
Journal of Shanghai Jiao Tong University (Medical Science) ›› 2023, Vol. 43 ›› Issue (6): 781-787.doi: 10.3969/j.issn.1674-8115.2023.06.016
• Review • Previous Articles Next Articles
CHENG Ran(
), HU Jiajia, LI Biao(
)
Received:2023-01-29
Accepted:2023-05-04
Online:2023-06-28
Published:2023-06-28
Contact:
LI Biao
E-mail:chengran354@163.com;lb10363@rjh.com.cn
Supported by:CLC Number:
CHENG Ran, HU Jiajia, LI Biao. Advances in the application of 18F-FDG PET/CT radiomics for diagnosis, treatment and prognosis prediction of lymphoma[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(6): 781-787.
Add to citation manager EndNote|Ris|BibTeX
URL: https://xuebao.shsmu.edu.cn/EN/10.3969/j.issn.1674-8115.2023.06.016
| 1 | SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. |
| 2 | LIU W P, LIU J M, SONG Y Q, et al. Burden of lymphoma in China, 1990—2019: an analysis of the global burden of diseases, injuries, and risk factors study 2019[J]. Aging, 2022, 14(7): 3175-3190. |
| 3 | MILLER K D, NOGUEIRA L, DEVASIA T, et al. Cancer treatment and survivorship statistics, 2022[J]. CA Cancer J Clin, 2022, 72(5): 409-436. |
| 4 | CHESON B D, FISHER R I, BARRINGTON S F, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification[J]. J Clin Oncol, 2014, 32(27): 3059-3068. |
| 5 | BARRINGTON S F, KLUGE R. FDG PET for therapy monitoring in Hodgkin and non-Hodgkin lymphomas[J]. Eur J Nucl Med Mol Imaging, 2017, 44(Suppl 1): S97-S110. |
| 6 | CHESON B D, MEIGNAN M. Current role of functional imaging in the management of lymphoma[J]. Curr Oncol Rep, 2021, 23(12): 144. |
| 7 | JIANG H, LI A, JI Z Y, et al. Role of radiomics-based baseline PET/CT imaging in lymphoma: diagnosis, prognosis, and response assessment[J]. Mol Imaging Biol, 2022, 24(4): 537-549. |
| 8 | CASALI M, LAURI C, ALTINI C, et al. State of the art of 18F-FDG PET/CT application in inflammation and infection: a guide for image acquisition and interpretation[J]. Clin Transl Imaging, 2021, 9(4): 299-339. |
| 9 | MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. |
| 10 | VAN TIMMEREN J E, CESTER D, TANADINI-LANG S, et al. Radiomics in medical imaging-"how-to" guide and critical reflection[J]. Insights Imaging, 2020, 11(1): 91. |
| 11 | VISVIKIS D, CHEZE LE REST C, JAOUEN V, et al. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications[J]. Eur J Nucl Med Mol Imaging, 2019, 46(13): 2630-2637. |
| 12 | DE JESUS F M, YIN Y, MANTZOROU-KYRIAKI E, et al. Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET/CT features[J]. Eur J Nucl Med Mol Imaging, 2022, 49(5): 1535-1543. |
| 13 | AIDE N, TALBOT M, FRUCHART C, et al. Diagnostic and prognostic value of baseline FDG PET/CT skeletal textural features in diffuse large B cell lymphoma[J]. Eur J Nucl Med Mol Imaging, 2018, 45(5): 699-711. |
| 14 | MAYERHOEFER M E, UMUTLU L, SCHÖDER H. Functional imaging using radiomic features in assessment of lymphoma[J]. Methods, 2021, 188: 105-111. |
| 15 | MAYERHOEFER M E, RIEDL C C, KUMAR A, et al. [18F]FDG-PET/CT radiomics for prediction of bone marrow involvement in mantle cell lymphoma: a retrospective study in 97 patients[J]. Cancers, 2020, 12(5): 1138. |
| 16 | KONG Z R, JIANG C D, ZHU R Z, et al. 18F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma[J]. Neuroimage Clin, 2019, 23: 101912. |
| 17 | OU X J, ZHANG J, WANG J, et al. Radiomics based on 18F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: a preliminary study[J]. Cancer Med, 2020, 9(2): 496-506. |
| 18 | ZHU S, XU H, SHEN C Y, et al. Differential diagnostic ability of 18F-FDG PET/CT radiomics features between renal cell carcinoma and renal lymphoma[J]. Q J Nucl Med Mol Imaging, 2021, 65(1): 72-78. |
| 19 | SIBILLE L, SEIFERT R, AVRAMOVIC N, et al. 18F-FDG PET/CT uptake classification in lymphoma and lung cancer by using deep convolutional neural networks[J]. Radiology, 2020, 294(2): 445-452. |
| 20 | LOVINFOSSE P, FERREIRA M, WITHOFS N, et al. Distinction of lymphoma from sarcoidosis on 18F-FDG PET/CT: evaluation of radiomics-feature-guided machine learning versus human reader performance[J]. J Nucl Med, 2022, 63(12): 1933-1940. |
| 21 | FROOD R, CLARK M, BURTON C, et al. Utility of pre-treatment FDG PET/CT-derived machine learning models for outcome prediction in classical Hodgkin lymphoma[J]. Eur Radiol, 2022, 32(10): 7237-7247. |
| 22 | AKHTARI M, MILGROM S A, PINNIX C C, et al. Reclassifying patients with early-stage Hodgkin lymphoma based on functional radiographic markers at presentation[J]. Blood, 2018, 131(1): 84-94. |
| 23 | MILGROM S A, ELHALAWANI H, LEE J, et al. A PET radiomics model to predict refractory mediastinal Hodgkin lymphoma[J]. Sci Rep, 2019, 9(1): 1322. |
| 24 | LUE K H, WU Y F, LIU S H, et al. Prognostic value of pretreatment radiomic features of 18F-FDG PET in patients with Hodgkin lymphoma[J]. Clin Nucl Med, 2019, 44(10): e559-e565. |
| 25 | MAYERHOEFER M E, RIEDL C C, KUMAR A, et al. Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma[J]. Eur J Nucl Med Mol Imaging, 2019, 46(13): 2760-2769. |
| 26 | LISSON C S, LISSON C G, MEZGER M F, et al. Deep neural networks and machine learning radiomics modelling for prediction of relapse in mantle cell lymphoma[J]. Cancers, 2022, 14(8): 2008. |
| 27 | WANG H X, ZHAO S N, LI L, et al. Development and validation of an 18F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma[J]. Eur Radiol, 2020, 30(10): 5578-5587. |
| 28 | PARVEZ A, TAU N, HUSSEY D, et al. 18F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin's lymphoma as predictors of treatment outcome and survival[J]. Ann Nucl Med, 2018, 32(6): 410-416. |
| 29 | AIDE N, FRUCHART C, NGANOA C, et al. Baseline 18F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy[J]. Eur Radiol, 2020, 30(8): 4623-4632. |
| 30 | JIANG C, LI A, TENG Y, et al. Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma[J]. Eur J Nucl Med Mol Imaging, 2022, 49(8): 2902-2916. |
| 31 | JIANG C, HUANG X J, LI A, et al. Radiomics signature from [18F]FDG PET images for prognosis predication of primary gastrointestinal diffuse large B cell lymphoma[J]. Eur Radiol, 2022, 32(8): 5730-5741. |
| 32 | EERTINK J J, VAN DE BRUG T, WIEGERS S E, et al. 18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma[J]. Eur J Nucl Med Mol Imaging, 2022, 49(3): 932-942. |
| 33 | FROOD R, CLARK M, BURTON C, et al. Discovery of pre-treatment FDG PET/CT-derived radiomics-based models for predicting outcome in diffuse large B-cell lymphoma[J]. Cancers, 2022, 14(7): 1711. |
| 34 | ZHANG X H, CHEN L, JIANG H, et al. A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT[J]. Eur J Nucl Med Mol Imaging, 2022, 49(4): 1298-1310. |
| 35 | COTTEREAU A S, NIOCHE C, DIRAND A S, et al. 18F-FDG PET dissemination features in diffuse large B-cell lymphoma are predictive of outcome[J]. J Nucl Med, 2020, 61(1): 40-45. |
| 36 | EERTINK J J, ZWEZERIJNEN G J C, CYSOUW M C F, et al. Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features[J]. Eur J Nucl Med Mol Imaging, 2022, 49(13): 4642-4651. |
| 37 | JIMENEZ J E, DAI D, XU G F, et al. Lesion-based radiomics signature in pretherapy 18F-FDG PET predicts treatment response to ibrutinib in lymphoma[J]. Clin Nucl Med, 2022, 47(3): 209-218. |
| 38 | HU X B, GUO R, CHEN J N, et al. Coarse-to-fine adversarial networks and zone-based uncertainty analysis for NK/T-cell lymphoma segmentation in CT/PET images[J]. IEEE J Biomed Health Inform, 2020, 24(9): 2599-2608. |
| 39 | GUO R, HU X B, SONG H M, et al. Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type[J]. Eur J Nucl Med Mol Imaging, 2021, 48(10): 3151-3161. |
| [1] | HE Jiayin, CHEN Siyuan, SHI Qing, ZHANG Muchen, YI Hongmei, DONG Lei, QIAN Ying, WANG Li, CHENG Shu, XU Pengpeng, ZHAO Weili. Clinicopathologic characteristics, gene mutation profile, and prognostic analysis of patients with adrenal diffuse large B-cell lymphoma [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(9): 1194-1201. |
| [2] | CHEN Siyuan, SHI Qing, FU Di, WANG Li, CHENG Shu, XU Pengpeng, ZHAO Weili. Clinicopathologic characteristics, gene mutation profile, and prognostic analysis of diffuse large B-cell lymphoma with lung involvement [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(9): 1214-1220. |
| [3] | HUANG Runyu, ZHANG Chunye, ZHANG Ying, ZHAO Zhengyan, YANG Yang, WU Lan. Features of oral peripheral T-cell lymphoma, not otherwise specified [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(5): 653-660. |
| [4] | WANG Boen, CHEN Siyuan, SHI Qing, ZHANG Muchen, YI Hongmei, DONG Lei, WANG Li, CHENG Shu, XU Pengpeng, ZHAO Weili. Clinicopathologic characteristics of patients with kidney-involved diffuse large B-cell lymphoma [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(9): 1162-1168. |
| [5] | REN Yixuan, CHEN Cheng, CAI Mingci, CHEN Jiamin, YANG Xinxin, WANG Chao, LIN Xiaozhu, CHENG Shu, JIANG Xufeng, CHEN Dongxu. Research on the characteristics of 18F-FDG PET/CT in mantle cell lymphoma and the discrimination between cellular morphological variants [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(12): 1561-1569. |
| [6] | TANG Sijie, MI Jianqing. Clinical advances in antibody-drug conjugates for hematological malignancies [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(12): 1607-1614. |
| [7] | DU Zhishan, WANG Yue, SHI Ziyang, SHI Qing, YI Hongmei, DONG Lei, WANG Li, CHENG Shu, XU Pengpeng, ZHAO Weili. Clinicopathologic characteristics, gene mutation profile and prognostic analysis of thyroid diffuse large B-cell lymphoma [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(1): 64-71. |
| [8] | LIU Qiming, LU Qifan, CHAI Yezi, JIANG Meng, PU Jun. Short-axis cine cardiac magnetic resonance images-derived radiomics for hypertrophic cardiomyopathy and healthy control classification [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(1): 79-86. |
| [9] | LIU Qiming, LU Qifan, CHAI Yezi, JIANG Meng, PU Jun. Radiomics-based left ventricular ejection fraction prediction: a feasibility study [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(9): 1162-1168. |
| [10] | MA Ben, ZHAO Cheng, SHU Yijun, DONG Ping. Application progress of CT radiomics in gastrointestinal stromal tumor [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(7): 923-930. |
| [11] | ZHANG Yirong, WEI Weiqing, MA Jiao, ZHANG Xue. Research on the role of SOX9 in regulating metabolic reprogramming in diffuse large B cell lymphoma [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(10): 1236-1244. |
| [12] | ZHAO Jie, JIANG Yan, HAO Siguo. Clinical characteristics and prognosis of patients with diffuse large B-cell lymphoma [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(10): 1282-1288. |
| [13] | HOU Shumin, SHAO Jingbo. Research progress in clinical characteristics, diagnosis and prognosis of TdT-negative lymphoblastic lymphoma/acute lymphoblastic leukemia [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(1): 120-124. |
| [14] | HU Zeyu, ZHOU Cheng, YANG Lin, MA Xiaoyan, XIAO Haijuan, SI Hailong. Monomorphic epitheliotropic intestinal T-cell lymphoma: a case with recurrent gastrointestinal hemorrhage [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(1): 132-136. |
| [15] | JIANG Zhenglin, GAO Yunge, WU Hao. Study on the expression of B-cell leukemia/lymphoma 11B in the organ of Corti during mice cochlea development [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2022, 42(10): 1361-1374. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||