Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: A Cancer Journal for Clinicians, vol.68, pp.394-424, 2018. ,
The unreasonable effectiveness of mathematics in the natural sciences. Richard courant lecture in mathematical sciences delivered at New York University, Communications on Pure and Applied Mathematics, vol.13, pp.1-14, 1959. ,
Essai d'une nouvelle analyse de la mortalité causée par la petite vérole, et des avantages de l'inoculation pour la prévenir. Histoire de l'Acad, Roy. Sci, pp.1-45, 1760. ,
Mathematical oncology: cancer summed up, Nature, vol.421, pp.321-321, 2003. ,
Computational oncology-mathematical modelling of drug regimens for precision medicine, Nature reviews Clinical oncology, vol.13, p.242, 2016. ,
The mathematics of cancer: integrating 11 ,
NCBI GEO: archive for functional genomics data sets-update ,
, Nucleic Acids Res, vol.41, pp.991-995, 2013.
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, vol.286, pp.531-537, 1999. ,
Multiclass cancer diagnosis using tumor gene expression signatures, Proc Natl Acad Sci, vol.98, pp.15149-15154, 2001. ,
Molecular portraits of human breast tumours, Nature, vol.406, pp.747-752, 2000. ,
Gene expression profiling predicts clinical outcome of breast cancer, Nature, vol.415, pp.530-536, 2002. ,
Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study, Nat Med, vol.14, pp.822-827, 2008. ,
The consensus molecular subtypes of colorectal cancer ,
, , vol.21, pp.1350-1356, 2015.
DNA methylation-based classification of central nervous system tumours, Nature, vol.555, pp.469-474, 2018. ,
Cluster analysis and display of genome-wide expression patterns, Proc Natl Acad Sci, vol.95, pp.14863-14868, 1998. ,
Analysis of breast cancer progression using principal component analysis and clustering, J. Biosci, vol.32, pp.1027-1039, 2007. ,
Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas, PLoS Comput Biol, vol.15, p.1006826, 2019. ,
Predictive Models for Breast Cancer Susceptibility from ,
, Multiple Single Nucleotide Polymorphisms, Clin Cancer Res, vol.10, pp.2725-2737, 2004.
The Application of Deep Learning in Cancer Prognosis Prediction, Cancers, vol.12, p.603, 2020. ,
Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data, PLoS Comput Biol, vol.14, p.1006076, 2018. ,
, Deep Learning-Based
, Multi-Omics Integration Robustly Predicts Survival in Liver Cancer, Clin. Cancer Res, vol.24, pp.1248-1259, 2018.
, Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer. Front Genet, vol.10, p.166, 2019.
Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models, Sci Rep, vol.7, pp.1-11, 2017. ,
Dermatologist-level classification of skin cancer with deep neural networks, Nature, vol.542, pp.115-118, 2017. ,
Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists, Arch. Pathol. Lab. Med, vol.143, pp.859-868, 2019. ,
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis, Nature Medicine, vol.25, pp.1453-1457, 2019. ,
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study, PLOS Medicine, vol.16, p.1002730, 2019. ,
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer, Nat. Med, vol.25, pp.1054-1056, 2019. ,
Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning, Nat. Med, vol.24, pp.1559-1567, 2018. ,
Radiomics: extracting more information from medical images using advanced feature analysis, Eur J Cancer, vol.48, pp.441-446, 2012. ,
,
, Artificial intelligence in radiology, Nature Reviews Cancer, vol.18, pp.500-510, 2018.
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma, Radiotherapy and Oncology, vol.114, pp.345-350, 2015. ,
Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer, Cancer Res, vol.77, pp.3922-3930, 2017. ,
Defining the biological basis of radiomic phenotypes in lung cancer, Elife, vol.6, 2017. ,
T2 -based MRI Delta-radiomics improve response prediction in soft-tissue sarcomas treated by neoadjuvant chemotherapy, J Magn Reson Imaging, vol.50, pp.497-510, 2019. ,
A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study, Lancet Oncol, vol.19, pp.1180-1191, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01958243
Vulnerabilities of radiomic signature development: The need for safeguards, Radiother Oncol, vol.130, pp.2-9, 2019. ,
Prognostic Value of Deep Learning PET/CT-based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma, Clin Cancer Res, 2019. ,
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study, PLoS Med, vol.15, 2018. ,
A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme, Sci Rep, vol.7, p.10353, 2017. ,
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images, EJNMMI Research, vol.7, p.11, 2017. ,
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning, Sci Rep, vol.7, p.8738, 2017. ,
Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging, Clin Cancer Res, vol.25, pp.3266-3275, 2019. ,
Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board, Ann Oncol, vol.29, pp.418-423, 2018. ,
Assessing Concordance With Watson for Oncology ,
, Cognitive Computing Decision Support System for Colon Cancer Treatment in Korea
, JCO Clin Cancer Inform, vol.2, pp.1-8, 2018.
, IBM pitched Watson as a revolution in cancer care. It's nowhere close, 2017.
Visible Machine Learning for, Biomedicine. Cell, vol.173, pp.1562-1565, 2018. ,
Comprehensive analysis of the clinical immuno-oncology landscape, Ann Oncol, vol.29, pp.84-91, 2018. ,
Modelling of individual pharmacokinetics for computer-aided drug dosage, Computers and Biomedical Research, vol.5, pp.441-459, 1972. ,
Model-Based Population Pharmacokinetic Analysis of ,
, Nivolumab in Patients With Solid Tumors, CPT Pharmacometrics Syst Pharmacol, vol.6, pp.58-66, 2017.
Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis, Clin. Pharmacol. Ther, vol.101, pp.657-666, 2017. ,
Time dependent pharmacokinetics of pembrolizumab in patients with solid tumor and its correlation with best overall response, J Pharmacokinet Pharmacodyn, vol.44, pp.403-414, 2017. ,
Model-based clinical pharmacology profiling of ipilimumab in patients with advanced melanoma, Br J Clin Pharmacol, vol.78, pp.106-117, 2014. ,
Population Pharmacokinetics of Ipilimumab in Combination With Nivolumab in Patients With Advanced Solid Tumors, CPT: Pharmacometrics & Systems Pharmacology, vol.9, pp.29-39, 2020. ,
Model-Based Characterization of the Pharmacokinetics of Pembrolizumab: A Humanized Anti-PD-1 Monoclonal Antibody in Advanced Solid Tumors, CPT Pharmacometrics Syst Pharmacol, vol.6, pp.49-57, 2017. ,
, Clinical Pharmacokinetics and Pharmacodynamics
, Atezolizumab in Metastatic Urothelial Carcinoma, Clin. Pharmacol. Ther, vol.102, pp.305-312, 2017.
Population Pharmacokinetics of Durvalumab in Cancer Patients and Association With Longitudinal Biomarkers of Disease Status, Clin. Pharmacol. Ther, vol.103, pp.631-642, 2018. ,
Time Is Money: Optimizing the Scheduling of Nivolumab, J Clin Oncol JCO, vol.18, pp.45-49, 2018. ,
Exposure-response relationships of the efficacy and safety of ipilimumab in patients with advanced melanoma, Clin Cancer Res, vol.19, pp.3977-3986, 2013. ,
Quantitative Characterization of the Exposure-Response Relationship for Cancer Immunotherapy: A Case Study of Nivolumab in Patients With Advanced Melanoma, CPT Pharmacometrics Syst Pharmacol, vol.6, pp.40-48, 2017. ,
Nivolumab Exposure-Response Analyses of Efficacy and Safety in Previously Treated Squamous or Nonsquamous Non-Small Cell Lung Cancer, Clin Cancer Res, vol.23, pp.5394-5405, 2017. ,
Pembrolizumab Exposure-Response Assessments Challenged by Association of Cancer Cachexia and Catabolic Clearance, Clin Cancer Res, 2018. ,
Alternative dosing regimens for atezolizumab: an example of model-informed drug development in the postmarketing setting, Cancer Chemother Pharmacol, vol.84, pp.1257-1267, 2019. ,
Assessment of nivolumab exposure and clinical safety of 480 ,
URL : https://hal.archives-ouvertes.fr/hal-02143540
, mg every 4 weeks flat-dosing schedule in patients with cancer, Ann Oncol, vol.29, pp.2208-2213, 2018.
Application of PK-PD Modeling and Simulation Approaches for Immuno-Oncology Drugs, Development of ,
, Antibody-Based Therapeutics, vol.4, pp.207-222, 2018.
Translational pharmacokinetic/pharmacodynamic modeling of tumor growth inhibition supports dose-range selection of the anti-PD-1 antibody pembrolizumab, CPT Pharmacometrics Syst Pharmacol, vol.6, pp.11-20, 2017. ,
, Population Pharmacokinetic/Pharmacodynamic
, Modeling of Tumor Size Dynamics in Pembrolizumab-Treated Advanced Melanoma
, CPT Pharmacometrics Syst Pharmacol, vol.6, pp.29-39, 2017.
Prediction of the Optimal Dosing Regimen Using a Mathematical Model of Tumor Uptake for Immunocytokine-Based Cancer Immunotherapy, Clin Cancer Res, vol.24, pp.3325-3333, 2018. ,
Adaptive, and Acquired Resistance to Cancer Immunotherapy, Cell, vol.168, pp.707-723, 2017. ,
Combination Cancer Therapy Can Confer Benefit via Patient-to-Patient Variability without Drug Additivity or Synergy, Cell, vol.171, pp.1678-1691, 2017. ,
, Mechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators? JCO Precision Oncology, pp.486-491, 2020.
A QSP Model for Predicting Clinical Responses to ,
Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade, Scientific Reports, vol.9, p.11286, 2019. ,
Mathematical Modeling of Cancer Immunotherapy and Its Synergy with Radiotherapy, Cancer Res, vol.76, pp.4931-4940, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01336779
Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model, J Immunother Cancer, vol.6, p.17, 2018. ,
Immunologically effective dose: a practical model for immuno-radiotherapy, Oncotarget, vol.9, pp.31812-31819, 2018. ,
Durvalumab after Chemoradiotherapy in Stage III Non-Small-Cell Lung Cancer, N Engl J Med, vol.377, pp.1919-1929, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01753457
Modeling and predicting optimal treatment scheduling between the antiangiogenic drug sunitinib and irinotecan in preclinical settings, CPT Pharmacometrics Syst. Pharmacol, vol.4, pp.720-727, 2015. ,
Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling: Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma, CPT Pharmacometrics Syst Pharmacol, vol.7, pp.42-50, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01624423
Metronomic reloaded: Theoretical models bringing chemotherapy into the era of precision medicine, Semin Cancer Biol, vol.35, pp.53-61, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01195547
Experimental evaluation of potential anticancer agents XIII. On the criteria and kinetics associated with 'curability' of experimental leukemia, Cancer Chemother Rep, vol.35, pp.1-111, 1964. ,
, Pharmacodynamics of Chemotherapeutic Effects: Dose-Time
, Response Relationships for Phase-Nonspecific Agents, JPharmSci, vol.60, pp.892-895, 1971.
A pharmacodynamic model for cell-cycle-specific chemotherapeutic agents, Journal of Pharmacokinetics and Biopharmaceutics, vol.1, pp.175-200, 1973. ,
Tumor size, sensitivity to therapy, and design of treatment schedules, Cancer Treat Rep, vol.61, pp.1307-1317, 1977. ,
A Gompertzian model of human breast cancer growth, Cancer Res, vol.48, pp.7067-7071, 1988. ,
Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741, J Clin Oncol, vol.21, pp.1431-1439, 2003. ,
Model of chemotherapy-induced myelosuppression with parameter consistency across drugs ,
, J. Clin. Oncol, vol.20, pp.4713-4721, 2002.
Revisiting Dosing Regimen Using Pharmacokinetic/Pharmacodynamic Mathematical Modeling: Densification and Intensification of Combination Cancer Therapy, Clin Pharmacokinet, vol.55, pp.1015-1025, 2016. ,
Revisiting dosing regimen using PK/PD modeling: the MODEL1 ,
, phase I/II trial of docetaxel plus epirubicin in metastatic breast cancer patients, Breast Cancer Res Treat, vol.156, pp.331-341, 2016.
Mathematical modeling for Phase I cancer trials: A study of metronomic vinorelbine for advanced non-small cell lung cancer (NSCLC) and mesothelioma patients, Oncotarget, vol.8, pp.47161-47166, 2017. ,
Intratumor heterogeneity and branched evolution revealed by multiregion sequencing, N. Engl. J. Med, vol.366, pp.883-892, 2012. ,
A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate, Cancer Treat Rep, vol.63, pp.1727-1733, 1979. ,
Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling, Sci Transl Med, vol.3, pp.90-59, 2011. ,
Phase 1 study of twice weekly pulse dose and daily low-dose erlotinib as initial treatment for patients with EGFR-mutant lung cancers, Ann Oncol, vol.28, pp.278-284, 2017. ,
A change of strategy in the war on cancer, Nature, vol.459, pp.508-509, 2009. ,
Evolution of resistance to targeted anti-cancer therapies during continuous and pulsed administration strategies, PLoS Comput Biol, vol.5, p.1000557, 2009. ,
Adaptive therapy, Cancer Res, vol.69, pp.4894-4903, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-01887358
Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer, Sci Transl Med, vol.8, pp.327-351, 2016. ,
Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer, Nature Communications, vol.8, p.1816, 2017. ,
New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1), European Journal of Cancer, vol.45, pp.228-247, 2009. ,
Linking Tumor Growth Dynamics to Survival in Ipilimumab-Treated Patients With Advanced Melanoma Using Mixture Tumor Growth Dynamic Modeling, CPT Pharmacometrics Syst Pharmacol, vol.8, pp.825-834, 2019. ,
A Pharmacodynamic Model for the Time Course of Tumor Shrinkage by Gemcitabine + Carboplatin in Non-Small Cell Lung Cancer Patients, Clin Cancer Res, vol.14, pp.4213-4218, 2008. ,
Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development, Clin Pharmacol Ther, vol.86, pp.167-174, 2009. ,
Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics, J Clin Oncol, vol.27, pp.4103-4108, 2009. ,
Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data, The Oncologist, vol.13, pp.1046-1054, 2008. ,
Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models, Clin Cancer Res, pp.1-22, 2019. ,
A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy, Clin Cancer Res, vol.18, pp.5071-80, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00744626
Population Modeling of Tumor Kinetics and Overall Survival to Identify Prognostic and Predictive Biomarkers of Efficacy for Durvalumab in Patients With Urothelial Carcinoma, Clin Pharmacol Ther, vol.103, pp.643-652, 2018. ,
A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics, Clin Cancer Res, vol.24, pp.3292-3298, 2018. ,
A PK/PDAnalysis of Circulating Biomarkers and Their Relationship to Tumor Response in Atezolizumab-Treated non-small Cell Lung Cancer Patients, Clin Pharmacol Ther, vol.105, pp.486-495, 2018. ,
Association Between Tumor Size Kinetics and Survival in Patients With Urothelial Carcinoma Treated With Atezolizumab: Implication for Patient Follow-Up, Clin Pharmacol Ther, vol.106, pp.810-820, 2019. ,
URL : https://hal.archives-ouvertes.fr/inserm-02103513
Nonlinear Mixed-Effect Models for Prostate-Specific Antigen Kinetics and Link with Survival in the Context of Metastatic Prostate Cancer: a Comparison by Simulation of Two-Stage and Joint Approaches, AAPS J, vol.17, pp.691-699, 2015. ,
Multivariate joint frailty model for the analysis of nonlinear tumor kinetics and dynamic predictions of death ,
, Stat Med, vol.37, pp.2148-2161, 2018.
Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach, Biostatistics, vol.10, pp.535-549, 2009. ,
URL : https://hal.archives-ouvertes.fr/inserm-00367752
,
, Mathematical Modeling of Tumor-Tumor Distant Interactions Supports a Systemic Control of Tumor Growth, Cancer Res, vol.77, pp.5183-5193, 2017.
Comparison of tumor size assessments in tumor growth inhibition-overall survival models with second-line colorectal cancer data from the VELOUR study, Cancer Chemother Pharmacol, vol.82, pp.49-54, 2018. ,
Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics, Cancer Chemother Pharmacol, 2019. ,
Study of metastatic kinetics in metastatic melanoma treated with B-RAF inhibitors: Introducing mathematical modelling of kinetics into the therapeutic decision, PLoS ONE, vol.12, p.176080, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01585748
Pharmacometric Modeling of Liver Metastases' Diameter, Volume, and Density and Their Relation to Clinical Outcome in Imatinib-Treated Patients With Gastrointestinal Stromal Tumors, CPT: Pharmacometrics & Systems Pharmacology, vol.6, pp.449-457, 2017. ,
PK-PD modeling of individual lesion FDG-PET response to predict overall survival in patients with sunitinib-treated gastrointestinal stromal tumor, CPT: Pharmacometrics & Systems Pharmacology, vol.5, pp.173-181, 2016. ,
A Dynamical Model for the Growth and Size Distribution of Multiple Metastatic Tumors, J Theor Biol, vol.203, pp.177-186, 2000. ,
Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies, Cell, vol.148, pp.362-375, 2012. ,
Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer, Sci Rep, vol.9, p.13018, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02509474
Mathematical modeling of tumor growth and metastatic spreading: validation in tumor-bearing mice, Cancer Res, vol.74, pp.6397-6407, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01107681
Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach, Cancer Res, vol.76, pp.535-547, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01222046
Computer simulation of a breast cancer metastasis model, Breast Cancer Res Treat, vol.45, pp.193-202, 1997. ,
A "universal" model of metastatic cancer, its parametric forms and their identification: what can be learned from sitespecific volumes of metastases, J Math Biol, vol.72, pp.1633-1662, 2016. ,
Pharmacometrics and Systems Pharmacology 2030, Clinical Pharmacology & Therapeutics, vol.107, pp.76-78, 2020. ,
A new approach to modeling covariate effects and individualization in population pharmacokinetics-pharmacodynamics, J Pharmacokinet Pharmacodyn, vol.33, pp.49-74, 2006. ,
Vertical Integration of Pharmacogenetics in Population PK/PD Modeling: A Novel Information Theoretic Method, CPT: Pharmacometrics & Systems Pharmacology, vol.2, p.25, 2013. ,
Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms, Clin. Pharmacol. Ther, vol.100, pp.371-379, 2016. ,
Machine Learning and Mechanistic Modeling for Prediction of ,
, Metastatic Relapse in Early-Stage Breast Cancer, JCO Clin Cancer Inform, vol.4, pp.259-274, 2020.
Deciphering response and resistance to immunecheckpoint inhibitors in lung cancer with artificial intelligence-based analysis: the PIONeeR and QUANTIC projects, British Journal of Cancer under review, 2020. ,
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, J Digit Imaging, vol.26, pp.1045-1057, 2013. ,
The Digital Slide Archive: A Software Platform for ,
, Analysis of Histology for Cancer Research, Cancer Res, vol.77, pp.75-78, 2017.
Project Data Sphere to Make Cancer Clinical Trial Data Publicly Available, J Natl Cancer Inst, vol.105, pp.1159-1160, 2013. ,