Quantification of Intratumor Heterogeneity in Mice and Patients with Machine Learning Models Trained on PET-MRI Data

Machine Learning


  • Eisenhower, EA et al. New Response Metrics in Solid Tumors: Revised RECIST Guidelines (Version 1.1). EUR. J. Cancer 45228–247 (2009).

    Papers CAS PubMed Google Scholar

  • O’Connor, JPB Imaging Biomarker Roadmap for Other Cancer Research. nut. Pastor Klin. Onkol. 14169–186 (2016).

    Papers PubMed PubMed Central Google Scholar

  • Judenhofer, MS et al. Simultaneous PET-MRI: A new approach for functional and morphological imaging. nut. medicine. 14459–465 (2008).

    Papers CAS PubMed Google Scholar

  • Disselhorst, JA, Bezrukov, I., Kolb, A., Parl, C., Pichler, BJ Principles of PET/MR imaging. J. Nucl. medicine. 552S–10S (2014).

    Papers PubMed Google Scholar

  • DL Bailey et al. Combined PET/MR: The real work is just beginning. Summary Report of the 3rd International Workshop on PET/MR Imaging. 17-21 February 2014, Tübingen, Germany. Mole. imaging biol. 17297–312 (2015).

    Articles CAS PubMed PubMed Central Google Scholar

  • Gillies, RJ & Bayer, T. PET, MRI: Is the whole greater than the sum of its parts? Cancer Institute 766163–6166 (2016).

    Articles CAS PubMed PubMed Central Google Scholar

  • Schmitz, J. et al. Decoding intratumoral heterogeneity in breast cancer by multiparameter in vivo imaging: a translational study. Cancer Institute 765512–5522 (2016).

    Articles CAS PubMed PubMed Central Google Scholar

  • O’Connor, JPB, et al. Imaging intratumoral heterogeneity: its role in therapeutic response, resistance, and clinical outcome. Clin.Cancer Institute twenty one249–257 (2015).

    Papers PubMed Google Scholar

  • Napel, S., Mu, W., Jardim-Perassi, BV, Aerts, HJWL & Gillies, RJ Quantitative imaging of cancer in the post-genome era: radio(geno)mics, deep learning, and habitat. cancer 1244633–4649 (2018).

  • Heinzmann, K., Carter, LM, Lewis, JS & Aboagye, EO Multiple image processing for diagnosis and treatment. nut. Biomed.engineering 1697–713 (2017).

    Papers PubMed Google Scholar

  • Junttila, MR & de Sauvage, FJ Effect of tumor microenvironment heterogeneity on therapeutic response. Nature 501346–354 (2013).

    Papers CAS PubMed Google Scholar

  • Schmidt, H. et al. Simultaneously acquired diffusion-weighted imaging and 2-deoxy-[18F] Fluoro-2-D-glucose positron emission tomography of pulmonary lesions with a dedicated whole-body magnetic resonance/positron emission tomography system. investment. Radiol. 48247–255 (2013).

    Papers PubMed Google Scholar

  • Divine, MR et al. A population-based Gaussian mixture model incorporating 18F-FDG PET and diffusion-weighted MRI quantifies tumor tissue class. J. Nucl. medicine. 57473–479 (2016).

    Papers CAS PubMed Google Scholar

  • Kim, J., Ryu, SY, Lee, SH, Lee, HY, Park, H. A clustering approach to identify intratumoral heterogeneity by combining FDG PET and diffusion-weighted MRI in lung adenocarcinoma. EUR. Radiol. 29468–475 (2019).

    Papers PubMed Google Scholar

  • Stoyanova, R. et al. Association between multiparametric MRI quantitative imaging features and prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget 753362–53376 (2016).

    Papers PubMed PubMed Central Google Scholar

  • Katiyar, P. et al. A novel unsupervised segmentation approach uses multiparameter MRI to quantify tumor tissue populations. The first result is histological verification. Mole. imaging biol. 19391–397 (2017).

    Papers PubMed Google Scholar

  • Katiyar, P. et al. Spectral clustering predicts tumor tissue heterogeneity using dynamic 18 F-FDG PET that complements standard compartmental modeling approaches. J. Nucl. medicine. 58651–657 (2017).

    Papers CAS PubMed Google Scholar

  • Carano, RAD et al. Quantification of tumor tissue populations by multispectral analysis. Magnitude Raison. medicine. 51542–551 (2004).

    Papers PubMed Google Scholar

  • Berry, LR et al. Quantification of microvascular properties of viable tumors by multispectral analysis. Magnitude Raison. medicine. 6064–72 (2008).

    Papers PubMed Google Scholar

  • Barck, KH et al. Detection of viable tumor tissue in mouse metastatic breast cancer by whole-body MRI and multispectral analysis. Magnitude Raison. medicine. 621423–1430 (2009).

    Papers PubMed Google Scholar

  • Henning, EC, Azuma, C., Sotak, CH & Helmer, KG Multispectral quantification of tissue types in a RIF-1 tumor model with histological validation. Part I. Magnitude Raison. medicine. 57501–512 (2007).

    Papers PubMed Google Scholar

  • Schölkopf, B. Artificial Intelligence: Learning to See and Act. Nature 518486–487 (2015).

    Papers PubMed Google Scholar

  • de Vries, EGE et al. Integrating molecular nuclear imaging into clinical research to improve anticancer therapy. nut. Pastor Klin. Onkol. 16241–255 (2019).

    Papers PubMed Google Scholar

  • Siegemund, M. et al. Superior antitumor activity of dimerized targeted single-chain TRAIL fusion proteins while retaining tumor selectivity. cell death disorder 3e295 (2012).

    Articles CAS PubMed PubMed Central Google Scholar

  • Gillies, RJ, Kinahan, PE & Hricak, H. Radiomics: Images are data, not just pictures. Radiology 278563–577 (2016).

    Papers PubMed Google Scholar

  • Arts, HJWL et al. Decoding tumor phenotypes by non-invasive imaging using a quantitative radiomics approach. nut. common. Five4006 (2014).

    Papers CAS PubMed Google Scholar

  • Kumar, V. et al. Radiomics: processes and challenges. Magnitude Raison.imaging 301234–1248 (2012).

    Papers PubMed PubMed Central Google Scholar

  • Stewart, GD, et al. Sunitinib treatment exacerbates intratumoral heterogeneity in metastatic renal cancer. Clin.Cancer Institute twenty one4212–4223 (2015).

    Papers CAS PubMed Google Scholar

  • Lee, BS et al. Induced phenotype-targeted therapy: Radiation-induced apoptosis-targeted chemotherapy. J. Natl Cancer Institute. 107dju403 (2015).

    Papers PubMed Google Scholar

  • Dieselhorst, JA et al. Linking Imaging and Omics Using Image-Guided Tissue Extraction. Procedure National Academy. Science.united states of america 115E2980–E2987 (2018).

    Articles CAS PubMed PubMed Central Google Scholar

  • Jaffray, DA Image-Guided Radiotherapy: From Current Concepts to Future Perspectives. nut. Pastor Klin. Onkol. 9688–699 (2012).

    Papers CAS PubMed Google Scholar

  • Hynynen, K. MRIgHIFU: Tools for image-guided therapy. J. Magn. Raison.imaging 34482–493 (2011).

    Papers PubMed Google Scholar

  • Reinke, A. et al. General Limitations of Image Processing Metrics: Picture Stories. Preprinted at https://arxiv.org/abs/2104.05642 (2021).

  • Button, KS et al. Blackout: Why small sample sizes undermine the reliability of neuroscience. nut. Neuroscience pastor. 14365–376 (2013).

    Papers CAS PubMed Google Scholar

  • Sequist, LV et al. Genotypic and histologic evolution of lung cancer with acquired resistance to EGFR inhibitors. Science. translation. medicine. 375ra26 (2011).

    Papers PubMed PubMed Central Google Scholar

  • Hody, FS et al. Evaluation of immune-related response criteria and RECIST v1.1 in patients with advanced melanoma treated with pembrolizumab. J. Clin. Onkol. 341510–1517 (2016).

    Articles CAS PubMed PubMed Central Google Scholar

  • Veuthey, TV, Herrera, G. & Dodero, VI Dyes and stains: from molecular structure to histological applications. front. biological sciences. 1991–112 (2014).

    Articles CAS Google Scholar

  • Gown, AM and Willingham, MC Enhanced detection of apoptotic cells in archive paraffin sections: immunohistochemistry using an antibody against cleaved caspase-3. J. Histochem. Cytochem. 50449–454 (2002).

    Papers CAS PubMed Google Scholar

  • Austyn, JM and Gordon, S. F4/80, a monoclonal antibody specifically directed against murine macrophages. EUR. J. Immunol. 11805–815 (1981).

    Papers CAS PubMed Google Scholar

  • Dubuisson, M.-P. & Jain, AK Modified Hausdorff distance for object matching.of Procedure 12th International Conference on Pattern Recognition 566–568 (IEEE, 1994).

  • Dice, LR A measure of the amount of ecological relatedness between species. ecology 26297–302 (1945).

    Articles Google Scholar

  • von Luxburg, U. A tutorial on spectral clustering. Calculate statistics. 17395–416 (2007).

    Articles Google Scholar

  • Kumar, A. & Daumé, H. A joint training approach for multi-view spectral clustering.of Procedure The 28th International Conference on Machine Learning (ICML-11) 393–400 (ACM, 2011).

  • Strobl, C., Boulesteix, A.-L., Zeileis, A. & Hothorn, T. Bias in random forest variable importance measures: figures, sources, and solutions. BMC bioinformatics 825 (2007).

    Papers PubMed PubMed Central Google Scholar



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