Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482

Initial sequencing and analysis of the human genome

International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216

Textural Features for Image Classification

Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886

Molecular portraits of human breast tumours

Perou, C., Sørlie, T., Eisen, M., van de Rijn, M., Jeffrey, S., Rees, C. A., Pollack, J., Ross, D., Johnsen, H., Akslen, L., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,551

Comprehensive molecular portraits of human breast tumours

The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129

Radiomics: Images Are More than Pictures, They Are Data

Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421

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

Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084

Trastuzumab plus Adjuvant Chemotherapy for Operable HER2-Positive Breast Cancer

Romond, E. H., Perez, E. A., Bryant, J., Suman, V. J., Geyer, C. E., Davidson, N. E., Tan-Chiu, E., Martino, S., Paik, S., Kaufman, P. A., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa052122 · Cited by 5,438

A New Initiative on Precision Medicine

Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599

Relationship Between Breast Ultrasound Background Echotexture and Magnetic Resonance Imaging Background Parenchymal Enhancement and the Effect of Hormonal Status Thereon

Kim, S. J., Jung, H. K., Han, B., Lee, J. H., Oh, K. S., Lee, J. S., & Lee, Y. (2020) · Ultrasound Quarterly · doi:10.1097/ruq.0000000000000487 · Cited by 2

A Nomogram Based on Conventional Ultrasound Radiomics for Differentiating Between Radial Scar and Invasive Ductal Carcinoma of the Breast

Su, H.-Z., Hong, L.-C., Su, Y.-M., Chen, X., Zhang, Z.-B., & Zhang, X.-D. (2024) · Ultrasound Quarterly · doi:10.1097/ruq.0000000000000685 · Cited by 1

AI for application solutions for healthcare services using AI detection and diagnosis of different diseases: A special emphasis on neuro-oncology

Dinesh, R. A., Shanmugam, J., & Biswas, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-18509-0.00009-8 · Cited by 4

Biomedical image characterization and radio genomics using machine learning techniques

Okunlola, F. O., Adetuyi, T. G., Olajide, P. A., Okunlola, A. R., Adetuyi, B. O., Adeyemo-Eleyode, V. O., Akomolafe, A. A., Yunana, N., Baba, F., Nwachukwu, K. C., et al. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15452-2.00019-4 · Cited by 2

Predicting progression in triple-negative breast cancer patients undergoing neoadjuvant chemotherapy: Insights from peritumoral radiomics

Kim, S., Woo, J., Lee, S., & Hong, H. (2024) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2024.110292 · Cited by 2

Human serum albumin-based KBiF4@HSA nanoclusters for dual-energy computed tomography and glutathione-scavenging radiotherapy of breast cancer

Huang, Y., Lu, Z., Liu, H., Yang, N., Chen, Y., Wang, C., Huang, W., Hu, J., Wang, D., & Yao, D. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03530-8 · Cited by 2

Equity and Generalizability of Radiomics in Orbital Disease: Challenges for Ophthalmology, Otolaryngology, and Plastic Surgery

Abbas, H., Taka, M. A., Imokhai, P. O., Singh, S. K., Gharib, C., Mehad, A. M., & Brooks, A. (2026) · Diagnostics · doi:10.3390/diagnostics16070968 · Cited by 2

Proteomic and metabolomic biomarkers of Alzheimer's disease: Opportunities to advance precision medicine and clinical trials

Petersen, M. E., Gross, T. J., O’Bryant, S. E., & Mapstone, M. E. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818845-3.00018-9 · Cited by 2

Radiomic Signature Based on Dynamic Contrast-Enhanced MRI for Evaluation of Axillary Lymph Node Metastasis in Breast Cancer

Tang, Y., Chen, L., Qiao, Y., Li, W., Deng, R., & Liang, M. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/1507125 · Cited by 3

Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics

Jia, T., Lv, Q., Zhang, B., Yu, C., Sang, S., & Deng, S. (2023) · BMC Medical Imaging · doi:10.1186/s12880-023-01052-z · Cited by 2

Precision Medicine in Brain Tumors: New Approaches

Akhlaghdoust, M., Bordbar, S., Nikoohemmat, M., Meftah, E., Rahimzadegan, M., Akbari, S., & Zali, A. (2024) · Interdisciplinary cancer research · doi:10.1007/16833_2024_274 · Cited by 2

Do We Need Optoacoustic Assessment of Hypoxia to Differentiate Molecular Subtypes of Breast Cancer?

Mann, R. M. (2019) · Radiology · doi:10.1148/radiol.2019191263 · Cited by 2

Editorial: Impact of Breast MRI on Breast Cancer Treatment and Prognosis

Bitencourt, A. G. V., Iima, M., Langs, G., & Pinker, K. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.825101 · Cited by 2

Systemic Metabolomic Changes Associated with Chemotherapy: Role in Personalized Therapy

Kalita, B., Barik, G. K., Sharma, T., Taunk, K., Bhavsar, P. P., Santra, M. K., & Rapole, S. (2022) · Wiley eBooks · doi:10.1002/9781119851042.ch24 · Cited by 2

Mitochondrial metabolism-related features guiding precision subtyping and prognosis in breast cancer, revealing FADS2 as a novel therapeutic target

Kang, Y., Meng, Y., Jin, J., Dai, Y., Li, F., Chen, N., Xie, H., & Cui, Y. (2025) · Translational Oncology · doi:10.1016/j.tranon.2025.102330 · Cited by 1

Alterations of drug-resistant proteins in nasopharyngeal carcinoma after intensity-modulated radiation therapy with or without chemotherapy and correlation with clinicopathologic features and short-term prognosis

Wang, G., & Li, Y. (2025) · Discover Oncology · doi:10.1007/s12672-025-02677-y · Cited by 1

Multidimensional evaluation of 3.0T HR-MRI, ultrasound imaging, and GATA3 protein expression in breast cancer, and their prognostic analysis

Bai, Y., Xie, T., Mo, X., Luo, Y., & Chen, M. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1587243 · Cited by 1

Decoding RNA therapeutics: Precision approaches in women's health

Far, N. P., Boroujerdi, H. N., Karimi, M. S., Vezvaei, P., Behfar, M., Mirzaei, R., Habibi, R., Varkiani, M. M., Aref, A. R., & Ebrahimi, N. (2025) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2025.178275 · Cited by 1

MRI-based radiomics model for predicting tumor regression patterns after neoadjuvant chemotherapy in breast cancer

Wang, L., Wang, Q., Zhang, J., Zhang, M., Guo, T., Gao, W., Zhang, B., & Wang, H. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1661448 · Cited by 1

‘Omics’ Big Data and Precision Medicine

Guan, J. (2025) · Springer eBooks · doi:10.1007/978-981-95-2806-6_12 · Cited by 1

Ultrasound radiomics predicts preoperative axillary lymph node metastasis status in early-stage breast cancer to support surgical decisions: a machine learning, monocenter study

Hong, Z.-L., Peng, X.-R., Liang, X., Zeng, X.-T., Yang, J.-C., & Wu, S.-Z. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2025.1680160 · Cited by 1

Predicting hormone receptor status in tumors: An innovative approach using breast ultrasound-radiomics combined model

Yin, X., Wang, Z., Zhao, Q.-X., Zhang, X.-C., Sang, T., & Shao, C.-P. (2026) · Medicine · doi:10.1097/md.0000000000047576

Counterfactual Multimodal Deep Learning for Personalized Breast Cancer Risk Intervention

Chugwani, S., & Kushwaha, P. A. S. (2026) · International Conference on Intelligent Systems in Engineering, Secured Systems and Cybersecurity (ICISESSC) · doi:10.1109/icisessc68634.2026.11542698 · Cited by 1

Breast cancer radiogenomics analysis via computational perturbation on AI-driven multi-omics guided image synthesis

Zhang, X.-Y., & Liu, Q. (2026) · bioRxiv · doi:10.1109/ccece68150.2026.11610465 · Cited by 1

Artificial Intelligence in Medicine and Privacy Preservation

Ziller, A., Passerat‐Palmbach, J., Trask, A., Braren, R., Rueckert, D., & Kaissis, G. A. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_261 · Cited by 1

Application of Big Data Analytics in Precision Medicine: Lesson for Ethiopia

Woldemariam, M. T., & Alemneh, D. G. (2023) · Journal of Information & Knowledge Management · doi:10.1142/s0219649223500533 · Cited by 1

Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis

Wang, M., Peng, Y., Wang, Y., & Luo, D. (2024) · Interactive Journal of Medical Research · doi:10.2196/51347 · Cited by 1

An Accelerated Computational Approach in Proteomics

Bhardwaj, S., Gudur, V. Y., & Acharyya, A. (2019) · Series in bioengineering · doi:10.1007/978-981-13-9097-5_16 · Cited by 1

Editor’s Pick: Regulatory and Ethical Issues in the New Era of Radiomics and Radiogenomics

Pesapane, F. (2020) · EMJ Radiology · doi:10.33590/emjradiol/19-00165 · Cited by 2

Imaging epigenetics and the radiogenomics

Tárnoki, Á. D., & Tárnoki, D. L. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820951-6.00011-9 · Cited by 1

Biomarkers, Prognosis, and Prediction Factors

Reig, B., Moy, L. P., Sigmund, E. E., & Heacock, L. C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-79702-3.00004-6 · Cited by 1

State-of-the-Art in Integrated Breast Imaging

Grezia, G. D., Gatta, G., Brunese, L., & Falco, G. (2019) · BioMed Research International · doi:10.1155/2019/7596059 · Cited by 1

The Role of Diffusion-weighted Imaging in Biopsy-proven Ductal Carcinoma in Situ

Baltzer, P. (2022) · Radiology · doi:10.1148/radiol.221329 · Cited by 1

Multiparametric MRI and Radiomics to Predict HER2-Low Cancer: The Potential Therapeutic Impact

Kataoka, M., & Honda, M. (2023) · Radiology · doi:10.1148/radiol.231649 · Cited by 1

Insights into innovative breast imaging techniques

Friedlander, L., & Ha, R. S. (2018) · Clinical Imaging · doi:10.1016/j.clinimag.2018.10.021

Merging Medical Imaging with Molecular Genetics for “Tailor-Made” Therapy in Oncology

Solomou, A. G. (2019) · Biomedical Journal of Scientific & Technical Research · doi:10.26717/bjstr.2019.17.003015

Genomics and Interventional Oncology in Metastatic Liver Cancer

Case, M. D., Slovak, R. J., & Kim, H. S. (2020) · Digestive Disease Interventions · doi:10.1055/s-0040-1712120

Surgical Decisions on Breast Cancer in the Elderly

Castaldi, M. T. (2020) · Springer eBooks · doi:10.1007/978-3-030-47963-3_14

Magnetic Resonance Features of Lower-grade Gliomas in Prediction of the Reverse Phase Protein A

Wang, J., & Guo, X. (2021) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001132

Bilder sind Daten: Eine Perspektive der Brustbildgebung auf ein zeitgenössisches Paradigma

Dietzel, M., Clauser, P., Kapetas, P., Schulz‐Wendtland, R., & Baltzer, P. (2021) · Senologie - Zeitschrift für Mammadiagnostik und -therapie · doi:10.1055/a-1557-1062

Sampling Statistical Errors in Big Data Research: 3 Cases of Breast Cancer ResearchPreprint

Cho, H.-J., & Jeong, E. S. (2021) · medRxiv · doi:10.1101/2021.10.07.21264601

6. Imaging Biopsy for Assisting Cancer Precision Therapy —Information Extracted from Radiomics—

Arimura, H., Kodama, T., Urakami, A., Kamezawa, H., Hirose, T., & Ninomiya, K. (2022) · Japanese Journal of Radiological Technology · doi:10.6009/jjrt.780213

Radiogenomics

Townsend, S., Sharma, R., & Jones, J. (2018) · Radiopaedia.org · doi:10.53347/rid-61490

Fortschrittsnachweise

Kinne, P. (2020) · Springer eBooks · doi:10.1007/978-3-662-61021-3_6

Deep learning-based breast cancer disease prediction framework for medical industries

Priya, G., & Radhika, A. (2022) · Advances in Complex Systems · doi:10.1142/s1793962323500125

Editorial for “TP53 Mutation Estimation Based on Radiomics Analysis for Breast Cancer”

Thakur, S. B., Chakraborthy, J., & Pinker, K. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28324

Conventional Breast Imaging

Mann, R. M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-79702-3.00002-2

MRI radiomics and radiogenomics for breast cancer

Belenky, V., Chitalia, R., & Despina Kontos, B. (2022) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-822729-9.00029-1

Artificial Intelligence in Breast Imaging

Bitencourt, A. G. V., & Pinker, K. (2023) · Springer eBooks · doi:10.1007/978-981-99-0035-0_36

Progress on radiomics and radiogenomics and their applications in breast cancer: A survey

(2023) · Data Science and Informetrics · doi:10.59494/dsi.2023.4.6 · Cited by 1

Additional file 1 of Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ

Wu, L., Zhao, Y., Lin, P., Qin, H., Liu, Y., Wan, D., Li, X., He, Y., & Yang, H. (2021) · Figshare · doi:10.6084/m9.figshare.14609062

Additional file 2 of Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ

Wu, L., Zhao, Y., Lin, P., Qin, H., Liu, Y., Wan, D., Li, X., He, Y., & Yang, H. (2021) · Figshare · doi:10.6084/m9.figshare.14609065

The Role of Texture Analysis of MRI in Prediction of Local Recurrence and Distant Metastasis in Locally Advanced Rectal Cancer: A retrospective Cohort study

Alrahawy, M., Aker, M., Ganeshan, B., zeinaldin, A., & Arulampalam, T. (2024) · Journal of Surgery and Research · doi:10.26502/jsr.10020356

Early Prediction of Breast Cancer by Genetic Screening Profile among Young Females in Najran area (Southern Saudi Arabia) 2022-/2024

TalbAllah, S. M. (2024) · Saudi Journal of Pathology and Microbiology · doi:10.36348/sjpm.2024.v09i05.004

Improving Breast Mass Classification Performance of Radiomics-Based Model by Image Enhancement with Discrete Wavelet Transformation

Nguyen, T. H., Vo, M. T. A., Nguyen, T. H. H., & Le, N. T. (2023) · International Conference on Health Science and Technology (ICHST) · doi:10.1109/ichst59286.2023.10565363

Revolutionizing Genomic Data Management in the Cloud- Novel Approaches for Secure Transfer and Storage of Sequencing VCF Data in the Realm of AI and Cybersecurity - A Perspective

Mohanty, J. P., & Mohanty, R. P. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724857

Machine learning based genome cancer dataset approaches: Review

Singh, S. J., & Agrawal, P. (2024) · AIP conference proceedings · doi:10.1063/5.0234976

Artificial Intelligence in Breast Imaging: Basic Principles for Radiologists

Rana, P., & Rangarajan, K. (2025) · Springer eBooks · doi:10.1007/978-981-97-9847-6_20

Enabling Privacy-Preserving Analysis of Medical Records from Heterogeneous Data Sources

Lax, G. (2025) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-88649-2_8

Development of a Radiomic-clinical Nomogram for Prediction of Survival in Patients with Nasal Extranodal Natural Killer/T-cell Lymphoma

Chen, L., Wang, Z., Fang, X., Yu, M., Ye, H., Han, L., Tian, Y., Guo, C., & Huang, H. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056319914250605053257

Additional file 2 of MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study

Park, A. Y., Han, M.-R., Seo, B. K., Ju, H.-Y., Son, G. S., Lee, H. Y., Chang, Y. W., Choi, J., Cho, K. R., Song, S. E., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23612385

Additional file 1 of Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions

Cui, H., Sun, Y., Zhao, D., Zhang, X., Kong, H., Hu, N., Wang, P., Zuo, X., Fan, W., Yao, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26563884

Additional file 5 of Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

Ju, H.-Y., Youn, S. Y., Kang, J., Whang, M. Y., Choi, Y. J., & Han, M.-R. (2024) · Figshare · doi:10.6084/m9.figshare.26759276

Additional file 1 of Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

Ju, H.-Y., Youn, S. Y., Kang, J., Whang, M. Y., Choi, Y. J., & Han, M.-R. (2024) · Figshare · doi:10.6084/m9.figshare.26759264

Additional file 2 of Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

Ju, H.-Y., Youn, S. Y., Kang, J., Whang, M. Y., Choi, Y. J., & Han, M.-R. (2024) · Figshare · doi:10.6084/m9.figshare.26759267

Additional file 7 of Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

Ju, H.-Y., Youn, S. Y., Kang, J., Whang, M. Y., Choi, Y. J., & Han, M.-R. (2024) · Figshare · doi:10.6084/m9.figshare.26759282

Additional file 3 of Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

Ju, H.-Y., Youn, S. Y., Kang, J., Whang, M. Y., Choi, Y. J., & Han, M.-R. (2024) · Figshare · doi:10.6084/m9.figshare.26759270

Additional file 6 of Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

Ju, H.-Y., Youn, S. Y., Kang, J., Whang, M. Y., Choi, Y. J., & Han, M.-R. (2024) · Figshare · doi:10.6084/m9.figshare.26759279

Additional file 4 of Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

Ju, H.-Y., Youn, S. Y., Kang, J., Whang, M. Y., Choi, Y. J., & Han, M.-R. (2024) · Figshare · doi:10.6084/m9.figshare.26759273

Additional file 1 of Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women

Tan, Y., Mai, H., Huang, Z., Zhang, L., Li, C., Wu, S., Huang, H., Tang, W., Liu, Y., & Jiang, K. (2021) · Figshare · doi:10.6084/m9.figshare.14205513

Additional file 1 of MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study

Park, A. Y., Han, M.-R., Seo, B. K., Ju, H.-Y., Son, G. S., Lee, H. Y., Chang, Y. W., Choi, J., Cho, K. R., Song, S. E., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23612382

Early prediction of pathologic complete response to neoadjuvant chemotherapy based on longitudinal total choline of MR spectroscopy in patients with breast cancer

Liu, S., Wang, X., Jiang, F., Tang, S., Cao, Y., Wang, L., Chen, H., Zeng, X., Huang, Y., Li, L., et al. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02160-2

Prediction of Neurological Functional Recovery after Carotid Endarterectomy Using Machine Learning and Carotid Computed Tomography Angiography Radiomics

Qiu, M., Xu, Y., Hu, B., Zheng, K., Shao, G., Yu, L., Cheng, L., Ma, J., & Li, S. (2026) · World Neurosurgery · doi:10.1016/j.wneu.2026.124874

Artificial intelligence in the management of acute kidney injury after cardiac surgery

Yang, S., Shen, H., Yang, J., Wang, Y., Jing, Z., Xing, L., Zhou, P., Chen, P., Ni, H., Yu, Y., et al. (2026) · Artificial Intelligence Surgery · doi:10.20517/ais.2025.100

Prediction of hormone receptor status in breast cancer brain metastases using an MRI-based multimodal deep learning framework

Sun, Q., Zhang, Q., Xiao, Y., & Pei, X. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1858163

Mechanistic study of antisense long non-coding RNA STEAP3-AS1 in breast cancer

Sun, J., Cui, J., Qiu, T., & Shen, G. (2026) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2026.1002380

Application and integration of deep learning in tumour radiomics: bibliometrics and visualisation analysis

Huang, Y., Liu, X., & Zhou, Y. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1779682

Peak uptake value on positron emission mammography addresses biopsy sampling limitations in stratifying proliferative activity of malignant non-mass enhancement

Mohamed, N. E., Sobhi, A., Wessam, R., Kamal, E., Omar, A. R., Refaie, R. M. A. E., & Elmesidy, D. S. (2026) · British Journal of Radiology · doi:10.1093/bjr/tqag188

Pixels to Prognosis: Translating Synthetic MRI Radiomics into Clinical Practice for Human Epidermal Growth Factor Receptor 2–Positive Breast Cancer

Hu, C. (2026) · Radiology Imaging Cancer · doi:10.1148/rycan.260448

Imaging in Cancer Diagnosis: Consolidating Progress and Charting the Next Steps

Sussan, G., & Crimì, F. (2026) · Tomography · doi:10.3390/tomography12100141

Explainable Radiogenomic Fusion Links Mri-derived Radiomic Phenotypes to Transcriptomic Molecular Subtypes in Breast Cancer

Thakar, S. B., Thakar, P. S., & Sangle, A. (2026) · Research Journal of Life Sciences Bioinformatics Pharmaceutical and Chemical Sciences · doi:10.26479/2026.1205.02

Radiogenomics: a key component of precision cancer medicine

Liu, Z.-Q., Duan, T., Zhang, Y.-Y., Weng, S.-Y., Xu, H.-X., Ren, Y.-Q., Zhang, Z.-Y., & Han, X.-W. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02317-8 · Cited by 98

Radiogenomics in breast cancer: a review

Гаранина, А. Е., & Васильевич, Х. А. (2024) · Diagnostic radiology and radiotherapy · doi:10.22328/2079-5343-2024-15-1-15-21 · Cited by 1

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

He, W., Huang, W., Zhang, L., Wu, X., Zhang, S., & Zhang, B. (2024) · MedComm · doi:10.1002/mco2.722 · Cited by 64

Progress on radiomics and radiogenomics and their applications in breast cancer: A survey

(2023) · Data Science and Informetrics · doi:10.59494/dsi.2023.4.6 · Cited by 1

Combining molecular and imaging metrics in cancer: radiogenomics

Gullo, R. L., Naranjo, I. D., Morris, E. A., & Pinker, K. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0795-6 · Cited by 183