Page 133 - 南京医科大学自然版
P. 133
第44卷第7期 许馨晨,顾莹莹,唐立钧. 影像组学在前列腺癌中的研究进展[J].
2024年7月 南京医科大学学报(自然科学版),2024,44(7):1010-1017 ·1017 ·
[57]THARMALINGAM H,TSANG Y M,ALONZI R,et al. spective radiomics study[J]. Br J Radiol,2019,92
Changes in magnetic resonance imaging radiomic features (1101):20190286
in response to androgen deprivation therapy in patients [64]PEEKEN J C,SHOUMAN M A,KROENKE M,et al. A
with intermediate⁃ and high⁃risk prostate cancer[J]. Clin CT ⁃ based radiomics model to detect prostate cancer
Oncol,2022,34(6):246-253 lymph node metastases in PSMA radioguided surgery pa⁃
[58]ZAMBOGLOU C,BETTERMANN A S,GRATZKE C,et tients[J]. Eur J Nucl Med Mol Imaging,2020,47(13):
al. Uncovering the invisible ⁃ prevalence,characteristics, 2968-2977
and radiomics feature ⁃ based detection of visually unde⁃ [65]MOSTAFAEI S,ABDOLLAHI H,KAZEMPOUR DEH⁃
68
tectable intraprostatic tumor lesions in GaPSMA⁃11 PET KORDI S,et al. CT imaging markers to improve radiation
images of patients with primary prostate cancer[J]. Eur J toxicity prediction in prostate cancer radiotherapy by
Nucl Med Mol Imaging,2021,48(6):1987-1997 stacking regression algorithm[J]. Radiol Med,2020,125
[59]FELICIANI G,CELLI M,FERRONI F,et al. Radiomics (1):87-97
68
analysis on[ Ga]Ga⁃PSMA⁃11 PET and MRI⁃ADC for [66]OU W,LEI J H,LI M H,et al. Ultrasound ⁃ based ra⁃
the prediction of prostate cancer ISUP grades:prelimi⁃ diomics score for pre⁃biopsy prediction of prostate cancer
nary results of the BIOPSTAGE trial[J]. Cancers,2022, to reduce unnecessary biopsies[J]. Prostate,2023,83
14(8):1888 (1):109-118
[60]ERLE A,MOAZEMI S,LÜTJE S,et al. Evaluating a ma⁃ [67]LIANG L,ZHI X,SUN Y,et al. A nomogram based on a
chine learning tool for the classification of pathological multiparametric ultrasound radiomics model for discrimi⁃
uptake in whole⁃body PSMA⁃PET⁃CT scans[J]. Tomogra⁃ nation between malignant and benign prostate lesions[J].
phy,2021,7(3):301-312 Front Oncol,2021,11:610785
[61]CYSOUW M C F,JANSEN B H E,VAN DE BRUG T,et [68]WILDEBOER R R,MANNAERTS C K,VAN SLOUN R J
18
al. Machine learning⁃based analysis of[ F]DCFPyL PET G,et al. Automated multiparametric localization of pros⁃
radiomics for risk stratification in primary prostate can⁃ tate cancer based on B⁃mode,shear⁃wave elastography,
cer[J]. Eur J Nucl Med Mol Imaging,2021,48(2):340- and contrast⁃enhanced ultrasound radiomics[J]. Eur Ra⁃
349 diol,2020,30(2):806⁃815
[62]OSMAN S S,LEIJENAAR R T,COLE A J,et al. Comput⁃ [69]ZHANG Q,XIONG J Y,CAI Y H,et al. Multimodal fea⁃
ed tomography ⁃ based radiomics for risk stratification in ture learning and fusion on B⁃mode ultrasonography and
prostate cancer[J]. Int J Radiat Oncol Biol Phys,2019, sonoelastography using point ⁃ wise gated deep networks
105(2):448-456 for prostate cancer diagnosis[J]. Biomed Tech,2020,65
[63]ACAR E,LEBLEBICI A,ELLIDOKUZ B E,et al. Ma⁃ (1):87-98
chine learning for differentiating metastatic and completely [收稿日期] 2024-03-11
responded sclerotic bone lesion in prostate cancer:a retro⁃ (本文编辑:陈汐敏)