Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy

[1]  G. Calin,et al.  Translational Modeling Identifies Synergy between Nanoparticle-Delivered miRNA-22 and Standard-of-Care Drugs in Triple-Negative Breast Cancer , 2021, Pharmaceutical Research.

[2]  R. Sidman,et al.  Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling , 2021, medRxiv.

[3]  K. Brock,et al.  The Effect of Slice Thickness on Contours of Brain Metastases for Stereotactic Radiosurgery , 2021, Advances in Radiation Oncology.

[4]  Jens Timmer,et al.  On structural and practical identifiability , 2021, 2102.05100.

[5]  G. Calin,et al.  A mathematical model for the quantification of a patient’s sensitivity to checkpoint inhibitors and long-term tumour burden , 2020, Nature biomedical engineering.

[6]  A. Mechelli,et al.  Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners , 2020, NeuroImage.

[7]  Vittorio Cristini,et al.  Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy , 2020, Science Advances.

[8]  C. Brinker,et al.  Image‐guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies , 2020, Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology.

[9]  Joseph D Butner,et al.  A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery , 2020, Computational and structural biotechnology journal.

[10]  Aaron Carass,et al.  DeepHarmony: A deep learning approach to contrast harmonization across scanner changes. , 2019, Magnetic resonance imaging.

[11]  V. Cristini,et al.  Size-Optimized Ultrasmall Porous Silica Nanoparticles Depict Vasculature-Based Differential Targeting in Triple Negative Breast Cancer. , 2019, Small.

[12]  Vinay Prasad,et al.  Estimation of the Percentage of US Patients With Cancer Who Are Eligible for and Respond to Checkpoint Inhibitor Immunotherapy Drugs , 2019, JAMA network open.

[13]  Colin M. Wilson,et al.  Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies. , 2019, JCI insight.

[14]  Jason L. Townson,et al.  Understanding the Connection between Nanoparticle Uptake and Cancer Treatment Efficacy using Mathematical Modeling , 2018, Scientific Reports.

[15]  Vittorio Cristini,et al.  An Introduction to Physical Oncology: How Mechanistic Mathematical Modeling Can Improve Cancer Therapy Outcomes , 2017 .

[16]  R. Laubenbacher,et al.  Addressing current challenges in cancer immunotherapy with mathematical and computational modeling , 2017, bioRxiv.

[17]  Mauro Ferrari,et al.  Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill , 2016, PLoS Comput. Biol..

[18]  Philipp M. Altrock,et al.  The mathematics of cancer: integrating quantitative models , 2015, Nature Reviews Cancer.

[19]  C. Rudin,et al.  Nivolumab versus Docetaxel in Advanced Nonsquamous Non-Small-Cell Lung Cancer. , 2015, The New England journal of medicine.

[20]  R. Motzer,et al.  Nivolumab for Metastatic Renal Cell Carcinoma: Results of a Randomized Phase II Trial. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[21]  P. Hegde,et al.  MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer , 2014, Nature.

[22]  Vittorio Cristini,et al.  Mechanistic modeling identifies drug-uptake history as predictor of tumor drug resistance and nano-carrier-mediated response. , 2013, ACS nano.

[23]  Vittorio Cristini,et al.  Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements , 2013, Proceedings of the National Academy of Sciences.

[24]  David C. Smith,et al.  Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. , 2012, The New England journal of medicine.

[25]  D. Green,et al.  Dual-phase evolution in complex adaptive systems , 2011, Journal of The Royal Society Interface.

[26]  Francesco Pappalardo,et al.  Optimal vaccination schedules using simulated annealing , 2008, Bioinform..

[27]  Carmen G. Moles,et al.  Parameter estimation in biochemical pathways: a comparison of global optimization methods. , 2003, Genome research.