Lambin, Philippe

Radiomics: extracting more information from medical images using advanced feature analysis

Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 6,084

Radiomics: the bridge between medical imaging and personalized medicine

Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 5,688

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 5,132

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 4,177

Radiomics: the process and the challenges

Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) · Magnetic Resonance Imaging

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 2,258

Imaging biomarker roadmap for cancer studies

O'Connor, J. P. B., Aboagye, E. O., Adams, J. E., Aerts, H. J. W. L., Barrington, S. F., Beer, A. J., Boellaard, R., Bohndiek, S. E., Brady, M., Brown, G., et al. (2017) · Nature Reviews Clinical Oncology

Medical Imaging Techniques and ApplicationsRadiomics and Machine Learning in Medical Imaging

Cited by 1,091

The unfolded protein response protects human tumor cells during hypoxia through regulation of the autophagy genes MAP1LC3B and ATG5

Rouschop, K. M. A., van den Beucken, T., Dubois, L., Niessen, H., Bussink, J., Savelkouls, K., Keulers, T., Mujcic, H., Landuyt, W., Voncken, J. W., et al. (2010) · Journal of Clinical Investigation

Autophagy in Disease and TherapyEndoplasmic Reticulum Stress and Disease

Cited by 829

CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma

Coroller, T. P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R. T. H., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R. H., & Aerts, H. J. W. L. (2015) · Radiotherapy and Oncology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 695

Transparency of deep neural networks for medical image analysis: A review of interpretability methods

Salahuddin, Z., Woodruff, H. C., Chatterjee, A., & Lambin, P. (2022) · Computers in Biology and Medicine

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 508

Defining the biological basis of radiomic phenotypes in lung cancer

Grossmann, P., Stringfield, O., El-Hachem, N., Bui, M. M., Rios Velazquez, E., Parmar, C., Leijenaar, R. T., Haibe-Kains, B., Lambin, P., Gillies, R. J., et al. (2017) · eLife

Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and Biomarkers

Cited by 365

Radical treatment of non-small-cell lung cancer patients with synchronous oligometastases: long-term results of a prospective phase II trial (Nct01282450)

De Ruysscher, D., Wanders, R., van Baardwijk, A., Dingemans, A.-M. C., Reymen, B., Houben, R., Bootsma, G., Pitz, C., van Eijsden, L., Geraedts, W., et al. (2012) · Journal of Thoracic Oncology

Lung Cancer Diagnosis and TreatmentLung Cancer Treatments and Mutations

Cited by 283

Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma

Grove, O., Berglund, A. E., Schabath, M. B., Aerts, H. J. W. L., Dekker, A., Wang, H., Velazquez, E. R., Lambin, P., Gu, Y., Balagurunathan, Y., et al. (2015) · PLOS ONE

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

Cited by 263

Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score

Sanduleanu, S., Woodruff, H. C., de Jong, E. E. C., van Timmeren, J. E., Jochems, A., Dubois, L., & Lambin, P. (2018) · Radiotherapy and Oncology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 242

Results and adverse events of personalized peptide receptor radionuclide therapy with 90Yttrium and 177Lutetium in 1048 patients with neuroendocrine neoplasms

Baum, R. P., Kulkarni, H. R., Singh, A., Kaemmerer, D., Mueller, D., Prasad, V., Hommann, M., Robiller, F. C., Niepsch, K., Franz, H., et al. (2018) · Oncotarget

Neuroendocrine Tumor Research AdvancesRadiopharmaceutical Chemistry and Applications

Cited by 141

Is there a causal relationship between genetic changes and radiomics-based image features? an in vivo preclinical experiment with doxycycline inducible gadd34 tumor cells

Panth, K. M., Leijenaar, R. T. H., Carvalho, S., Lieuwes, N. G., Yaromina, A., Dubois, L., & Lambin, P. (2015) · Radiotherapy and Oncology

Radiomics and Machine Learning in Medical ImagingAdvanced Radiotherapy Techniques

Cited by 133

A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen

Rios Velazquez, E., Aerts, H. J. W. L., Gu, Y., Goldgof, D. B., De Ruysscher, D., Dekker, A., Korn, R., Gillies, R. J., & Lambin, P. (2012) · Radiotherapy and Oncology

Medical Image Segmentation TechniquesLung Cancer Diagnosis and Treatment

Cited by 120

Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)

Zerka, F., Urovi, V., Vaidyanathan, A., Barakat, S., Leijenaar, R. T. H., Walsh, S., Gabrani-Juma, H., Miraglio, B., Woodruff, H. C., Dumontier, M., et al. (2020) · IEEE Access

Blockchain Technology Applications and SecurityPrivacy-Preserving Technologies in Data

Cited by 89

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., Tsakou, G., Aussó, S., Alberich, L. C., Marias, K., Tsiknakis, M., et al. (2021) · arXiv

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 58

Mitochondrial Dysfunction Inhibits Hypoxia-Induced HIF-1α Stabilization and Expression of Its Downstream Targets

van Gisbergen, M. W., Offermans, K., Voets, A. M., Lieuwes, N. G., Biemans, R., Hoffmann, R. F., Dubois, L. J., & Lambin, P. (2020) · Frontiers in Oncology

Cancer, Hypoxia, and MetabolismEnzyme function and inhibition

Cited by 30

Relocatable fixation systems in intracranial stereotactic radiotherapy: Accuracy of serial CT scans and patient acceptance in a randomized design

Theelen, A., Martens, J., Bosmans, G., Houben, R., Jager, J. J., Rutten, I., Lambin, P., Minken, A. W., & Baumert, B. G. (2012) · Strahlentherapie und Onkologie

Advanced Radiotherapy TechniquesBrain Metastases and Treatment

Cited by 18