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Publications 2023 - Journal papers (175)

(138)

  1. LIFEx-texture: Qiu YJ, Zhou LL, Li J, Zhang YF, Wang Y, Yang YS. The repeatability and consistency of different methods for measuring the volume parameters of the primary rectal cancer on diffusion weighted images. Front Oncol. 2023 Mar 9;13:993888. https://doi.org/10.3389/fonc.2023.993888. PMID: 36969078; PMCID: PMC10034158.
  2. LIFEx-texture: Agüloğlu N, Acar Akkaya E, Binicier ÖB. Volumetric and radiomics assessment of lesions with incidental [18F]FDG uptake in the colon: a retrospective radiomics study. Q J Nucl Med Mol Imaging. 2023 Jun;67(2):145-151. https://doi.org/10.23736/S1824-4785.21.03346-X. Epub 2021 Oct 19. PMID: 34664824.
  3. LIFEx-texture: Murtas F, Landoni V, Ordòñez P, Greco L, Ferranti FR, Russo A, Perracchio L, Vidiri A. Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis. Front Oncol. 2023 May 12;13:1152158. https://doi.org/10.3389/fonc.2023.1152158. PMID: 37251915; PMCID: PMC10213670.
  4. LIFEx-texture: Kawashima Y, Miyakoshi M, Kawabata Y, Indo H. Efficacy of texture analysis of ultrasonographic images in the differentiation of metastatic and non-metastatic cervical lymph nodes in patients with squamous cell carcinoma of the tongue. Oral Surg Oral Med Oral Pathol Oral Radiol. 2023 Aug;136(2):247-254. https://doi.org/10.1016/j.oooo.2023.04.012. Epub 2023 May 1. PMID: 37353468.
  5. LIFEx-texture: Agüloğlu N, Aksu A, Unat DS. Machine learning approach using 18 F-FDG PET-based radiomics in differentiation of lung adenocarcinoma with bronchoalveolar distribution and infection. Nucl Med Commun. 2023 Apr 1;44(4):302-308. https://doi.org/10.1097/MNM.0000000000001667. Epub 2023 Feb 9. PMID: 36756766.
  6. LIFEx-texture-MTV: Agüloğlu N, Aksu A. Evaluation of survival of the patients with metastatic rectal cancer by staging 18F-FDG PET/CT radiomic and volumetric parameters. Rev Esp Med Nucl Imagen Mol (Engl Ed). 2023 Mar-Apr;42(2):122-128. https://doi.org/10.1016/j.remnie.2022.09.010. Epub 2022 Sep 24. PMID: 36162744.
  7. LIFEx-texture: Bomhals B, Cossement L, Maes A, Sathekge M, Mokoala KMG, Sathekge C, Ghysen K, Van de Wiele C. Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules. J Clin Med. 2023 Dec 17;12(24):7731. https://doi.org/10.3390/jcm12247731. PMID: 38137800; PMCID: PMC10743692.
  8. LIFEx-texture: van Eijnatten EJM, Camps G, Guerville M, Fogliano V, Hettinga K, Smeets PAM. Milk coagulation and gastric emptying in women experiencing gastrointestinal symptoms after ingestion of cow's milk. Neurogastroenterology & Motility. 2024;36:e14696. https://doi.org/10.1111/nmo.14696
  9. LIFEx-MTV: Voltin, CA., Paccagnella, A., Winkelmann, M. et al. Multicenter development of a PET-based risk assessment tool for product-specific outcome prediction in large B-cell lymphoma patients undergoing CAR T-cell therapy. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06554-0
  10. LIFEx-texture: E. Babu, Ravi Krishna, Dathu Anushka, Medharimetla Lokesh, Boda Laila, Begari Mohan. Customized 3D CNN Model-based Lung Cancer Classification from Chest X-ray Images. IJARST. Volume 13, Issue 12, Dec 2023 ISSN 2457-0362, Page 268. https://www.ijarst.in/public/uploads/paper/516491702544047.pdf
  11. LIFEx-MTV: Alexander Dierks, Alexander Gäble, Andreas Rinscheid, Georgine Wienand, Christian H. Pfob, MalteKircher, Johanna S. Enke, Tilman Janzen, Marianne Patt, Martin Trepel, Dorothea Weckermann, Ralph A. Bundschuh, Constantin Lapa. First Safety and Efficacy Data with the Radiohybrid 177Lu-rhPSMA-10.1 for the Treatment of Metastatic Prostate Cancer. Journal of Nuclear Medicine Dec 2023, jnumed.123.266741; https://doi.org/10.2967/jnumed.123.266741
  12. LIFEx-Main: Pitarch, G., Rotstein Habarnau, Y., Chirico, R. et al. Exploring the applicability of a lesion segmentation method on [18F]fluorothymidine PET/CT images in diffuse large B-cell lymphoma. European J Hybrid Imaging 7, 28 (2023). https://doi.org/10.1186/s41824-023-00184-3
  13. LIFEx-MTV: Voltin, CA., Paccagnella, A., Winkelmann, M. et al. Multicenter development of a PET-based risk assessment tool for product-specific outcome prediction in large B-cell lymphoma patients undergoing CAR T-cell therapy. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06554-0
  14. LIFEx-texture: Hsiao C-C, Peng C-H, Wu F-Z, Cheng D-C. Impact of Voxel Normalization on a Machine Learning-Based Method: A Study on Pulmonary Nodule Malignancy Diagnosis Using Low-Dose Computed Tomography (LDCT). Diagnostics. 2023; 13(24):3690. https://doi.org/10.3390/diagnostics13243690
  15. LIFEx-TMTV: Jafari, E., Zarei, A., Dadgar, H. et al. A convolutional neural network–based system for fully automatic segmentation of whole-body [68Ga]Ga-PSMA PET images in prostate cancer. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06555-z
  16. LIFEx-texture: Zhang Jianyua, Zhao Xinming, Zhao Yan, Zhang Jingmian, Zhang Zhaoqi. Prediction of epidermal growth factor receptor mutation subtypes in patients with non-small cell lung cancer by 18F-FDG PET/CT radiomic ; (6): 480-485, 2023. Chinese Journal of Nuclear Medicine and Molecular Imaging. https://doi.org/ 10.3760/cma.j.cn321828-20220109-00008
  17. LIFEx-texture: Zhao, W., Ozawa, Y., Hara, M. et al. Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes. Jpn J Radiol (2023). https://doi.org/10.1007/s11604-023-01512-0
  18. LIFEx-texture: Philip Whybra, Alex Zwanenburg, Vincent Andrearczyk, Roger Schaer, Aditya P Apte, et al.. The Image Biomarker Standardization Initiative: Standardized convolutional filters for quantitative radiomics Authors and affiliations. 2023. https://hal.science/hal-04305625
  19. LIFEx-MTV: Kibrom B Girum, Anne-Ségolène Cottereau, Laetitia Vercellino, Louis Rebaud, Jérôme Clerc, et al.. Tumor location relative to the spleen is a prognostic factor in lymphoma patients: a demonstration from the REMARC trial. Journal of Nuclear Medicine, In press. https://hal.science/hal-04305558
  20. LIFEx-MTV: Zhaoting Cheng, Sijuan Zou, Jianyuan Zhou, Shuang Song, Yuankai Zhu, Jun Zhao, Xiaohua Zhu; Prognostic Value of Somatostatin Receptor-Derived Volumetric Parameters from a Hybrid Standardized Uptake Value Thresholding Method in Patients with 68Ga-DOTATATE-Avid Stage IV Neuroendocrine Neoplasms: A Preliminary Study. Neuroendocrinology 2023 ; https://doi.org/10.1159/000530771
  21. LIFEx-MTV: Aksu, A., Küçüker, K.A., Solmaz, Ş. et al. A different perspective on PET/CT before treatment in patients with Hodgkin lymphoma: importance of volumetric and dissemination parameters. Ann Hematol (2023). https://doi.org/10.1007/s00277-023-05547-1
  22. LIFEx-MTV: Dang, J., Peng, X., Wu, P. et al. Predictive value of Dmax and %ΔSUVmax of 18F-FDG PET/CT for the prognosis of patients with diffuse large B-cell lymphoma. BMC Med Imaging 23, 173 (2023). https://doi.org/10.1186/s12880-023-01138-8
  23. LIFEx-texture: Liu, J., Tang, M., Zhu, D. et al. The remodeling of metabolic brain pattern in patients with extracranial diffuse large B-cell lymphoma. EJNMMI Res 13, 94 (2023). https://doi.org/10.1186/s13550-023-01046-6
  24. LIFEx-texture: Frood, R., Mercer, J., Brown, P. et al. Training and external validation of pre-treatment FDG PET-CT-based models for outcome prediction in anal squamous cell carcinoma. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10340-9
  25. LIFEx-texture: Lara Cavinato, Michela Carlotta Massi, Martina Sollini, Margarita Kirienko &
    Francesca Ieva. Dual adversarial deconfounding autoencoder for joint batch‑effects removal from multi‑center and multi‑scanner radiomics data. Scientific Reports | (2023) 13:18857.  https://doi.org/10.1038/s41598-023-45983-7
  26. LIFEx-texture: Magdalena Belyanova, Martin Krupev. Texture analysis of adenomatous and metastatic adrenal lesions on native and contrast-enhanced computed tomography. Comptes rendus de l’Academie bulgare des Sciences. Tome 76, No 10, 2023. https://doig.org/10.7546/CRABS.2023.10.16
  27. LIFEx-MTV: Jing, F., Liu, Y., Zhao, X. et al. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma. EJNMMI Res 13, 92 (2023). https://doi.org/10.1186/s13550-023-01047-5
  28. LIFEx-main: Berbís, M.Á., Godino, F.P., Rodríguez-Comas, J. et al. Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application. Abdom Radiol (2023). https://doi.org/10.1007/s00261-023-04071-0
  29. LIFEx-texture: Bülbül, H.M., Burakgazi, G. & Kesimal, U. Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer. Jpn J Radiol (2023). https://doi.org/10.1007/s11604-023-01502-2
  30. LIFEx-MTV: Jing, F., Liu, Y., Zhao, X. et al. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma. EJNMMI Res 13, 92 (2023). https://doi.org/10.1186/s13550-023-01047-5
  31. LIFEx-texture: van Eijnatten EJM, Camps G,Guerville M, Fogliano V, Hettinga K, Smeets PAM. Milkcoagulation and gastric emptying in women experiencinggastrointestinal symptoms after ingestion of cow's milk. Neurogastroenterology & Motility. 2023;00:e14696. https://doi.org/10.1111/nmo.14696
  32. LIFEx-texture: Yoon, H.; Choi, W.H.; Joo, M.W.; Ha, S.; Chung, Y.-A. SPECT/CT Radiomics for Differentiating between Enchondroma and Grade I Chondrosarcoma. Tomography 2023, 9, 1868–1875. https://doi.org/10.3390/tomography9050148
  33. LIFEx-main: Xiyao Lei, Zhuo Cao, Yibo Wu, Jie Lin, Zhenhua Zhang, Juebin Jin, Yao Ai, Ji Zhang, Dexi Du, Zhifeng Tian, Congying Xie, Weiwei Yin and Xiance Jin. Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics. Insights into Imaging. (2023) 14:174 https://doi.org/10.1186/s13244-023-01528-0
  34. LIFEx-texture: Taleie, H., Hajianfar, G., Sabouri, M. et al. Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms. J Digit Imaging (2023). https://doi.org/10.1007/s10278-023-00891-0
  35. LIFEx-texture: Marchal, E., Palard-Novello, X., Lhomme, F. et al. Baseline [18F]FDG PET features are associated with survival and toxicity in patients treated with CAR T cells for large B cell lymphoma. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06427-6
  36. LIFEx-texture: Ninatti, G., Pini, C., Bono, B.C. et al. The prognostic power of [11C]methionine PET in IDH-wildtype diffuse gliomas with lower-grade histological features: venturing beyond WHO classification. J Neurooncol (2023). https://doi.org/10.1007/s11060-023-04438-9
  37. LIFEx-Main: Duwe G, Müller L, Ruckes C, Fischer ND, Frey LJ, Börner JH, Rölz N, Haack M, Sparwasser P, Jorg T, et al. Change in Splenic Volume as a Surrogate Marker for Immunotherapy Response in Patients with Advanced Urothelial and Renal Cell Carcinoma—Evaluation of a Novel Approach of Fully Automated Artificial Intelligence Based Splenic Segmentation. Biomedicines. 2023; 11(9):2482. https://doi.org/10.3390/biomedicines11092482 
  38. LIFEx-QualityControl: Koffi N’guessan Placide Gabin Allangba, Annick Kouame Koutouan, Alessia Giuliano, Zié Traoré, Antonio Traino. Partial Volume Effect (PVE) Correction in Single Photon Emission Computed Tomography (SPECT) Imaging. Radiation Science and Technology. Vol. 9, No. 3, 2023, pp. 26-35. https://doi.org/10.11648/j.rst.20230903.11
  39. LIFEx-texture: Duwe, G.; Müller, L.; Ruckes, C.; Fischer, N.D.; Frey, L.J.; Börner, J.H.; Rölz, N.; Haack, M.; Sparwasser, P.; Jorg, T.; et al. Change in Splenic Volume as a Surrogate Marker for Immunotherapy Response in Patients with Advanced Urothelial and Renal Cell Carcinoma—Evaluation of a Novel Approach of Fully Automated Artificial Intelligence Based Splenic Segmentation. Biomedicines 2023, 11, 2482. https://doi.org/10.3390/biomedicines11092482
  40. LIFEx-texture: Xian He, Zhi Chen, Yutao Gao, Wanjing Wang, Meng You. Reproducibility and location-stability of radiomic features derived from Cone-Beam Computed Tomography: a phantom study. The British Institute of Radiology. 4 Sep 2023 https://doi.org/10.1259/dmfr.20230180
  41. LIFEx-MTV: van Heek L, Weindler J, Gorniak C, et al. Prognostic value of baseline metabolic tumor volume (MTV) for forecasting chemotherapy outcome in early-stage unfavorable Hodgkin lymphoma: Data from the phase III HD17 trial. Eur J Haematol. 2023;1‐7. https://doi.org/10.1111/ejh.14093
  42. LIFEx-texture: Nakamori, A., Tsuyoshi, H., Tsujikawa, T. et al. Evaluation of calcification distribution by CT-based textural analysis for discrimination of immature teratoma. J Ovarian Res 16, 179 (2023). https://doi.org/10.1186/s13048-023-01268-1
  43. LIFEx-MTV: Nalan Alan-Selcuk, Gamze Beydagi, Emre Demirci, Meltem Ocak, Serkan Celik, Bala B. Oven, Turkay Toklu, Ipek Karaaslan, Kaan Akcay, Omer Sonmez and Levent Kabasakal. Clinical Experience with [225Ac]Ac-PSMA Treatment in Patients with [177Lu]Lu-PSMA–Refractory Metastatic Castration-Resistant Prostate Cancer. Journal of Nuclear Medicine, published on August 24, 2023, http://doi.org/10.2967/jnumed.123.265546
  44. LIFEx-texture: Chilaca-Rosas, M.-F.; Contreras-Aguilar, M.-T.; Garcia-Lezama, M.; Salazar-Calderon, D.-R.; Vargas-Del-Angel, R.-G.; Moreno-Jimenez, S.; Piña-Sanchez, P.; Trejo-Rosales, R.-R.; Delgado-Martinez, F.-A.; Roldan-Valadez, E. Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation. Diagnostics 2023, 13, 2669. https://doi.org/10.3390/diagnostics13162669
  45. Hajri, R.; Nicod-Lalonde, M.; Hottinger, A.F.; Prior, J.O.; Dunet, V. Prediction of Glioma Grade and IDH Status Using 18F-FET PET/CT Dynamic and Multiparametric Texture Analysis. Diagnostics 2023, 13, 2604. https://doi.org/10.3390/diagnostics13152604
  46. LIFEx-texture: Li, J., Cui, N., Jiang, Z. et al. Differentiating thymic epithelial tumors from mediastinal lymphomas: preoperative nomograms based on PET/CT radiomic features to minimize unnecessary anterior mediastinal surgery. J Cancer Res Clin Oncol (2023). https://doi.org/10.1007/s00432-023-05054-w
  47. LIFEx-texture: Samimi, R., Shiri, I., Ahmadyar, Y. et al. Radiomics predictive modeling from dual-time-point FDG PET Ki parametric maps: application to chemotherapy response in lymphoma. EJNMMI Res 13, 70 (2023). https://doi.org/10.1186/s13550-023-01022-0
  48. LIFEx-texture: Balma, M.; Laudicella, R.; Gallio, E.; Gusella, S.; Lorenzon, L.; Peano, S.; Costa, R.P.; Rampado, O.; Farsad, M.; Evangelista, L.; et al. Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs). Life 2023, 13, 1647. https://doi.org/10.3390/life13081647
  49. LIFEx-texture: Lee, H.; Moon, S.H.; Hong, J.Y.; Lee, J.; Hyun, S.H. A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer. Cancers 2023, 15, 3841. https://doi.org/10.3390/cancers15153841
  50. LIFEx-texture: Shuilin Zhao, Jing Wang, Chentao Jin, Xiang Zhang, Chenxi Xue, Rui Zhou, Yan Zhong, Yuwei Liu, Xuexin He, Youyou Zhou, Caiyun Xu, Lixia Zhang, Wenbin Qian, Hong Zhang, Xiaohui Zhang, and Mei Tian. Stacking Ensemble Learning–Based [18F]FDG PET Radiomics for Outcome Prediction in Diffuse Large B-Cell Lymphoma Journal of Nuclear Medicine, published on July 27, 2023 as doi: https://doi.org/10.2967/jnumed.122.265244
  51. LIFEx-texture: Jia T, Lv Q, Cai X, Ge S, Sang S, Zhang B, Yu C and Deng S (2023) Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer. Front. Oncol. 13:1210125. doi: https://doi.org/10.3389/fonc.2023.1210125
  52. LIFEx-texture: Pellegrino, S.; Fonti, R.; Hakkak Moghadam Torbati, A.; Bologna, R.; Morra, R.; Damiano, V.; Matano, E.; De Placido, S.; Del Vecchio, S. Heterogeneity of Glycolytic Phenotype Determined by 18F-FDG PET/CT Using Coefficient of Variation in Patients with Advanced Non-Small Cell Lung Cancer. Diagnostics 2023, 13, 2448. https://doi.org/10.3390/diagnostics13142448
  53. LIFEx-texture: Jia, T., Lv, Q., Zhang, B. et al. Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics. BMC Med Imaging 23, 93 (2023). https://doi.org/10.1186/s12880-023-01052-z
  54. LIFEx-texture: Yaltırık Bilgin E, Ünal Ö, Törenek Ş, et al. (July 16, 2023) Computerized Tomography Texture Analysis in the Differential Diagnosis of Intracranial Epidermoid and Arachnoid Cysts. Cureus 15(7): e41945. DOI https://doi.org/10.7759/cureus.41945
  55. LIFEx-texture: Amirhossein Sanaat, Hossein Shooli, Andrew Stephen Böhringer, Maryam Sadeghi, Isaac Shiri, Yazdan Salimi, Nathalie Ginovart, Valentina Garibotto, Hossein Arabi, Habib Zaidi. A cycle‑consistent adversarial network for brain PET partial volume correction without prior anatomical information. European Journal of Nuclear Medicine and Molecular Imaging (2023) 50:1881–1896. https://doi.org/10.1007/s00259-023-06152-0
  56. LIFEx-texture: Hanekamp, B.A., Viktil, E., Slørdahl, K.S. et al. Magnetic resonance imaging of anal cancer: tumor characteristics and early prediction of treatment outcome. Strahlenther Onkol (2023). https://doi.org/10.1007/s00066-023-02114-5
  57. LIFEx-texture: Sheen, H., Shin, HB., Kim, H. et al. Application of error classification model using indices based on dose distribution for characteristics evaluation of multileaf collimator position errors. Sci Rep 13, 11027 (2023). https://doi.org/10.1038/s41598-023-35570-1
  58. LIFEx-texture: Park, YJ., Park, Y.S., Kim, S.T. et al. A Machine Learning Approach Using [18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor. Mol Imaging Biol (2023). https://doi.org/10.1007/s11307-023-01832-7
  59. LIFEx-texture: Zhou, Y., Zhang, B., Han, J. et al. Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells. J Cancer Res Clin Oncol (2023). https://doi.org/10.1007/s00432-023-05038-w
  60. LIFEx-MTV: Peng X, Yu S, Kou Y, Dang J, Wu P, Yao Y, Shen J, Liu Y, Wang X, Cheng Z. Prediction nomogram based on 18F-FDG PET/CT and clinical parameters for patients with diffuse large B-cell lymphoma. Ann Hematol. 2023 Jul 3. doi: 10.1007/s00277-023-05336-w. Epub ahead of print. PMID: 37400729
  61. LIFEx-Viewer: Zhong, H., Huang, D., Wu, J. et al. 18F‑FDG PET/CT based radiomics features improve prediction of prognosis: multiple machine learning algorithms and multimodality applications for multiple myeloma. BMC Med Imaging 23, 87 (2023). https://doi.org/10.1186/s12880-023-01033-2
  62. LIFEx-texture: Fournier, C.; Leguillette, C.; Leblanc, E.; Le Deley, M.-C.; Carnot, A.; Pasquier, D.; Escande, A.; Taieb, S.; Ceugnart, L.; Lebellec, L. Diagnostic Value of the Texture Analysis Parameters of Retroperitoneal Residual Masses on Computed Tomographic Scan after Chemotherapy in Non-Seminomatous Germ Cell Tumors. Cancers 2023, 15, 2997. https://doi.org/10.3390/cancers15112997
  63. LIFEx-texture: Weiyue Tan, Yi Zhang, Jie Wang, Zhonghang Zheng, Ligang Xing, Xiaorong Sun, FDG PET/CT Tumor Dissemination Characteristic Predicts the Outcome of First-Line Systemic Therapy in Non-small Cell Lung Cancer, Academic Radiology, 2023, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2023.03.027
  64. LIFEx-texture: Kawaji, K., Nakajo, M., Shinden, Y. et al. Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer. Mol Imaging Biol (2023). https://doi.org/10.1007/s11307-023-01823-8
  65. LIFEx-texture: Mustafa Orhan Nalbant, Ozkan Oner, Ozlem Akinci, Elif Hocaoglu, Ercan Inci, Analysis of Pancreatobiliary and Intestinal Type Periampullary Carcinomas Using Volumetric Apparent Diffusion Coefficient Histograms, Academic Radiology, 2023, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2023.04.031
  66. LIFEx-texture: Partial tumor irradiation plus pembrolizumab in treating large advanced solid tumor metastases. Mark C. Korpics, Benjamin E. Onderdonk, Rebekah E. Dadey, Jared H. Hara, Lilit Karapetyan, Yuanyuan Zha, Theodore G. Karrison, Adam C. Olson, Gini F. Fleming, Ralph R. Weichselbaum, Riyue Bao, Steven J. Chmura and Jason J. Luke. J Clin Invest. 2023;133(10):e162260. https://doi.org/10.1172/JCI162260
  67. LIFEx-texture: Abdollahi Hamid, Dehesh Tania, Abdalvand Neda, Rahmim Arman. Radiomics and dosiomics-based prediction of radiotherapy-induced xerostomia in head and neck cancer patients. International Journal of Radiation Biology. 2023/05/12. https://doi.org/10.1080/09553002.2023.2214206
  68. LIFEx-content: Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah, Hugo J. W. L. Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Spyridon Bakas, Roelof J. Beukinga, Ronald Boellaard, Marta Bogowicz, Luca Boldrini, Irène Buvat, Gary J. R. Cook, Christos Davatzikos, Adrien Depeursinge, Marie-Charlotte Desseroit, Nicola Dinapoli, Cuong Viet Dinh, Sebastian Echegaray, Issam El Naqa, Andriy Y. Fedorov, Roberto Gatta, Robert J. Gillies, Vicky Goh, Michael Götz, Matthias Guckenberger, Sung Min Ha, Mathieu Hatt, Fabian Isensee, Philippe Lambin, Stefan Leger, Ralph T.H. Leijenaar, Jacopo Lenkowicz, Fiona Lippert, Are Losnegård, Klaus H. Maier-Hein, Olivier Morin, Henning Müller, Sandy Napel, Christophe Nioche, Fanny Orlhac, Sarthak Pati, Elisabeth A.G. Pfaehler, Arman Rahmim, Arvind U.K. Rao, Jonas Scherer, Muhammad Musib Siddique, Nanna M. Sijtsema, Jairo Socarras Fernandez, Emiliano Spezi, Roel J.H.M. Steenbakkers, Stephanie Tanadini-Lang, Daniela Thorwarth, Esther G.C. Troost, Taman Upadhaya, Vincenzo Valentini, Lisanne V. van Dijk, Joost van Griethuysen, Floris H.P. van Velden, Philip Whybra, Christian Richter, Steffen Löck. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. RadiologyVol. 295, No. 2. Mar 10 2020. https://doi.org/10.1148/radiol.2020191145
  69. LIFEx-texture: Yusuke Kawashima DDS, PhD , Masaaki Miyakoshi DDS, PhD, Yoshihiro Kawabata DDS, PhD , Hiroko Indo DDS, PhD , Efficacy of texture analysis of ultrasono-graphic images in the differentiation of metastatic and non-metastatic cervical lymph nodes in patients with squamous cell carcinoma of the tongue, Oral Surg Oral Med Oral Pathol Oral Radiol (2023), doi: https://doi.org/10.1016/j.oooo.2023.04.012
  70. LIFEx-Main: Boursier, C., Zaragori, T., Bros, M. et al. Semi-automated segmentation methods of SSTR PET for dosimetry prediction in refractory meningioma patients treated by SSTR-targeted peptide receptor radionuclide therapy. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-09697-8
  71. LIFEx-texture: Ishimura, M., Norikane, T., Mitamura, K. et al. FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma. Sci Rep 13, 6742 (2023). https://doi.org/10.1038/s41598-023-34061-7
  72. LIFEx-texture: Ishimura, M., Norikane, T., Mitamura, K. et al. FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma. Sci Rep 13, 6742 (2023). https://doi.org/10.1038/s41598-023-34061-7
  73. LIFEx-texture: Vural Topuz Ö, Aksu A, Yılmaz Özgüven MB. Una perspectiva diferente sobre la radiómica PET con 18F-FDG en pacientes con cáncer colorrectal; la relación entre el análisis intra y peritumoral y los hallazgos patológicos. Rev Esp Med Nucl Imagen Mol. 2023. https://doi.org/10.1016/j.remn.2023.04.002
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  80. LIFEx-texture: Maki Amano, Katsuhiro Sano,Shohei Fujita, Naoyuki Takei, Akihiko Wada, Kanako Sato, Junko Kikuta, Yoshiki Kuwatsuru, Rina Tachibana, Towa Sekine, Yoshiya Horimoto, and Shigeki Aoki. Feasibility of Quantitative MRI Using 3D-QALAS for Discriminating Immunohistochemical Status in Invasive Ductal Carcinoma of the Breast. J. MAGN. RESON. IMAGING 2023. https://doi.org/10.1002/jmri.28683
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  115. LIFEx-texture: She, J., Huang, H., Ye, Z. et al. Automatic biometry of fetal brain MRIs using deep and machine learning techniques. Sci Rep 13, 17860 (2023). https://doi.org/10.1038/s41598-023-43867-4
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  122. LIFEx-texture: Hermet P, Delache B, Herate C, Wolf E, Kivi G, Juronen E, et al. (2023) Broadly neutralizing humanized SARS-CoV-2 antibody binds to a conserved epitope on Spike and provides antiviral protection through inhalation-based delivery in non-human primates. PLoS Pathog 19(8): e1011532. https://doi.org/10.1371/journal.ppat.1011532
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  124. LIFEx-texture: Ma, H., Zhang, D., Wang, Y. et al. Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI. BMC Psychiatry 23, 466 (2023). https://doi.org/10.1186/s12888-023-04966-8
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  126. Šedienė, S.; Kulakienė, I.; Urbonavičius, B.G.; Korobeinikova, E.; Rudžianskas, V.; Povilonis, P.A.; Jaselskė, E.; Adlienė, D.; Juozaitytė, E. Development of a Model Based on Delta-Radiomic Features for the Optimization of Head and Neck Squamous Cell Carcinoma Patient Treatment. Medicina 2023, 59, 1173. https://doi.org/10.3390/medicina59061173
  127. LIFEx-texture: Malet J, Ancel J, Moubtakir A, Papathanassiou D, Deslée G, Dewolf M. Assessment of the Association between Entropy in PET/CT and Response to Anti-PD-1/PD-L1 Monotherapy in Stage III or IV NSCLC. Life. 2023; 13(4):1051. https://doi.org/10.3390/life13041051
  128. LIFEx-texture: Vani Rajasekar, M.P. Vaishnnave, S. Premkumar, Velliangiri Sarveshwaran, V. Rangaraaj, Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques, Results in Engineering, Volume 18, 2023, 101111, ISSN 2590-1230, https://doi.org/10.1016/j.rineng.2023.101111
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  132. LIFEx-texture: Ari Lee, Gun-Chan Park, Eunae Sandra Cho, Yoon Joo Choi, Kug Jin Jeon, Sang Sun Han, Chena Lee. Radiomics-based sialadenitis staging in contrast-enhanced computed tomography and ultrasonography: A preliminary rat model study, Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 2023, ISSN 2212-4403, https://doi.org/10.1016/j.oooo.2023.04.005
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Thesis (6):

  1. LIFEx-texture: Emre Uysal. Nazofarenks karsinomunda tedavi oncesi cekilen kontrastli manyetik rezonans goruntulemeden erken tedavi yaniti ongorulebilir mi? Thesis · October 2023. https://doi.org/10.13140/RG.2.2.22343.47524
  2. LIFEx-texture: SANAAT, Amirhossein. Strategies for improvement of PET instrumentation performance and imaging methodology. 2023. https://doi.org/10.13097/archive-ouverte/unige:171571
  3. LIFEx-texture: Giulia Colelli, Artificial Intelligence, Mathematical Modeling and Magnetic Resonance Imaging for Precision Medicine in Neurology and Neuroradiology, Università degli Studi di Pavia, Università della Svizzera italiana, 2022 (link)
  4. LIFEx-texture: Hamza CHEGRAOUI. Machine learning for genomics and imaging data integration applied to neuro-oncology. Paris-Saclay, le 23 mars 2023 (link)
  5. LIFEx-texture: Federico Loi. Radiogenomic features of CNS tumors and MiRNAs correlation phenotypes analysis. Jan 2020. Università degli Studi di Cagliari. (link)
  6. LIFEx-texture: Artificial intelligence in molecular imaging: from machine to deep learning. Riccardo Laudicella. https://iris.unime.it/retrieve/e234d14a-b211-415e-8bef-0640248279de/Tesi.pdf

 

Poster (1):

  1. LIFEx-texture: Giulia Martini, Valerio Nardone, Davide Ciardiello, Marco De Chiara, Teresa Troiani, Luca D'ambrosio, Stefania Napolitano, Claudia Cardone, Chiara Cremolini, Filippo Pietrantonio, Evaristo Maiello, Antonio Avallone, Salvatore Cappabianca, Fortunato Ciardiello, Alfonso Reginelli, and Erika Martinelli. Journal of Clinical Oncology 2023 41:4_suppl, 241-241(link)

 

Article not in English (1):

  1. LIFEx-texture: SG Ates, GB Dilek, G Uçmak. Heterogeneidad del tumor primario en la18F-FDG PET/TC pretratamiento para predecir el pronóstico en pacientes con cáncer de recto sometidos a cirugía tras. Revista Española de Medicina Nuclear e Imagen …, 2023, ISSN 2253-654X. https://doi.org/10.1016/j.remn.2023.01.001

 

Review (30):

  1. LIFEx-texture: Xue Yang, Kexin Huang, Dewei Yang, Weiling Zhao, and Xiaobo Zho. Biomedical Big Data Technologies, Applications, andChallenges for Precision Medicine: A Review. 2023 2300163 . Global Challenges published. http://doi/org/10.1002/gch2.202300163
  2. LIFEx-texture: Hugo C. Temperley, Niall J. O’Sullivan, Caitlin Waters, Alison Corr, Brian J. Mehigan, Grainne O’Kane, Paul McCormick, Charles Gillham, Emanuele Rausa, John O. Larkin, James F. Meaney, Ian Brennan,and Michael E. Kelly. Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review. The American Surgeon 2023, Vol. 0(0) 1–10. http://doi.org/10.1177/00031348231216494
  3. LIFEx-texture: Akin O, Lema-Dopico A, Paudyal R, Konar AS, Chenevert TL, Malyarenko D, Hadjiiski L, Al-Ahmadie H, Goh AC, Bochner B, et al. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers. 2023; 15(22):5468. https://doi.org/10.3390/cancers15225468
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