Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer
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Joseph R. Peterson | John A. Cole | M. Biancalana | V. Takiar | D. Lopez-Ramos | H. Esslinger | Tushar Pandey | J. Pfeiffer | Yuhan Zhang | Gregory H. Norris | A. Antony
[1] L. Esserman,et al. Association of Residual Ductal Carcinoma In Situ With Breast Cancer Recurrence in the Neoadjuvant I-SPY2 Trial. , 2022, JAMA surgery.
[2] Frederick M. Howard,et al. Highly accurate response prediction in high-risk early breast cancer patients using a biophysical simulation platform , 2022, Breast Cancer Research and Treatment.
[3] J. Weis,et al. Dynamic characterization of breast cancer response to neoadjuvant therapy using biophysical metrics of spatial proliferation , 2022, Scientific Reports.
[4] S. Schnitt,et al. Impact of the Histologic Pattern of Residual Tumor After Neoadjuvant Chemotherapy on Recurrence and Survival in Stage I–III Breast Cancer , 2022, Annals of Surgical Oncology.
[5] Bowen Zheng,et al. Spatially resolved transcriptomics provide a new method for cancer research , 2022, Journal of experimental & clinical cancer research : CR.
[6] G. Sala,et al. Breast cancer in the era of integrating “Omics” approaches , 2022, Oncogenesis.
[7] Joseph R. Peterson,et al. Abstract P1-08-31: Simbiosys tumorscope: Biophysical modeling of patient-specific response to chemotherapy , 2022, Cancer Research.
[8] Frederick M. Howard,et al. Abstract P4-05-03: Evaluation of the prognostic accuracy of SimBioSys TumorScope in early breast cancer , 2022, Cancer Research.
[9] Gabor T. Marth,et al. A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology , 2022, Nature Cancer.
[10] R. Gelber,et al. Evaluation of pathological complete response as surrogate endpoint in neoadjuvant randomised clinical trials of early stage breast cancer: systematic review and meta-analysis , 2021, BMJ.
[11] L. Esserman,et al. Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multicentre pooled analysis of 5161 patients , 2021, The Lancet. Oncology.
[12] David A. Ekrut,et al. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting , 2021, Nature Protocols.
[13] Eun Sook Ko,et al. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer , 2021, Scientific Reports.
[14] K. Hoyt,et al. Multiscale computational modeling of cancer growth using features derived from microCT images , 2021, Scientific Reports.
[15] E. Jaffee,et al. Forecasting cancer: from precision to predictive medicine. , 2021, Med.
[16] Stephen R. Williams,et al. A single-cell and spatially resolved atlas of human breast cancers , 2021, Nature Genetics.
[17] Brian M. Larsen,et al. A pan-cancer organoid platform for precision medicine. , 2021, Cell reports.
[18] K. Gourevich,et al. Tumor Volume as Predictor of Pathologic Complete Response Following Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer , 2021, American journal of clinical oncology.
[19] J. Rosen,et al. Breast cancer heterogeneity through the lens of single-cell analysis and spatial pathologies. , 2021, Seminars in cancer biology.
[20] E. Rutgers,et al. Customizing local and systemic therapies for women with early breast cancer: the St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021 , 2021, Annals of oncology : official journal of the European Society for Medical Oncology.
[21] T. Yankeelov,et al. Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation , 2021, Scientific Reports.
[22] E. Winer,et al. Road Map to Safe and Well-Designed De-escalation Trials of Systemic Adjuvant Therapy for Solid Tumors. , 2020, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[23] I. Ricci-Cabello,et al. Adherence to breast cancer guidelines is associated with better survival outcomes: a systematic review and meta-analysis of observational studies in EU countries , 2020, BMC Health Services Research.
[24] K. Rahbar,et al. PSMA PET total tumor volume predicts outcome of patients with advanced prostate cancer receiving [177Lu]Lu-PSMA-617 radioligand therapy in a bicentric analysis , 2020, European Journal of Nuclear Medicine and Molecular Imaging.
[25] I. Endo,et al. Intra-Tumoral Angiogenesis Is Associated with Inflammation, Immune Reaction and Metastatic Recurrence in Breast Cancer , 2020, International journal of molecular sciences.
[26] G. Johnson,et al. PET Imaging of Tumor Perfusion: A Potential Cancer Biomarker? , 2020, Seminars in nuclear medicine.
[27] T. Go,et al. Correlation of Pathological Complete Response With Tumor Volume Reduction During Neoadjuvant Chemoradiotherapy in Lung Cancer , 2020, AntiCancer Research.
[28] Joseph R. Peterson,et al. Perfusion kinetics from clinical DCE mris increase the accuracy of predictions of tumor response to chemotherapy. , 2020 .
[29] Joseph R. Peterson,et al. SimBioSys TumorScope: Spatio-temporal modeling of the tumor microenvironment to predict chemotherapeutic response. , 2020 .
[30] Joseph R. Peterson,et al. Spatiotemporal modeling with SimBioSys TumorScope to predict chemotherapeutic response in breast tumor microenvironments. , 2020 .
[31] K. Rahbar,et al. Semiautomatically Quantified Tumor Volume Using 68Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate Cancer , 2020, The Journal of Nuclear Medicine.
[32] J. Fütterer,et al. The Effect of Higher Level Computerized Clinical Decision Support Systems on Oncology Care: A Systematic Review , 2020, Cancers.
[33] Joseph R. Peterson,et al. Abstract P1-06-04: SimBioSys TumorScope: Spatio-temporal modeling of the breast tumor microenvironment accurately predicts chemotherapeutic response , 2020 .
[34] Shivajirao M. Jadhav,et al. Deep convolutional neural network based medical image classification for disease diagnosis , 2019, Journal of Big Data.
[35] S. Kim,et al. Advances in Diffusion and Perfusion MRI for Quantitative Cancer Imaging , 2019, Current Pathobiology Reports.
[36] Rhea D. Chitalia,et al. Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence , 2019, Clinical Cancer Research.
[37] E. Matano,et al. Total metabolic tumor volume by 18F-FDG PET/CT for the prediction of outcome in patients with non-small cell lung cancer , 2019, Annals of Nuclear Medicine.
[38] Laura Keller,et al. Unravelling tumour heterogeneity by single-cell profiling of circulating tumour cells , 2019, Nature Reviews Cancer.
[39] Michael Hinczewski,et al. The 2019 mathematical oncology roadmap , 2019, Physical biology.
[40] K. Kitajima,et al. Significance of Metabolic Tumor Volume at Baseline and Reduction of Mean Standardized Uptake Value in 18F-FDG-PET/CT Imaging for Predicting Pathological Complete Response in Breast Cancers Treated with Preoperative Chemotherapy , 2019, Annals of Surgical Oncology.
[41] Kun Wang,et al. Tumor location of the central and nipple portion is associated with impaired survival for women with breast cancer , 2019, Cancer management and research.
[42] Chengyue Wu,et al. Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors , 2018, Magnetic resonance in medicine.
[43] L. Kooreman,et al. Correlation Between Pathologic Complete Response in the Breast and Absence of Axillary Lymph Node Metastases After Neoadjuvant Systemic Therapy. , 2020, Annals of surgery.
[44] Y. Tsushima,et al. Prognostic value of metabolic tumor volume of pretreatment 18F-FAMT PET/CT in non-small cell lung Cancer , 2018, BMC Medical Imaging.
[45] Maciej A. Mazurowski,et al. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set , 2018, Breast Cancer Research and Treatment.
[46] J. Boughey,et al. The Clinical Significance of Breast-only and Node-only Pathologic Complete Response (pCR) After Neoadjuvant Chemotherapy (NACT): A Review of 20,000 Breast Cancer Patients in the National Cancer Data Base (NCDB) , 2018, Annals of surgery.
[47] S. Bhatia,et al. Impact of Nonconcordance With NCCN Guidelines on Resource Utilization, Cost, and Mortality in De Novo Metastatic Breast Cancer. , 2018, Journal of the National Comprehensive Cancer Network : JNCCN.
[48] M. Nishino. Tumor Response Assessment for Precision Cancer Therapy: Response Evaluation Criteria in Solid Tumors and Beyond. , 2018, American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting.
[49] A. Thompson,et al. Breast cancer: influence of tumour volume estimation method at MRI on prediction of pathological response to neoadjuvant chemotherapy. , 2018, The British journal of radiology.
[50] A. Shaw,et al. Tumour heterogeneity and resistance to cancer therapies , 2018, Nature Reviews Clinical Oncology.
[51] G. Turashvili,et al. Tumor Heterogeneity in Breast Cancer , 2017, Front. Med..
[52] Jeong Eon Lee,et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer , 2017, Nature Communications.
[53] Patrick Soon-Shiong,et al. Molecular heterogeneity in breast cancer: State of the science and implications for patient care. , 2017, Seminars in cell & developmental biology.
[54] C. Compton,et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population‐based to a more “personalized” approach to cancer staging , 2017, CA: a cancer journal for clinicians.
[55] S. Masood. Neoadjuvant chemotherapy in breast cancers , 2016, Women's health.
[56] Seyed-Ahmad Ahmadi,et al. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[57] F. Rojo,et al. High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer , 2016, The oncologist.
[58] Ash A. Alizadeh,et al. Toward understanding and exploiting tumor heterogeneity , 2015, Nature Medicine.
[59] J. Wildberger,et al. The Quality of Tumor Size Assessment by Contrast-Enhanced Spectral Mammography and the Benefit of Additional Breast MRI , 2015, Journal of Cancer.
[60] John Kornak,et al. Optimized breast MRI functional tumor volume as a biomarker of recurrence‐free survival following neoadjuvant chemotherapy , 2014, Journal of magnetic resonance imaging : JMRI.
[61] Gideon Blumenthal,et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis , 2014, The Lancet.
[62] Thomas E Yankeelov,et al. Clinically Relevant Modeling of Tumor Growth and Treatment Response , 2013, Science Translational Medicine.
[63] Quynh-Thu Le,et al. Metabolic tumor volume is an independent prognostic factor in patients treated definitively for non-small-cell lung cancer. , 2012, Clinical lung cancer.
[64] Andrew H. Beck,et al. Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival , 2011, Science Translational Medicine.
[65] S. Paik,et al. Association between the 21-gene recurrence score assay and risk of locoregional recurrence in node-negative, estrogen receptor-positive breast cancer: results from NSABP B-14 and NSABP B-20. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[66] A. Nobel,et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[67] F. Bertucci,et al. Association of GATA3, P53, Ki67 status and vascular peritumoral invasion are strongly prognostic in luminal breast cancer , 2009, Breast Cancer Research.
[68] S. Paik,et al. Development of the 21-gene assay and its application in clinical practice and clinical trials. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[69] Christos Hatzis,et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[70] L. Esserman,et al. MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival. , 2005, AJR. American journal of roentgenology.
[71] Hilde van der Togt,et al. Publisher's Note , 2003, J. Netw. Comput. Appl..
[72] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[73] P. Tofts,et al. Measurement of the blood‐brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts , 1991, Magnetic resonance in medicine.
[74] E. S. Pearson,et al. THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL , 1934 .