Radiogenomics: bridging the gap between imaging and genomics for precision oncology

Abstract

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Keywords

artificial intelligence / oncology / precision medicine / radiogenomics / radiomics

References

[1] Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021; 71(3): 209-249.

[2] Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009; 458(7239): 719-724.

[3] De Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell. 2023; 41(3): 374-403.

[4] Akhoundova D, Rubin MA. Clinical application of advanced multi-omics tumor profiling: shaping precision oncology of the future. Cancer Cell. 2022; 40(9): 920-938.

[5] Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016; 278(2): 563-577.

[6] Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017; 14(12): 749-762.

[7] Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology. 2018; 287(3): 732-747.

[8] Hartmann K, Sadée CY, Satwah I, Carrillo-Perez F, Gevaert O. Imaging genomics: data fusion in uncovering disease heritability. Trends Mol Med. 2023; 29(2): 141-151.

[9] Li ZC, Yan J, Zhang S, et al. Glioma survival prediction from whole-brain MRI without tumor segmentation using deep attention network: a multicenter study. Eur Radiol. 2022; 32(8): 5719-5729.

[10] Wang S, Yu H, Gan Y, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health. 2022; 4(5): e309-e319.

[11] Van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017; 77(21): e104-e107.

[12] Van der Voort SR, Incekara F, Wijnenga MMJ, et al. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning. Neuro-Oncol. 2023; 25(2): 279-289.

[13] Decuyper M, Bonte S, Deblaere K, Van Holen R. Automated MRI based pipeline for segmentation and prediction of grade, IDH mutation and 1p19q co-deletion in glioma. Comput Med Imaging Graph. 2021; 88: 101831. October 2020.

[14] Zhang B, Wu X, Zhang S, et al. Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients. Published online February 20, 2024.

[15] Wei Y, Chen X, Zhu L, et al. Multi-modal learning for predicting the genotype of glioma. IEEE Trans Med Imaging. 2023. PP.

[16] Yang N, Xiao X, Gu G, et al. Diffusion MRI-based connectomics features improve the noninvasive prediction of H3K27M mutation in brainstem gliomas. Radiother Oncol. 2023; 186: 109789.

[17] Ismail M, Prasanna P, Bera K, et al. Radiomic deformation and textural heterogeneity (R-depth) descriptor to characterize tumor field effect: application to survival prediction in glioblastoma. IEEE Trans Med Imaging. 2022; 41(7): 1764-1777.

[18] Liu X, Zhang Q, Li J, et al. Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas. Eur Radiol. 2023;33(12):8776-8787.

[19] Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol. 2023; 24(10): 695-713.

[20] Nam AS, Chaligne R, Landau DA. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat Rev Genet. 2021; 22(1): 3-18.

[21] Siravegna G, Marsoni S, Siena S, Bardelli A. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol. 2017; 14(9): 531-548.

[22] Passaro A, Al Bakir M, Hamilton EG, et al. Cancer biomarkers: emerging trends and clinical implications for personalized treatment. Cell. 2024; 187(7): 1617-1635.

[23] Li MM, Cottrell CE, Pullambhatla M, et al. Assessments of somatic variant classification using the Association for Molecular Pathology/American Society of Clinical Oncology/College of American Pathologists Guidelines: a Report from the Association for Molecular Pathology. J Mol Diagn. 2023; 25(2): 69-86.

[24] Jose A, Kulkarni P, Thilakan J, et al. Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine. Mol Cancer. 2024; 23(1): 50.

[25] Bressan D, Battistoni G, Hannon GJ. The dawn of spatial omics. Science. 2023; 381(6657): eabq4964.

[26] Nikanjam M, Kato S, Kurzrock R. Liquid biopsy: current technology and clinical applications. J Hematol Oncol. 2022; 15(1): 131.

[27] Wang H, Zhang Y, Zhang H, et al. Liquid biopsy for human cancer: cancer screening, monitoring, and treatment. Medcomm. 2024; 5(6): e564.

[28] Holder AM, Dedeilia A, Sierra-Davidson K, et al. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer. 2024; 24(7): 498-512.

[29] Kagawa Y, Smith JJ, Fokas E, et al. Future direction of total neoadjuvant therapy for locally advanced rectal cancer. Nat Rev Gastroenterol Hepatol. 2024; 21(6): 444-455.

[30] Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018; 15(2): 81-94.

[31] Liang Y, Zhang H, Song X, Yang Q. Metastatic heterogeneity of breast cancer: molecular mechanism and potential therapeutic targets. Semin Cancer Biol. 2020; 60: 14-27.

[32] Zhu S, Lu Y, Fei X, Shen K, Chen X. Pathological complete response, category change, and prognostic significance of HER2-low breast cancer receiving neoadjuvant treatment: a multicenter analysis of 2489 cases. Br J Cancer. 2023; 129(8): 1274-1283.

[33] Zhang L, Wang R, Gao J, et al. A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma. Eur Radiol. 2024;34(1):391-399.

[34] Ma C, Wang L, Song D, et al. Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study. BMC Med. 2023; 21(1): 198.

[35] Duan J, Zhang Z, Chen Y, et al. Imaging phenotypes from MRI for the prediction of glioma immune subtypes from RNA sequencing: a multicenter study. Mol Oncol. 2023; 17(4): 629-646.

[36] Zhang L, Pan H, Liu Z, et al. Multicenter clinical radiomics-integrated model based on [(18)F]FDG PET and multi-modal MRI predict ATRX mutation status in IDH-mutant lower-grade gliomas. Eur Radiol. 2023; 33(2): 872-883.

[37] Sha Y, Yan Q, Tan Y, Wang X, Zhang H, Yang G. Prediction of the molecular subtype of IDH mutation combined with MGMT promoter methylation in gliomas via radiomics based on preoperative MRI. Cancers. 2023; 15(5).

[38] Kim BH, Lee H, Choi KS, et al. Validation of MRI-based models to predict MGMT promoter methylation in gliomas: brats 2021 radiogenomics challenge. Cancers. 2022; 14(19).

[39] Cluceru J, Interian Y, Phillips JJ, et al. Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging. Neuro-Oncol. 2022; 24(4): 639-652.

[40] Wu C, Zheng H, Li J, et al. MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain. Eur Radiol. 2022; 32(3): 1813-1822.

[41] Soldatelli MD, Namdar K, Tabori U, et al. Identification of multiclass pediatric low-grade neuroepithelial tumor molecular subtype with ADC MR imaging and machine learning. AJNR Am J Neuroradiol. 2024; 45(6): 753-760.

[42] Li Y, Wei D, Liu X, et al. Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning. Eur Radiol. 2022; 32(2): 747-758.

[43] Zhuo Z, Qu L, Zhang P, et al. Prediction of H3K27M-mutant brainstem glioma by amide proton transfer-weighted imaging and its derived radiomics. Eur J Nucl Med Mol Imaging. 2021; 48(13): 4426-4436.

[44] Li Z, Kaiser L, Holzgreve A, et al. Prediction of tertp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [18F]FET PET radiomics. Eur J Nucl Med Mol Imaging. 2021; 48(13): 4415-4425.

[45] Beig N, Singh S, Bera K, et al. Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in glioblastoma. Neuro-Oncol. 2021; 23(2): 251-263.

[46] Yan J, Zhang B, Zhang S, et al. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. NPJ Precis Oncol. 2021; 5(1): 72.

[47] Qian J, Herman MG, Brinkmann DH, et al. Prediction of MGMT status for glioblastoma patients using radiomics feature extraction from 18F-DOPA-PET imaging. Int J Radiat Oncol Biol Phys. 2020; 108(5): 1339-1346.

[48] Park JE, Kim HS, Park SY, et al. Prediction of core signaling pathway by using diffusionand perfusion-based mri radiomics and next-generation sequencing in isocitrate dehydrogenase wild-type glioblastoma. Radiology. 2020; 294(2): 388-397.

[49] Buda M, Albadawy EA, Saha A, Mazurowski MA. Deep radiogenomics of lower-grade gliomas: convolutional neural networks predict tumor genomic subtypes using MR images. Radiol Artif Intell. 2020; 2(1): e180050.

[50] Choi KS, Choi SH, Jeong B. Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network. Neuro-Oncol. 2019; 21(9): 1197-1209.

[51] Zinn PO, Singh SK, Kotrotsou A, et al. A coclinical radiogenomic validation study: conserved magnetic resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models. Clin Cancer Res. 2018; 24(24): 6288-6299.

[52] Yan J, Sun Q, Tan X, et al. Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study. Eur Radiol. 2023; 33(2): 904-914.

[53] Yan J, Zhao Y, Chen Y, et al. Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities. Ebiomedicine. 2021; 72: 103583.

[54] Wang J, Zheng X, Zhang J, et al. An MRI-based radiomics signature as a pretreatment noninvasive predictor of overall survival and chemotherapeutic benefits in lower-grade gliomas. Eur Radiol. 2021; 31(4): 1785-1794.

[55] Guo J, Fathi Kazerooni A, Toorens E, et al. Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach. Sci Rep. 2024; 14(1): 4922.

[56] Sun Y, Zhang Y, Gan J, et al. Comprehensive quantitative radiogenomic evaluation reveals novel radiomic subtypes with distinct immune pattern in glioma. Comput Biol Med. 2024; 177: 108636.

[57] Chen M, Lu H, Copley SJ, et al. A novel radiogenomics biomarker for predicting treatment response and pneumotoxicity from programmed cell death protein or ligand-1 inhibition immunotherapy in NSCLC. J Thorac Oncol. 2023; 18(6): 718-730.

[58] Ju HM, Kim BC, Lim I, Byun BH, Woo SK. Estimation of an image biomarker for distant recurrence prediction in NSCLC using proliferation-related genes. Int J Mol Sci. 2023; 24(3).

[59] Shao J, Ma J, Zhang S, et al. Radiogenomic system for non-invasive identification of multiple actionable mutations and PD-L1 expression in non-small cell lung cancer based on CT images. Cancers. 2022; 14(19): 1-18.

[60] Shiri I, Amini M, Nazari M, et al. Impact of feature harmonization on radiogenomics analysis: prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images. Comput Biol Med. 2022; 142: 105230.

[61] Wang C, Ma J, Shao J, et al. Non-invasive measurement using deep learning algorithm based on multi-source features fusion to predict PD-L1 expression and survival in NSCLC. Front Immunol. 2022; 13: 828560.

[62] Wang C, Ma J, Shao J, et al. Predicting EGFR and PD-L1 status in NSCLC patients using multitask AI system based on CT images. Front Immunol. 2022; 13: 813072.

[63] Yang X, Fang C, Li C, et al. Can CT radiomics detect acquired T790M mutation and predict prognosis in advanced lung adenocarcinoma with progression after first-or second-generation EGFR tkis? Front Oncol. 2022; 12(July): 1-10.

[64] She Y, He B, Wang F, et al. Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicentre study. Ebiomedicine. 2022; 86: 104364.

[65] Perez-Johnston R, Araujo-Filho JA, Connolly JG, et al. CT-based radiogenomic analysis of clinical stage I lung adenocarcinoma with histopathologic features and oncologic outcomes. Radiology. 2022; 303(3): 664-672.

[66] Song Z, Liu T, Shi L, et al. The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients. Eur J Nucl Med Mol Imaging. 2021; 48(2): 361-371.

[67] Tian P, He B, Mu W, et al. Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images. Theranostics. 2021; 11(5): 2098-2107.

[68] Mu W, Jiang L, Zhang JY, et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun. 2020; 11(1): 5228.

[69] He B, Dong D, She Y, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer. 2020; 8(2).

[70] Park C, Na KJ, Choi H, et al. Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma. Theranostics. 2020; 10(23): 10838-10848.

[71] Ju H, Kim K, Kim BI, Woo SK. Graph neural network model for prediction of non-small cell lung cancer lymph node metastasis using protein-protein interaction network and (18)F-FDG PET/CT radiomics. Int J Mol Sci. 2024; 25(2).

[72] Guo Y, Xie X, Tang W, et al. Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer. Eur Radiol. 2024;34(2):899-913.

[73] Liu HQ, Lin SY, Song YD, et al. Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy. Eur Radiol. 2023; 33(4): 2965-2974.

[74] Jiang L, You C, Xiao Y, et al. Radiogenomic analysis reveals tumor heterogeneity of triple-negative breast cancer. Cell Rep Med. 2022; 3(7): 100694.

[75] Lee JY, Lee KS, Seo BK, et al. Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI. Eur Radiol. 2022; 32(1): 650-660.

[76] Ming W, Li F, Zhu Y, et al. Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique. Comput Biol Med. 2022; 150: 106147.

[77] Petrillo A, Fusco R, Di Bernardo E, et al. Prediction of breast cancer histological outcome by radiomics and artificial intelligence analysis in contrast-enhanced mammography. Cancers. 2022; 14(9).

[78] Jeon SH, Kim SW, Na K, Seo M, Sohn YM, Lim YJ. Radiomic models based on magnetic resonance imaging predict the spatial distribution of CD8+ tumor-infiltrating lymphocytes in breast cancer. Front Immunol. 2022; 13(December): 1-12.

[79] Fan M, Yuan C, Huang G, et al. A framework for deep multitask learning with multiparametric magnetic resonance imaging for the joint prediction of histological characteristics in breast cancer. IEEE J Biomed Health Inform. 2022; 26(8): 3884-3895.

[80] Su GH, Xiao Y, Jiang L, et al. Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer. J Transl Med. 2022; 20(1): 471.

[81] Jiang M, Zhang D, Tang SCC, et al. Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study. Eur Radiol. 2021; 31(6): 3673-3682.

[82] Han X, Cao W, Wu L, Liang C. Radiomics assessment of the tumor immune microenvironment to predict outcomes in breast cancer. Front Immunol. 2022; 12: 1-9. January.

[83] Lo Gullo R, Wen H, Reiner JS, et al. Assessing pd-l1 expression status using radiomic features from contrast-enhanced breast mri in breast cancer patients: initial results. Cancers. 2021; 13(24): 1-13.

[84] Lv T, Hong X, Liu Y, et al. AI-powered interpretable imaging phenotypes noninvasively characterize tumor microenvironment associated with diverse molecular signatures and survival in breast cancer. Comput Methods Programs Biomed. 2024; 243: 107857.

[85] Fan M, Zhang P, Wang Y, et al. Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer. Eur Radiol. 2019; 29(8): 4456-4467.

[86] Quan MY, Huang YX, Wang CY, Zhang Q, Chang C, Zhou SC. Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status. Front Endocrinol. 2023; 14: 1144812.

[87] Zhang T, Tan T, Han L, et al. Predicting breast cancer types on and beyond molecular level in a multi-modal fashion. NPJ Breast Cancer. 2023; 9(1): 16.

[88] Zhang M, Wang C, Cai L, et al. Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images. Comput Struct Biotechnol J. 2023; 22: 17-26.

[89] Fan M, Wang K, Pan D, et al. Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study. J Transl Med. 2024; 22(1): 637.

[90] Zhao J, Sun Z, Yu Y, et al. Radiomic and clinical data integration using machine learning predict the efficacy of anti-PD-1 antibodies-based combinational treatment in advanced breast cancer: a multicentered study. J Immunother Cancer. 2023; 11(5).

[91] Zhang Y, You C, Pei Y, et al. Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets. J Transl Med. 2022; 20(1): 256.

[92] Zhou J, Bai Y, Zhang Y, et al. A preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy. Cancer Imaging. 2024; 24(1): 98.

[93] Lai J, Chen Z, Liu J, et al. A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study. Int J Surg. 2024; 110(4): 2162-2177.

[94] Lu N, Guan X, Zhu J, Li Y, Zhang J. A contrast-enhanced CT-based deep learning system for preoperative prediction of colorectal cancer staging and RAS mutation. Cancers. 2023; 15(18): 4497.

[95] Cao W, Hu H, Guo J, et al. CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study. J Transl Med. 2023; 21(1): 214.

[96] Lee H, Moon SH, Hong JY, Lee J, Hyun SH. A machine learning approach using FDG PET-based radiomics for prediction of tumor mutational burden and prognosis in stage IV colorectal cancer. Cancers. 2023; 15(15): 3841.

[97] Saber R, Henault D, Messaoudi N, et al. Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases. J Transl Med. 2023; 21(1): 507.

[98] Shi R, Chen W, Yang B, et al. Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features. Am J Cancer Res. 2020; 10(12): 4513-4526.

[99] Zhong ME, Duan X, li Ni-jia-tiMyidi , et al. CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective study. J Transl Med. 2022; 20(1): 574.

[100] Gao J, Ye F, Han F, Jiang H, Zhang J. A radiogenomics biomarker based on immunological heterogeneity for non-invasive prognosis of renal clear cell carcinoma. Front Immunol. 2022; 13.

[101] He H, Xie Y, Song F, Feng Z, Rong P. Radiogenomic analysis based on lipid metabolism-related subset for non-invasive prediction for prognosis of renal clear cell carcinoma. Eur J Radiol. 2024; 175: 111433.

[102] Zeng H, Chen L, Wang M, Luo Y, Huang Y, Ma X. Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma. Aging. 2021; 13(7): 9960-9975.

[103] Morrison G, Buckley J, Ostrow D, et al. Non-invasive profiling of advanced prostate cancer via multi-parametric liquid biopsy and radiomic analysis. Int J Mol Sci. 2022; 23(5): 2571.

[104] Ogbonnaya CN, Alsaedi BSO, Alhussaini AJ, et al. Radiogenomics map-based molecular and imaging phenotypical characterization in localised prostate cancer using pre-biopsy biparametric MR imaging. Int J Mol Sci. 2024; 25(10).

[105] Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-Oncol. 2021; 23(8): 1231-1251.

[106] Weller M, Wen PY, Chang SM, et al. Glioma. Nat Rev Dis Primer. 2024; 10(1): 33.

[107] Pellerino A, Caccese M, Padovan M, Cerretti G, Lombardi G. Epidemiology, risk factors, and prognostic factors of gliomas. Clin Transl Imaging. 2022; 10(5): 467-475.

[108] Sanai N, Berger MS. Surgical oncology for gliomas: the state of the art. Nat Rev Clin Oncol. 2018; 15(2): 112-125.

[109] Gempt J, Förschler A, Buchmann N, et al. Postoperative ischemic changes following resection of newly diagnosed and recurrent gliomas and their clinical relevance. J Neurosurg. 2013; 118(4): 801-808.

[110] Pei D, Guan F, Hong X, et al. Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas. Eur Radiol. 2023; 33(5): 3455-3466.

[111] Yoo RE, Yun TJ, Hwang I, et al. Arterial spin labeling perfusion-weighted imaging aids in prediction of molecular biomarkers and survival in glioblastomas. Eur Radiol. 2020; 30(2): 1202-1211.

[112] Wei Y, Li C, Cui Z, et al. Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients. Brain. 2023; 146(4): 1714-1727.

[113] Karremann M, Gielen GH, Hoffmann M, et al. Diffuse high-grade gliomas with H3 K27M mutations carry a dismal prognosis independent of tumor location. Neuro-Oncol. 2018; 20(1): 123-131.

[114] Ryall S, Krishnatry R, Arnoldo A, et al. Targeted detection of genetic alterations reveal the prognostic impact of H3K27M and MAPK pathway aberrations in paediatric thalamic glioma. Acta Neuropathol Commun. 2016; 4(1): 93.

[115] Pan CC CUN, Liu J, Tang J, et al. A machine learning-based prediction model of H3K27M mutations in brainstem gliomas using conventional MRI and clinical features. Radiother Oncol. 2019; 130: 172-179.

[116] Su X, Chen N, Sun H, et al. Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain. Neuro-Oncol. 2020; 22(3): 393-401.

[117] Esteller M, Garcia-Foncillas J, Andion E, et al. Inactivation of the DNA-repair gene MGMT and the clinical response of gliomas to alkylating agents. N Engl J Med. 2000;343(19):1350-4.

[118] Li L, Xiao F, Wang S, et al. Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis. Sci Rep. 2024; 14(1): 16031.

[119] Doniselli FM, Pascuzzo R, Mazzi F, et al. Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis. Eur Radiol. 2024; 3.

[120] Saeed N, Ridzuan M, Alasmawi H, Sobirov I, Yaqub M. MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models. Med Image Anal. 2023; 90: 102989.

[121] Śledzińska P, Bebyn MG, Furtak J, Kowalewski J, Lewandowska MA. Prognostic and predictive biomarkers in gliomas. Int J Mol Sci. 2021; 22(19): 10373.

[122] Yang K, Wu Z, Zhang H, et al. Glioma targeted therapy: insight into future of molecular approaches. Mol Cancer. 2022; 21(1).

[123] Haubold J, Hosch R, Parmar V, et al. Fully automated MR based virtual biopsy of cerebral gliomas. Cancers. 2021; 13(24).

[124] Verduin M, Primakov S, Compter I, et al. Prognostic and predictive value of integrated qualitative and quantitative magnetic resonance imaging analysis in glioblastoma. Cancers. 2021; 13(4).

[125] Tak D, Ye Z, Zapaischykova A, et al. Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning. Radiol Artif Intell. 2024; 6(3): e230333.

[126] Sun C, Jiang C, Wang X, Ma S, Zhang D, Jia W. MR-based radiomics predicts CDK6 expression and prognostic value in high-grade glioma. Acad Radiol. 2024; S1076-6332(24): 00364-00367. Published online July 3.

[127] Zhao K, Zhang H, Lin J, et al. Radiomic prediction of CCND1 expression levels and prognosis in low-grade glioma based on magnetic resonance imaging. Acad Radiol. 2024; S1076-6332(24): 00196-X. Published online.

[128] Hu LS, Ning S, Eschbacher JM, et al. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro-Oncol. 2017; 19(1): 128-137.

[129] Xu PF, Li C, Chen YS, et al. Radiomics-based survival risk stratification of glioblastoma is associated with different genome alteration. Comput Biol Med. 2023; 159: 106878.

[130] Luan J, Zhang D, Liu B, et al. Immune-related lncrnas signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme. J Transl Med. 2024; 22(1): 107.

[131] Hu LS, Hawkins-Daarud A, Wang L, Li J, Swanson KR. Imaging of intratumoral heterogeneity in high-grade glioma. Cancer Lett. 2020; 477: 97-106.

[132] Yoon JH, Kim H. CT radiomics in oncology: insights into the tumor microenvironment of hepatocellular carcinoma. Radiology. 2023; 307(1).

[133] Choi YS, Ahn SS, Chang JH, et al. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction. Eur Radiol. 2020; 30(7): 3834-3842.

[134] Jang BSS, Park AJ, Jeon SH, et al. Machine learning model to predict pseudoprogression versus progression in glioblastoma using mri: a multi-institutional study (krog 18-07). Cancers. 2020; 12(9): 1-14.

[135] Leiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol. 2023; 20(9): 624-639.

[136] Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. Lung cancer. The Lancet. 2021; 398(10299): 535-554.

[137] Lahiri A, Maji A, Potdar PD, et al. Lung cancer immunotherapy: progress, pitfalls, and promises. Mol Cancer. 2023; 22(1): 40.

[138] Tan AC, Tan DSW. Targeted therapies for lung cancer patients with oncogenic driver molecular alterations. J Clin Oncol. 2022; 40(6): 611-625.

[139] Blumenthal GM, Bunn PA, Chaft JE, et al. Current status and future perspectives on neoadjuvant therapy in lung cancer. J Thorac Oncol. 2018; 13(12): 1818-1831.

[140] Arbour KC, Riely GJ. Systemic therapy for locally advanced and metastatic non–small cell lung cancer. JAMA. 2019; 322(8): 764.

[141] Da Cunha Santos G, Shepherd FA, Tsao MS. EGFR mutations and lung cancer. Annu Rev Pathol Mech Dis. 2011; 6(1): 49-69.

[142] Blaquier JB, Ortiz-Cuaran S, Ricciuti B, Mezquita L, Cardona AF, Recondo G. Tackling osimertinib resistance in EGFR-mutant non-small cell lung cancer. Clin Cancer Res. 2023; 29(18): 3579-3591.

[143] Passaro A, Jänne PA, Mok T, Peters S. Overcoming therapy resistance in EGFR-mutant lung cancer. Nat Cancer. 2021; 2(4): 377-391.

[144] Chang C, Zhou S, Yu H, et al. A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma. Eur Radiol. 2021; 31(8): 6259-6268.

[145] Yang X, Liu M, Ren Y, et al. Using contrast-enhanced CT and non-contrast-enhanced CT to predict EGFR mutation status in NSCLC patients—a radiomics nomogram analysis. Eur Radiol. 2022; 32(4): 2693-2703.

[146] Zhao W, Yang J, Ni B, et al. Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning. Cancer Med. 2019; 8(7): 3532-3543.

[147] Wang S, Shi J, Ye Z, et al. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J. 2019; 53(3).

[148] Wang S, Yu H, Gan Y, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health. 2022; 4(5): e309-e319.

[149] Schneider JL, Lin JJ, Shaw AT. ALK-positive lung cancer: a moving target. Nat Cancer. 2023; 4(3): 330-343.

[150] Le NQK, Kha QH, Nguyen VH, Chen YC, Cheng SJ, Chen CY. Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer. Int J Mol Sci. 2021; 22(17): 9254.

[151] Mountzios G, Remon J, Hendriks LEL, et al. Immune-checkpoint inhibition for resectable non-small-cell lung cancer — opportunities and challenges. Nat Rev Clin Oncol. 2023; 20(10): 664-677.

[152] Mu W, Tunali I, Gray JE, Qi J, Schabath MB, Gillies RJ. Abstract 868: prediction of clinical benefit to checkpoint blockade in advanced NSCLC patients using radiomics of PET/CT images. Cancer Res. 2020; 80(16_Supplement): 868-868.

[153] Sun R, Limkin EJ, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. 2018; 19(9): 1180-1191.

[154] Hinzpeter R, Kulanthaivelu R, Kohan A, et al. Predictive [18F]-FDG PET/CT-based radiogenomics modelling of driver gene mutations in non-small cell lung cancer. Acad Radiol. 2024; S1076-6332(24): 00423-00429. Published online July 11.

[155] Li L, Duan J, Gao Y, et al. Multi-omics predictive model based on clinical, radiomic and genomic features for predicting the response of limited-stage small cell lung cancer to definitive chemoradiotherapy. Clin Transl Med. 2024; 14(1): e1522.

[156] Verma S, Magazzù G, Eftekhari N, et al. Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients. Cell Rep Methods. 2024; 4(7): 100817.

[157] De Miguel-Perez D, Ak M, Mamindla P, et al. Validation of a multiomic model of plasma extracellular vesicle PD-L1 and radiomics for prediction of response to immunotherapy in NSCLC. J Exp Clin Cancer Res CR. 2024; 43(1): 81.

[158] Huppert LA, Gumusay O, Idossa D, Rugo HS. Systemic therapy for hormone receptor-positive/human epidermal growth factor receptor 2-negative early stage and metastatic breast cancer. CA Cancer J Clin. 2023; 73(5): 480-515.

[159] Loibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. The Lancet. 2021; 397(10286): 1750-1769.

[160] Acciavatti RJ, Lee SH, Reig B, et al. Beyond breast density: risk measures for breast cancer in multiple imaging modalities. Radiology. 2023; 306(3): e222575.

[161] Cui H, Sun Y, Zhao D, et al. Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions. J Transl Med. 2023; 21(1): 44.

[162] Nicosia L, Bozzini AC, Ballerini D, et al. Radiomic features applied to contrast enhancement spectral mammography: possibility to predict breast cancer molecular subtypes in a non-invasive manner. Int J Mol Sci. 2022; 23(23).

[163] Bitencourt AGV, Gibbs P, Rossi Saccarelli C, et al. MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer. Ebiomedicine. 2020; 61: 103042.

[164] Zhu Z, Albadawy E, Saha A, Zhang J, Harowicz MR, Mazurowski MA. Deep learning for identifying radiogenomic associations in breast cancer. Comput Biol Med. 2019; 109: 85-90.

[165] Huang ZH, Chen L, Sun Y, Liu Q, Hu P. Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer. J Transl Med. 2024; 22(1): 226.

[166] Mathews JC, Nadeem S, Levine AJ, Pouryahya M, Deasy JO, Tannenbaum A. Robust and interpretable PAM50 reclassification exhibits survival advantage for myoepithelial and immune phenotypes. Npj Breast Cancer. 2019; 5(1): 30.

[167] Liang S, Xu S, Zhou S, et al. IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes. J Genet Genomics Yi Chuan Xue Bao. 2024; 51(4): 443-453.

[168] Gallivanone F, Cava C, Corsi F, Bertoli G, Castiglioni I. In silico approach for the definition of radiomirnomic signatures for breast cancer differential diagnosis. Int J Mol Sci. 2019; 20(23).

[169] Harris MA, Savas P, Virassamy B, et al. Towards targeting the breast cancer immune microenvironment. Nat Rev Cancer. 2024; 24(8): 554-577.

[170] Wang H, Ding XH, Liu CL, et al. Genomic alterations affecting tumor-infiltrating lymphocytes and PD-L1 expression patterns in triple-negative breast cancer. J Natl Cancer Inst. 2023; 115(12): 1586-1596.

[171] Han X, Gong Z, Guo Y, Tang W, Wei X. Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction. Eur J Radiol. 2024; 175: 111441.

[172] Pesapane F, Rotili A, Botta F, et al. Radiomics of MRI for the prediction of the pathological response to neoadjuvant chemotherapy in breast cancer patients: a single referral centre analysis. Cancers. 2021; 13(17).

[173] Sutton EJ, Onishi N, Fehr DA, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy. Breast Cancer Res. 2020; 22(1): 57.

[174] Fan M, Chen H, You C, et al. Radiomics of tumor heterogeneity in longitudinal dynamic contrast-enhanced magnetic resonance imaging for predicting response to neoadjuvant chemotherapy in breast cancer. Front Mol Biosci. 2021; 8: 622219.

[175] Huang Y, Chen W, Zhang X, et al. Prediction of tumor shrinkage pattern to neoadjuvant chemotherapy using a multiparametric MRI-based machine learning model in patients with breast cancer. Front Bioeng Biotechnol. 2021; 9: 662749.

[176] Kocak B, Durmaz ES, Ates E, Ulusan MB. Radiogenomics in clear cell renal cell carcinoma: machine learning–based high-dimensional quantitative CT texture analysis in predicting PBRM1 mutation status. Am J Roentgenol. 2019; 212(3): W55-W63.

[177] Gao R, Pang J, Lin P, et al. Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics. Heliyon. 2024; 10(11): e31816.

[178] Gu J, Zhu J, Qiu Q, Wang Y, Bai T, Yin Y. Prediction of immunohistochemistry of suspected thyroid nodules by use of machine learning-based radiomics. Am J Roentgenol. 2019; 213(6): 1348-1357.

[179] Roy S, Whitehead TD, Quirk JD, et al. Optimal co-clinical radiomics: sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. Ebiomedicine. 2020; 59: 102963.

[180] Moher Alsady T, Voskrebenzev A, Behrendt L, et al. Multicenter standardization of phase-resolved functional lung MRI in patients with suspected chronic thromboembolic pulmonary hypertension. J Magn Reson Imaging. 2024; 59(6): 1953-1964.

[181] Whybra P, Zwanenburg A, Andrearczyk V, et al. The image biomarker standardization initiative: standardized convolutional filters for reproducible radiomics and enhanced clinical insights. Radiology. 2024; 310(2): e231319.

[182] Varriano G, Guerriero P, Santone A, Mercaldo F, Brunese L. Explainability of radiomics through formal methods. Comput Methods Programs Biomed. 2022; 220: 106824.

[183] Liu M, Ning Y, Teixayavong S, et al. A translational perspective towards clinical AI fairness. Npj Digit Med. 2023; 6(1): 172.

[184] Andaur Navarro CL, Damen JAA, Takada T, et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ. 2021:n2281. Published online October 20.

[185] Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021; 3(11): e745-e750.

[186] Azodi CB, Tang J, Shiu SH. Opening the black box: interpretable machine learning for GenetICIsts. Trends Genet. 2020; 36(6): 442-455.

[187] Tomaszewski MR, Gillies RJ. The biological meaning of radiomic features. Radiology. 2021; 298(3): 505-516.

[188] Jiang Y, Zhang Z, Wang W, et al. Biology-guided deep learning predicts prognosis and cancer immunotherapy response. Nat Commun. 2023; 14(1): 5135.

[189] Liu Q, Hu P. Extendable and explainable deep learning for pan-cancer radiogenomics research. Curr Opin Chem Biol. 2022; 66: 102111.

[190] Zanfardino M, Pane K, Mirabelli P, Salvatore M, Franzese M. TCGA-TCIA impact on radiogenomics cancer research: a systematic review. Int J Mol Sci. 2019; 20(23): 6033.

[191] Prior F, Smith K, Sharma A, et al. The public cancer radiology imaging collections of The Cancer Imaging Archive. Sci Data. 2017; 4(1): 170124.

[192] Ganini C, Amelio I, Bertolo R, et al. Global mapping of cancers: the Cancer Genome Atlas and beyond. Mol Oncol. 2021; 15(11): 2823-2840.

[193] Kirby J, Tarbox L, Freymann J, Jaffe C, Prior F. TU-AB-BRA-03: the cancer imaging archive: supporting radiomic and imaging genomic research with open-access data sets. Med Phys. 2015; 42(6Part31): 3587-3587.

[194] Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: techniques and applications. Comput Biol Med. 2023; 158: 106848.

[195] Tang R, Liang H, Guo Y, et al. Pan-mediastinal neoplasm diagnosis via nationwide federated learning: a multicentre cohort study. Lancet Digit Health. 2023; 5(9): e560-e570.

[196] Zhou J, Chen S, Wu Y, et al. PPML-Omics: a privacy-preserving federated machine learning method protects patients’ privacy in omic data. Sci Adv. 2024; 10(5): eadh8601.

[197] Huang P, Li D, Jiao Z, et al. Common feature learning for brain tumor MRI synthesis by context-aware generative adversarial network. Med Image Anal. 2022; 79: 102472.

[198] Ahmadian M, Bodalal Z, van der Hulst HJ, et al. Overcoming data scarcity in radiomics/radiogenomics using synthetic radiomic features. Comput Biol Med. 2024; 174: 108389.

[199] Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018; 18(8): 500-510.

[200] Mitra S. Deep learning with radiogenomics towards personalized management of gliomas. IEEE Rev Biomed Eng. 2023; 16: 579-593.

[201] Tippareddy C, Onyewadume L, Sloan AE, et al. Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study. Eur Radiol. 2023; 33(2): 836-844.

[202] Wu X, Zhang B. Chatgpt promotes healthcare: current applications and potential challenges. Int J Surg. 2024; 110(1): 606-608.

[203] Mehandru N, Miao BY, Almaraz ER, Sushil M, Butte AJ, Alaa A. Evaluating large language models as agents in the clinic. Npj Digit Med. 2024; 7(1): 84.

[204] Bhayana R. Chatbots and large language models in radiology: a practical primer for clinical and research applications. Radiology. 2024; 310(1): e232756.

[205] Li J, Wang S, Zhang M, et al. Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents. Published online May 5, 2024.

[206] Hsu JBK, Lee GA, Chang TH, et al. Radiomic immunophenotyping of GSEA-assessed immunophenotypes of glioblastoma and its implications for prognosis: a feasibility study. Cancers. 2020; 12(10).

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