Radiomics: Current Applications and Future Directions

Abstract

Radiomics enables high-throughput extraction of quantitative imaging features to decode tumor phenotypes and biological behaviors, representing a transformative noninvasive tool for precision oncology. In recent years, radiomics has rapidly evolved from static feature analysis to dynamic multi-dimensional assessment, and it has been widely explored in various solid tumors, yet its pan-cancer generalization, biological interpretability, and clinical translation still face prominent bottlenecks. Cancer remains the leading cause of global mortality, and solid tumors account for more than 90% of adult malignant cases, while conventional medical imaging and invasive biopsies have inherent limitations in reflecting tumor heterogeneity and dynamic evolution. This review outlines the unified technical pipeline of radiomics across solid tumors, highlights cancer-specific imaging considerations, and summarizes standardization strategies for multi-center, multi-scanner, and multi-cancer heterogeneity. We systematically review pan-cancer clinical applications covering early detection, molecular characterization, treatment response prediction, and prognostic stratification, with lung cancer as a paradigmatic example while integrating evidence from breast, colorectal, liver, glioma, and prostate cancers. We also discuss multi-omics integration, biological interpretability, and translational bottlenecks including domain shift and reproducibility crisis. Finally, we prospect cutting-edge directions including foundation models, causal inference, and federated learning to advance generalizable and clinically actionable radiomics toward routine clinical practice.

Keywords

artificial intelligence / clinical translation / multi-omics fusion / pan-cancer / radiomics

References

[1] P. Potok, W. Woźniak-Laszczyńska, R. Wieczorek, et al., “Substrate-Mimicking Peptides as MMP-1 Inhibitors: Impact of Zinc-Binding Group Position on Ternary Complex Stability,” Inorganic Chemistry 65, no. 4 (2026): 2203–2216.

[2] Z. Zhang, D. Cui, H. Wang, L. Wu, and X. Liu, “Unleashing CAR-T Potential in Solid Tumors: Overcoming Intrinsic and Extrinsic Hurdles to Improve Therapy,” Cancer Immunology, Immunotherapy 75, no. 2 (2026): 37.

[3] Y. Li, L. Zhang, G. Xu, et al., “Exploration and Validation of a Novel Reactive Oxygen Species-Related Signature for Predicting the Prognosis and Chemotherapy Response of Patients With Bladder Cancer,” Frontiers in Immunology 15 (2024): 1493528.

[4] J. Xiao, Z. Zhu, F. Zhang, and X. Wang, “The Potential of MMP14 as a Prognostic and Immune Biomarker in Lung Cancer,” BMC Cancer 26, no. 1 (2026): 256.

[5] M. Delgado-Ortet, V. Bura, I. G. Funingana, et al., “Imaging-Based Assessment of Response to Olaparib in Platinum-Sensitive Relapsed Ovarian Cancer Patients,” Frontiers in Oncology 15 (2025): 1546324.

[6] J. Alfred Witjes, H. Max Bruins, A. Carrión, et al., “European Association of Urology Guidelines on Muscle-Invasive and Metastatic Bladder Cancer: Summary of the 2023 Guidelines,” European Urology 85, no. 1 (2024): 17–31.

[7] Y. Xia, L. Li, P. Liu, T. Zhai, and Y. Shi, “Machine Learning Prediction Model for Functional Prognosis of Acute Ischemic Stroke Based on MRI Radiomics of White Matter Hyperintensities,” BMC Medical Imaging 25, no. 1 (2025): 91.

[8] E. J. Inarejos Clemente, M. Navallas, I. Barber Martínez de la Torre, et al., “MRI of Rhabdomyosarcoma and Other Soft-Tissue Sarcomas in Children,” Radiographics 40, no. 3 (2020): 791–814.

[9] P. Habert, A. Decoux, L. Chermati, et al., “Best Imaging Signs Identified by Radiomics Could Outperform the Model: Application to Differentiating Lung Carcinoid Tumors From Atypical Hamartomas,” Insights Imaging 14, no. 1 (2023): 148.

[10] L. L. Zhang, M. Y. Huang, Y. Li, et al., “Pretreatment MRI Radiomics Analysis Allows for Reliable Prediction of Local Recurrence in Non-Metastatic T4 Nasopharyngeal Carcinoma,” EBioMedicine 42 (2019): 270–280.

[11] R. J. Beukinga, F. B. Poelmann, G. Kats-Ugurlu, et al., “Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients With 18F-FDG PET Radiomics Based Machine Learning Classification,” Diagnostics 12, no. 5 (2022): 1070.

[12] X. Huang, X. Huang, K. Wang, H. Bai, B. Ye, and G. Jin, “Habitat-Based Radiomics From Contrast-Enhanced CT and Clinical Data to Predict Lymph Node Metastasis in Clinical N0 Peripheral Lung Adenocarcinoma ≤ 3 cm,” Scientific Reports 15, no. 1 (2025): 17085.

[13] Z. Shi, Z. Zhang, Z. Liu, et al., “Methodological Quality of Machine Learning-Based Quantitative Imaging Analysis Studies in Esophageal Cancer: A Systematic Review of Clinical Outcome Prediction After Concurrent Chemoradiotherapy,” European Journal of Nuclear Medicine and Molecular Imaging 49, no. 8 (2022): 2462–2481.

[14] S. Yin, J. Ming, H. Chen, Y. Sun, and C. Jiang, “Integrating Deep Learning and Radiomics for Preoperative Glioma Grading Using Multi-Center MRI Data,” Scientific Reports 15, no. 1 (2025): 36756.

[15] I. Vanni, L. Pastorino, V. Andreotti, et al., “Combining Germline, Tissue and Liquid Biopsy Analysis by Comprehensive Genomic Profiling to Improve the Yield of Actionable Variants in a Real-World Cancer Cohort,” Journal of Translational Medicine 22, no. 1 (2024): 462.

[16] E. R. Malone, M. Oliva, P. J. B. Sabatini, T. L. Stockley, and L. L. Siu, “Molecular Profiling for Precision Cancer Therapies,” Genome Medicine 12, no. 1 (2020): 8.

[17] Q. Zhong, J. Jiang, W. Bai, Z. Yin, Z. Liao, and X. Zhong, “Application of Digital-Intelligent Technologies in Physical Education: A Systematic Review,” Frontiers in Public Health 13 (2025): 1626603.

[18] J. Park, K. Cho, J. Lee, and J. Kim, “Controllability of the Gene Regulatory Network in Zebrafish Embryogenesis,” Scientific Reports 16, no. 1 (2025): 3783.

[19] F. Wang and H. Huangfu, “Laryngeal Squamous Cell Carcinoma: A Functional “Two-Hit” Model of Papillomavirus-Epstein-Barr Virus Synergistic Carcinogenesis,” Frontiers in Cellular and Infection Microbiology 16 (2026): 1768893.

[20] K. Zhou, Y. Li, W. Wang, et al., “SLFN11: A Pan-Cancer Biomarker for DNA-Targeted Drugs Sensitivity and Therapeutic Strategy Guidance,” Frontiers in Oncology 15 (2025): 1582738.

[21] L. Wang, A. Aireti, A. Aihaiti, and K. Li, “Expression of microRNA-150 and Its Target Gene IGF2BP1 in Human Osteosarcoma and Their Clinical Implications,” Pathology Oncology Research 25, no. 2 (2019): 527–533.

[22] H. V. Ege, C. Goutallier, L. B. Ellis, et al., “Human Papillomavirus in Reproductive Health and Pregnancy: Clinical Implications, Outcomes, and a Comprehensive Review of Vaccination,” Vaccines 14, no. 2 (2026): 180.

[23] E. I. Obeagu, “Venous Thromboembolism in Ovarian Cancer: Pathophysiology, Risk, and Management,” Annals of Medicine and Surgery 87, no. 12 (2025): 8587–8596.

[24] H. Li, X. Li, Y. Fan, J. Li, and X. Qi, “Exploring the Trajectory of Post-Traumatic Growth in Patients in Intensive Care Unit: A Phenomenological Longitudinal Study,” International Journal of Qualitative Studies on Health and Well-being 20, no. 1 (2025): 2535838.

[25] L. Nommsen-Rivers, M. M. Black, P. Christian, et al., “An Equitable, Community-Engaged Translational Framework for Science in Human Lactation and Infant Feeding—A Report From “Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)” Working Group 5,” American Journal of Clinical Nutrition 117, no. supplement, S1 (2023): S87–S105.

[26] A. Crombé, M. Roulleau-Dugage, and A. Italiano, “The Diagnosis, Classification, and Treatment of Sarcoma in This Era of Artificial Intelligence and Immunotherapy,” Cancer Communications 42, no. 12 (2022): 1288–1313.

[27] R. R. Colen, C. Rolfo, M. Ak, et al., “Radiomics Analysis for Predicting Pembrolizumab Response in Patients With Advanced Rare Cancers,” Journal for ImmunoTherapy of Cancer 9, no. 4 (2021): e001752.

[28] D. Xie, C. Sun, M. Xue, and X. Xiao, “Development and Validation of a CT-Based Habitat Radiomics Model for Predicting Pathological Grading in Non-Small Cell Lung Cancer,” Frontiers in Medicine 13 (2026): 1722634.

[29] G. L. Cipriano, I. Raffaele, A. Floramo, et al., “Beyond Neurotrophins: A Proposed Neurotrophic-Epigenetic Axis Mediated by Non-Coding RNA Networks for Hericium Erinaceus Bioactives—A Hypothesis-Driven Review,” International Journal of Molecular Sciences 27, no. 3 (2026): 1269.

[30] H. Piotrzkowska-Wróblewska, “The Role of Quantitative Ultrasound in Monitoring Neoadjuvant Chemotherapy in Breast Cancer: A Narrative Review,” Cancers 17, no. 22 (2025): 3676.

[31] S. Zhang, L. Huo, Z. Zhu, et al., “Preoperative Multiparametric MRI-Based Tumour-Periprostatic Adipose Tissue Interface Characterisation for Extraprostatic Extension Prediction in Prostate Cancer,” Cancer Medicine 15, no. 2 (2026): e71613.

[32] Y. Deng, J. Xu, T. Yu, and D. Zhu, “Advances in Tissue Optical Clearing for 3D Imaging in Large Animal,” Frontiers of Optoelectronics 18, no. 1 (2025): 18.

[33] S. Feng, Y. Pan, P. Lu, N. Li, W. Zhu, and Z. Hao, “From Bench to Bedside: The Application of Cannabidiol in Glioma,” Journal of Translational Medicine 22, no. 1 (2024): 648.

[34] Y. Chen, D. Chen, X. Liu, H. Jiang, and X. Wang, “Deep Learning-Driven Multimodal Integration of miRNA and Radiomic for Lung Cancer Diagnosis,” Biosensors 15, no. 9 (2025): 610.

[35] B. Zheng, P. Yu, Z. Zhu, Y. Liang, and H. Liu, “Predicting Conservative Treatment Failure in Postmenopausal Women With Osteoporotic Vertebral Compression Fractures: A CT and MRI-Based Radiomics Machine Learning Approach,” Global Spine Journal 16, no. 4 (2025): 21925682251391687.

[36] J. Zhong, Z. Pan, Y. Chen, et al., “Robustness of Radiomics Features of Virtual Unenhanced and Virtual Monoenergetic Images in Dual-Energy CT Among Different Imaging Platforms and Potential Role of CT Number Variability,” Insights Imaging 14, no. 1 (2023): 79.

[37] E. Suero Molina, G. Azemi, and Z. Özdemir, “Predicting Intraoperative 5-ALA-Induced Tumor Fluorescence via MRI and Deep Learning in Gliomas With Radiographic Lower-Grade Characteristics,” Journal of Neuro-Oncology 171, no. 3 (2025): 589–598.

[38] A. Decoux, L. Duron, P. Habert, et al., “Comparative Performances of Machine Learning Algorithms in Radiomics and Impacting Factors,” Scientific Reports 13, no. 1 (2023): 14069.

[39] A. Hertel, H. Tharmaseelan, L. T. Rotkopf, et al., “Phantom-Based Radiomics Feature Test-Retest Stability Analysis on Photon-Counting Detector CT,” European Radiology 33, no. 7 (2023): 4905–4914.

[40] C. Xu, W. Liu, Q. Zhao, et al., “CT-Based Radiomics Nomogram for Overall Survival Prediction in Patients With Cervical Cancer Treated With Concurrent Chemoradiotherapy,” Frontiers in Oncology 13 (2023): 1287121.

[41] Z. N. Huang, H. X. Zhang, Y. Q. Sun, et al., “Multi-Cohort Study in Gastric Cancer to Develop CT-Based Radiomic Models to Predict Pathological Response to Neoadjuvant Immunotherapy,” Journal of Translational Medicine 23, no. 1 (2025): 362.

[42] Q. Ren, P. Zhu, C. Li, et al., “Pretreatment Computed Tomography-Based Machine Learning Models to Predict Outcomes in Hepatocellular Carcinoma Patients Who Received Combined Treatment of Trans-Arterial Chemoembolization and Tyrosine Kinase Inhibitor,” Frontiers in Bioengineering and Biotechnology 10 (2022): 872044.

[43] X. Zhang, Y. Zhang, G. Zhang, et al., “Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential,” Frontiers in Oncology 12 (2022): 773840.

[44] H. Xiao, X. He, W. Zhou, X. Guo, X. Cai, and T. Li, “The Application of Radiomics in the Diagnosis and Evaluation of Cognitive Impairment Related to Neurological Diseases,” Frontiers in Neuroscience 19 (2025): 1591605.

[45] L. Xie, Y. Xu, M. Zheng, et al., “An Anthropomorphic Diagnosis System of Pulmonary Nodules Using Weak Annotation-Based Deep Learning,” Computerized Medical Imaging and Graphics 118 (2024): 102438.

[46] X. Liu, Z. X. Zhang, B. Zheng, M. Xu, X. Y. Cao, and H. M. Huang, “A Retrospective Study on Predicting Clinically Significant Prostate Cancer via a Bi-parametric Ultrasound-Based Deep Learning Radiomics Model,” Frontiers in Oncology 15 (2025): 1538854.

[47] S. Gou, N. Liu, W. Liu, and Y. Yao, “Knowledge Relay: Synergetic Generation and Transfer Learning for Pancreatic Tumor Segmentation on Multimodal Images,” Medical Physics 52, no. 6 (2025): 4828–4843.

[48] P. Ruusuvuori, M. Valkonen, K. Kartasalo, et al., “Spatial Analysis of Histology in 3D: Quantification and Visualization of Organ and Tumor Level Tissue Environment,” Heliyon 8, no. 1 (2022): e08762.

[49] S. Rathore, S. Mohan, S. Bakas, et al., “Multi-Institutional Noninvasive in Vivo Characterization of IDH, 1p/19q, and EGFRvIII in Glioma Using Neuro-Cancer Imaging Phenomics Toolkit (Neuro-CaPTk),” Neuro-Oncology Advances 2, no. supplement, S4 (2020): iv22–iv34.

[50] H. H. Cho, H. Y. Lee, E. Kim, et al., “Radiomics-Guided Deep Neural Networks Stratify Lung Adenocarcinoma Prognosis From CT Scans,” Communications Biology 4, no. 1 (2021): 1286.

[51] H. W. Chung, K. S. Park, I. Lim, et al., “PET/MRI and Novel Targets for Breast Cancer,” Biomedicines 12, no. 1 (2024): 172.

[52] H. Farrokhzadeh, M. J. Tarrahi, A. Baradaran, et al., “Altered Trace and Toxic Element Profiles in Breast Tissue: A Case-Control Study of Cancer Risk,” Scientific Reports 16, no. 1 (2026): 9405.

[53] W. Gong, Y. Guo, H. Yuan, et al., “HMGB3 is a Potential Therapeutic Target by Affecting the Migration and Proliferation of Colorectal Cancer,” Frontiers in Cell and Developmental Biology 10 (2022): 891482.

[54] S. Mulé, M. Ronot, M. Ghosn, et al., “Automated CT LI-RADS v2018 Scoring of Liver Observations Using Machine Learning: A Multivendor, Multicentre Retrospective Study,” JHEP Reports 5, no. 10 (2023): 100857.

[55] G. Santo, R. Laudicella, F. Linguanti, et al., “The Utility of Conventional Amino Acid PET Radiotracers in the Evaluation of Glioma Recurrence Also in Comparison With MRI,” Diagnostics 12, no. 4 (2022): 844.

[56] F. Khalvati, J. Zhang, A. G. Chung, M. J. Shafiee, A. Wong, and M. A. Haider, “MPCaD: A Multi-Scale Radiomics-Driven Framework for Automated Prostate Cancer Localization and Detection,” BMC Medical Imaging 18, no. 1 (2018): 16.

[57] Y. Liu, J. Yao, J. Deng, et al., “Attenuated Bacteria-Based Tumor Therapy: Clinical Application Risks, Marketing Approval Restrictions, and Coping Strategies,” Advanced Science 13, no. 16 (2026): e17730.

[58] Z. Y. Zhao, S. Z. Liu, X. P. Chen, et al., “Exploring the Relationships Between CT and Pathological Characteristics and Gene Mutations in Neoplastic Ground Glass Nodules,” BMC Medical Imaging 25, no. 1 (2025): 314.

[59] Y. Zhang, W. Huang, H. Jiao, and L. Kang, “PET Radiomics in Lung Cancer: Advances and Translational Challenges,” EJNMMI Physics 11, no. 1 (2024): 81.

[60] M. Omar, W. Dinalankara, L. Mulder, et al., “Using Biological Constraints to Improve Prediction in Precision Oncology,” Iscience 26, no. 3 (2023): 106108.

[61] L. Gatto, E. Franceschi, A. Tosoni, et al., “Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology,” Biomedicines 10, no. 12 (2022): 3205.

[62] W. Yang, T. Pan, Y. Chen, et al., “Role of CD28+ PD-1+ Tc Cells in Immune Response and Prognosis Prediction in Hepatocellular Carcinoma,” Frontiers in Immunology 16 (2025): 1576193.

[63] D. C. Marcu, C. Grava, and L. G. Marcu, “Current Role of Delta Radiomics in Head and Neck Oncology,” International Journal of Molecular Sciences 24, no. 3 (2023): 2214.

[64] J. Zhou, Y. Zhang, and S. Shi, “Ultrasound Elastography: Advances and Challenges in Early Detection of Breast Cancer,” Frontiers in Oncology 15 (2025): 1589142.

[65] J. Wu, A. T. Mayer, and R. Li, “Integrated Imaging and Molecular Analysis to Decipher Tumor Microenvironment in the Era of Immunotherapy,” Seminars in Cancer Biology 84 (2022): 310–328.

[66] M. S. Alshuhri, H. F. Al-Mubarak, A. Qaisi, et al., “MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model,” Biomedicines 13, no. 4 (2025): 815.

[67] Y. Hong, P. Zhang, Z. Teng, et al., “Muscle-Driven Prognostication in Gastric Cancer: A Multicenter Deep Learning Framework Integrating Iliopsoas and Erector Spinae Radiomics for 5-Year Survival Prediction,” Scientific Reports 15, no. 1 (2025): 22347.

[68] Z. P. Tang, Z. Ma, Y. He, et al., “Ultrasound-Based Radiomics for Predicting Different Pathological Subtypes of Epithelial Ovarian Cancer Before Surgery,” BMC Medical Imaging 22, no. 1 (2022): 147.

[69] Z. Yang, J. Gong, J. Li, H. Sun, Y. Pan, and L. Zhao, “The Gap Before Real Clinical Application of Imaging-Based Machine-Learning and Radiomic Models for Chemoradiation Outcome Prediction in Esophageal Cancer: A Systematic Review and Meta-Analysis,” International Journal of Surgery 109, no. 8 (2023): 2451–2466.

[70] M. Suo, C. Ma, X. Chen, Y. Guan, X. Wang, and Z. Li, “A Novel Multimodal AI-Based Radiomics Approach for Precision Assessment of Pain Intensity in Chronic Nonspecific Low Back Pain,” Journal of Orthopaedic Translation 57 (2026): 101062.

[71] M. A. Jones, N. Sadeghipour, X. Chen, W. Islam, and B. Zheng, “A Multi-Stage Fusion Framework to Classify Breast Lesions Using Deep Learning and Radiomics Features Computed From Four-View Mammograms,” Medical Physics 50, no. 12 (2023): 7670–7683.

[72] J. Xiao, E. Kendal, and F. A. A. Kwa, “Harnessing the Power of AI to Improve Detection, Monitoring, and Public Health Interventions for Japanese Encephalitis,” Biomedicines 13, no. 1 (2024): 42.

[73] W. Weng, Y. Chen, and R. Jin, “Application of Artificial Intelligence in Differentiating IgG4-related Ophthalmic Disease and Orbital MALT Lymphoma: A Review of Radiomics and Deep Learning Advances,” Frontiers in Immunology 17 (2026): 1722733.

[74] B. Lan, C. Y. Chang, S. W. Liu, C. C. Wu, K. M. Lai, and H. P. Liu, “Machine Learning-Assisted Detection of Canine Mammary Tumors Using Serum Autoantibody Signatures,” Veterinary Quarterly 46, no. 1 (2026): 2617470.

[75] S. Bao, F. Zheng, L. Jiang, Q. Wang, and Y. Lyu, “TA-SSM Net: Tri-Directional Attention and Structured State-Space Model for Enhanced MRI-Based Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment,” BMC Medical Imaging 25, no. 1 (2025): 309.

[76] S. H. Tian, Y. Q. Fang, and Y. S. Zhang, “Retrieval-Augmented Patch Generation for Geosynchronous Satellite Status Forecasting,” Scientific Reports 16, no. 1 (2026): 6916.

[77] L. Wu, Y. Zhao, P. Lin, et al., “Preoperative Ultrasound Radiomics Analysis for Expression of Multiple Molecular Biomarkers in Mass Type of Breast Ductal Carcinoma in Situ,” BMC Medical Imaging 21, no. 1 (2021): 84.

[78] X. Chen, A. Burgun, O. Boyer, B. Knebelmann, and N. Garcelon, “Artificial Intelligence and Perspective for Rare Genetic Kidney Diseases,” Kidney International 108, no. 3 (2025): 388–393.

[79] M. J. Meinders, L. Heathers, K. C. Ho, et al., “Optimizing Wrist-Worn Wearable Compliance With Insights From Two Parkinson's Disease Cohort Studies,” NPJ Parkinson's Disease 11, no. 1 (2025): 152.

[80] Y. J. Qi, G. H. Su, C. You, et al., “Radiomics in Breast Cancer: Current Advances and Future Directions,” Cell Reports Medicine 5, no. 9 (2024): 101719.

[81] M. Cui, H. Dong, W. Duan, et al., “The Relationship Between Cancer Associated Fibroblasts Biomarkers and Prognosis of Breast Cancer: A Systematic Review and Meta-Analysis,” PeerJ 12 (2024): e16958.

[82] A. Rastkhiz, A. Rastkhiz, F. Erb, et al., “Preoperative Arterial and Venous CT Radiomics for Survival Prediction After Pylorus Preserving Pancreatoduodenectomy in Pancreatic Head Cancer,” Scientific Reports 15, no. 1 (2025): 24924.

[83] M. Fan, K. Wang, Y. Zhang, Y. Ge, Z. Lü, and L. Li, “Radiogenomic Analysis of Cellular Tumor-Stroma Heterogeneity as a Prognostic Predictor in Breast Cancer,” Journal of Translational Medicine 21, no. 1 (2023): 851.

[84] M. Sha, Y. Gao, X. Yin, X. Li, C. Liu, and S. Li, “Engineered Exosomes: A Promising Approach for Overcoming Challenges in Pancreatic Cancer Therapy,” Journal of Nanobiotechnology 23, no. 1 (2025): 619.

[85] L. Gerratana, A. A. Davis, M. Velimirovic, et al., “Interplay Between ESR1/PIK3CA Codon Variants, Oncogenic Pathway Alterations and Clinical Phenotype in Patients With Metastatic Breast Cancer (MBC): Comprehensive Circulating Tumor DNA (ctDNA) Analysis,” Breast Cancer Research 25, no. 1 (2023): 112.

[86] V. Anagnostou and C. Aggarwal, “Toward Response-Adaptive Therapy in Locally Advanced NSCLC: Integrating ctDNA and Radiomics for Risk Stratification,” Cancer Discovery 15, no. 8 (2025): 1534–1536.

[87] X. Pan, W. Wang, and X. Li, “Dual-Channel Deep Learning Captures Intratumoural Heterogeneity on CECT for Preoperative Risk Stratification of Thymic Epithelial Tumors,” Iscience 28, no. 11 (2025): 113683.

[88] Z. Jiao, H. Li, Y. Xiao, et al., “Integration of Deep Learning Radiomics and Counts of Circulating Tumor Cells Improves Prediction of Outcomes of Early Stage NSCLC Patients Treated With Stereotactic Body Radiation Therapy,” International Journal of Radiation and Oncology in Biology and Physics 112, no. 4 (2022): 1045–1054.

[89] H. Xu, S. N. Russell, K. Steiner, E. O'Neill, and K. I. Jones, “Targeting PI3K-Gamma in Myeloid Driven Tumour Immune Suppression: A Systematic Review and Meta-analysis of the Preclinical Literature,” Cancer Immunology, Immunotherapy 73, no. 10 (2024): 204.

[90] T. P. Theodore Armand, K. A. Nfor, J. I. Kim, and H. C. Kim, “Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review,” Nutrients 16, no. 7 (2024): 1073.

[91] T. Yang, L. Zhang, W. Xu, C. Zheng, and B. Liang, “Multimodal Imaging Techniques in Yttrium-90 Radioembolization for Hepatocellular Carcinoma: A Modality-Oriented Clinical Review,” Frontiers in Oncology 16 (2026): 1748836.

[92] X. Wang, G. Wang, L. Li, H. Zou, and J. Cui, “MFF-ClassificationNet: CNN-Transformer Hybrid With Multi-Feature Fusion for Breast Cancer Histopathology Classification,” Biosensors 15, no. 11 (2025): 718.

[93] Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li, “Deep Learning-Based Image Segmentation on Multimodal Medical Imaging,” IEEE Transactions on Radiation and Plasma Medical Sciences 3, no. 2 (2019): 162–169.

[94] X. Wang, S. Zhong, K. Fang, Y. Du, and J. Huang, “Application and Prospect of Artificial Intelligence in Diagnostic Imaging of Prostate Cancer,” NPJ Digital Medicine 9, no. 1 (2026): 168.

[95] M. Eftekharian and Z. Hashemi, “Artificial Intelligence for Lung Cancer: A Systematic Review of Head‑to‑Head CT, FDG PET/CT, and Multimodal Models Across Screening, Staging, and Prognosis,” BMC Medical Imaging 26, no. 1 (2026): 141.

[96] F. Yan, J. Wu, J. Li, et al., “PathOrchestra: A Comprehensive Foundation Model for Computational Pathology With Over 100 Diverse Clinical-Grade Tasks,” NPJ Digital Medicine 8, no. 1 (2025): 695.

[97] T. Lin, F. Gan, Y. Chen, et al., “Mitochondrial Gene Expression as a Novel Biomarker for Detecting and Discriminating Neurotoxic Pesticide Exposure in Ramulus Phyllodeus (Chen & He, 2008),” Insects 17, no. 2 (2026): 220.

[98] D. M. Cristea, D. Onita, and L. B. Iantovics, “Biomimetic Transfer Learning-Based Complex Gastrointestinal Polyp Classification,” Biomimetics 10, no. 10 (2025): 699.

[99] J. Wu, C. Li, M. Gensheimer, et al., “Radiological Tumor Classification Across Imaging Modality and Histology,” Nature Machine Intelligence 3 (2021): 787–798.

[100] G. Feliciani, L. Mellini, A. Carnevale, et al., “The Potential Role of MR Based Radiomic Biomarkers in the Characterization of Focal Testicular Lesions,” Scientific Reports 11, no. 1 (2021): 3456.

[101] J. Han, S. Park, L. A. Kim, et al., “Machine Learning-Enabled Non-Invasive Screening of Tumor-Associated Circulating Transcripts for Early Detection of Colorectal Cancer,” International Journal of Molecular Sciences 26, no. 4 (2025): 1477.

[102] D. Zeng, Z. Song, Q. Liu, J. Huang, X. Wang, and Z. Tang, “Radiomics Analysis of Dual-Layer Detector Spectral CT-Derived Iodine Maps for Predicting Ki-67 PI in Pancreatic Ductal Adenocarcinoma,” BMC Medical Imaging 25, no. 1 (2025): 124.

[103] M. E. Dindere, A. Tanca, M. Rusu, E. A. Liehn, and O. Bucur, “Intraoperative Tumor Detection Using Pafolacianine,” International Journal of Molecular Sciences 23, no. 21 (2022): 12842.

[104] J. C. M. van Zelst and R. M. Mann, “Automated Three-Dimensional Breast US for Screening: Technique, Artifacts, and Lesion Characterization,” Radiographics 38, no. 3 (2018): 663–683.

[105] S. Grosu, P. Wesp, A. Graser, et al., “Machine Learning-Based Differentiation of Benign and Premalignant Colorectal Polyps Detected With CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study,” Radiology 299, no. 2 (2021): 326–335.

[106] H. Chandarana, L. Feng, J. Ream, et al., “Respiratory Motion-Resolved Compressed Sensing Reconstruction of Free-Breathing Radial Acquisition for Dynamic Liver Magnetic Resonance Imaging,” Investigative Radiology 50, no. 11 (2015): 749–756.

[107] M. D. Greer, A. M. Brown, J. H. Shih, et al., “Accuracy and Agreement of PIRADSv2 for Prostate Cancer mpMRI: A Multireader Study,” Journal of Magnetic Resonance Imaging 45, no. 2 (2017): 579–585.

[108] X. Chen, L. Zhang, B. Chen, and J. Lu, “Building Radiomics Models Based on ACR TI-RADS Combining Clinical Features for Discriminating Benign and Malignant Thyroid Nodules,” Frontiers in Endocrinology 16 (2025): 1486920.

[109] R. D. Chitalia and D. Kontos, “Role of Texture Analysis in Breast MRI as a Cancer Biomarker: A Review,” Journal of Magnetic Resonance Imaging 49, no. 4 (2019): 927–938.

[110] X. Liu, R. Hong, P. Du, et al., “The Metabolic Genomic Atlas Reveals Potential Drivers and Clinically Relevant Insights Into the Etiology of Esophageal Squamous Cell Carcinoma,” Theranostics 12, no. 14 (2022): 6160–6178.

[111] J. Sun, X. Xu, S. Wei, and Y. Yu, “In-Cell Proteomics Enables High-Resolution Spatial and Temporal Mapping of Early Xenopus Tropicalis Embryos,” Molecular & Cellular Proteomics 25, no. 2 (2026): 101481.

[112] L. Peng, F. Li, F. Zhang, et al., “An Interpretable Machine Learning Model Combining MRI-DKI Habitat Radiomic Features and Clinical Biomarkers for Noninvasive Prediction of Lymphatic Metastasis in Rectal Cancer: A Prospective Study,” Insights Imaging 17, no. 1 (2026): 79.

[113] K. Qiao, L. Wu, L. Yang, et al., “Ubiquitin-Centered Post-Translational Modification Crosstalk Orchestrates Tumor Immunity and Immunotherapy Response,” Experimental Hematology & Oncology 15, no. 1 (2026): 22.

[114] W. He, W. Huang, L. Zhang, X. Wu, S. Zhang, and B. Zhang, “Radiogenomics: Bridging the Gap Between Imaging and Genomics for Precision Oncology,” MedComm 5, no. 9 (2024): e722.

[115] C. M. Chiesa-Estomba, M. Mayo-Yanez, O. Guntinas-Lichius, et al., “Radiomics in Hypopharyngeal Cancer Management: A State-of-the-Art Review,” Biomedicines 11, no. 3 (2023): 805.

[116] G. L. Banna, T. Olivier, F. Rundo, et al., “The Promise of Digital Biopsy for the Prediction of Tumor Molecular Features and Clinical Outcomes Associated With Immunotherapy,” Frontiers in Medicine 6 (2019): 172.

[117] A. Zwanenburg, M. Vallières, M. A. Abdalah, et al., “The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-Based Phenotyping,” Radiology 295, no. 2 (2020): 328–338.

[118] E. Schaafsma, B. Zhang, M. Schaafsma, C. Y. Tong, L. Zhang, and C. Cheng, “Impact of Oncotype DX Testing on ER+ Breast Cancer Treatment and Survival in the First Decade of Use,” Breast Cancer Research 23, no. 1 (2021): 74.

[119] X. Xie, D. Li, Y. Pei, et al., “Personalized Anti-Tumor Drug Efficacy Prediction Based on Clinical Data,” Heliyon 10, no. 6 (2024): e27300.

[120] I. Pan and R. Y. Huang, “Artificial Intelligence in Neuroimaging of Brain Tumors: Reality or Still Promise?,” Current Opinion in Neurology 36, no. 6 (2023): 549–556.

[121] H. H. Chu, J. H. Kim, P. N. Kim, et al., “Surgical Resection Versus Radiofrequency Ablation Very Early-Stage HCC (≤2 cm Single HCC): A Propensity Score Analysis,” Liver International 39, no. 12 (2019): 2397–2407.

[122] V. Romeo, P. Kapetas, P. Clauser, et al., “Simultaneous 18F-FDG PET/MRI Radiomics and Machine Learning Analysis of the Primary Breast Tumor for the Preoperative Prediction of Axillary Lymph Node Status in Breast Cancer,” Cancers 15, no. 20 (2023): 5088.

[123] R. García-Figueiras, S. Baleato-González, A. R. Padhani, et al., “How Clinical Imaging Can Assess Cancer Biology,” Insights Imaging 10, no. 1 (2019): 28.

[124] J. Li, C. Miao, H. Guo, et al., “Lactylation in Cancer: Molecular Mechanisms and Advances in Clinical Study,” Molecular Cancer 25, no. 1 (2026): 53.

[125] G. Gorji-Bahri, B. M. Krishna, C. Hagerling, et al., “Stromal Cartilage Oligomeric Matrix Protein as a Tumorigenic Driver in Ovarian Cancer via Notch3 Signaling and Epithelial-to-Mesenchymal Transition,” Journal of Translational Medicine 22, no. 1 (2024): 351.

[126] B. Mairhörmann, A. Castelblanco, F. Häfner, et al., “Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease,” Radiology: Artificial Intelligence 5, no. 6 (2023): e220239.

[127] Y. P. Zhang, X. Y. Zhang, Y. T. Cheng, et al., “Artificial Intelligence-Driven Radiomics Study in Cancer: The Role of Feature Engineering and Modeling,” Military Medical Research 10, no. 1 (2023): 22.

[128] A. Saburi, M. S. Kahrizi, N. Naghsh, et al., “A Comprehensive Survey Into the Role of microRNAs in Ovarian Cancer Chemoresistance: An Updated Overview,” Journal of Ovarian Research 15, no. 1 (2022): 81.

[129] Z. Tan, H. Mei, C. Qin, et al., “The Diagnostic Value of Dual-Layer CT in the Assessment of Lymph Nodes in Lymphoma Patients With PET/CT as a Reference Standard,” Scientific Reports 13, no. 1 (2023): 18323.

[130] M. Avanzo, P. Soda, M. Bertolini, et al., “Robust Radiomics: A Review of Guidelines for Radiomics in Medical Imaging,” Frontiers in Radiology 5 (2025): 1701110.

[131] J. Silos-Sánchez, J. A. Ruiz-Vanoye, F. R. Trejo-Macotela, et al., “Early Lung Cancer Detection via AI-Enhanced CT Image Processing Software,” Diagnostics 15, no. 21 (2025): 2691.

[132] J. Gong, Q. Wang, J. Li, et al., “Using High-Repeatable Radiomic Features Improves the Cross-Institutional Generalization of Prognostic Model in Esophageal Squamous Cell Cancer Receiving Definitive Chemoradiotherapy,” Insights Imaging 15, no. 1 (2024): 239.

[133] E. Montin, S. Namireddy, H. S. Ponniah, et al., “Radiomics for Precision Diagnosis of FAI: How Close Are We to Clinical Translation? A Multi-Center Validation of a Single-Center Trained Model,” Journal of Clinical Medicine 14, no. 12 (2025): 4042.

[134] Q. He, X. Kong, X. Meng, X. Shen, and N. Li, “The Value of PET/CT-Based Radiomics in Predicting Adrenal Metastases in Patients With Cancer,” Diagnostics 15, no. 11 (2025): 1356.

[135] M. Atzeni, G. Cappon, J. K. Quint, F. Kelly, B. Barratt, and M. Vettoretti, “A Machine Learning Framework for Short-Term Prediction of Chronic Obstructive Pulmonary Disease Exacerbations Using Personal Air Quality Monitors and Lifestyle Data,” Scientific Reports 15, no. 1 (2025): 2385.

[136] Y. Huang, T. Zhu, X. Zhang, et al., “Longitudinal MRI-Based Fusion Novel Model Predicts Pathological Complete Response in Breast Cancer Treated With Neoadjuvant Chemotherapy: A Multicenter, Retrospective Study,” EClinicalMedicine 58 (2023): 101899.

[137] I. Kołodziejczak-Guglas, R. L. S. Simões, E. de Souza Santos, et al., “Proteomic-Based Stemness Score Measures Oncogenic Dedifferentiation and Enables the Identification of Druggable Targets,” Cell Genomics 5, no. 6 (2025): 100851.

[138] M. Li, B. Mu, M. Li, et al., “Magnetic Resonance Imaging Studies on Acupuncture Therapy in Cerebral Ischemic Stroke: A Systematic Review,” Heliyon 10, no. 20 (2024): e39059.

[139] A. S. Pinho, A. P. Salazar, E. M. Hennig, B. C. Spessato, A. Domingo, and A. S. Pagnussat, “Can We Rely on Mobile Devices and Other Gadgets to Assess the Postural Balance of Healthy Individuals? A Systematic Review,” Sensors 19, no. 13 (2019): 2972.

[140] Y. Zhou, Z. Yao, L. Zhu, Y. Tang, J. Chen, and J. Wu, “Safety of Dabigatran as an Anticoagulant: A Systematic Review and Meta-Analysis,” Frontiers in Pharmacology 12 (2021): 626063.

[141] C. Kousparou, M. Fyrilla, A. Stephanou, and I. Patrikios, “DHA/EPA (Omega-3) and LA/GLA (Omega-6) as Bioactive Molecules in Neurodegenerative Diseases,” International Journal of Molecular Sciences 24, no. 13 (2023): 10717.

[142] N. L. Zabik, A. S. Iadipaolo, H. A. Marusak, C. Peters, K. Burghardt, and C. A. Rabinak, “A Common Genetic Variant in Fatty Acid Amide Hydrolase Is Linked to Alterations in Fear Extinction Neural Circuitry in a Racially Diverse, Nonclinical Sample of Adults,” Journal of Neuroscience Research 100, no. 3 (2022): 744–761.

[143] M. Anstruther, B. Rossini, T. Zhang, T. Liang, Y. Xiao, and M. Fortin, “PILLAR: ParaspInaL muscLe segmentAtion pRoject—a Comprehensive Online Resource to Guide Manual Segmentation of Paraspinal Muscles From Magnetic Resonance Imaging,” BMC Musculoskeletal Disorders 24, no. 1 (2023): 909.

[144] T. Sagawa and R. Kojima, “ReactionT5: A Pre-Trained Transformer Model for Accurate Chemical Reaction Prediction With Limited Data,” Journal of Cheminformatics 17, no. 1 (2025): 126.

[145] R. Wang, Q. Zhou, and G. Zheng, “EDRL: Entropy-Guided Disentangled Representation Learning for Unsupervised Domain Adaptation in Semantic Segmentation,” Computer Methods and Programs in Biomedicine 240 (2023): 107729.

[146] B. Khan, M. Usman, and M. Binsawad, “A Novel Hybrid Model for Emotion Detection in Text Through Sequential and Transformer-Based Approaches: LSTM Enhanced RoBERTa (LER),” Scientific Reports 16, no. 1 (2026): 2224.

[147] H. Salmon, R. Remark, S. Gnjatic, and M. Merad, “Host Tissue Determinants of Tumour Immunity,” Nature Reviews Cancer 19, no. 4 (2019): 215–227.

[148] W. A. Hall, E. Paulson, X. A. Li, et al., “Magnetic Resonance Linear Accelerator Technology and Adaptive Radiation Therapy: An Overview for Clinicians,” CA: A Cancer Journal for Clinicians 72, no. 1 (2022): 34–56.

[149] L. Chang, H. Li, W. Wu, et al., “Applications of Artificial Intelligence in Non-Small Cell Lung Cancer: From Precision Diagnosis to Personalized Prognosis and Therapy,” Journal of Translational Medicine 24, no. 1 (2025): 108.

[150] B. Zhao, “Understanding Sources of Variation to Improve the Reproducibility of Radiomics,” Frontiers in Oncology 11 (2021): 633176.

[151] F. J. Dorfner, J. B. Patel, J. Kalpathy-Cramer, E. R. Gerstner, and C. P. Bridge, “A Review of Deep Learning for Brain Tumor Analysis in MRI,” npj Precision Oncology 9, no. 1 (2025): 2.

[152] T. Akinci D'Antonoli, A. U. Cavallo, F. Vernuccio, et al., “Reproducibility of Radiomics Quality Score: An Intra- and Inter-rater Reliability Study,” European Radiology 34, no. 4 (2024): 2791–2804.

[153] P. Rai, S. Ahmed, and A. Mahajan, “Radiomics in Pediatric Brain Tumors: From Images to Insights,” Discover Oncology 16, no. 1 (2025): 1563.

[154] B. Sun, S. Huang, and J. Zhou, “Perspectives of Antidiabetic Drugs in Diabetes With Coronavirus Infections,” Frontiers in Pharmacology 11 (2020): 592439.

[155] J. Qiu, J. Li, S. Xu, et al., “Association Between the Newly Proposed Triglyceride Glucose-A Body Shape Index (TyG-ABSI) and Atherosclerotic Cardiovascular Disease in the General Population: Modest Diagnostic Improvement Compared With Traditional TyG-Related Parameters,” Cardiovascular Diabetology 25, no. 1 (2026): 40.

[156] A. Tenditnaya, R. Y. Gabriels, W. T. R. Hooghiemstra, et al., “Performance Assessment and Quality Control of Fluorescence Molecular Endoscopy With a Multi-Parametric Rigid Standard,” IEEE Transactions on Medical Imaging 43, no. 11 (2024): 3710–3718.

[157] B. H. Chew and K. Y. Ngiam, “Artificial Intelligence Tool Development: What Clinicians Need to Know?,” BMC Medicine 23, no. 1 (2025): 244.

[158] B. M. Mervak, J. G. Fried, and A. P. Wasnik, “A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging,” Diagnostics 13, no. 18 (2023): 2889.

[159] O. Bergengren, K. R. Pekala, K. Matsoukas, et al., “2022 Update on Prostate Cancer Epidemiology and Risk Factors—A Systematic Review,” European Urology 84, no. 2 (2023): 191–206.

[160] M. Deng, Y. Lin, L. Yan, C. Chen, Z. Fei, and J. Ding, “A Bibliometric Analysis of Nasopharyngeal Carcinoma Radiomics: Trends and Insights,” Frontiers in Oncology 15 (2025): 1506778.

[161] B. Yuan, Y. Hu, Y. Liang, et al., “Comparative Analysis of Convolutional Neural Networks and Transformer Architectures for Breast Cancer Histopathological Image Classification,” Frontiers in Medicine 12 (2025): 1606336.

[162] P. Hadweh, A. Niset, M. Salvagno, et al., “Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations From Rule-Based Systems to Frontier Models,” Journal of Clinical Medicine 14, no. 12 (2025): 4026.

[163] Q. Niu, J. Han, Z. Sui, and F. Xu, “Lightweight Deep Neural Networks: Optimization of Vehicle Classification Using ICBAM Based on Depthwise Separable Convolutions,” PLoS ONE 20, no. 11 (2025): e0335967.

[164] M. A. Chia, F. Antaki, Y. Zhou, A. W. Turner, A. Y. Lee, and P. A. Keane, “Foundation Models in Ophthalmology,” British Journal of Ophthalmology 108, no. 10 (2024): 1341–1348.

[165] GBD 2023 Cancer Collaborators. The Global, Regional, and National Burden of Cancer, 1990–2023, With Forecasts to 2050: A Systematic Analysis for the Global Burden of Disease Study 2023. Lancet 2025; 406(10512): 1565–1586.

[166] A. E. Jakab, M. Bukva, Z. Maróti, et al., “The ASAP Study: Association of Atherosclerosis With Pathobiology in a Caucasian Cohort—A Study of 3400 Autopsy Reports,” Scientific Reports 14, no. 1 (2024): 25179.

[167] L. L. Benster, C. R. Weissman, F. Suprani, et al., “Predictive Modeling of Response to Repetitive Transcranial Magnetic Stimulation in Treatment-Resistant Depression,” Translational Psychiatry 15, no. 1 (2025): 160.

[168] S. Zhao, D. P. Chen, T. Fu, et al., “Single-Cell Morphological and Topological Atlas Reveals the Ecosystem Diversity of Human Breast Cancer,” Nature Communications 14, no. 1 (2023): 6796.

[169] O. Dumas, N. Rozza, D. Cheishvili, et al., “The epiMelanoma Test Enables Plasma-Based Detection of Melanoma and Prediction of Immunotherapy Response,” Scientific Reports 15, no. 1 (2025): 30710.

[170] L. Dercle, J. McGale, B. Zhao, et al., “Artificial Intelligence and Radiomics Biomarkers for Treatment Response Prediction in Advanced HER2-Negative Breast Cancer,” Breast 84 (2025): 104571.

[171] M. Hatt, A. K. Krizsan, A. Rahmim, et al., “Joint EANM/SNMMI Guideline on Radiomics in Nuclear Medicine : Jointly Supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council,” European Journal of Nuclear Medicine and Molecular Imaging 50, no. 2 (2023): 352–375.

[172] Y. Liu, J. Sun, H. Sun, and Y. Chang, “Identification of Key Somatic Oncogenic Mutation Based on a Confounder-Free Causal Inference Model,” Plos Computational Biology 18, no. 9 (2022): e1010529.

[173] G. Lin, X. Zhang, Z. Ren, et al., “TAPB: An Interventional Debiasing Framework for Alleviating Target Prior Bias in Drug-Target Interaction Prediction,” Nature Communications 16, no. 1 (2025): 10867.

[174] A. M. B. Amorim, U. Orzeł, A. B. Caniceiro, N. Rosário-Ferreira, and I. S. Moreira, “Artificial Intelligence in Rare Diseases: Toward Clinical Impact,” Trends in Pharmacological Sciences 46, no. 12 (2025): 1241–1268.

[175] D. Kebsi, C. Barki, I. Dergaa, et al., “Personalized Hearing Loss Care Using SNOMED CT-Aligned Ontology and Random Forest Machine Learning: A Hybrid Decision-Support Framework,” Audiology Research 16, no. 2 (2026): 37.

[176] M. Tölle, P. Garthe, C. Scherer, et al., “Real World Federated Learning With a Knowledge Distilled Transformer for Cardiac CT Imaging,” npj Digital Medicine 8, no. 1 (2025): 88.

[177] K. Venkatesan and S. B. Rahayu, “Blockchain Security Enhancement: An Approach Towards Hybrid Consensus Algorithms and Machine Learning Techniques,” Scientific Reports 14, no. 1 (2024): 1149.

[178] V. Padmavathi and R. Saminathan, “A Federated Edge Intelligence Framework With Trust Based Access Control for Secure and Privacy Preserving IoT Systems,” Scientific Reports 15, no. 1 (2025): 35832.

[179] T. Liu, J. Wang, Z. Zhao, et al., “Efficacy and Safety of L-Oxiracetam on Cognitive Function in Patients With Traumatic Brain Injury: A Multicentre, Randomised, Double-Blind, Phase 3 Clinical Trial,” Signal Transduction and Targeted Therapy 10, no. 1 (2025): 401.

[180] W. Zhang, L. Luo, M. He, J. Hai, and J. Ye, “DescriptorMedSAM: Language-Image Fusion With Multi-Aspect Text Guidance for Medical Image Segmentation,” Scientific Reports 16, no. 1 (2026): 3758.

[181] S. L. Savaridas, U. Agrawal, A. F. Fagbamigbe, S. L. Tennant, and C. McCowan, “Radiomic Analysis in Contrast-Enhanced Mammography Using a Multivendor Data Set: Accuracy of Models According to Segmentation Techniques,” Bjr 96, no. 1145 (2023): 20220980.

[182] R. Xu, H. Tian, S. Zhao, and S. Gu, “Construction and Validation of a Risk Prediction Model for Complications in Patients With Acute Leukemia Based on Machine Learning,” Scientific Reports 15, no. 1 (2025): 40787.

[183] L. B. Callesen, T. Takacova, J. Hamfjord, et al., “Circulating DNA in Patients Undergoing Loco-regional Treatment of Colorectal Cancer Metastases: A Systematic Review and Meta-Analysis,” Therapeutic Advances in Medical Oncology 14 (2022): 17588359221133171.

[184] Q. Huang, H. Zhang, L. Song, Z. Jiao, and X. Ni, “EPIDSeg-Net: A Multi-Modal Fusion Framework Based on DRR Guidance in Radiotherapy Is Used for Precise Segmentation of MV-EPID Lung Targets,” Technology in Cancer Research & Treatment 25 (2026): 15330338251414224.

[185] A. Pearce, B. Terrill, J. W. Alffenaar, et al., “Pharmacogenomic Testing: Perception of Clinical Utility, Enablers and Barriers to Adoption in Australian Hospitals,” Internal Medicine Journal 52, no. 7 (2022): 1135–1143.

[186] L. Yang, S. W. F. Mok, H. H. Li, I. N. Wong, and L. J. Yang, “DDX27 in Cancer: Molecular Mechanisms, Clinical Implications, and Therapeutic Potential,” Journal of Translational Medicine 23, no. 1 (2025): 971.

[187] A. Daher, D. Trucu, and R. Eftimie, “Calibrating Tissue Level PDE Models of Ligand Dynamics Using Single Cell and Spatial Transcriptomics Data,” npj Systems Biology and Applications 12, no. 1 (2026): 44.

[188] K. Wadowska, I. Bil-Lula, Ł. Trembecki, and M. Śliwińska-Mossoń, “Genetic Markers in Lung Cancer Diagnosis: A Review,” International Journal of Molecular Sciences 21, no. 13 (2020): 4569.

[189] L. Han, K. Peng, L. Y. Qiu, et al., “Hitchhiking on Controlled-Release Drug Delivery Systems: Opportunities and Challenges for Cancer Vaccines,” Frontiers in Pharmacology 12 (2021): 679602.

[190] G. Wu, A. Jochems, T. Refaee, et al., “Structural and Functional Radiomics for Lung Cancer,” European Journal of Nuclear Medicine and Molecular Imaging 48, no. 12 (2021): 3961–3974.

[191] F. W. Chi, S. Parthasarathy, V. A. Palzes, et al., “Alcohol Brief Intervention, Specialty Treatment and Drinking Outcomes at 12 Months: Results From a Systematic Alcohol Screening and Brief Intervention Initiative in Adult Primary Care,” Drug and Alcohol Dependence 235 (2022): 109458.

RIGHTS & PERMISSIONS

2026 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.