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Publications 2025 - Journal papers (53)

(45)

  1. LIFEx-texture: Peng, Y., Shi, M., Xiong, D. et al. Preoperative assessment and prognostic prediction of gastric cancer patients with peritoneal metastasis using 18F-FDG PET/CT before conversion surgery. EJNMMI Res 15, 46 (2025). https://doi.org/10.1186/s13550-025-01244-4
  2. LIFEx-texture: Gemmell, A.J., Brown, C.M., Ray, S. et al. Robustness of textural analysis features in quantitative 99 mTc and 177Lu SPECT-CT phantom acquisitions. EJNMMI Phys 12, 40 (2025). https://doi.org/10.1186/s40658-025-00749-0
  3. LIFEx-Texture: Bilgin E, Bilgin EY, Bayrak A, Torenek S. Advanced Radiomics for Predicting Extracapsular Invasion of Metastatic Axillary Lymph Nodes in Breast Cancer Patients Using CT Imaging. J Coll Physicians Surg Pak 2025; 35(04):415-419 https://doi.org/10.29271/jcpsp.2025.04.415
  4. LIFEx-main: Stien, G., Zinsz, A., Ahrari, S. et al. PET-based response assessment criteria for diffuse gliomas (PET RANO 1.0): methodological application in [18F]-FDOPA PET imaging.EJNMMI Res 15, 44 (2025). https://doi.org/10.1186/s13550-025-01239-1 
  5. LIFEx-texture: Yali Cui, Yao Li, Wenhao Hu, Zhifang Wu, Sijin Li, Hongliang Wang. Evaluating ΔMTV%, ΔDmax%, and %ΔSUVmax of 18F-FDG PET/CT for mid-treatment efficacy and prognosis in diffuse large B-cell lymphoma. Discover Oncology; New York Vol. 16, Iss. 1,  (Dec 2025): 411. https://doi.org/10.1007/s12672-025-02126-w
  6. LIFEx-texture: Melek YAKAR, Durmus ETIZ, Eyyup GULBANDILAR, Kerem DURUER, Ergin ERDEN. Radiomic-Assistant Response Prediction to Stereotactic Body Radiotherapy in Early Stage Lung Cancer. UHOD Number: 1 Volume: 35 Year: 2025. https://doi.org/10.4999/uhod.257762
  7. LIFEx-texture: Zhao, J., Zhao, W., Chen, M. et al. 18F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study. J Cancer Res Clin Oncol 151, 125 (2025). https://doi.org/10.1007/s00432-025-06172-3
  8. LIFEx-texture: Cui, Y., Li, Y., Hu, W. et al. Evaluating ΔMTV%, ΔDmax%, and %ΔSUVmax of 18F-FDG PET/CT for mid-treatment efficacy and prognosis in diffuse large B-cell lymphoma. Discov Onc 16, 411 (2025). https://doi.org/10.1007/s12672-025-02126-w
  9. LIFEx-texture: Takeharu Kiso, Yukinori Okada, Satoru Kawata, Kouta Shichiji, Eiichiro Okumura, Noritaka Hatsumi, Ryohei Matsuura, Masaki Kaminaga, Hikaru Kuwano, Erika Okumura. Ultrasound-based radiomics and machine learning for enhanced diagnosis of knee osteoarthritis: Evaluation of diagnostic accuracy, sensitivity, specificity, and predictive value. European Journal of Radiology Open, Volume 14, 2025, 100649, ISSN 2352-0477, https://doi.org/10.1016/j.ejro.2025.100649
  10. LIFEx-main: Yexin Su, Hongyue Zhao, Zhehao Lyu, Peng Xu, Ziyue Zhang, Huiting Zhang, Mengjiao Wang, Lin Tian, Peng Fu. Quantification of Intratumoral Heterogeneity Based on Habitat Analysis for Preoperative Assessment of Lymphovascular Invasion in Colorectal Cancer. Academic Radiology, 2025, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2025.03.014
  11. LIFEx-texture: Mengye Peng, Menglu Wang, Xinyue Yang, Yanmei Wang, Lizhi Xie, Wenxin An, Fan Ge, Chen Yang, Kezheng Wang. Prediction of PD-L1 Expression in NSCLC patients Using PET/CT Radiomics and Prognostic Modeling for Immunotherapy in PD-L1-Positive NSCLC Patients. Clinical Radiology. 2025. 106915, ISSN 0009-9260, https://doi.org/10.1016/j.crad.2025.106915
  12. LIFEx-MTV: Hu, J., Cheng, R., Quan, M. et al. Hypermetabolic pulmonary lesions detection and diagnosis based on PET/CT imaging and deep learning models. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-025-07215-0
  13. LIFEx-texture: Liao Z, Luo D, Tang X, Huang F, Zhang X. MRI-based radiomics for predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis. Front Oncol. 2025 Mar 10;15:1550838. https://doi.org/10.3389/fonc.2025.1550838 PMID: 40129922; PMCID: PMC11930822.
  14. LIFEx-main: Shrestha et al., (2025). RT-utils: A Minimal Python Library for RT-struct Manipulation. Journal of Open Source Software, 10(107), 7361, https://doi.org/10.21105/joss.07361
  15. LIFEx-texture: Isemoto, K., Waseda, Y., Fujiwara, M., Kimura, K., Hirahara, D., Saho, T., Takaya, E., Arita, Y., Kwee, T. C., Fukuda, S., Tanaka, H., Yoshida, S., & Fujii, Y. (2025). Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer. Diagnostics, 15(7), 801. https://doi.org/10.3390/diagnostics15070801
  16. LIFEx-texture: Esat Kaba, Hande Melike Bülbül, Mehmet Kıvrak, Nur Hürsoy. Multisequence combined magnetic resonance imaging radiomics model to noninvasively predict nuclear grade of clear cell renal cell carcinoma: interpretable model development. Rev Assoc Med Bras. 2025;71(1):e20241012. https://doi.org/10.1590/1806-9282.20241012
  17. LIFEx-texture: J. Wang, W. Tang, J. Zhu, J. Cui, Y. Chen, M. Gu, H. Xu, M. Zhan, Q. Chen, B. Xu. Predicting the pathological subdiagnosis of benign prostatic hyperplasia with MRI radiomics: A noninvasive approach. VIEW. 2025, 20240092. https://doi.org/10.1002/VIW.20240092
  18. LIFEx-texture: Magnin Cheryl Y., Lauer David, Ammeter Michael, Gote-Schniering Janine. From images to clinical insights: an educational review on radiomics in lung diseases. 21:1, 230225. https://doi.org/10.1183/20734735.0225-2023
  19. LIFEx-texture:  Sriramganesh Gokavarapu, K Venkata Rao, Gorla Srinivas, Dasari Siva Krishna, Enhancing Lung Cancer Detection: Optimizing Deep Learning with Convolutional Block Attention Module, Journal of Artificial Intelligence and Technology (2025), DOI: https://doi.org/10.37965/jait.2025.0668
  20. LIFEx-texture: Liu, H., Meng, X., Wang, G. et al. Differentiating second primary lung cancer from pulmonary metastasis in patients of single solitary pulmonary lesion with extrapulmonary tumor using multiparametric analysis of FDG PET/CT. Ann Nucl Med(2025). https://doi.org/10.1007/s12149-025-02034-7
  21. LIFEx-texture: Alireza Safarian, Seyed Ali Mirshahvalad, Hadi Nasrollahi, Theresa Jung, Christian Pirich, Hossein Arabi, Mohsen Beheshti. Impact of [18F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role, Seminars in Nuclear Medicine, 2025, ISSN 0001-2998, https://doi.org/10.1053/j.semnuclmed.2025.02.006
  22. LIFEx-texture: Liu, H., Meng, X., Wang, G. et al. Differentiating second primary lung cancer from pulmonary metastasis in patients of single solitary pulmonary lesion with extrapulmonary tumor using multiparametric analysis of FDG PET/CT. Ann Nucl Med(2025). https://doi.org/10.1007/s12149-025-02034-7
  23. LIFEx-texture: Tan Qianqian, Teng Yue, Sun Yiwen, Xu Pei, Xu Yiduo, Chen Qiaoliang, He Jian, Lai Ruihe. Prognostic value of 18F-FDG PET/CT metabolic parameters TMTV in patients with stage Ⅳ endometrial cancer[J]. Int J Radiat Med Nucl Med. https://doi.org/10.3760/cma.j.cn121381-202403022-00506
  24. LIFEx-texture: Lazaros K, Adam S, Krokidis MG, Exarchos T, Vlamos P, Vrahatis AG. Non-Invasive Biomarkers in the Era of Big Data and Machine Learning. Sensors. 2025; 25(5):1396. https://doi.org/10.3390/s25051396
  25. LIFEx-texture: Yang, Mao et al. Multimodal integration of liquid biopsy and radiology for the noninvasive diagnosis of gallbladder cancer and benign disorders. Cancer Cell, Volume 43, Issue 3, 398 - 412.e4. https://www.cell.com/cancer-cell/fulltext/S1535-6108(25)00063-7
  26. LIFEx-texture: Lazaros K, Adam S, Krokidis MG, Exarchos T, Vlamos P, Vrahatis AG. Non-Invasive Biomarkers in the Era of Big Data and Machine Learning. Sensors. 2025; 25(5):1396. https://doi.org/10.3390/s25051396
  27. LIFEx-texture: Peng, M., Wang, M., An, W. et al. Predictive classification of lung cancer pathological based on PET/CT radiomics. Jpn J Radiol (2025). https://doi.org/10.1007/s11604-025-01742-4
  28. LIFEx-texture: Gennaro, N.; Soliman, M.; Borhani, A.A.; Kelahan, L.; Savas, H.; Avery, R.; Subedi, K.; Trabzonlu, T.A.; Krumpelman, C.; Yaghmai, V.; et al. Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer. Tomography 2025, 11, 20. https://doi.org/10.3390/tomography11030020
  29. LIFEx-Main: Ronga MG, Gesualdi F, Bonfrate A, et al. Comparison of secondary radiation dose between pencil beam scanning and scattered delivery for proton and VHEE radiotherapy. Med Phys. 2025;1-10. https://doi.org/10.1002/mp.17700
  30. Zhang, Yichi and Xue, Le and Zhang, Wenbo and Li, Lanlan and Liu, Yuchen and Jiang, Chen and Cheng, Yuan and Qi, Yuan. SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images. https://arxiv.org/pdf/2502.14351
  31. LIFEx-MTV: Albano, D., Temponi, A., Bertagna, F. et al. The prognostic role of staging [18F]PSMA-1007 PET/CT volumetric and dissemination features in prostate cancer. Ann Nucl Med (2025). https://doi.org/10.1007/s12149-025-02026-7
  32. LIFEx-main: Liu, Y., Wang, J., Du, B. et al. Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point 18F-FDG PET/CT. Cancer Imaging 25, 17 (2025). https://doi.org/10.1186/s40644-025-00834-8
  33. LIFEx-texture: Pellegrino S, Fonti R, Morra R, Di Donna E, Servetto A, Bianco R, Del Vecchio S. Prognostic Value of Tumor Dissemination (Dmax) Derived from Basal 18F-FDG Positron Emission Tomography/Computed Tomography in Patients with Advanced Non-Small-Cell Lung Cancer. Biomedicines. 2025; 13(2):477. https://www.mdpi.com/2227-9059/13/2/477
  34. LIFEx-texture: Dwivedi P, Sagar S, AK Jha, S Choudhury, Venkatesh R. Robustness of 18F-FDG PET Radiomic Features in Lung Cancer: Impact of Advanced Reconstruction Algorithm.  J. Nucl. Med. Technol. 2025/02/05. http://doi.org/10.2967/jnmt.124.268252
  35. LIFEx-texture: Filippi, L., Bianconi, F., Minestrini, M. et al. Multi-centre data harmonisation applied to heart-to-mediastinum quantification in parkinsonism (ITA-MIBG): a cross-calibration phantom study with tube and bottle. Clin Transl Imaging (2025). https://doi.org/10.1007/s40336-025-00681-4
  36. LIFEx-texture: Jiang, C., Jiang, Z., Zhang, Z. et al. An explainable transformer model integrating PET and tabular data for histologic grading and prognosis of follicular lymphoma: a multi-institutional digital biopsy study. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-025-07090-9
  37. LIFEx-texture: Beaumont H, Iannessi A, Thinnes A, Jacques S, Quintás-Cardama A. Radiomics-Based Prediction of Treatment Response to TRuC-T Cell Therapy in Patients with Mesothelioma: A Pilot Study. Cancers. 2025; 17(3):463. https://doi.org/10.3390/cancers17030463
  38. LIFEx-texture: Jiang, C., Qian, C., Jiang, Q. et al. Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT. BMC Med 23, 49 (2025). https://doi.org/10.1186/s12916-025-03893-7
  39. LIFEx-MTV: Wen, Z., Gao, X., Wu, Q. et al. Baseline [18F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line R-CHOP. BMC Cancer 25, 128 (2025). https://doi.org/10.1186/s12885-025-13507-3
  40. LIFEx-MTV-texture: Kleiburg, F., de Geus-Oei, LF., Spijkerman, R. et al. Baseline PSMA PET/CT parameters predict overall survival and treatment response in metastatic castration-resistant prostate cancer patients. Eur Radiol (2025). https://doi.org/10.1007/s00330-025-11360-3
  41. LIFEx-texture: Qi, L., Li, X., Ni, J. et al. Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT, 18F-FDG PET/CT, DNA mutation, and CA199. Cancer Cell Int 25, 19 (2025). https://doi.org/10.1186/s12935-025-03639-8
  42. LIFEx-texture: Ahrari, S., Zaragori, T., Zinsz, A. et al. Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-024-07053-6
  43. LIFEx-texture: Captier, N., Lerousseau, M., Orlhac, F. et al. Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer. Nat Commun 16, 614 (2025). https://doi.org/10.1038/s41467-025-55847-5
  44. LIFEx-texture: Zhou Y, Zhou XY, Xu YC, Ma XL, Tian R. Radiomics based on 18 F-FDG PET for predicting treatment response and prognosis in newly diagnosed diffuse large B-cell lymphoma patients: do lesion selection and segmentation methods matter? Quant Imaging Med Surg 2025;15(1):103-120. https://doi.org/10.21037/qims-24-585
  45. LIFEx-texture: Bei-Hui Xue, Shuang-Li Chen, Jun-Ping Lan, Li-Li Wang, Jia-Geng Xie, Xiang-wu Zheng, Liang-Xing Wang, Kun Tang. Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study, Academic Radiology, 2025, ISSN 1076-6332. https://doi.org/10.1016/j.acra.2024.12.038

Review (3):

  1. LIFEx-texture: Asaf Raza, Antonella Guzzo, Michele Ianni, Rosamaria Lappano, Alfredo Zanolini, Marcello Maggiolini, Giancarlo Fortino. Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis. Computer Methods and Programs in Biomedicine. 2025, 108768, ISSN 0169-2607, https://doi.org/10.1016/j.cmpb.2025.108768
  2. LIFEx-texture: Salvatore Pezzino, Tonia Luca, Mariacarla Castorina, Stefano Puleo, Sergio Casorina. Current Trends and Emerging Themes in Utilizing Artificial Intelligence to Enhance Anatomical Diagnostic Accuracy and Efficiency in Radiotherapy.  Salvatore Pezzino et al 2025 Prog. Biomed. Eng. https://doi.org/10.1088/2516-1091/adc85e
  3. LIFEx-texture: Keshavarz, Pedram et al. Prediction of treatment response and outcome of transarterial chemoembolization in patients with hepatocellular carcinoma using artificial intelligence: A systematic review of efficacy. European Journal of Radiology, Volume 0, Issue 0, 111948. https://doi.org/10.1016/j.ejrad.2025.111948

Others (5):

  1. LIFEx-texture: HangYu Watson, Seher Berzingi, Karthik Seetharam, Samuel A. Mensah, Syed Ahmad and Brijesh D. Patel. Myocardial tissue texture improves diagnostic accuracy of echocardiogram to diagnosis of hypertrophic cardiomyopathy. JACC. 2025 Apr, 85 (12_Supplement) 2575. https://www.jacc.org/doi/abs/10.1016/S0735-1097%2825%2903059-1
  2. LIFEx-texture:TERZI ATHINA Marina. Radiogenomic Analysis of Lung Cancer. MSc program “Biomedical Engineering and Technology”. https://polynoe.lib.uniwa.gr/xmlui/bitstream/handle/11400/8616/Terzi_bmet2305.pdf
  3. LIFEx-texture: Yunus Soleymani, Farahnaz Aghahosseini, Peyman Sheikhzadeh. Correlation of radiomics features extracted from nuclear medicine images with lesion metabolism in patients with colon cancer. February 2025Tehran University Medical Journal 82(5). link
  4. LIFEx-texture: Malhaire C thesis. Optimization of the Prediction of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer by Breast MRI : Contributions of Semantic Descriptors, Radiomics, and Segmentation Methods. HAL Id: tel-04931114. https://theses.hal.science/tel-04931114v1
  5. LIFEx-texture: S. Gülbahar Ates, B. B. Demirel, E. Kekilli, E. Öztürk, G. Uçmak. Primary Tumor Heterogeneity on Pre-treatment [68Ga]Ga-PSMA PET/CT for the Prediction of Biochemical Recurrence in Prostate Cancer. Revista española de medicina nuclear e imagen molecular, ISSN 2253-654X, Vol. 43, Nº. 6 (Noviembre-Diciembre), 2024, págs. 4-4. https://dialnet.unirioja.es/ejemplar/686513