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.
|