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Publications 2021 - Journal papers (157)

  1. LIFEx-texture: Zhou J, Zou S, Kuang D, Yan J, Zhao J, Zhu X. A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer. Front Oncol. 2021 Nov 17;11:769272. https://doi.org/10.3389/fonc.2021.769272. PMID: 34868999; PMCID: PMC8635743.
  2. LIFEx-texture: Wei M, Gu B, Song S, Zhang B, Wang W, Xu J, Yu X, Shi S. A Novel Validated Recurrence Stratification System Based on 18F-FDG PET/CT Radiomics to Guide Surveillance After Resection of Pancreatic Cancer. Front Oncol. 2021 May 12;11:650266. https://doi.org/10.3389/fonc.2021.650266. PMID: 34055620; PMCID: PMC8149949.
  3. LIFEx-texture: Vara G, Rustici A, Sechi A, Mosconi C, Lucidi V, Golfieri R. Texture Analysis on Ultrasound: The Effect of Time Gain Compensation on Histogram Metrics and Gray-Level Matrices. J Med Phys. 2020 Oct-Dec;45(4):249-255. https://doi.org/10.4103/jmp.JMP_82_20. Epub 2021 Feb 2. PMID: 33953501; PMCID: PMC8074715.
  4. LIFEx-texture: Han X, Yang J, Luo J, Chen P, Zhang Z, Alu A, Xiao Y, Ma X. Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods. Front Oncol. 2021 Jul 22;11:606677. https://doi.org/10.3389/fonc.2021.606677. PMID: 34367940; PMCID: PMC8339967.
  5. LIFEx-texture: Thuillier P, Liberini V, Grimaldi S, Rampado O, Gallio E, Santi B, Arvat E, Piovesan A, Filippi R, Abgral R, Molinari F, Deandreis D. Prognostic Value of Whole-Body PET Volumetric Parameters Extracted from 68Ga-DOTATOC PET/CT in Well-Differentiated Neuroendocrine Tumors. J Nucl Med. 2022 Jul;63(7):1014-1020. https://doi.org/10.2967/jnumed.121.262652. Epub 2021 Nov 5. PMID: 34740949.
  6. LIFEx-texture:Aksu A, Karahan Şen NP, Tuna EB, Aslan G, Çapa Kaya G. Evaluation of 68Ga-PSMA PET/CT with volumetric parameters for staging of prostate cancer patients. Nucl Med Commun. 2021 May 1;42(5):503-509. doi: 10.1097/MNM.0000000000001370. Erratum in: Nucl Med Commun. 2021 Oct 1;42(10):1186. https://doi.org/10.1097/MNM.0000000000001482. PMID: 33560717.
  7. LIFEx-texture:Yamashita S, Okuda K, Nakaichi T, Yamamoto H, Yokoyama K. Texture Feature Comparison Between Step-and-Shoot and Continuous-Bed-Motion 18F-FDG PET. J Nucl Med Technol. 2021 Mar;49(1):58-64. https://doi.org/10.2967/jnmt.120.246157. Epub 2020 Oct 5. PMID: 33020230
  8. LIFEx-viewer: Martinez- Perez R, Kortz MW, Ung TH, Rayo N, Lagares A, Cepeda S. Third Ventricle Volume Predicts Functional Outcome in Chronic Subdural Hematoma. Acta Neurol Scand.  2021;00:1– 8 (doi)
  9. LIFEx-texture: Urraro F, Nardone V, Reginelli A, Varelli C, Angrisani A, Patanè V, D’Ambrosio L, Roccatagliata P, Russo GM, Gallo L, De Chiara M, Altucci L and Cappabianca S (2021) MRI Radiomics in Prostate Cancer: A Reliability Study. Front. Oncol. 11:805137 (doi)
  10. LIFEx-texture: Guilherme D. Kolinger, David Vállez García, Gerbrand Maria Kramer, Virginie Frings, Gerben J.C. Zwezerijnen, Egbert F. Smit, Adrianus Johannes de Langen, Irène Buvat and Ronald Boellaard. Effects of tracer uptake time in non-small cell lung cancer 18F-FDG PET radiomics. Journal of Nuclear Medicine, published on December 21, 2021 (doi)
  11. LIFEx-texture: Fatma Ceren Sarioglu, Orkun Sarioglu, Handan Guleryuz, Burak Deliloglu, Funda Tuzun, Nuray Duman, Hasan Ozkan. The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia. British Institute of Radiology. Published Online:17 Dec 2021 (doi)
  12. LIFEx-texture:  Li J, Ge S, Sang S, Hu C and Deng S (2021) Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics. Front. Oncol 11:789014 (doi)
  13. Russo, G.; Stefano, A.; Alongi, P.; Comelli, A.; Catalfamo, B.; Mantarro, C.; Longo, C.; Altieri, R.; Certo, F.; Cosentino, S.; et al. Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model. Curr. Oncol. 2021, 28, 5318–5331 (doi)
  14. LIFEx-texture: Ahrari, S.; Zaragori, T.; Rozenblum, L.; Oster, J.; Imbert, L.; Kas, A.; Verger, A. Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from TreatmentRelated Changes. Biomedicines 2021, 9, 1924 (doi)
  15. LIFEx-texture: Julie Faudemer, Nicolas Aide, Anne‑Claire Gac, Ghandi Damaj, Jean‑Pierre Vilque, Charline Lasnon. Diagnostic value of baseline 18 FDG PET/CT skeletal textural features in follicular lymphoma. Scientific Reports (2021) 11:23812 (doi)
  16. LIFEx-viewer: Martina Sollini, Francesco Bartoli, Lara Cavinato, Francesca Ieva, Alessandra Ragni, Andrea Marciano, Roberta Zanca, Luca Galli, Fabiola Paiar, Francesco Pasqualetti and Paola Anna Erba. [18 F]FMCH PET/CT biomarkers and similarity analysis to refine the definition of oligometastatic prostate cancer. EJNMMI Research (2021) 11:119 (doi)
  17. LIFEx-texture: Sollini, M., Bartoli, F., Cavinato, L. et al. [18F]FMCH PET/CT biomarkers and similarity analysis to refine the definition of oligometastatic prostate cancer. EJNMMI Res 11, 119 (2021) (doi)
  18. LIFEx-texture: Guilherme D. Kolinger, David Vállez García, Gerbrand M. Kramer, Virginie Frings, Gerben J.C. Zwezerijnen, Egbert F. Smit, Adrianus J. de Langen, Irène Buvat, and Ronald Boellaard. Use-cases for 18F-FDG PET radiomics based on uptake time dependence in non-small cell lung cancer. Journal of Nuclear Medicine, SNMMI, 2021 (doi)
  19. LIFEx-texture:Yeye Zhou, Yuchun Zhu, Zhiqiang Chen, Jihui Li, Shibiao Sang and Shengming Deng. Radiomic Features of 18F-FDG PET in Hodgkin Lymphoma Are Predictive of Outcomes. Contrast Media & Molecular Imaging Volume 2021, Article ID 6347404, 8 pages (doi)
  20. LIFEx-MTV-texture: M Kimura, I Kato, K Ishibashi, K Hashimoto, H Tsuji, Y Sone, M Umemura, T Nagao. Texture analysis of 18F-FDG PET images for the detection of cervical lymph node metastases in patients with oral squamous cell carcinoma. Advances in Oral and Maxillofacial Surgery. Volume 5, January–March 2022, 100228 (doi)
  21. LIFEx-MTV-texture: Hyein Ahn, Jeong Won Lee, Si-Hyong Jang, Hyun Ju Lee, Ji-Hye Lee, Mee-Hye Oh, Sang Mi Lee,
    Prognostic significance of imaging features of peritumoral adipose tissue in FDG PET/CT of patients with colorectal cancer,
    European Journal of Radiology, Volume 145, 2021 (doi)
  22. LIFEx-texture: Kotaro Ito, Hirotaka Muraoka, Naohisa Hirahara, Eri Sawada, Shoya Hirohata, Kohei Otsuka, Shunya Okada, Takashi Kaneda. Quantitative assessment of mandibular bone marrow using computed tomography texture analysis for detect stage 0 medication-related osteonecrosis of the jaw. European Journal of Radiology, Volume 145, 110030, December 01, 2021 (doi)
  23. LIFEx-MTV-texture: Xue Beihui, Jiang Jia, Chen Lei, Wu Sunjie, Zheng Xuan, Zheng Xiangwu, Tang Kun. Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis. Front. Oncol., 26 October 2021 (doi)
  24. LIFEx-viewer: Stefano, A.; Leal, A.; Richiusa, S.; Trang, P.; Comelli, A.; Benfante, V.; Cosentino, S.; Sabini, M.G.; Tuttolomondo, A.; Altieri, R.;et al. Robustness of PET Radiomics Features: Impact of Co-Registration with MRI. Appl. Sci. 2021, 11, 10170 (doi)
  25. LIFEx-texture: Mendes, B.; Domingues, I.; Silva, A.; Santos, J. Prostate Cancer Aggressiveness Prediction Using CT Images. Life 2021, 11, 1164 (doi)
  26. LIFEx-viewer: Lee, JH., Kim, S., Lee, H.S. et al. Different prognostic impact of glucose uptake in visceral adipose tissue according to sex in patients with colorectal cancer. Sci Rep 11, 21556 (2021) (doi)
  27. LIFEx-texture: Kim, J.; Jeong, S.Y.; Kim, B.-C.; Byun, B.-H.; Lim, I.; Kong, C.-B.; Song, W.S.; Lim, S.M.; Woo, S.-K. Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Using Convolutional Neural Network of Tumor Center 18F-FDG PET Images. Diagnostics 2021, 11, 1976 (doi)
  28. LIFEx-texture: Xue B, Jiang J, Chen L, Wu S, Zheng X, Zheng X and Tang K (2021) Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis. Front. Oncol. 11:740111 (doi)
  29. LIFEx-texture: Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim. Artificial Intelligence-Based Detection, Classification and Prediction/Prognosis in PET Imaging: Towards Radiophenomics. PET Clinics, Volume 17, Issue 1, 2021 (doi)
  30. LIFEx-texture: Zhao Y, Chen R, Zhang T, Chen C, Muhelisa M, Huang J, Xu Y and Ma X (2021) MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions. Front. Oncol. 11:552634 (doi)
  31. LIFEx-MTV: Hande Melike Bülbül, Ogün Bülbül, Sülen Sarıoğlu, Özhan Özdoğan, Ersoy Doğan, Nuri Karabay. Relationships Between DCE-MRI, DWI, and 18 F-FDG PET/CT Parameters with Tumor Grade and Stage in Patients with Head and Neck Squamous Cell Carcinoma. Mol Imaging Radionucl Ther 2021;30:177-186 (doi)
  32. LIFEx-texture: Lee JW, Kim SY, Han SW, Lee JE, Hong SH, Lee SM, Jo IY. Clinical Significance of Peritumoral Adipose Tissue PET/CT Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer. Journal of Personalized Medicine. 2021; 11(10):1029 (doi)
  33. LIFEx-PT-COMP: Zaragori T, Doyen M, Rech F, Blonski M, Taillandier L, Imbert L and Verger A (2021) Dynamic 18F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient? Front. Oncol. 11:735257. doi: 10.3389/fonc.2021.735257(doi)
  34. LIFEx-texture: Maëlle Dade, Marine Giry, Giulia Berzero, Marion Benazra, Gilles Huberfeld, Delphine Leclercq, Vincent Navarro, Jean-Yves Delattre, Dimitri Psimaras, Agusti Alentorn. Quantitative brain imaging analysis of neurological syndromes associated with anti-GAD antibodies. NeuroImage: Clinical, Volume 32, 2021, 102826, ISSN 2213-1582 (doi)
  35. LIFEx-texture: valuation of 68Ga-PSMA PET/CT with Volumetric Parameters for Staging of Prostate Cancer Patients: Erratum, Nuclear Medicine Communications: October 2021 - Volume 42 - Issue 10 - p 1186 (doi)
  36. LIFEx-texture: Flaus, A., Habouzit, V., De Leiris, N. et al. FDG PET biomarkers for prediction of survival in metastatic melanoma prior to anti-PD1 immunotherapy. Sci Rep 11, 18795 (2021) (doi)
  37. LIFEx-texture: Shozo Yamashita, Koichi Okuda, Tetsu Nakaichi, Haruki Yamamoto and Kunihiko Yokoyama.Texture Feature Comparison Between Step-and-Shoot and Continuous-Bed-Motion 18F-FDG PET. Journal of Nuclear Medicine Technology March 2021, 49 (1) 58-64 (doi)
  38. LIFEx-texture: Mengmeng Yan, Weidong Wang. A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy. Science Progress
    2021, Vol. 104(1) 1-10 (doi)
  39. LIFEx-texture: Zhang, L., Zhao, H., Jiang, H. et al. 18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdom Radiol (2021) (doi)
  40. LIFEx-texture: Xu, Hanyue; Zou, Xiuhe; Zhao, Yunuo; Zhang, Tao; Tang, Youyin; Zheng, Aiping; Zhou, Xianghong; Ma, Xuelei. Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography. Technol Cancer Res Treat ; 20: 15330338211039125, 2021 (doi)
  41. LIFEx-viewer: Wallis, D., Soussan, M., Lacroix, M. et al. An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging (2021). (doi)
  42. LIFEx-texture: Zhong-Wei Chen, Kun Tang, You-Fan Zhao, Yang-Zong Chen, Liang-Jie Tang, Gang Li, Ou-Yang Huang, Xiao-Dong Wang, Giovanni Targher, Christopher D. Byrne, Xiang-Wu Zheng and Ming-Hua Zheng. Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study. Int. J. Med. Sci. 2021, Vol 18(16): 3624-3630 (doi)
  43. LIFEx-texture: Yang, N., Liu, F., Li, C. et al. Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images. Sci Rep 11, 17885 (2021) (doi)
  44. LIFEx-texture: Chong, G.O.; Park, S.-H.; Jeong, S.Y.; Kim, S.J.; Park, N.J.-Y.; Lee, Y.H.; Lee, S.-W.; Hong, D.G.; Park, J.Y.; Han, H.S. Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer. Diagnostics 2021, 11, 1517 (doi)
  45. LIFEx-texture: Yuki Arita and Soichiro Yoshida and Thomas C. Kwee and Hirotaka Akita and Shigeo and Yuki Iwaita and Kiyoko Mukai and Shunya Matsumoto and Ryo and Ryota and Ryuichi Mizuno and Yasuhisa Fujii and Mototsugu Oya and Masahiro Jinzaki. Diagnostic Value of Texture Analysis of Apparent Diffusion Coefficient Maps for Differentiating Fat-Poor Angiomyolipoma from Non-Clear-Cell Renal Cell Carcinoma. European Journal of Radiology. p109895, 0720-048X; 2021 (doi)
  46. LIFEx-texture: Beaumont, H., Iannessi, A., Cucchi, J.M. et al. Intra-scan inter-tissue variability can help harmonize radiomics features in CT. Eur Radiol (2021) (doi)
  47. LIFEx-texture: Ren, H., Mori, N., Mugikura, S. et al. Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging. Abdom Radiol (2021) (doi)
  48. LIFEx-texture: Tang, Y., Zhang, T., Zhou, X. et al. The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma. World J Surg Onc 19, 45 (2021) (doi)
  49. LIFEx-texture: Lafata, K.J., Wang, Y., Konkel, B. et al. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (2021) (doi)
  50. LIFEx-texture: Su C-W, Lee J-C, Chang Y-F, et al. Delta-volume radiomics of induction chemotherapy to predict outcome of subsequent chemoradiotherapy for locally advanced hypopharyngeal cancer. Tumori Journal. August 2021 (doi)
  51. LIFEx-texture: Javier González-Viguera, Gabriel Reynés-Llompart, Alicia Lozano. Outcomes and computed tomography radiomic features extraction in soft tissue sarcomas treated with neoadjuvant radiation therapy. Reports of practical oncology and radiotherapy. 2021-08-13 (doi)
  52. LIFEx-texture: Karmazanovsky, G., Gruzdev, I., Tikhonova, V. et al. Computed tomography-based radiomics approach in pancreatic tumors characterization. Radiol med (2021) (doi)
  53. LIFEx-viewer: Chen C, Cheng Y, Xu J, Zhang T, Shu X, Huang W, Hua Y, Zhang Y, Teng Y, Zhang L, Xu J. Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method. Journal of Personalized Medicine. 2021; 11(8):786 (doi)
  54. LIFEx-texture: Bracci, S., Dolciami, M., Trobiani, C. et al. Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients. Radiol med (2021) (doi)
  55. LIFEx-texture: Tang, Y., Zhang, T., Zhou, X. et al. The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma. World J Surg Onc 19, 45 (2021) (doi)
  56. LIFEx-texture: Hainan Ren, Naoko Mori, Shunji Mugikura, Hiroaki Shimizu, Sakiko Kageyama, Masatoshi Saito, Kei Takase. Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2‑weighted imaging. Abdominal Radiology. 20 July 2021 (doi)
  57. LIFEx-texture: Orkun Sarioglu, Fatma C Sarioglu, Ahmet E Capar, Demet Fb Sokmez, Berna D Mete, Umit Belet. Clot-based radiomics features predict first pass effect in acute ischemic stroke. Interv Neuroradiol. 2021 May 18;15910199211019176 (doi)
  58. LIFEx-texture: Baba, A., Kessoku, H., Akutsu, T. et al. Pre-treatment MRI predictor of high-grade malignant parotid gland cancer. Oral Radiol (2021)(doi)
  59. LIFEx-texture: Chen B, Chen C, Wang J, Teng Y, Ma X and Xu J (2021) Differentiation of Low-Grade Astrocytoma From Anaplastic Astrocytoma Using Radiomics-Based Machine Learning Techniques. Front. Oncol. 11:521313. (doi)
  60. LIFEx-texture: Girot, C., Volk, A., Walczak, C. et al. New method for quantification of intratumoral heterogeneity: a feasibility study on Ktrans maps from preclinical DCE-MRI. Magn Reson Mater Phy (2021) (doi)
  61. LIFEx-texture: Elkilany, A., Fehrenbach, U., Auer, T.A. et al. A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI. Sci Rep 11, 10778 (2021) (doi)
  62. LIFEx-texture: Timothée Zaragori, Julien Oster, Veronique Roch, Gabriela Hossu, Mohammad Bilal Chawki, Rachel Grignon, Celso Pouget, Guillaume Gauchotte, Fabien Rech, Marie Blonski, Luc Taillandier, Laëtitia Imbert and Antoine Verger. 18F-FDOPA PET for the non-invasive prediction of glioma molecular parameters: a radiomics study. Journal of Nuclear Medicine May 2021, jnumed.120.261545 (doi)
  63. LIFEx-MTV: François Allioux, Damaj Gandhi, Jean-Pierre Vilque, Cathy Nganoa, AnneClaire Gac, Nicolas Aide & Charline Lasnon (2021): End-of-treatment 18F-FDG PET/CT in diffuse large B cell lymphoma patients: ∆SUV outperforms Deauville score, Leukemia & Lymphoma (doi)
  64. LIFEx-viewer: Francesco Bianconi, Mario Luca Fravolini, Sofia Pizzoli, Isabella Palumbo, Matteo Minestrini, Maria Rondini, Susanna Nuvoli, Angela Spanu, Barbara Palumbo. Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT. Quant Imaging Med Surg 2021;11(7):3286-3305 (doi)
  65. LIFEx-texture: Mahmoud M.A., Shihab M., Saad SS., Elhussiny F., Houseni M. Imaging differentiation of malignant hepatic tumors: radiomics and metabolic features of 18F-FDG PET/CT. REJR 2021; 11(2):165-170 (doi)
  66. LIFEx-texture: Friconnet, G. Exploring the correlation between semantic descriptors and texture analysis features in brain MRI. Chin J Acad Radiol 4, 105–115 (2021)(doi)
  67. LIFEx-texture: Zeydanli, T., Kilic, H.K. Performance of quantitative CT texture analysis in differentiation of gastric tumors. Jpn J Radiol (2021) (doi)
  68. Zeydanli, T., Kilic, H.K. Performance of quantitative CT texture analysis in differentiation of gastric tumors. Jpn J Radiol 2021 (doi)
  69. LIFEx-texture: P Vincenta, ME. Maeder, B Hunt, B Linn, T MangelsDick, T Hasan, KK. Wan and BW. Pogue. CT Radiomic Features of Photodynamic Priming in Clinical Pancreatic Adenocarcinoma Treatment. 2021 Phys. Med. Biol (doi)
  70. LIFEx-texture: Lee JW, Park S-H, Ahn H, Lee SM, Jang SJ. Predicting Survival in Patients with Pancreatic Cancer by Integrating Bone Marrow FDG Uptake and Radiomic Features of Primary Tumor in PET/CT. Cancers. 2021; 13(14):3563 (doi)
  71. LIFEx-texture: Tu SJ, Tran VT, Teo JM, Chong WC, Tseng JR. Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous 11 C-choline PET/MRI acquisition in prostate cancer patients. Med Phys. 2021 Jul 2 (doi)
  72. LIFEx-texture: Comment on Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848 (doi)
  73. LIFEx-texture: Kimura, K., Yoshida, S., Tsuchiya, J. et al. Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer. Eur Radiol (2021)(doi)
  74. LIFEx-texture: Mitamuraa K, Norikanea T, Yamamotoa Y＊, Nishishitaa AI, Kobataa T, Fujimotoa K, Takamia Y, Kudomib N, Hoshikawac H , and Nishiyamaa Y. Texture Indices of 18F-FDG PET/CT for Differentiating Squamous Cell Carcinoma and Non-Hodgkin’s Lymphoma of the Oropharynx. Acta Med. Okayama, 2021 Vol. 75, No. 3, pp. 351-356 (doi)
  75. LIFEx-texture: Costa, G.; Cavinato, L.; Masci, C.; Fiz, F.; Sollini, M.; Politi, L.S.; Chiti, A.; Balzarini, L.; Aghemo, A.; di Tommaso, L.; et al. Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases. Cancers 2021, 13, 3077 (doi)
  76. LIFEx-texture: The Clinical Impact of the Late Imaging with 18F-Fluorodeoxyglucose PET Texture Analysis in Invasive Lobular Breast Cancer. FO FALAY, H SEYMEN - Turk J Oncol 2021;36(3):273–84 (doi)
  77. LIFEx-texture: Fiz, F.; Costa, G.; Gennaro, N.; la Bella, L.; Boichuk, A.; Sollini, M.; Politi, L.S.; Balzarini, L.; Torzilli, G.; Chiti, A.; et al. Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the “Radiological” Tumour Microenvironment. Diagnostics 2021, 11, 1162 (doi)
  78. LIFEx-texture: Y Chen, H Li, J Feng, S Suo, Q Feng, J Shen. A Novel Radiomics Nomogram for the Prediction of Secondary Loss of Response to Infliximab in Crohn's Disease - Journal of Inflammation Research, june 2021 (doi)
  79. LIFEx-texture: Orlhac, F.; Buvat, I. Comment on Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848. Cancers 2021, 13, 3037 (doi)
  80. LIFEx-texture: Ai Y, Zhang J, Jin J, Zhang J, Zhu H and Jin X (2021) Preoperative Prediction of Metastasis for Ovarian Cancer Based on Computed Tomography Radiomics Features and Clinical Factors. Front. Oncol. 11:610742. (doi)
  81. LIFEx-texture: Gill A.B., Rundo L, Wan  JCM, Lau D, Zawaideh JP,  Woitek R, Zaccagna F,  Beer L, Gale D, Sala E, Couturier DL, Corrie PG, Rosenfeld N, Gallagher FA. Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma. 2020 Cancers Nov 24;12(12):3493 (doi).
  82. LIFEx-texture: Karahan Şen, N.P., Aksu, A. & Çapa Kaya, G. A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods. Ann Nucl Med (2021) (doi)
  83. LIFEx-texture: Masci, G.M., Iafrate, F., Ciccarelli, F. et al. Tocilizumab effects in COVID-19 pneumonia: role of CT texture analysis in quantitative assessment of response to therapy. Radiol med (2021) (doi)
  84. LIFEx-texture: Aboelyazid Elkilany, Uli Fehrenbach, Timo Alexander Auer, Tobias Müller, Wenzel Schöning, Bernd Hamm1 & Dominik Geisel.  A radiomics‑based model to classify the etiology of liver cirrhosis using gadoxetic acid‑enhanced MRIScientific Reports | (2021) 11:10778 (doi)
  85. LIFEx-viewer: Bordonneet al. High-quality brain perfusion SPECT images may be achieved with a high-speed recording using 360° CZT camera. EJNMMI Physics (2020) 7:65 (doi)
  86. LIFEx-texture: Kim, M., Gu, W., Nakajima, T. et al. Texture analysis of [18F]-fluorodeoxyglucose-positron emission tomography/computed tomography for predicting the treatment response of postoperative recurrent or metastatic oral squamous cell carcinoma treated with cetuximab. Ann Nucl Med (2021) (doi)
  87. LIFEx-texture: Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status by Lukas Lenga,Simon Bernatz,Simon S. Martin,Christian Booz,Christine Solbach,Rotraud Mulert-Ernst,Thomas J. Vogl andDoris Leithner.Cancers 2021, 13(10), 2431 (doi)
  88. LIFEx-texture: MA Mazzei, L Di Giacomo, GBagnacci, V Nardone, & al. Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer—a multicenter study of GIRCG (Italian Research Group for Gastric Cancer) Quant Imaging Med Surg2021;11(6):2376-238 (doi)
  89. LIFEx-texture: Friconnet, G. Exploring the correlation between semantic descriptors and texture analysis features in brain MRI. Chin J Acad Radiol (2021) (doi)
  90. LIFEx-texture: Tomita, H., Yamashiro, T., Heianna, J. et al. Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography. Eur Radiol (2021) (doi)
  91. LIFEx-texture: Daria Ripani, Carmelo Caldarella, Tommaso Za, Elena Rossi, Valerio De Stefano, Alessandro Giordano. Progression to symptomatic multiple myeloma predicted by texture analysis-derived parameters in patients without focal disease at 18F-FDG PET/CT. Clinical Lymphoma Myeloma and Leukemia 2021 (doi)
  92. LIFEx-texture: Mazzei, M., Giacomo, L., Bagnacci, G., Nardone, V., Gentili, F., Lucii, G., Tini, P., Marrelli, D., Morgagni, P., Mura, G., Baiocchi, G., Pittiani, F., Volterrani, L., Roviello, F. Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer - a multicenter study of GIRCG (Italian Research Group for Gastric Cancer ; Quant Imaging Med Surg 2021;11(6):2376-2387 (doi)
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