Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer
| العنوان: | Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer |
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| المؤلفون: | Lusine Tumyan, Daniel Schmolze, Joanne E. Mortimer, Daniel Abler, David A. Hormuth, Angela M. Jarrett, Vikram Adhikarla, Prativa Sahoo, Thomas E. Yankeelov, Russell C. Rockne |
| المصدر: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-14 (2020) Jarrett, Angela M.; Hormuth, David A.; Adhikarla, Vikram; Sahoo, Prativa; Abler, Daniel; Tumyan, Lusine; Schmolze, Daniel; Mortimer, Joanne; Rockne, Russell C.; Yankeelov, Thomas E. (2020). Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer. Scientific reports, 10(1), p. 20518. Springer Nature 10.1038/s41598-020-77397-0 <http://dx.doi.org/10.1038/s41598-020-77397-0Test> |
| بيانات النشر: | Springer Nature, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | medicine.medical_specialty, Mathematics and computing, Receptor, ErbB-2, Science, medicine.medical_treatment, 610 Medicine & health, Breast Neoplasms, Models, Biological, Article, 030218 nuclear medicine & medical imaging, Targeted therapy, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, Text mining, Targeted therapies, 510 Mathematics, Positron Emission Tomography Computed Tomography, medicine, Image Processing, Computer-Assisted, Organometallic Compounds, Chemotherapy, Humans, skin and connective tissue diseases, Neoadjuvant therapy, PET-CT, Multidisciplinary, medicine.diagnostic_test, business.industry, Cancer, Magnetic resonance imaging, Translational research, Trastuzumab, medicine.disease, 620 Engineering, Magnetic Resonance Imaging, Neoadjuvant Therapy, Positron emission tomography, 030220 oncology & carcinogenesis, Medicine, Cancer imaging, Female, Radiology, business, Biomedical engineering |
| الوصف: | While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response. |
| وصف الملف: | application/pdf |
| DOI: | 10.48350/150780 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34d692269c2e8a618712ec30483dae71Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....34d692269c2e8a618712ec30483dae71 |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.48350/150780 |
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