Imaging Bone Metastases: Research Progress
-
摘要:
骨是恶性肿瘤常见的转移部位,骨转移瘤的发生率高于原发性骨肿瘤。近年来,各种恶性肿瘤患者的5年生存率逐渐提高,骨转移及骨骼相关事件发生的概率也有所增大。及时、准确地诊断骨转移以及治疗反应评估对临床制定个性化治疗方案、改善患者生存质量至关重要。本文对肿瘤骨转移诊断的影像学检查技术进行综述,探讨影像学新技术对肿瘤骨转移诊断的可行性。
Abstract:Bone is a common site for cancer metastasis, with a higher incidence than primary bone malignancies. As various cancer treatment regimens continue to improve, the five-year survival rate for cancer patients has risen steadily. However, this has also led to an increased likelihood of bone metastasis and skeletal-related events. Timely and accurate diagnosis of bone metastases, along with proper assessment of therapeutic response, is crucial for developing personalized treatment plans and improving patient survival. This paper reviews current imaging examinations for bone metastases, and explores the potential of new imaging techniques for diagnosing tumor-related bone metastases.
-
Keywords:
- CT /
- MRI /
- PET-CT /
- imagomics /
- bone metastases
-
随着人们生活水平和审美观的日益提高,我国的整形外科手术量和技术方法也越来越多,其中隆鼻术最为常见。临床多采用自体软骨(肋软骨、耳软骨及鼻中隔软骨等)进行隆鼻术,已成为主流充填材料[1-2],其中自体肋软骨为首选材料,多排螺旋CT检查及三维重建可为相关患者的肋软骨定性定量分析提供准确的术前影像信息[3-4]。
根据需求不同,多排螺旋CT可以使用不同的重建算法生成不同特点的图像。由于目前国内外相关文献报道极少,本研究对比研究全模型迭代重建算法(iterative model reconstruction,IMR)、混合迭代重建算法(iDose4)和传统滤波反投影(filtered back projection,FBP)三种算法[5-7]对肋软骨重建图像质量的影响,探讨多拍螺旋CT在隆鼻术前肋软骨选材最理想的检查方法。
1. 资料与方法
1.1 研究对象
2020年4月至2021年10月期间,在自愿要求拟行自体肋软骨隆鼻术的患者群体内,收集相应患者,排除因年龄过小及过大、先天畸形、患有甲状腺激素异常、缺钙、应用糖皮质激素、脂肪肝等易导致肋软骨异常的检查者后,随机选取其中符合标准并计划进行自体肋软骨隆鼻手术的60例患者的相关资料作为研究对象,进行回顾性分析比较。女性56例,男性4例,年龄范围17~58岁,平均年龄(27.5±3.2)岁,检查前均已告知 CT检查的利与弊,并签署书面知情同意书;本课题经医院伦理委员会审批通过。
1.2 仪器与方法
60例患者均于术前行MSCT检查,采用Philips Brilliance iCT 256层扫描仪,采用仰卧位、双臂上举抱头姿势、吸气后屏气时扫描,扫描范围胸廓开口~脐部。
扫描参数:管电压120 kV,智能管电流100 mAs,机架转速0.5 s,层厚和间隔均为1.00 mm,螺距1.375∶1,冠状位MIP像窗位200、窗宽500。对每例患者原始数据分别使用3种算法技术进行图像重建:IMR算法、iDose4算法和 FBP算法。IMR重建的Level为1,iDOS4重建Level为3。
1.3 图像后处理及分析
研究组所有病例的MSCT原始数据经3种算法重建后的横断位图像,传送至EBW工作站,采用同步重建模式下进行容积再现(volumetric rendering,VR)成像和最大密度投影(maximum intensity projection,MIP)成像,以明确胸廓肋骨整体外貌及手术备选区肋软骨的具体情况。同步重建模式下使用相同的重建参数:冠状位、层面中心位置相同、MIP图像层厚70 mm、窗位200、窗宽500、患者信息屏蔽。
由两名高年资放射科医生及1名整形外科医生(具有丰富隆鼻术经验)共同组成阅片组,对每位患者的CT影像结合临床资料进行综合分析,取得一致意见后,对肋软骨钙化程度分级和图像质量评分[8-9]。
1.4 影像重建质量评估方法
主观评分:全部图像在EBW工作站上,使用同步重建模式和相同参数进行冠状位MIP重建和VR重建以充分显示肋软骨,由3名医生(两名高年资放射科医生及1名整形外科医生)对肋软骨钙化程度评估并分级,并采用4分法对所得MIP和VR重建图像进行质量评分[4]:4分,图像质量非常好,肋软骨边缘结构显示清晰,无明显噪声及伪影,诊断信心充足;3分,图像质量比较好,肋软骨边缘可见,噪声及伪影很小,诊断可靠;2分,图像质量总体一般,肋软骨边缘隐约可见,噪声及伪影局限影响解剖结构评估,但可以用于术前诊断评估;1分,图像质量差,无法用于诊断。
客观评价:在每例MIP图像右侧第6~8肋软骨和肝脏位置上分别画出规定范围感兴趣区ROI,测量并记录肋软骨CT值、标准差(standard deviation,SD),同时记录肝脏CT值,并计算出肋软骨的对比噪声比(contrast noise ratio,CNR)和信噪比(signal noise ratio,SNR),将肋软骨CT值的标准差(SD肋软骨)作为图像噪声[10],计算公式:SNR=CT值肋软骨/SD肋软骨,CNR=(CT值肋软骨 - CT值肝脏)/SD肋软骨。用统计学方法进行组间比较。
1.5 统计学分析
采用SPSS 24.0软件进行分析,对图像质量评分计量资料采用均值±标准差表示。肋软骨的SD、CNR、SNR的分析使用两两组间配对t检验统计比较,并分别进行加权Kappa检验观察一致性。P<0.05认为差异有统计学意义。
2. 结果
2.1 肋软骨钙化程度评估
研究组60例患者中,以冠状位MIP图像和VR图像为依据,分别统计每一例患者全部肋软骨总面积、各肋软骨上所有钙化斑面积总和及所占比例,然后分级计算有1级(肋软骨无钙化)19例(31.7%,19/60)、2级(轻度钙化,钙化面积占肋软骨总面积25% 以下)34例(56.7%,34/60)、3级(中度钙化,钙化面积占肋软骨总面积的25%~50%)5例(8.3%,5/60)和4级(重度钙化,钙化面积超过50%)2例(3.3%,2/60)。3种算法得到的CT图像均能评价肋软骨上有无钙化,及钙化程度(图1)。
2.2 主观评价
与FBP组相比,IMR、iDose4的评分均值在MIP图像上分别增加了50.5% 和33.6%,在VR图像上评分均值分别增加了51.0% 和19.0%。IMR组较iDose4组的评分均值在MIP图像和VR图像上分别增加了11.1% 和21.1%。明显可见IMR组图像主观评分最高,在图像上具体主要表现在IMR算法重建的图像肋软骨边缘显示更加清晰,与周围组织对比度更好,能更清楚显示肋软骨上的钙化斑范围及边缘,尤其在VR图像上可见周边肝脏组织背景对肋软骨显示的干扰最小(图2和图3)。
2.3 三种算法肋软骨重建图像客观评价
所有记录的客观测量数据肋如肋软骨CT值、标准差SD、肝脏CT值,方差均具有齐性,IMR组、iDose4组和FBP组的SD、SNR和CNR组间统计学分析均具有明显统计学差异。IMR组的噪声SD较iDose4组和FBP组分别降低了39.2%和47.4%;信噪比SNR较iDose4组和FBP组分别增加了41.4% 和77.0%;对比噪声比CNR较iDose4组和FBP组分别增加了143.0% 和217.6%。
IMR组的图像质量客观评价最优(表1和表2)。应用IMR重建算法的MIP图像、VR图像在抑制肝脏、胃内容物及心脏波动伪影方面较FBP和iDose4有明显的优势(图4)。
表 1 3种不同重建算法图像的客观评价数据测量结果Table 1. Objective evaluation data measurement results of three different reconstruction algorithms组别 SD SNR CNR FBP组 8.31±2.13 17.30±4.82 4.14±2.13 IMR组 4.37±0.95 30.63±7.19 13.15±4.41 iDose4组 7.19±1.72 21.65±6.18 5.41±2.83 注:表内数据为测量计算均值±标准差。iDose4指混合迭代算法。 表 2 3组图像每两组间的统计分析结果Table 2. Statistical comparison results between each two groups组间统计 SD SNR CNR t P t P t P FBP组 VS IMR组 25.45 0.00 -21.26 0.00 -24.92 0.00 FBP组 VS iDose4组 6.42 0.00 -9.35 0.00 -8.10 0.00 IMR组R VS iDose4组 -22.59 0.00 17.15 0.00 22.32 0.00 注: iDose4指混合迭代算法。 3. 讨论
近些年来,整形美容越来越受到爱美人士的追求,其中隆鼻术是最为常见的。既往做隆鼻术需要在鼻部植入人工假体,由于多存在并发症,如外形不佳、假体易移位、易发生排斥反应甚至感染等并不十分理想,已逐步被自体软骨(包括肋软骨、耳软骨、鼻中隔软骨)以及筋膜组织等充填材料所取代。其中自体肋软骨是首选的材料,相对于其他自体材料,肋软骨供应量充足,感染和移位的发生率较低,术后整体质感良好,鼻翼皮肤表面软组织自然,更易于术者接受。
对于拟行自体肋软骨隆鼻术的患者需在术前准确评估自体肋软骨是否可用,从而提前制定最佳的手术方案,常需要借助于胸部多排螺旋CT扫描[4]。多排螺旋CT具有成像速度快,密度及空间分辨率高的优点,配合CT后处理重建功能可在多个角度准确评价肋软骨有无钙化及测量肋软骨的长度、宽度、厚度等。
因肋软骨解剖结构的限定和CT机具有X射线辐射的特点,在较低X射线辐射水平下如何提高肋软骨的成像细节显示能力一直是我们追求的目标。CT机提高成像显示能力离不开重建算法的发展进步,以往经典的CT数据重建算法多采用FBP重建算法,后来又出现了混合迭代重建技术,如Philips的iDose4重建算法,是在FBP基础上后再行数学模型及矩阵代数识别降噪,但此过程耗时较长,与FBP相比,iDose4可降低图像噪声,提高CNR及SNR,但仍不能满意显示FBP所不能清楚显示的解剖细节,受重建模型的影响,提升图像质量的空间有限。
随着计算机技术的发展,最新出现的IMR重建算法是一种全新的全模型迭代重建技术,是依据硬件平台以结构化模型为基础,设定精确的数据模型和图像统计模型,通过重复迭代采集纯净的源数据最终接近理性模型以产生最优的图像质量,为低剂量CT扫描提供了新的手段,与iDose4相比,能够获得更好的图像质量和进一步降低辐射剂量。理论上IMR重建算法可在更低辐射剂量条件下重建出满足临床需求的图像,并明显降低图像噪声,显著提升图像的空间、密度分辨力、改善图像的锐利度,尤其在薄层重建图像中更能体现其优势,提高微小细节结构诊断的准确性[10-12]。
近几年,关于IMR算法重建在头颈部、泌尿系统、腹部和血管CT成像的应用研究中已见广泛报道。IMR在冠脉CTA和冠脉钙化积分分析中的应用表明,IMR在评价重建的冠脉图像上,降低了图像噪声及严重钙化斑块边缘的光晕伪影[13],可以准确评估钙化且边缘更清晰,IMR算法重建的图像质量最佳,图像噪声最小,iDose4次之,FBP最差。在评价泌尿系结石的相关文献研究结果表明:同等剂量下,IMR算法重建的图像质量及对结石的大小显示最佳,其噪声最低,诊断可行性最好;当辐射剂量较常规剂量下降76.4%时,IMR算法重建在图像显示及诊断方面仍可与常规剂量相当。更有研究表明在应用IMR算法重建时,剂量降低至20 mAs时,钙化显示仍与常规剂量FBP重建组相当[14]。
本研究过程中,使用了iCT机配套EBW后处理工作站的同步重建模式功能,可以实现IMR、iDose4和FBP三种算法的图像在相同参数下同步重建,比如相同层面中心、层厚、窗宽、窗位等,便于更快的测量具体数据和更直观可靠的对图像作评估分析。应用IMR重建算法较FBP和iDose4在评估肋软骨上的钙化斑边缘及范围,以及在抑制肝脏、胃内容物及心脏波动伪影方面有明显的优势。目前国内外对于CT重建算法对自体肋软骨图像质量影响的相关研究未见类似报道。
综上所述,自体肋软骨是理想的自体软骨取材原料,应用多排螺旋CT及三维重建技术在相关患者术前检查是目前最可靠的影像学评估方法。相比目前临床应用的传统FBP算法和iD0se4算法重建技术,IMR算法重建能显著减低图像噪声,提高重建图像质量,尤其提高MIP图像和VR图像主观质量、CNR及SNR,能更清楚地评估自体肋软骨是否存在钙化、范围、边缘及程度,有助于更精准地测量各肋软骨实际走行、长度、宽度及厚度,为相关患者提供可靠的影像学信息,用以制定最佳的手术方案,明显提高手术成功率及术后满意度,可在临床大力推广。
-
[1] CECCHINI M G, WETTERWALD A, PLUIJM G V D, et al. Molecular and biological mechanisms of bone metastasis[J]. European Association of Urology Update Series, 2005, (3): 214-226. DOI: 10.1016/j.euus.2005.09.006.
[2] BUSSARD K M, GAY C V, MASTRO A M. The bone microenvironment in metastasis; What is special about bone?[J]. Cancer Metastasis Reviews, 2008, 27(1). DOI: 10.1007/s10555-007-9109-4.
[3] COLEMAN R, HADJI P, BODY J J, et al. Bone health in cancer: ESMO clinical practice guidelines[J]. Ann Oncol, 2020, 31(12). DOI: 10.1016/j.annonc.2020.07.019.
[4] O’SULLIVAN, CARTY F L, CRONIN C G. Imaging of bone metastasis: An update[J]. World Journal of Radiology, 2015, 7(8). DOI: 10.4329/wjr.v7.i8.202.
[5] YU H H, TSAI Y Y, HOFFE S E. Overview of diagnosis and management of metastatic disease to bone[J]. Cancer Control, 2012, 19(2). DOI: 10.1177/107327481201900202.
[6] CHOI J, RAGHAVAN M. Diagnostic imaging and image-guided therapy of skeletal metastases[J]. Cancer Control, 2012, 19(2). DOI: 10.1177/107327481201900204.
[7] MILLER T T. Bone tumors and tumorlike conditions: Analysis with conventional radiography[J]. Radiology, 2008, 246(3). DOI: 10.1148/radiol.2463061038.
[8] SUN W K, LIU S L, GUO J, et al. A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours[J]. Cancer Imaging, 2021, 21(1). DOI: 10.1186/s40644-021-00387-6.
[9] WANG Q, SUN B, MENG X, et al. Density of bone metastatic lesions increases after radiotherapy in patients with breast cancer[J]. Journal of Radiation Research, 2019, 60(3): 394-400. DOI: 10.1093/jrr/rry098.
[10] ISHIWATA Y, HIEDA Y, KAKI S, et al. Improved diagnostic accuracy of bone metastasis detection by water-hap associated to non-contrast CT[J]. Diagnostics (Basel), 2020, 10(10): 853-864. DOI: 10.3390/diagnostics10100853.
[11] BÄUERLE T, SEMMLER W. Imaging response to systemic therapy for bone metastases[J]. European Society of Radiology, 2009, 19(10): 2495-2507. DOI: 10.1007/s00330-009-1443-1.
[12] YANG H L, LIU T, WANG X M, et al. Diagnosis of bone metastases: A meta-analysis comparing 18F-FDG PET, CT, MRI and bone scintigraphy[J]. European Society of Radiology, 2011, 21(12): 2604-2617. DOI: 10.1007/s00330-011-2221-4.
[13] SADIK M, SUURKULA M, HöGLUND P, et al. Quality of planar whole-body bone scan interpretations: A nationwide survey[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2008, 35(8): 1464-1472. DOI: 10.1007/s00259-008-0721-5.
[14] IMBRIACO M, LASON S M, YEUNG H W, et al. A new parameter for measuring metastatic bone involvement by prostate cancer: The bone scan index[J]. Clinical Cancer Research, 1998, 4(7): 1765-1772.
[15] WUESTEMANN J, HUPFELD S, KUPITZ D, et al. Analysis of bone scans in various tumor entities using a deep-learning-based artificial neural network algorithm-evaluation of diagnostic performance[J]. Cancers (Basel), 2020, 12(9). DOI: 10.3390/cancers12092654.
[16] ISODA T, BABA S, MARUOKA Y, et al. Influence of the different primary cancers and different types of bone metastasis on the lesion-based artificial neural network value calculated by a computer-aided diagnostic system, bonenavi, on bone scintigraphy images[J]. Asia Oceania Journal of Nuclear Medicine & Biology, 2017, 5(1). DOI: 10.22038/aojnmb.2016.7606.
[17] SHIBAHARA I, SAITO R, OSADA Y, et al. Incidence of initial spinal metastasis in glioblastoma patients and the importance of spinal screening using MRI[J]. Journal of Neuro-Oncology, 2019, 141(2). DOI: 10.1007/s11060-018-03036-4.
[18] PARK S, PARK J G, JUN S, et al. Differentiation of bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone with multiparametric MRI[J]. Magnetic Resonance Imaging, 2020, 73. DOI: 10.1016/j.mri.2020.08.019.
[19] KUMAR V, GU Y, BASU S, et al. Radiomics: The process and the challenges[J]. Magnetic Resonance Imaging, 2012, 30(9). DOI: 10.1016/j.mri.2012.06.010.
[20] GUAN Y, PECK K K, JUN S, et al. T1-weighted dynamic contrast-enhanced MRI to differentiate nonneoplastic and malignant vertebral body lesions in the spine[J]. Radiology, 2020, 297(2). DOI: 10.1148/radiol.2020190553.
[21] SUN G, ZHANG Y X, LIU F, et al. Whole-body magnetic resonance imaging is superior to skeletal scintigraphy for the detection of bone metastatic tumors: A meta-analysis[J]. European Review for Medicaland Pharmacological Sciences, 2020, 24(13): 7240-7252. DOI: 10.26355/eurrev_202007_21879.
[22] KOSMIN M, PADHANI A R, GOGBASHIAN A, et al. Comparison of whole-body MRI, CT, and bone scintigraphy for response evaluation of cancer therapeutics in metastatic breast cancer to bone[J]. Radiology, 2020, 297(3): 622-629. DOI: 10.1148/RADIOL.2020192683.
[23] OTTOSSON F, BACO E, LAURITZEN, P M, et al. The prevalence and locations of bone metastases using whole-body MRI in treatment-naïve intermediate- and high-risk prostate cancer[J]. European Radiology, 2021, 31(5): 2747-2753. DOI: 10.1007/s00330-020-07363-x.
[24] VARGAS H A, SCHOR-BARDACH R, LONG N, et al. Prostate cancer bone metastases on staging prostate MRI: Prevalence and clinical features associated with their diagnosis[J]. Abdominal Radiology (NY), 2017, 42(1): 271-277. DOI: 10.1007/s00261-016-0851-3.
[25] LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: Extracting more information from medical images ssing advanced feature analysis[J]. European Journal of Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[26] FILOGRANA L, LENKOWICZ J, CELLINI F, et al. Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: A feasibility study[J]. La Radiologia Medica, 2019, 124(1). DOI: 10.1007/s11547-018-0935-y.
[27] LANG N, ZHANG Y, ZHANG E L, et al. Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI[J]. Magnetic Resonance Imaging, 2019, 64: 4-12. DOI: 10.1016/j.mri.2019.02.013.
[28] XIONG X, WANG J, HU S, et al. Differentiating between multiple myeloma and metastasis subtypes of lumbar vertebra lesions using machine learning-based radiomics[J]. Frontiers in Oncology, 2021: 601699. DOI: 10.3389/fonc.2021.601699.
[29] CHEN K, CAO J, ZHANG X, et al. Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network[J]. Frontiers in Oncology, 2022, 12: 981769. DOI: 10.3389/fonc.2022.981769.
[30] YIN P, MAO N, ZHANG C, et al. A triple-classification radiomics model for the differentiation of primary chordoma, giant cell tumor, and metastatic tumor of sacrum based on T2-weighted and contrast-enhanced T1-weighted MRI[J]. Journal of Magnetic Resonance Imaging, 2019, 49(3): 1-8. DOI: 10.1002/jmri.26238.
[31] WANG Y R, YU B, ZHONG F, et al. MRI-based texture analysis of the primary tumor for pre-treatment prediction of bone metastases in prostate Cancer[J]. Magnetic Resonance Imaging, 2019, (3): 7-18. DOI: 10.1016/j.mri.2019.03.007.
[32] JIANG X, REN M, SHUANG X, et al. Multiparametric MRI-based radiomics approaches for preoperative prediction of EGFR mutation status in spinal bone metastases in patients with lung adenocarcinoma[J]. Journal of Magnetic Resonance Imaging, 2021, 54(2): 497-507. DOI: 10.1002/jmri.27579.
[33] REN M L, YANG H Z, LAI Q Y, et al. MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients[J]. Medical Physics, 2021, 48(9): 1-10. DOI: 10.1002/mp.15137.
[34] CAO R, DONG Y, WANG X, et al. MRI-based radiomics nomogram as a potential biomarker to predict the EGFR mutations in exon 19 and 21 based on thoracic spinal metastases in lung adenocarcinoma[J]. Academic Radiology, 2022, 29(3): e9-e17. DOI: 10.1016/j.acra.2021.06.004.
[35] HAMAOKA T, MADEWELL J E, PODOLOFF D A, et al. Bone imaging in metastatic breast cancer[J]. Journal Clinical Oncology, 2004, 22(14): 2942-2953. DOI: 10.1200/JCO.2004.08.181.
[36] EVEN-SAPIR E, METSER U I, GENNADY, et al. The detection of bone metastases in patients with high-risk prostate cancer: 99mTc-MDP planar bone scintigraphy, single- and multi-field-of-view SPECT, 18F-fluoride PET, and 18F-fluoride PET/CT[J]. Journal of Nuclear Medicine, 2006, 47(2): 287-297.
[37] CUCCURULLO V, CASCINI G L, TAMBURRINI O, et al. Bone metastases radiopharmaceuticals: An overview[J]. Current Radiopharmaceuticals, 2013, 6(1): 41-47. DOI: 10.2174/1874471011306010007.
[38] BASTAWROUS S, BHARGAVA P, BEHNIA F, et al. Newer PET application with an old tracer: Role of 18F-Naf skeletal PET/CT in oncologic practice[J]. Radiographics, 2014, 34(5): 1295-1316. DOI: 10.1148/rg.345130061.
[39] WOUTER A M, BROOS, FRISO M, et al. Accuracy of 18F-Naf PET/CT in bone metastasis detection and its effect on patient management in patients with breast carcinoma[J]. Nuclear Medicine Communication, 2018, 39(4): 325-333. DOI: 10.1097/MNM.0000000000000807.
[40] LEE J W, PARK Y J, JEON Y S, et al. Clinical value of dual-phase F-18 sodium fluoride PET/CT for diagnosing bone metastasis in cancer patients with solitary bone lesion[J]. Quantitative Imaging in Medicine and Surg, 2020, 10(11): 2098-2111. DOI: 10.21037/qims-20-607.
[41] HEIDENREICH A, BASTIAN P J, BELLMUNT J, et al. EAU guidelines on prostate cancer. Part 1: Screening, diagnosis, and local treatment with curative intent-update 2013[J]. European Urology, 2014, 65(1): 124-137. DOI: 10.1016/j.eururo.2013.09.046.
[42] AFSHAR-OROMIEH A, HOLLAND-LETZ T, GIESEL F L, et al. Diagnostic performance of 68Ga-PSMA-11 (HBED-CC) PET/CT in patients with recurrent prostate cancer: Evaluation in 1007 patients[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2017, 44(8): 1258-1268. DOI: 10.1007/s00259-017-3711-7.
[43] JANSSEN J C, MEISSNER S, WOYTHAL N, et al. Comparison of hybrid 68Ga-PSMA-PET/CT and 99mTc-DPD-SPECT/CT for the detection of bone metastases in prostate cancer patients: Additional value of morphologic information from low dose CT[J]. European Radiology, 2018, 28(2): 610-619. DOI: 10.1007/s00330-017-4994-6.
[44] WU J, WANG Y, LIAO T, et al. Comparison of the relative diagnostic performance of [68G9]Ga-DOTA-FAPI-04 and 18F-FDG PET/CT for the detection of bone metastasis in patients with different cancers[J]. Frontiers in Oncology, 2021, 11(0): 737827. DOI: 10.3389/fonc.2021.737827.
[45] CATALANO O A, NICOLAI E, ROSEN B R, et al. Comparison of CE-FDG-PET/CT with CE-FDG-PET/MR in the evaluation of osseous metastases in breast cancer patients[J]. British Journal of Cancer, 2015, 112(9): 1452-1460. DOI: 10.1038/bjc.2015.112.
[46] QIAO Z Y, WANG S D, WANG H Y, et al. Diagnostic capability of 18F-PSMA PET-MRI and pelvic MRI plus bone scan in treatment-naive prostate cancer: A single-center paired validating confirmatory study[J]. International Journal of Surgery, 2024, 110(1): 87-94. DOI: 10.1097/JS9.0000000000000787.
[47] ÇELEBI F. What is the diagnostic performance of 18F-FDG-PET/MRI in the detection of bone metastasis in patients with breast cancer?[J]. European Journal of Breast Health, 2019, 15(4): 213-216. DOI: 10.5152/ejbh.2019.4885.
-
期刊类型引用(1)
1. 魏昊业,柳青,宗会迁. GE LightSpeed Pro 16 CT伪影故障维修3例. 医疗卫生装备. 2024(10): 118-120 . 百度学术
其他类型引用(0)
计量
- 文章访问数: 162
- HTML全文浏览量: 174
- PDF下载量: 38
- 被引次数: 1