oncol (R Coll Radiol) 2021;33:683-93. [PMID: 34535358 DOI: 10.1016/j.clon.2021.08.015] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Abrahamsson L, Isheden G, Czene K, Humphreys K. Continuous tumour growth models, lead time estimation and length bias in breast cancer screening studies. Stat Methods Med Res 2020;29:374-95. [DOI: 10.1177/0962280219832901] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
9 Sugimori M, Hayakawa Y, Tamura R, Kuroda S. The combined efficacy of OTS964 and temozolomide for reducing the size of power-law coded heterogeneous glioma stem cell populations. Oncotarget 2019;10:2397-415. [PMID: 31040930 DOI: 10.18632/oncotarget.26800] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Pettersson OJ, Fröss-Baron K, Crona J, Sundin A. Tumor growth rate in pancreatic neuroendocrine tumor patients undergoing PRRT with 177Lu-DOTATATE. Endocr Connect 2021;10:422-31. [PMID: 33875614 DOI: 10.1530/EC-21-0027] [Reference Citation Analysis]
11 Patmanidis S, Charalampidis AC, Kordonis I, Strati K, Mitsis GD, Papavassilopoulos GP. Individualized growth prediction of mice skin tumors with maximum likelihood estimators. Comput Methods Programs Biomed 2020;185:105165. [PMID: 31710982 DOI: 10.1016/j.cmpb.2019.105165] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
12 Pérez-García VM, Calvo GF, Bosque JJ, León-Triana O, Jiménez J, Perez-Beteta J, Belmonte-Beitia J, Valiente M, Zhu L, García-Gómez P, Sánchez-Gómez P, Hernández-San Miguel E, Hortigüela R, Azimzade Y, Molina-García D, Martinez Á, Rojas ÁA, de Mendivil AO, Vallette F, Schucht P, Murek M, Pérez-Cano M, Albillo D, Honguero Martínez AF, Jiménez Londoño GA, Arana E, García Vicente AM. Universal scaling laws rule explosive growth in human cancers. Nat Phys 2020;16:1232-7. [PMID: 33329756 DOI: 10.1038/s41567-020-0978-6] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 4.5] [Reference Citation Analysis]
13 Xiong H, Wang Z, Wang C, Yao J. Transforming Complexity to Simplicity: Protein-Like Nanotransformer for Improving Tumor Drug Delivery Programmatically. Nano Lett 2020;20:1781-90. [DOI: 10.1021/acs.nanolett.9b05008] [Cited by in Crossref: 21] [Cited by in F6Publishing: 15] [Article Influence: 10.5] [Reference Citation Analysis]
14 Rich NE, Marrero JA, Singal AG. REPLY. Hepatology 2021;73:2618-9. [PMID: 33205463 DOI: 10.1002/hep.31638] [Reference Citation Analysis]
15 Siah KW, Khozin S, Wong CH, Lo AW. Machine-Learning and Stochastic Tumor Growth Models for Predicting Outcomes in Patients With Advanced Non-Small-Cell Lung Cancer. JCO Clin Cancer Inform 2019;3:1-11. [PMID: 31539267 DOI: 10.1200/CCI.19.00046] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Patmanidis S, Chignola R, Charalampidis AC, Papavassilopoulos GP. A comparison between Nonlinear Least Squares and Maximum Likelihood estimation for the prediction of tumor growth on experimental data of human and rat origin. Biomedical Signal Processing and Control 2019;54:101639. [DOI: 10.1016/j.bspc.2019.101639] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
17 Heesterman BL, Bokhorst JM, de Pont LMH, Verbist BM, Bayley JP, van der Mey AGL, Corssmit EPM, Hes FJ, van Benthem PPG, Jansen JC. Mathematical Models for Tumor Growth and the Reduction of Overtreatment. J Neurol Surg B Skull Base 2019;80:72-8. [PMID: 30733904 DOI: 10.1055/s-0038-1667148] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.8] [Reference Citation Analysis]
18 Wang KF, Mo LQ, Kong DX. Role of mathematical medicine in gastrointestinal carcinoma: Current status and perspectives. Shijie Huaren Xiaohua Zazhi 2017; 25(2): 114-121 [DOI: 10.11569/wcjd.v25.i2.114] [Reference Citation Analysis]
19 Moreira HM, Guerra Liberal FD, O'sullivan JM, Mcmahon SJ, Prise KM. Mechanistic Modeling of Radium-223 Treatment of Bone Metastases. International Journal of Radiation Oncology*Biology*Physics 2019;103:1221-30. [DOI: 10.1016/j.ijrobp.2018.12.015] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
20 Zhang H, Almuqbil RM, Alhudaithi SS, Sunbul FS, da Rocha SRP. Pulmonary administration of a CSF-1R inhibitor alters the balance of tumor-associated macrophages and supports first-line chemotherapy in a lung cancer model. Int J Pharm 2021;598:120350. [PMID: 33545279 DOI: 10.1016/j.ijpharm.2021.120350] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
21 Pham H. . MBE 2022;19:8975-9004. [DOI: 10.3934/mbe.2022417] [Reference Citation Analysis]
22 Chen AJ, Zhang J, Agarwal A, Lakdawalla DN. Value of Reducing Wait Times for Chimeric Antigen Receptor T-Cell Treatment: Evidence From Randomized Controlled Trial Data on Tisagenlecleucel for Diffuse Large B-Cell Lymphoma. Value Health 2022:S1098-3015(22)00109-7. [PMID: 35341689 DOI: 10.1016/j.jval.2022.02.007] [Reference Citation Analysis]
23 Tseng YJ, Huang CE, Wen CN, Lai PY, Wu MH, Sun YC, Wang HY, Lu JJ. Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies. Int J Med Inform 2019;128:79-86. [PMID: 31103449 DOI: 10.1016/j.ijmedinf.2019.05.003] [Cited by in Crossref: 28] [Cited by in F6Publishing: 13] [Article Influence: 9.3] [Reference Citation Analysis]
24 Hong WS, Wang SG, Zhang GQ. Lung Cancer Radiotherapy: Simulation and Analysis Based on a Multicomponent Mathematical Model. Comput Math Methods Med 2021;2021:6640051. [PMID: 34012477 DOI: 10.1155/2021/6640051] [Reference Citation Analysis]
25 Patmanidis S, Charalampidis AC, Kordonis I, Mitsis GD, Papavassilopoulos GP. Tumor growth modeling: Parameter estimation with Maximum Likelihood methods. Comput Methods Programs Biomed 2018;160:1-10. [PMID: 29728236 DOI: 10.1016/j.cmpb.2018.03.014] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
26 Stepien TL, Kostelich EJ, Kuang Y. Mathematics + Cancer: An Undergraduate "Bridge" Course in Applied Mathematics. SIAM Rev 2020;62:244-63. [DOI: 10.1137/18m1191865] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
27 Bajpai S, Tiwary SK, Sonker M, Joshi A, Gupta V, Kumar Y, Shreyash N, Biswas S. Recent Advances in Nanoparticle-Based Cancer Treatment: A Review. ACS Appl Nano Mater 2021;4:6441-70. [DOI: 10.1021/acsanm.1c00779] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 11.0] [Reference Citation Analysis]
28 Eftimie R, Hamam H. Modelling and investigation of the CD4+ T cells – Macrophages paradox in melanoma immunotherapies. Journal of Theoretical Biology 2017;420:82-104. [DOI: 10.1016/j.jtbi.2017.02.022] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 2.8] [Reference Citation Analysis]
29 Werner B, Scott JG, Sottoriva A, Anderson AR, Traulsen A, Altrock PM. The Cancer Stem Cell Fraction in Hierarchically Organized Tumors Can Be Estimated Using Mathematical Modeling and Patient-Specific Treatment Trajectories. Cancer Res 2016;76:1705-13. [PMID: 26833122 DOI: 10.1158/0008-5472.CAN-15-2069] [Cited by in Crossref: 49] [Cited by in F6Publishing: 17] [Article Influence: 8.2] [Reference Citation Analysis]
30 Rodriguez-Brenes IA, Wodarz D, Komarova NL. Beyond the pair approximation: Modeling colonization population dynamics. Phys Rev E 2020;101:032404. [PMID: 32289892 DOI: 10.1103/PhysRevE.101.032404] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Yu JS, Bagheri N. Agent-Based Models Predict Emergent Behavior of Heterogeneous Cell Populations in Dynamic Microenvironments. Front Bioeng Biotechnol 2020;8:249. [PMID: 32596213 DOI: 10.3389/fbioe.2020.00249] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
32 Albano G, Giorno V, Román-Román P, Román-Román S, Serrano-Pérez JJ, Torres-Ruiz F. Inference on an heteroscedastic Gompertz tumor growth model. Math Biosci 2020;328:108428. [PMID: 32712317 DOI: 10.1016/j.mbs.2020.108428] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
33 Wang J, Gottschal P, Ding L, Veldhuizen DAV, Lu W, Houssami N, Greuter MJW, de Bock GH. Mammographic sensitivity as a function of tumor size: A novel estimation based on population-based screening data. Breast 2021;55:69-74. [PMID: 33348148 DOI: 10.1016/j.breast.2020.12.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
34 Heesterman BL, de Pont LMH, Verbist BM, van der Mey AGL, Corssmit EPM, Hes FJ, van Benthem PPG, Jansen JC. Age and Tumor Volume Predict Growth of Carotid and Vagal Body Paragangliomas. J Neurol Surg B Skull Base 2017;78:497-505. [PMID: 29134169 DOI: 10.1055/s-0037-1604347] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 1.4] [Reference Citation Analysis]
35 Bhattarai S, Klimov S, Aleskandarany MA, Burrell H, Wormall A, Green AR, Rida P, Ellis IO, Osan RM, Rakha EA, Aneja R. Machine learning-based prediction of breast cancer growth rate in vivo. Br J Cancer 2019;121:497-504. [PMID: 31395950 DOI: 10.1038/s41416-019-0539-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
36 Bürger R, Chowell G, Lara-Díaz LY. Measuring differences between phenomenological growth models applied to epidemiology. Math Biosci 2021;334:108558. [PMID: 33571534 DOI: 10.1016/j.mbs.2021.108558] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Hoffmann B, Lange T, Labitzky V, Riecken K, Wree A, Schumacher U, Wedemann G. The initial engraftment of tumor cells is critical for the future growth pattern: a mathematical study based on simulations and animal experiments. BMC Cancer 2020;20:524. [PMID: 32503458 DOI: 10.1186/s12885-020-07015-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
38 Lesbats C, Kelly CL, Czanner G, Poptani H. Diffusion kurtosis imaging for characterizing tumor heterogeneity in an intracranial rat glioblastoma model. NMR in Biomedicine 2020;33. [DOI: 10.1002/nbm.4386] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
39 Nilsson A, Nielsen J. Genome scale metabolic modeling of cancer. Metab Eng 2017;43:103-12. [PMID: 27825806 DOI: 10.1016/j.ymben.2016.10.022] [Cited by in Crossref: 44] [Cited by in F6Publishing: 32] [Article Influence: 7.3] [Reference Citation Analysis]
40 Oberg AL, Heinzen EP, Hou X, Al Hilli MM, Hurley RM, Wahner Hendrickson AE, Goergen KM, Larson MC, Becker MA, Eckel-Passow JE, Maurer MJ, Kaufmann SH, Haluska P, Weroha SJ. Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting. Sci Rep 2021;11:8076. [PMID: 33850213 DOI: 10.1038/s41598-021-87470-x] [Reference Citation Analysis]
41 Pal A, Bhowmick AR, Yeasmin F, Bhattacharya S. Evolution of model specific relative growth rate: Its genesis and performance over Fisher's growth rates. J Theor Biol 2018;444:11-27. [PMID: 29452171 DOI: 10.1016/j.jtbi.2018.02.012] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
42 Bozic I, Wu CJ. Delineating the evolutionary dynamics of cancer from theory to reality. Nat Cancer 2020;1:580-8. [DOI: 10.1038/s43018-020-0079-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
43 Atsou K, Anjuère F, Braud VM, Goudon T. A size and space structured model describing interactions of tumor cells with immune cells reveals cancer persistent equilibrium states in tumorigenesis. J Theor Biol 2020;490:110163. [PMID: 31981572 DOI: 10.1016/j.jtbi.2020.110163] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
44 Di Crescenzo A, Spina S. Analysis of a growth model inspired by Gompertz and Korf laws, and an analogous birth-death process. Mathematical Biosciences 2016;282:121-34. [DOI: 10.1016/j.mbs.2016.10.005] [Cited by in Crossref: 20] [Cited by in F6Publishing: 8] [Article Influence: 3.3] [Reference Citation Analysis]
45 Jo G, Cho SI, Cho Y, Ohn J, Mun JH. Tumor growth rate as a prognostic factor of acral melanoma in a Korean population. Medicine (Baltimore) 2020;99:e19936. [PMID: 32481257 DOI: 10.1097/MD.0000000000019936] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
46 Okuneye K, Bergman D, Bloodworth JC, Pearson AT, Sweis RF, Jackson TL. A validated mathematical model of FGFR3-mediated tumor growth reveals pathways to harness the benefits of combination targeted therapy and immunotherapy in bladder cancer. Comput Syst Oncol 2021;1:e1019. [PMID: 34984415 DOI: 10.1002/cso2.1019] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
47 Gruber M, Bozic I, Leshchiner I, Livitz D, Stevenson K, Rassenti L, Rosebrock D, Taylor-Weiner A, Olive O, Goyetche R, Fernandes SM, Sun J, Stewart C, Wong A, Cibulskis C, Zhang W, Reiter JG, Gerold JM, Gribben JG, Rai KR, Keating MJ, Brown JR, Neuberg D, Kipps TJ, Nowak MA, Getz G, Wu CJ. Growth dynamics in naturally progressing chronic lymphocytic leukaemia. Nature 2019;570:474-9. [PMID: 31142838 DOI: 10.1038/s41586-019-1252-x] [Cited by in Crossref: 41] [Cited by in F6Publishing: 33] [Article Influence: 13.7] [Reference Citation Analysis]
48 Horrigan SK; Reproducibility Project: Cancer Biology. Replication Study: The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. Elife 2017;6:e18173. [PMID: 28100392 DOI: 10.7554/eLife.18173] [Cited by in Crossref: 37] [Cited by in F6Publishing: 30] [Article Influence: 7.4] [Reference Citation Analysis]
49 Li Y, Fanous MJ, Kilian KA, Popescu G. Quantitative phase imaging reveals matrix stiffness-dependent growth and migration of cancer cells. Sci Rep 2019;9:248. [PMID: 30670739 DOI: 10.1038/s41598-018-36551-5] [Cited by in Crossref: 24] [Cited by in F6Publishing: 19] [Article Influence: 8.0] [Reference Citation Analysis]
50 Schneider U, Besserer J. Tumour volume distribution can yield information on tumour growth and tumour control. Z Med Phys 2021:S0939-3889(21)00053-2. [PMID: 34119384 DOI: 10.1016/j.zemedi.2021.04.002] [Reference Citation Analysis]
51 Rizvi F, Shaukat L, Azhar A, Jafri A, Aslam U, Imran-Ul-Haq H. Preclinical meritorious anticancer effects of Metformin against breast cancer: An In vivo trial. J Taibah Univ Med Sci 2021;16:504-12. [PMID: 34408607 DOI: 10.1016/j.jtumed.2021.02.006] [Reference Citation Analysis]
52 Sego TJ, Glazier JA, Tovar A. Unification of aggregate growth models by emergence from cellular and intracellular mechanisms. R Soc Open Sci 2020;7:192148. [PMID: 32968501 DOI: 10.1098/rsos.192148] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
53 Kim J, Afshari A, Sengupta R, Sebastiano V, Gupta A, Kim YH; Reproducibility Project: Cancer Biology. Replication study: Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Elife 2018;7:e39944. [PMID: 30526855 DOI: 10.7554/eLife.39944] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 2.8] [Reference Citation Analysis]
54 Graillon T, Ferrer L, Siffre J, Sanson M, Peyre M, Peyrière H, Mougel G, Autran D, Tabouret E, Figarella-Branger D, Barlier A, Kalamarides M, Dufour H, Colin T, Chinot O. Role of 3D volume growth rate for drug activity evaluation in meningioma clinical trials: the example of the CEVOREM study. Neuro Oncol 2021;23:1139-47. [PMID: 33556177 DOI: 10.1093/neuonc/noab019] [Reference Citation Analysis]
55 Mercier F, Meneses-Lorente G, Grimsey P, Phipps A, Michielin F. Therapeutically-induced stable disease in oncology early clinical trials. PLoS One 2020;15:e0233882. [PMID: 32470048 DOI: 10.1371/journal.pone.0233882] [Reference Citation Analysis]
56 Burke JR, Brown P, Quyn A, Lambie H, Tolan D, Sagar P. Tumour growth rate of carcinoma of the colon and rectum: retrospective cohort study. BJS Open 2020. [PMID: 32996713 DOI: 10.1002/bjs5.50355] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
57 Zhou D, Luo Y, Dingli D, Traulsen A. The invasion of de-differentiating cancer cells into hierarchical tissues. PLoS Comput Biol 2019;15:e1007167. [PMID: 31260442 DOI: 10.1371/journal.pcbi.1007167] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
58 Pham H. A Dynamic Model of Multiple Time-Delay Interactions between the Virus-Infected Cells and Body’s Immune System with Autoimmune Diseases. Axioms 2021;10:216. [DOI: 10.3390/axioms10030216] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
59 Bergman D, Sweis RF, Pearson AT, Nazari F, Jackson TL. A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues. iScience 2022;25:104387. [PMID: 35637730 DOI: 10.1016/j.isci.2022.104387] [Reference Citation Analysis]
60 Arabameri A, Asemani D, Hadjati J. A structural methodology for modeling immune-tumor interactions including pro- and anti-tumor factors for clinical applications. Math Biosci 2018;304:48-61. [PMID: 30055212 DOI: 10.1016/j.mbs.2018.07.006] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 2.8] [Reference Citation Analysis]