Decoding global gene expression programs in liver cancer by noninvasive imaging

Gene Ontology: tool for the unification of biology

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206

Gene expression profiling predicts clinical outcome of breast cancer

van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536

A Gene-Expression Signature as a Predictor of Survival in Breast Cancer

van de Vijver, M. J., He, Y. D., Veer, L. V. ‘., Dai, H. A., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa021967 · Cited by 6,474

A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer

Paik, S., Shak, S., Tang, G., Kim, C., Baker, J. B., Cronin, M. T., Baehner, F. L., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,321

Gene Expression and Benefit of Chemotherapy in Women With Node-Negative, Estrogen Receptor–Positive Breast Cancer

Paik, S., Tang, G., Shak, S., Kim, C., Baker, J. B., Kim, W., Cronin, M. T., Baehner, F. L., Watson, D., Bryant, J., et al. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2005.04.7985 · Cited by 2,597

Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., & Friedman, N. (2003) · Nature Genetics · doi:10.1038/ng1165 · Cited by 1,678

Gene expression profiling identifies clinically relevant subtypes of prostate cancer

Lapointe, J., Li, C., Higgins, J., van de Rijn, M., Bair, E., Montgomery, K. D., Ferrari, M. K., Egevad, L., Rayford, W., Bergerheim, U. S. R., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0304146101 · Cited by 1,282

Use of Gene-Expression Profiling to Identify Prognostic Subclasses in Adult Acute Myeloid Leukemia

Bullinger, L., Döhner, K., Bair, E., Fröhling, S., Schlenk, R. F., Tibshirani, R. J., Döhner, H., & Pollack, J. R. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa031046 · Cited by 992

Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival

Chang, H. Y., Nuyten, D. S. A., Sneddon, J. B., Hastie, T., Tibshirani, R., Sørlie, T., Dai, H., He, Y. D., van't Veer, L. J., Bartelink, H., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409462102 · Cited by 981

MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study

Leporq, B., Bouhamama, A., Pilleul, F., Lame, F., Bihane, C., Sdika, M., Blay, J., & Beuf, O. (2020) · Cancer Imaging · doi:10.1186/s40644-020-00354-7 · Cited by 43

Molecular imaging of breast cancer: present and future directions

Alcántara, D., Leal, M. P., García-Bocanegra, I., & García‐Martín, M. L. (2014) · Frontiers in Chemistry · doi:10.3389/fchem.2014.00112 · Cited by 43

Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment

Larkin, T. J., Canuto, H. C., Kettunen, M. I., Booth, T. C., Hu, D., Krishnan, A. S., Bohndiek, S. E., Neves, A., McLachlan, C., Hobson, M., et al. (2013) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24644 · Cited by 44

Radiogenomic Analysis Demonstrates Associations between18F-Fluoro-2-Deoxyglucose PET, Prognosis, and Epithelial-Mesenchymal Transition in Non–Small Cell Lung Cancer

Yamamoto, S., Huang, D., Du, L., Korn, R. L., Jamshidi, N., Burnette, B., & Kuo, M. (2016) · Radiology · doi:10.1148/radiol.2016160259 · Cited by 43

Development of a Patient-specific Tumor Mold Using Magnetic Resonance Imaging and 3-Dimensional Printing Technology for Targeted Tissue Procurement and Radiomics Analysis of Renal Masses

Dwivedi, D. K., Chatzinoff, Y., Zhang, Y., Yuan, Q., Fulkerson, M., Chopra, R., Brugarolas, J., Cadeddu, J. A., Kapur, P., & Pedrosa, I. (2017) · Urology · doi:10.1016/j.urology.2017.08.056 · Cited by 42

Radiomic Profiling of Head and Neck Cancer: 18F-FDG PET Texture Analysis as Predictor of Patient Survival

Feliciani, G., Fioroni, F., Grassi, E., Bertolini, M., Rosca, A., Timon, G., Galaverni, M., Iotti, C., Versari, A., Iori, M., et al. (2018) · Contrast Media & Molecular Imaging · doi:10.1155/2018/3574310 · Cited by 42

Radiomics-based predictive risk score: A scoring system for preoperatively predicting risk of lymph node metastasis in patients with resectable non-small cell lung cancer

He, L., Huang, Y., Yan, L., Zheng, J., Liang, C., & Liu, Z. (2019) · Chinese Journal of Cancer Research · doi:10.21147/j.issn.1000-9604.2019.04.08 · Cited by 42

Gallbladder cancer revisited: the evolving role of a radiologist

Ramachandran, A., Srivastava, D. N., & Madhusudhan, K. S. (2020) · British Journal of Radiology · doi:10.1259/bjr.20200726 · Cited by 42

Current status and future perspectives of radiomics in hepatocellular carcinoma

Miranda, J., Horvat, N. D. S. M. R., Fonseca, G. M., Araújo-Filho, J. D. A. B., Fernandes, M. C., Charbel, C., Chakraborty, J., Coelho, F. F., Nomura, C. H., & Herman, P. (2023) · World Journal of Gastroenterology · doi:10.3748/wjg.v29.i1.43 · Cited by 42

Radiomics: a critical step towards integrated healthcare

Bodalal, Z., Trebeschi, S., & Beets‐Tan, R. G. H. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0669-3 · Cited by 41

Image-based biomarkers for solid tumor quantification

Savadjiev, P., Chong, J., Dohan, A. J. J., Agnus, V., Forghani, R., Reinhold, C., & Gallix, B. (2019) · European Radiology · doi:10.1007/s00330-019-06169-w · Cited by 42

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

Castiglioni, I., Gallivanone, F., Soda, P., Avanzo, M., Stancanello, J., Aiello, M., Interlenghi, M., & Salvatore, M. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04414-4 · Cited by 42

Application of Radiomics for Personalized Treatment of Cancer Patients

Meng, Y., Sun, J. Y., Qu, N., Zhang, G., Yu, T., & Piao, H. (2019) · Cancer Management and Research · doi:10.2147/cmar.s232473 · Cited by 41

PVE Correction in PET-CT Whole-Body Oncological Studies From PVE-Affected Images

Gallivanone, F., Stefano, A., Grosso, E., Canevari, C., Gianolli, L., Messa, C., Gilardi, M. C., & Castiglioni, I. (2011) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2011.2108316 · Cited by 41

The age of reason for FDG PET image-derived indices

Visvikis, D., Hatt, M., Tixier, F., & Rest, C. C. L. (2012) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-012-2239-0 · Cited by 39

New Paradigm for Management of Hepatocellular Carcinoma by Imaging

Joo, I., & Choi, B. I. (2012) · Liver Cancer · doi:10.1159/000342404 · Cited by 38

Rapid development of image analysis research tools: Bridging the gap between researcher and clinician with pyOsiriX

Blackledge, M., Collins, D. J., Koh, D., & Leach, M. O. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.12.002 · Cited by 39

Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma

Veeraraghavan, H., Vargas, H., Jiménez-Sánchez, A., Miccò, M., Mema, E., Lakhman, Y., Crispin‐Ortuzar, M., Huang, E. P., Levine, D. A., Grisham, R. N., et al. (2020) · Cancers · doi:10.3390/cancers12113403 · Cited by 38

Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma

Wu, C., Chen, J., Fan, Y., Zhao, M. D., He, X., Wei, Y., Ge, W., & Liu, Y. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.943942 · Cited by 38

Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group

Olsen, A., Babikian, T., Bigler, E. D., Caeyenberghs, K., Conde, V., Dams-O’Connor, K., Dobryakova, E., Genova, H. M., Grafman, J. H., Haberg, A. K., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00313-7 · Cited by 37

Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer

Zeng, S., Wang, X., & Yang, H. (2024) · Military Medical Research · doi:10.1186/s40779-024-00580-1 · Cited by 41

Identification of biomarkers for pseudo and true progression of GBM based on radiogenomics study

Qian, X., Tan, H., Zhang, J., Liu, K., Yang, T., Wang, M., Debinskie, W., Zhao, W., Chan, M. D., & Zhou, X. (2016) · Oncotarget · doi:10.18632/oncotarget.10553 · Cited by 36

Potential Complementary Value of Noncontrast and Contrast Enhanced CT Radiomics in Colorectal Cancers

Badic, B., Desseroit, M.-C., Hatt, M., & Visvikis, D. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.06.004 · Cited by 36

Preoperative MR imaging for predicting early recurrence of solitary hepatocellular carcinoma without microvascular invasion

Wei, Y., Pei, W., Qin, Y., Su, D., & Liao, H. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109663 · Cited by 36

Evaluation of perfusion CT and dual-energy CT for predicting microvascular invasion of hepatocellular carcinoma

Lewin, M., Laurent-Bellue, A., Desterke, C., Radu, A., Feghali, J. A., Farah, J., Agostini, H., Nault, J., Vibert, É., & Guettier, C. (2022) · Abdominal Radiology · doi:10.1007/s00261-022-03511-7 · Cited by 36

CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer

Wan, S., Zhou, T., Che, R., Li, Y., Peng, J., Wu, Y., Gu, S., Cheng, J., & Hua, X. (2023) · Journal of Ovarian Research · doi:10.1186/s13048-022-01089-8 · Cited by 36

The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial

Jamshidi, N., Jonasch, E. W., Zapala, M. A., Korn, R. L., Brooks, J. D., Ljungberg, B., & Kuo, M. (2015) · European Radiology · doi:10.1007/s00330-015-4082-8 · Cited by 36

The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

Masokano, I. B., Liu, W., Xie, S., Marcellin, D. F. H., Pei, Y., & Li, W. (2020) · Cancer Imaging · doi:10.1186/s40644-020-00341-y · Cited by 35

Comparison of imaging findings of macrotrabecular-massive hepatocellular carcinoma using CT and gadoxetic acid–enhanced MRI

Cha, H., Choi, J., Park, Y. N., Han, K., Jang, M., Kim, M., Park, M., & Rhee, H. (2022) · European Radiology · doi:10.1007/s00330-022-09105-7 · Cited by 35

Next Generation Radiologic-Pathologic Correlation in Oncology: Rad-Path 2.0

Kuo, M., & Yamamoto, S. (2011) · American Journal of Roentgenology · doi:10.2214/ajr.11.7163 · Cited by 35

CT Image-based Decision Support System for Categorization of Liver Metastases Into Primary Cancer Sites

Ben-Cohen, A., Klang, E., Diamant, I., Rozendorn, N., Raskin, S., Konen, E., Amitai, M. M., & Greenspan, H. K. (2017) · Academic Radiology · doi:10.1016/j.acra.2017.06.008 · Cited by 35

Radiomics-based nomogram using CT imaging for noninvasive preoperative prediction of early recurrence in patients with hepatocellular carcinoma

Zhu, H., Zheng, Z., Zhao, H., Zhang, J., Zhu, H., Li, Y., Dong, Z., Xiao, L., Kuang, J., Zhang, X., et al. (2020) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2020.19623 · Cited by 34

The use of molecular imaging combined with genomic techniques to understand the heterogeneity in cancer metastasis

Chowdhury, R., Ganeshan, B., Irshad, S., Lawler, K., Eisenblätter, M., Milewicz, H., Rodriguez‐Justo, M., Miles, K. A., Ellis, P., Groves, A. M., et al. (2014) · BJR|case reports · doi:10.1259/bjr.20140065 · Cited by 34

Signal Processing Challenges in Quantitative 3-D Cell Morphology: More than meets the eye

Dufour, A., Liu, T., Gérardin, C., Tournemenne, R., Cummings, B. B., Thibeaux, R., Guillén, N., Hero, A. O., & Olivo‐Marín, J. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2359131 · Cited by 34

Multiregional Radiogenomic Assessment of Prostate Microenvironments with Multiparametric MR Imaging and DNA Whole-Exome Sequencing of Prostate Glands with Adenocarcinoma

Jamshidi, N., Margolis, D., Raman, S. S., Huang, J., Reiter, R. E., & Kuo, M. (2017) · Radiology · doi:10.1148/radiol.2017162827 · Cited by 35

Evolution of lymphoma staging and response evaluation: current limitations and future directions

Cunningham, J., Iyengar, S., & Sharma, B. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.78 · Cited by 33

Non-invasive and real-time proliferative activity estimation based on a quantitative radiomics approach for patients with acromegaly: a multicenter study

Fan, Y. G., Chai, Y., Li, K., Fang, H., Mou, A., Feng, S., Feng, M., & Wang, R.-Z. (2019) · Journal of Endocrinological Investigation · doi:10.1007/s40618-019-01159-7 · Cited by 33

Cholangiocarcinoma: new perspectives for new horizons

Rimini, M., Puzzoni, M., Pedica, F., Silvestris, N., Fornaro, L., Aprile, G., Loi, E., Brunetti, O., Vivaldi, C., Simionato, F., et al. (2021) · Expert Review of Gastroenterology & Hepatology · doi:10.1080/17474124.2021.1991313 · Cited by 36

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning

Jaberipour, M., Soliman, H., Sahgal, A., & Naini, A. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-01024-9 · Cited by 35

Hepatocellular Carcinoma

Bargellini, I., Coletti, L., & Lorenzoni, G. (2018) · Cancer dissemination pathways · doi:10.1007/978-3-319-50296-0_3 · Cited by 33

A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration

Tan, M., Li, Z., Qiu, Y., McMeekin, S., Thai, T., Ding, K., Moore, K. N., Liu, H., & Zheng, B. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2473823 · Cited by 32

Radiomics and imaging genomics in precision medicine

Lee, G., Lee, H. Y., Ko, E. S., & Jeong, W. K. (2017) · Precision and Future Medicine · doi:10.23838/pfm.2017.00101 · Cited by 36

Validated preoperative computed tomography risk estimation for postoperative hepatocellular carcinoma recurrence

Zhang, W., Lai, S., Chen, J., Xie, D., Wu, F., Jin, G., & Su, D. (2017) · World Journal of Gastroenterology · doi:10.3748/wjg.v23.i35.6467 · Cited by 32

Radiomics and Texture Analysis in Laryngeal Cancer. Looking for New Frontiers in Precision Medicine through Imaging Analysis

Chiesa-Estomba, C. M., Echaniz, O., Larruscain, E., González-García, J. Á., Sistiaga-Suárez, J. A., & Graña, M. (2019) · Cancers · doi:10.3390/cancers11101409 · Cited by 31

Application of radiomics signature captured from pretreatment thoracic CT to predict brain metastases in stage III/IV ALK-positive non-small cell lung cancer patients

Xu, X., Huang, L., Chen, J., Wen, J., Liu, D., Cao, J., Wang, J., & Fan, M. (2019) · Journal of Thoracic Disease · doi:10.21037/jtd.2019.11.01 · Cited by 31

Radiomics: an overview in lung cancer management—a narrative review

Chen, B., Yang, L., Zhang, R., Luo, W., & Li, W. (2020) · Annals of Translational Medicine · doi:10.21037/atm-20-4589 · Cited by 31

Prediction of microvascular invasion of hepatocellular carcinoma: value of volumetric iodine quantification using preoperative dual-energy computed tomography

Kim, T. M., Lee, J. M., Yoon, J. H., Joo, I., Park, S., Jeon, S. K., Schmidt, B. T., & Sedlmair, M. (2020) · Cancer Imaging · doi:10.1186/s40644-020-00338-7 · Cited by 31

Imaging genomics: data fusion in uncovering disease heritability

Hartmann, K., Sadée, C., Satwah, I., Carrillo‐Pérez, F., & Gevaert, O. (2022) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2022.11.002 · Cited by 33

Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging

Bernatz, S., Zhdanovich, Y., Ackermann, J., Koch, I., Wild, P. J., dos Santos, D. P., Vogl, T. J., Kaltenbach, B., & Rosbach, N. (2021) · Scientific Reports · doi:10.1038/s41598-021-93756-x · Cited by 30

Robustness of dual-energy CT-derived radiomic features across three different scanner types

Lennartz, S., O’Shea, A., Parakh, A., ten Persigehl, T., Baeßler, B., & Kambadakone, A. R. (2021) · European Radiology · doi:10.1007/s00330-021-08249-2 · Cited by 30

A radiomics signature associated with underlying gene expression pattern for the prediction of prognosis and treatment response in hepatocellular carcinoma

Wang, D., Zhang, L., Sun, Z., Jiang, H., & Zhang, J. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.111086 · Cited by 30

Model Matters: Differences in Orthotopic Rat Hepatocellular Carcinoma Physiology Determine Therapy Response to Sorafenib

Gross, C., Steiger, K., Sayyed, S., Heid, I., Feuchtinger, A., Walch, A., Hess, J., Unger, K., Zitzelsberger, H., Settles, M., et al. (2015) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-14-2018 · Cited by 25

Définition et applications cliniques des radiomics

Bourgier, C., Colinge, J., Aillères, N., Fenoglietto, P., Brengues, M., Pèlegrin, A., & Azria, D. (2015) · Cancer/Radiothérapie · doi:10.1016/j.canrad.2015.06.008 · Cited by 29

Prediction of Microvascular Invasion in Solitary AFP-Negative Hepatocellular Carcinoma ≤ 5 cm Using a Combination of Imaging Features and Quantitative Dual-Layer Spectral-Detector CT Parameters

Zhu, Y., Feng, B., Cai, W., Wang, B., Meng, X., Wang, S., Ma, X., & Zhao, X. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.02.015 · Cited by 29

An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer

Lenkinski, R. E., Bloch, B., Liu, F., Frangioni, J. V., Perner, S., Rubin, M. A., Genega, E. M., Rofsky, N. M., & Gaston, S. M. (2008) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-008-0133-3 · Cited by 29

Use of DNA Microarray and Small Animal Positron Emission Tomography in Preclinical Drug Evaluation of RAF265, a Novel B-Raf/VEGFR-2 Inhibitor

Tseng, J. R., Stuart, D. D., Aardalen, K., Kaplan, A., Aziz, N. A., Hughes, N. P., & Gambhir, S. S. (2011) · Neoplasia · doi:10.1593/neo.101466 · Cited by 28

Repeatability of Cardiac Magnetic Resonance Radiomics: A Multi-Centre Multi-Vendor Test-Retest Study

Raisi‐Estabragh, Z., Gkontra, P., Jaggi, A., Cooper, J. A., Augusto, J. B., Bhuva, A., Davies, R. H., Manisty, C., Moon, J., Munroe, P. B., et al. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.586236 · Cited by 28

CAM-CM: a signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues

Chen, L., Chan, T., Choyke, P. L., Hillman, E. M. C., Chi, C., Bhujwalla, Z. M., Wang, G., Wang, S. S., Szabó, Z. R., & Wang, Y. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr436 · Cited by 28

Deep Fusion Models of Multi-Phase CT and Selected Clinical Data for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma

Wang, W., Chen, Q., Iwamoto, Y., Aonpong, P., Lin, L., Hu, H., Zhang, Q., & Chen, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3011145 · Cited by 27

Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs

Li, X., Qi, Z., Du, H., Geng, Z., Li, Z., Qin, S., Zhang, X., Liang, J., Zhang, X., Liang, W., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08198-w · Cited by 27

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions

Gallivanone, F., Bertoli, G. R., & Porro, D. (2022) · Methods and Protocols · doi:10.3390/mps5050078 · Cited by 27

AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD

Gu, P., Mendonca, O., Carter, D., Dube, S., Wang, Z. P., Huang, X., Li, D., Moore, J. H., & McGovern, D. P. B. (2024) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izae030 · Cited by 27

Cancer Genomics and Important Oncologic Mutations: A Contemporary Guide for Body Imagers

Cox, V. L., Bhosale, P., Varadhachary, G. R., Wagner‐Bartak, N. A., Glitza, I. C., Gold, K. A., Atkins, J. T., Soliman, P. T., Hong, D. S., & Qayyum, A. (2017) · Radiology · doi:10.1148/radiol.2017152224 · Cited by 27

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept

Vallières, M., Laberge, S., Diamant, A., & Naqa, I. E. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa8a49 · Cited by 26

Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol

Monti, S., Brancato, V., Costanzo, G., Basso, L., Puglia, M., Ragozzino, A., Salvatore, M., & Cavaliere, C. (2020) · Cancers · doi:10.3390/cancers12020390 · Cited by 26

Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation

Shen, J., Zhou, Q., Chen, Z., Chen, Q., Chen, S., Feng, S., Li, X. S., Wu, T., Peng, S., & Kuang, M. (2020) · Translational Oncology · doi:10.1016/j.tranon.2020.100866 · Cited by 26

Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status

Liu, X., Elbanan, M., Luna, A., Haider, M. A., Smith, A. D., Sabottke, C. F., Spieler, B. M., Türkbey, B., Fuentes, D., Moawad, A. W., et al. (2022) · American Journal of Roentgenology · doi:10.2214/ajr.22.27695 · Cited by 27

DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference

Jiao, Y., Lawler, K., Patel, G. S., Purushotham, A. D., Jones, A. F., Grigoriadis, A. E., Tutt, A. N. J., Ng, T., & Teschendorff, A. E. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-403 · Cited by 26

Detailed identification of epidermal growth factor receptor mutations in lung adenocarcinoma: Combining radiomics with machine learning

Li, S., Luo, T., Ding, C., Huang, Q.-L., Guan, Z., & Zhang, H. (2020) · Medical Physics · doi:10.1002/mp.14238 · Cited by 25

Radiogenomics in Interventional Oncology

Moussa, A. M., & Ziv, E. (2021) · Current Oncology Reports · doi:10.1007/s11912-020-00994-9 · Cited by 26

Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response

Borhani, A. A., Catania, R., Velichko, Y., Hectors, S. J. C. G., Taouli, B. A., & Lewis, S. C. (2021) · Abdominal Radiology · doi:10.1007/s00261-021-03085-w · Cited by 25

Nomograms for prediction of breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: A single-center retrospective study based on radiomics features

Hong, Z.-L., Chen, S., Peng, X.-R., Li, J., Yang, J.-C., & Wu, S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.894476 · Cited by 25

Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma

Yang, J., Dong, X., Wang, G., Chen, J., Zhang, B., Pan, W., Zhang, H., Jin, S., & Ji, W. (2022) · Abdominal Radiology · doi:10.1007/s00261-022-03740-w · Cited by 25

Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma

Wei, J., Jiang, H., Zhou, Y., Tian, J., Furtado, F., & Catalano, O. A. (2023) · Digestive and Liver Disease · doi:10.1016/j.dld.2022.12.015 · Cited by 25

Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer

Libling, W. A., Korn, R. L., & Weiss, G. J. (2023) · Translational Lung Cancer Research · doi:10.21037/tlcr-23-5 · Cited by 25

Integrating Radio Imaging With Gene Expressions Toward a Personalized Management of Cancer

Mitra, S., & Shankar, B. U. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2325744 · Cited by 25

An Empirical Approach for Avoiding False Discoveries When Applying High-Dimensional Radiomics to Small Datasets

Chatterjee, A., Vallières, M., Dohan, A. J. J., Levesque, I. R., Ueno, Y. R., Bist, V., Saif, S., Reinhold, C., & Seuntjens, J. P. (2018) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2018.2880617 · Cited by 25

Radiological Diagnosis and Characterization of HCC

Violi, N. V., Lewis, S. C., Hectors, S. J. C. G., Said, D., & Taouli, B. A. (2019) · Molecular and translational medicine · doi:10.1007/978-3-030-21540-8_4 · Cited by 24

Deep Learning With Radiogenomics Towards Personalized Management of Gliomas

Mitra, S. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3075500 · Cited by 26

The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma

Tang, Y., Zhang, T., Zhou, X., Zhao, Y., Xu, H., Liu, Y., Wang, H., Chen, Z., & Ma, X. (2021) · World Journal of Surgical Oncology · doi:10.1186/s12957-021-02162-0 · Cited by 24

The quest for standards in medical imaging

Bernard Gibaud, A. (2010) · European Journal of Radiology · doi:10.1016/j.ejrad.2010.05.003 · Cited by 24

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Tixier, F., Rest, C. C. L., Schick, U., Simon, B., Dufour, X., Key, S., Pradier, O., Aubry, M., Hatt, M., Corcos, L., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-62414-z · Cited by 24

Hepatocellular Carcinoma

Hesketh, R. L., Zhu, A. X., & Öklü, R. (2014) · American Journal of Clinical Oncology · doi:10.1097/coc.0000000000000123 · Cited by 23

In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis

Gallivanone, F., Cava, C., Corsi, F., Bertoli, G. R., & Castiglioni, I. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20235825 · Cited by 23

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies

Doran, S., Sa’d, M. A., Petts, J., d’Arcy, J. A., Alpert, K., Cho, W., Sánchez, L. E., Alle, S., Harouni, A., Genereaux, B. W., et al. (2022) · Tomography · doi:10.3390/tomography8010040 · Cited by 22

Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study

Xie, C., Hu, Y., Ho, J. W. K., Han, L., Yang, H., Wen, J., Lam, K. O., Wong, Y., Law, S. Y.-K., Chiu, K. W.-H., et al. (2021) · Cancers · doi:10.3390/cancers13092145 · Cited by 22

A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer

Cai, D., Duan, X., Wang, W., Huang, Z.-P., Zhu, Q., Zhong, M., Lv, M., Li, C., Kou, W.-B., Wu, X., et al. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.613918 · Cited by 22

Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges

Hassanien, A. E., Milanova, M. G., Smolinski, T. G., & Abraham, A. (2009) · Studies in computational intelligence · doi:10.1007/978-3-540-70778-3_1 · Cited by 22

High-dimensional biology to comprehend hepatocellular carcinoma

Vivekanandan, P., & Singh, O. V. (2008) · Expert Review of Proteomics · doi:10.1586/14789450.5.1.45 · Cited by 22

Rethinking future development of molecular therapies in hepatocellular carcinoma: A bottom-up approach

Villanueva, A. (2013) · Journal of Hepatology · doi:10.1016/j.jhep.2013.03.025 · Cited by 21

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

Coates, J. T. T., Pirovano, G., & Naqa, I. E. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.3.031902 · Cited by 21

Radiomics Signature: A potential biomarker for the prediction of survival in Advanced Hepatocellular Carcinoma

Li, L., Kan, X., Zhao, Y., Liang, B., Ye, T., Yang, L., & Zheng, C. (2021) · International Journal of Medical Sciences · doi:10.7150/ijms.55510 · Cited by 21

Towards interpretable imaging genomics analysis: Methodological developments and applications

Cen, X., Dong, W., Lv, W., Zhao, Y. C., Dubee, F., Mentis, A. A., Jovic, D., Yang, H., & Li, Y. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102032 · Cited by 21

Radiomics and circulating tumor cells: personalized care in hepatocellular carcinoma?

Hesketh, R. L., Zhu, A. X., & Öklü, R. (2014) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2014.14237 · Cited by 21

Prediction of sorafenib treatment–related gene expression for hepatocellular carcinoma: preoperative MRI and histopathological correlation

Dong, Z., Huang, K., Liao, B., Cai, H., Dong, Y., Huang, M., Zhou, X., Jia, Y., Xu, L., Luo, Y., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5882-4 · Cited by 21

A novel signature based on microvascular invasion predicts the recurrence of HCC

Du, B., Wang, F., Jarad, B., Wang, Z., & Zhang, Y. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02432-7 · Cited by 21

Systematic imaging in medicine: a comprehensive review

Zhang, K., Sun, Y., Wu, S., Zhou, M., Zhang, X., Zhou, R., Zhang, T., Gao, Y., Chen, T., Chen, Y., et al. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05107-z · Cited by 21

Facile Fabrication of BiF3: Ln (Ln = Gd, Yb, Er)@PVP Nanoparticles for High-Efficiency Computed Tomography Imaging

Xie, J., Zhou, Z., Ma, S., Luo, X., Liu, J., Wang, S., Chen, Y., Yan, J., & Luo, F. (2021) · Nanoscale Research Letters · doi:10.1186/s11671-021-03591-2 · Cited by 20

CT-based radiomics model for preoperative prediction of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt

Cheng, S., Yu, X., Chen, X., Jin, Z., Xue, H., Wang, Z., & Xie, P. (2022) · British Journal of Radiology · doi:10.1259/bjr.20210792 · Cited by 20

Investigation of the Inter‐ and Intrascanner Reproducibility and Repeatability of Radiomics Features in T1 ‐Weighted Brain MRI

Mitchell-Hay, R., Ahearn, T. S., Murray, A. D., & Waiter, G. D. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28191 · Cited by 20

Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data—a preliminary study

Xia, W., Chen, Y., Zhang, R., Yan, Z., Zhou, X., Zhang, B., & Gao, X. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aaa609 · Cited by 63

Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results

Taouli, B. A., Hoshida, Y., Kakite, S., Chen, X., Tan, P. S., Sun, X., Kihira, S., Kojima, K., Toffanin, S., Fiel, M. I., et al. (2017) · European Radiology · doi:10.1007/s00330-017-4844-6 · Cited by 55

Delineation of prognostic biomarkers from the global gene expression profile of liver cancer (Abstract)

Cheung, S., Chen, X., Fong, D., Leung, K., Ip, Y., Ng, I. O., So, S., & Fan, S. (2004)

Gene expression liver cancer from TCGA

González, J. R. (2021) · Figshare · doi:10.6084/m9.figshare.13560578

Radiogenomic Analysis to Identify Imaging Phenotypes Associated with Drug Response Gene Expression Programs in Hepatocellular Carcinoma

Kuo, M. D., Gollub, J., Sirlin, C. B., Ooi, C., & Chen, X. (2007) · Journal of Vascular and Interventional Radiology · doi:10.1016/j.jvir.2007.04.031 · Cited by 168