摘要
目的：利用ADC Histogram分析,探讨不同类型的ADC值与宫颈癌病理学特征的关系。
材料与方法：回顾性收集111例经宫颈活检证实的宫颈癌患者,所有患者于根治术前2周内行常规MRI平扫及扩散梯度为b=0和800s/mm2的DWI检查,利用GE后处理软件重建出ADC图及ADC Histogram,测量宫颈癌的ADC值。沿病灶边缘逐层手动绘制感兴趣区(ROI),同时避免ROI内的出血、坏死及囊变区域。计算所有病灶包含层面ROI的平均ADC值(ADCmean)及最小ADC值(ADCmin)的均值；记录病灶最大层面5%-95%每间隔10%的百分比ADC值(ADC5%, ADC15%…ADC95%)。比较不同病理学特征(病理类型,分化程度,宫颈基质浸润深度,伴或不伴有淋巴结转移,伴或不伴脉管间隙浸润)宫颈癌的ADCmin、百分比ADC值及ADCmean,通过ROC曲线分析评价ADC值判断宫颈癌病理学特征的能力,并确定其最佳阈值。利用多元逐步回归分析,判断宫颈癌ADC值的相关因素。
结果：宫颈鳞癌的ADCmin、各百分比ADC值、ADCmean均低于宫颈腺癌,差异具有统计学意义(P<0.0001),ADCmean鉴别宫颈鳞癌及腺癌的Az(0.882)最大,p<0.05,以ADCmean=1100.2×10-6mm2/s作为鉴别宫颈鳞癌及腺癌的阈值,特异性及敏感性分别为89.2%、88.9%。不同分化程度宫颈鳞癌的ADCmin及低百分比ADC值(ADC5%-ADC555%),均有统计学差异(P癌栓及不同宫颈基质浸润深度的宫颈癌各种ADC均无统计学差异,P>0.05。多元逐步回归分析仅病理类型及分化程度选入回归方程。
结论：ADC值有助于鉴别宫颈鳞癌及腺癌、不同分化程度的鳞癌,不同类型的ADC值鉴别能力不同。病理类型及分化程度与宫颈癌ADC值具有相关关系。
目的：探讨弥散加权成像(DWI)诊断宫颈癌淋巴结转移的能力是否优于常规MRI。建立ADC值及形态学指标对宫颈癌转移性淋巴结的诊断阈值。
材料与方法：回顾性收集经手术病理证实的宫颈癌患者42例,所有患者均在术前2周内行盆腔常规MRI及扩散梯度为b=0和800s/mm2的DWI检查,两名放射科医师在不知晓临床资料及手术病理结果的前提下,分析宫颈癌患者术前MR图像,将轴位图像上所有短径≥5mm的淋巴结纳入本研究。随机抽取伴或不伴盆腔淋巴结转移的宫颈癌患者各3例,两名医师共同确定轴位图像上所有短径≥5mm的淋巴结,并达成共识,然后分别独自测量淋巴结的长径(L)、短径(S)、ADCmin、ADCmean及原发癌灶的ADCmin、ADCmean,计算淋巴结的S/L,淋巴结ADCmin与癌灶ADCmin之比,淋巴结ADCmean与癌灶ADCmean之比,即rADCmin>, ADCmean,利用Bland-Altman分析,评价不同观察者测量结果的一致性。两名医师独自分析剩余36例宫颈癌患者的术前MRI图像(伴或不伴盆腔淋巴结转移各18例),记录淋巴结所在的区域,测量淋巴结上述各形态学指标及ADC值,将磁共振成像结果与术后病理结果对照,比较转移与非转移淋巴结形态学指标及ADC值的差异,通过ROC曲线分析,确定L、S、S/L、 ADCmin、ADCmean、rADCmean、rADCmin判断淋巴结转移的最佳阈值。将最佳阈值作为诊断淋巴转移的标准,利用Kappa检验分别评价不同观察者在分组水平上利用常规MRI或DWI诊断淋巴结转移的一致性。
结果：淋巴结的形态学指标及ADC值不同测量者间的一致性均较好。转移淋巴结的L.S.S/L均显著大于非转移淋巴结,P<0.05；转移淋巴结的ADCmin、ADCmean、rADCmin、 rADCmean均显著小于非转移淋巴结,P<0.05; ADCmin鉴别转移及非转移淋巴结的Az(0.918)最大,P<0.05,以ADCmin=757.9×10-6mm2/s作为鉴别淋巴结转移的阈值,敏感性及特异性分别为88.0%、84.7%。ADCmin、ADCmean、rADCmin、rADCmean诊断盆腔淋巴结转移的Az均显著大于L、S/L, P<0.05;仅ADCmin诊断淋巴结转移的Az大于S,P<0.05。两名医师在分组水平通过常规MRI或DWI诊断盆腔淋巴结转移的Kappa值分别为0.543、0.528,P<0.05。
结论：DWI诊断宫颈癌淋巴结转移的能力优于常规MRI。不同医师在分组水平上诊断宫颈癌盆腔淋巴结转移的一致性中等。
目的：探讨单指数衰减模型Histogram分析及多b值双指数衰减模型的弥散加权成像,评估宫颈癌同步放化疗疗效的价值。
材料与方法：前瞻性收集28例经宫颈活检证实的以同步放化疗为治疗方案的宫颈癌患者,所有患者分别于放疗前2周内、放化疗开始后第7天、第21天及治疗结束后1个月行盆腔常规MRI、b值=0,800s/mam2及b=0,50,450,850s/mam2的弥散加权成像。在每个时间点上测量肿瘤体积、ADCmin、5%-95%每间隔10%的百分比ADC值(ADC5%,ADC15%…ADC95%)、ADCmean、快速表观弥散系数(ADCfast)、慢速表观弥散系数(ADCslow)、快速弥散所占容积分数(Ffast)值。按治疗后随访(9-25.5个月)结果,根据原发病灶有无残留、复发、远处转移将治疗结局分成结局不良组及结局良好组。比较不同结局组治疗前肿瘤体积及治疗结束后1个月肿瘤体积缩小率有无差异；分析不同结局组治疗前后不同时间点肿瘤体积及ADC值的变化趋势,比较不同结局组不同时间点ADC值及ADC值变化率有无差异。
结果：治疗前不同结局组肿瘤体积及治疗结束后1个月肿瘤体积缩小率均无统计学差异(P>0.05)；治疗前结局良好组的ADCmin、ADC5%及ADCslow显著高于结局不良组(P<0.05)；治疗前后不同结局组间ADCfast及其变化率,均无统计学差异(P>0.05)；治疗开始后第7天,结局良好组Ffast值及其变化率显著高于结局不良组,治疗过程中各时间点结局良好组Ffast值均高于治疗前基线值(P<0.05),而结局不良组Ffast值虽呈升高趋势,但与治疗前相比差异不显著(P>0.05)；治疗结束后1个月,仅ADCslow变化率的绝对值显著高于结局不良组(P<0.05)。
结论：治疗前ADCmin、ADC5%、ADCslow值、治疗前后Ffast值变化趋势及治疗结束后1个月ADCslow变化率,有助于预测宫颈癌同步放化疗的治疗结局。
Purpose:To explore the correlation of Apparent Diffusion Coefficient (ADC) and pathological features of cervical cancer by ADC histogram analysis.
Material and Methods:111patients with cervical cancer proven by biopsy who performed preoperative MRI including diffusion weighted imaging with b values of0and800s/mm2before radical resection within2weeks were retrospectively studied. ADC maps and ADC Histogram were reformated by GE post-processing software. Regions of interest were drawn around entire tumor excluding hemorrhagic, necrotic and cystic regions on each consecutive tumor containing slice. The average of ADCmean,ADCmin obtained from all tumor containing slices and the5th to95th percentile ADC values every10percent derived from the largest cross-sectional area of lesions were measured for each patient. ADCmin, percentile ADC values and ADCmean were compared between subgroups according to pathologic subtype, histological differentiation, depth of cervical infiltration and presence of lymph node metastasis or lymphovascular invasion. Receiver Operating characteristics (ROC) analysis was performed in order to evaluate the diagnostic performance of ADC value in differentiating pathological features of cervical cancer. According to the ROC curves, the optimal cut off value was extracted. Relevant factors of ADC values were analyzed by multiple stepwise regression.
Results:ADCmean, any percentile ADC value and ADCmin for squamous cell carcinoma were significantly lower than that of adenocarcinoma (P<0.0001) ADCmean for differentiating squamous cell carcinoma from adenocarcinoma had a largest Az (0.882), p<0.05, A cut-off value of1100.2×10-6mm2/s for ADCmean in differentiating squamous cell carcinoma from adenocarcinoma with a specificity of89.2%, a sensitivity of88.9%. Only ADCmin and low percentile
ADC values(ADC50%-ADC55%) were significantly different among different grades of squamous cell carcinoma, P<0.05. ADC5%for differentiating well/moderately from poorly differentiated squamous cell carcinoma had a largest Az(0.831), p<0.05, Threshold value of686.5X10"6mm2/s for ADC5%in differentiating well/moderately from poorly differentiated squamous cell carcinoma with a specificity of82.6%, a sensitivity of82.9%. There was no statistical difference in ADC values for different depth of cervical infiltration, lymph node with/without metastasis, lymphovascular with/without invasion, p>0.05. Only pathological type and degree of differentiation were selected to multivariate regression equation.
Conclusion:ADC values are helpful in identifying squamous cell carcinoma from adenocarcinoma, differentiation of cervical squamous cell carcinoma. The role of different type of ADC value is different.There is a correlation between ADC values of cervical cancer and pathological subtype, differentiation, respectively.
Purpose:To explore whether DWI is superior to conventional MRI in diagnosis of lymph nodes metastasis in patients with cervical cancer. To establish the thresholds of Apparent Diffusion Coefficients(ADC) values and morphology indexes in diagnosis of lymph nodes metastasis of cervical cancer.
Material and Methods:42cervical cancer patients confirmed by operation and pathology examination who performed preoperative MRI including diffusion weighted imaging with b values of0and800s/mm2before radical resection within2weeks were retrospectively studied. Two radiological doctors fully blinded to the clinical data and histological characteristics of cervical cancer. Preoperative MR images of cervical cancer patients were analyzed and all lymph nodes with a short diameter≥5mm in axial sequence were included in this study.3cervical cancer patients with pelvic lymphatic metastasis and3patients without lymphatic metastasis were randomly selected, lymph nodes of these patients with a short diameter>5mm were identified by two doctors in consensus, short-axis(S) and long-axis diameters (L), ADCmin, ADCmean of lymph nodes and ADCmin, ADCmean of primary tumors were independently measured. L/S ratio, rADCmin and rADCmean(defined as the ratio of lymph node ADC value to the primary tumor ADC value) were independently calculated by two doctors. The inter-observer consistency of the measurements were assessed by using Bland-Altman analysis. MR images of the remaining36patients(18patients with pelvic lymphatic metastasis and18patients without lymphatic metastasis) were independently analysed by two doctors. The location of lymph nodes were recorded. The above morphology indexes and ADC values were measured. MRI findings were compared with the post operative pathologic findings in all cases. The morphology indexes and ADC values were compared between metastatic and non-metastatic lymph nodes. Receiver operator characteristic curve(ROC) analyses were performed in order to identify optimal cut-off value for L, S, S/L ratio, ADCmin, ADCmean, rADCmin, rADCmeain the diagnosis of lymphatic metastasis. The optimal thresholds were selected as the diagnostic criteria of lymphatic metastasis.Inter-observer agreement for classification of metastasis on regional level by conventional MRI or DWI were respectively tested by Kappa statistics.
Results:The inter-observer consistency of morphology indexes and ADC values measurement were good. Long-axis diameter, Short-axis diameter, S/L ratio of metastatic lymph nodes were significantly longer than non-metastatic ones, P<0.05. The ADCmin, ADCmean, rADCmin, rADCmean of metastatic lymph nodes were significantly lower than non-metastatic ones, P<0.05. The Az of the ADCmin(0.918) was largest, P<0.05. An optimal cut-off ADCmin of757.9×10-6mm2/s for differentiating metastatic from non-metastatic lymph nodes with a sensitivity of88.0%, a specificity of84.7%. The Az of ADCmin, ADCmean, rADCm=in, rADCmean were significantly greater than L or S/L ratio-based criteria, P<0.05. Only the Az of ADCmin was significantly greater than S-based criteria, P<0.05. The Kappa value for agreement of metastasis diagnosis on regional level by conventional MRI or DWI was0.543,0.528, respectively, P<0.05.
Conclusion:Diagnostic performance of DWI in differentiating metastatic from non-metastatic pelvic lymph nodes in patients with cervical cancer is better than the conventional MRI. Inter-observer agreement for classification of pelvic lymphatic metastasis on regional level in cervical cancer is moderate.
Purpose:To investigate the value of ADC histogram analysis based on monoexponential signal decay model and DWI with multiple b values based on biexponential signal decay model in accessing cervical cancer response to concurrent chemoradiotherapy.
Material and Methods:Twenty-eight patients with cervical cancer proven by biopsy who received concurrent chemoradiotherapy were prospectively included. Pelvic MR scans were performed before therapy within2weeks,7days and21days after the therapy initiated,1month after the treatment completed. DW1with b values of0,800s/mm2and b values of0,50,450,850s/mm2were performed respectively in all cases. Tumor volumes, ADCmin,5th to95th percentile ADC values every10percent (ADC5%, ADC15%…ADC955%), ADCmean,ADCslow、 ADCfast、Ffas were measured at each time point of MR examination. Poor or good clinical outcome was defined according to with/without tumor residue, recurrence, distant metastases during follow-up(9-25.5months). Tumor volumes before therapy and tumor shrinkage rate1month after conclusion of therapy were compared between groups of good and poor clinical outcome. Variation tends of tumor volumes and ADC values at different time points before and after treatment were analysed. ADC values and its rate of change at each time point were compared between different outcome groups.
Results:Tumor volume before therapy and tumor shrinkage rate1month after conclusion of therapy were not statistically different between good and poor outcome group(P>0.05). Pretreatment ADCmin,ADC5%and ADCslow for good clinical outcome were significantly higher than those of poor clinical outcome (P<0.05). ADCfast and its rate of change before and after treatment were not significantly different between good and poor outcome group(P>0.05).Ffast andits rate of change were significantly higher in good outcome group than that in poor outcome group7days after the therapy initiated (P<0.05). Ffast during treatment in good outcome group significantly higher compared with pretreatment ones(P<0.05), while Ffast gradually increasing in poor outcome group, were not significantly higher than that of pretreatment(P<0.05). Only the absolute value of change rate of ADCslow in good outcome group was significantly higher than that in poor outcome group1month after conclusion of therapy.
Conclusion:ADCmin, ADC5%, ADCslow before the start of the treatment, variation tendency of Ffast before and after therapy, change rate of ADCslow1month after conclusion of therapy have the potential to predict the outcome of cervicalcancer to concurrent chemoradiotherapy.
引文
[1]Gien LT, Beauchemin MC, Thomas G. Adenocarcinoma:a unique cervical cancer[J]. Gynecol Oncol,2010,116(1):140-146.
[2]Macdonald OK, Chen J, Dodson M, et al. Prognostic significance of histology and positive lymph node involvement following radical hysterectomy in carcinoma of the cervix[J]. Am J Clin Oncol,2009,32(4):411-416.
[3]Biewenga P, vander Velden J, Mol BW, et al. Prognostic model for survival in patients with early stage cervical cancer [J]. Cancer,2011,117(4):768-776
[4]Rezvani M, Shaaban A. Imaging of cervical pathology[J]. Clin Obstet Gynecol,2009,52(1):94-111.
[5]Tozer DJ, Jager HR, Danchaivijitr N, et al. Apparent diffusion coefficient histograms may predict low-grade glioma subtype [J]. NMR Biomed, 2007,20(1):49-57.
[6]Costantini M, Belli P, Rinaldi P, et al. Diffusion-weighted imaging in breast cancer:relationship between apparent diffusion coefficient and tumour aggressiveness[J].Clin Radiol,2010,65(12):1005-1012.
[7]Itou Y, Nakanishi K, Narumi Y, et al. Clinical Utility of Apparent Diffusion Coefficient (ADC) Values in Patients With Prostate Cancer:Can ADC Values Contribute to Assess the Aggressiveness of Prostate Cancer? [J]. J Magn Reson Imaging,2011,33(1):167-172.
[8]Barajas RF Jr, Rubenstein JL, Chang JS, et al.Diffusion-Weighted MR Imaging Derived Apparent Diffusion Coefficient Is Predictive of Clinical Outcome in Primary Central Nervous System Lymphoma [J]. AJNR Am J Neuroradiol 2010,31(1):60-66.
[9]Kundu S, Chopra S, Verma A, et al. Functional magnetic resonance imaging in cervical cancer:current evidence and future directions [J]. J Cancer Res Ther,2012,8(1):11-18.
[10]Chen YB, Hu CM, Chen GL, et al. Staging of uterine cervical carcinoma: whole-body diffusion-weighted magnetic resonance imaging[J]. Abdom Imaging, 2011,36(5):619-626.
[11]Hoogendam JP, Klerkx WM, de Kort GA, et al. The influence of the b-value combination on apparent diffusion coefficient based differentiation between malignant and benign tissue in cervical cancer [J]. J Magn Reson Imaging,2010, 32(2):376-382.
[12]Charles-Edwards EM, Messiou C, Morgan VA, et al. Diffusion-weighted imaging in cervical cancer with an endovaginal technique:potential value for improving tumor detection in stage la and Ibl disease [J]. Radiology,2008, 249(2):541-550.
[13]Punwani S. Diffusion weighted imaging of female pelvic cancers:concepts and clinical applications[J]. Eur J Radio,2011,78(1):21-29.
[14]Kang Y, Choi SH, Kim YJ, et al. Gliomas:Histogram analysis of apparent diffusion coefficient maps with standard-or high-b-value diffusion-weighted MR imaging--correlation with tumor grade [J]. Radiology, 2011,261 (3):882-890.
[15]Kyriazi S, Collins DJ, Messiou C, et al. Metastatic ovarian and primary peritoneal cancer:assessing chemotherapy response with diffusion-weighted MR imaging--value of histogram analysis of apparent diffusion coefficients [J]. Radiology,2011,261(1):182-192.
[16]Woodhams R, Kakita S, Hata H, et al. Diffusion-weighted imaging of mucinous carcinoma of the breast:evaluation of apparent diffusion coefficient and signal intensity in correlation with histologic findings[J]. AJR Am J Roentgenol,2009,193(1):260-266.
[17]Higano S, Yun X, Kumabe T, et al. Malignant astrocytic tumors:clinical importance of apparent diffusion coefficient in prediction of grade and prognosis[J]. Radiology,2006,241(3):839-846.
[18]Whittaker CS, Coady A, Culver L, et al. Diffusion-weighted MR imaging of female pelvic tumors:a pictorial review[J]. Radiographics,2009,29(3): 759-774.
[19]Motoshima S, Irie H, Nakazono T, et al. Diffusion-weighted MR imaging in gynecologic cancers[J].J Gynecol Oncol,2011,22(4):275-287.
[20]Curvo-Semedo L, Lambregts DM, Maas M, et al. Diffusion-weighted MRI in rectal cancer:apparent diffusion coefficient as a potential noninvasive marker of tumor aggressiveness[J]. J Magn Reson Imaging,2012,35(6):1365-1371.
[21]Nishie A, Tajima T, Asayama Y, et al. Diagnostic performance of apparent diffusion coefficient for predicting histological grade of hepatocellular carcinoma[J].Eur J Radiol,2011,80(2):e29-33.
[22]Liu Y, Bai R, Sun H, et al. Diffusion-weighted magnetic resonance imaging of uterine cervical cancer [J]. J Comput Assist Tomogr,2009,33(6):858-862.
[23]Matoba M, Tonami H, Kondou T, et al. Lung carcinoma:diffusion weighted MR imaging—preliminary evaluation with apparent diffusion coefficient [J]. Radiology,2007,243(2):570-577.
[24]Payne GS, Schmidt M, Morgan VA, et al. Evaluation of magnetic resonance diffusion and spectroscopy measurements as predictive biomarkers in stage 1 cervical cancer[J].Gynecol Oncol,2010,116(2):246-252.
[25]Clayton F. Bronchioloalveolar carcinomas. Cell types, patterns of growth, and prognostic correlates[J]. Cancer,1986,57(8):1555-1564.
[26]Brambilla E, Travis WD, Colby TV et al. The new World Health Organization classification of lung tumours[J]. Eur Respir J,2001,18(6):1059-1068.
[1]Tanaka Y, Sawada S, Murata T. Relationship between lymph node metastases and prognosis in patients irradiated postoperatively for carcinoma of the uterine cervix[J].Acta Radiol Oncol,1984,23(6):455-459.
[2]Choi EK, Kim JK, Choi HJ, et al. Node-by-node correlation between MR and PET/CT in patients with uterine cervical cancer:diffusion-weighted imaging versus size-based criteria on T2WI[J].Eur Radiol,2009,19(8):2024-2032.
[3]Whittaker CS, Coady A, Culver L, et al. Diffusion-weighted MR imaging of female pelvic tumors:a pictorial review[J]. Radiographics,2009,29(3): 759-774.
[4]Scheidler J, Hricak H, Yu KK, et al. Radiological evaluation of lymph node metastases in patients with cervical cancer. A meta-analysis[J]. JAMA. 1997,278(13)-.1096-1101.
[5]Rockall AG, Sohaib SA, Harisinghani MG, et al. Diagnostic performance of nanoparticle-enhanced magnetic resonance imaging in the diagnosis of lymphnode metastases in patients with endometrial and cervical cancer[J]. J Clin Oncol,2005,23(12):2813-2821.
[6]Choi HJ, Kim SH, Seo SS, et al.MRI for pretreatment lymph node staging in uterine cervical cancer[J]. AJR Am J Roentgenol,2006,187(5):W538-43.
[7]Bipat S, Glas AS, van der Velden J, et al. Computed tomography and magnetic resonance imaging in staging of uterine cervical carcinoma:a systematic review[J]. Gynecol Oncol,2003,91(1):59-66.
[8]Chung HH, Kang KW, Cho JY, et al. Role of magnetic resonance imaging and positron emission tomography/computed tomography in preoperative lymph node detection of uterine cervical cancer[J]. Am J Obstet Gynecol,2010,203 (2):156. e1-5.
[9]Marti-Bonmati L. Lymph node assessment by diffusion weighted imaging in cervical cancer[J]. Eur Radiol,2011,21(3):474-477.
[10]LiuY, Liu H, BaiX, et al. Differentiation of metastatic from non-metastatic lymph nodes in patients with uterine cervical cancer using diffusion-weighted imaging[J]. Gynecol Oncol,2011,122(1):19-24.
[11]Kim JK, Kim KA, Park BW, et al. Feasibility of diffusion-weighted imaging in the differentiation of metastatic from nonmetastatic lymph nodes:early experience[J]. J Magn Reson Imaging,2008,28(3):714-719.
[12]Park SO, Kim JK, Kim KA, et al. Relative apparent diffusion coefficient: determination of reference site and validation of benefit for detecting metastatic lymph nodes in uterine cervical cancer[J].J Magn Reson Imaging,2009,29(2):383-390.
[13]Klerkx WM, Veldhuis WB, Spijkerboer AM, et al. The value of 3. OTesla diffusion-weighted MRI for pelvic nodal staging in patients with early stage cervical cancer[J]. Eur J Cancer,2012,48(18):3414-3421.
[14]Nakai G, Matsuki M, Inada Yn, et al. Detection and evaluation of pelvic lymph nodes in patients with gynecologic malignancies using body diffusion-weighted magnetic resonance imaging[J]. J Comput Assist Tomogr, 2008,32 (5):764-768.
[15]Williams AD, Cousins C, Soutter WP, et al. Detection of pelvic lymph node metastases in gynecologic malignancy:a comparison of CT, MR imaging, and positron emission tomography[J]. AJR Am J Roentgenol,2001,177(2):343-348.
[16]Benedetti-Panici P, Maneschi F, Scambia G, et al. Lymphatic spread of cervical cancer:an anatomical and pathological study based on 225 radical hysterectomies with systematic pelvic and aortic lymphadenectomy[J]. Gynecol Oncol,1996,62(1):19-24.
[17]Koh DM, Hughes M, Husband JE, et al.Cross-sectional imaging of nodal metastases in the abdomen and pelvis [J]. Abdom Imaging,2006,31(6):632-463. [18]Thoeny HC, Forstner R, De Keyzer F, et al. Genitourinary applications of diffusion-weighted MR imaging in the pelvis[J]. Radiology,2012,263 (2):326-342.
[19]Lai G, Rockall AG. Lymph node imaging in gynecologic malignancy. Semin Ultrasound CT MR,2010,31(5):363-76.
[20]Fukuya T, Honda H, Hayashi T, et al. Lymph-node metastases:efficacy for detection with helical CT in patients with gastric cancer[J]. Radiology, 1995,197(3):705-711.
[21]Chen YB, Liao J, Xie R, et al. Discrimination of metastatic from hyperplastic pelvic lymph nodes in patients with cervical cancer by diffusion-weighted magnetic resonance imaging [J]. Abdom Imaging,2011,36(1): 102-109.
[22]Klerkx WM, Mali WM, Peter Heintz A, et al. Observer variation of magnetic resonance imaging and diffusion weighted imaging in pelvic lymph node detection[J].Eur J Radiol,2011,78(1):71-74.
[1]Monk BJ, Tewari KS, Koh WJ. Multimodality therapy for locally advanced cervical carcinoma:state of the art and future directions[J]. J Clin Oncol,2007,25(20):2952-2965.
[2]Engin G. Cervical cancer:MR imaging findings before, during, and after radiation therapy[J]. Eur Radiol,2006,16(2):313-324.
[3]Guo Y, Cai Y1, Cai ZL, et al. Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging[J]. J Magn Reson Imaging,2002,16(2):172-178.
[4]Koh DM, Collins DJ. Diffusion-weighted MRI in the body:application and challenges in oncology[J].AJR Am J Roentgenol,2007,188(6):1622-1635.
[5]Kim CK, Park BK, Kim B. Diffusion-weighted MRI at 3 T for the evaluation of prostate cancer[J].AJR Am J Roentgenol,2010,194(6):1461-1469.
[6]Padhani AR, Liu G, Koh DM, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker:consensus and recom-mendations[J]. Neoplasia, 2009,11 (2):102-125.
[7]Hamstra DA, Rehemtulla A, Ross BD. Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology [J]. J Clin Oncol,2007,25(26): 4104-4109.
[8]Mulkern RV, Barnes AS, Haker SJ, et al. Biexponential characterization of prostate tissue water diffusion decay curves over an extended b-factor range[J]. Magn Reson Imaging,2006,24(5):563-568.
[9]Andreou A, Koh DM, Collins DJ, et al. Measurement reproducibility of perfusion fraction and pseudodiffusion coefficient derived by intravoxel incoherent motion diffusion-weighted MR imaging in normal liver and metastases[J]. Eur Radiol,2013,23(2):428-434.
[10]Rizzo S, Summers P, Raimondi S, et al. Diffusion-weighted MR imaging in assessing cervical tumour response to nonsurgical therapy[J]. Radiol Med, 2011,116(5):766-780.
[11]Chenevert TL, McKeever PE, Ross BD, et al.Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging[J]. Clin Cancer Res.1997,3(9):1457-1466.
[12]Kim SH, Lee JY, Lee JM, et al. Apparent diffusion coefficient for evaluating tumour response to neoadjuvant chemoradiation therapy for locally advanced rectal cancer[J]. Eur Radiol.2011,21(5):987-995.
[13]Park SH, Moon WK, Cho N, et al. Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer[J]. Radiology,257(1):56-63.
[14]Sun YS, Cui Y, Tang L, et al. Early evaluation of cancer response by a new functional biomarker:apparent diffusion coefficient. AJR Am J Roentgenol[J].2011,197(1):W23-29.
[15]McVeigh PZ, Syed AM, Milosevic M, et al. Diffusion-weighted MRI in cervical cancer. Eur Radiol[J].2008,18(5):1058-1064.
[16]Kim HS, Kim CK, Park BK, et al. Evaluation of therapeutic response to concurrent chemoradiotherapy in patients with cervical cancer using diffusion-weighted MR imaging. J Magn Reson Imaging [J].2013,37(1):187-193.
[17]Zhang Y, Chen JY, Xie CM, et al. Diffusion-weighted magnetic resonance imaging for prediction of response of advanced cervical cancer to chemoradiation[J]. J Comput Assist Tomogr,2011,35(1):102-107.
[18]Higano S, Yun X, Kumabe T, et al. Malignant astrocytic tumors:clinical importance of apparent diffusion coefficient in prediction of grade and prognosis[J]. Radiology,2006,241(3):839-846.
[19]Barajas RF Jr, Rubenstein JL, Chang JS, et al. Diffusion-weighted MR imaging derived apparent diffusion coefficient is predictive of clinical outcome in primary central nervous system lymphoma[J]. AJNR Am J Neuroradiol. 2010,31(1):60-66.
[20]Liu Y, Bai R, Sun H, et al. Diffusion-weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation[J]. Clin Radiol.2010,64(11):1067-1074.
[21]Harry VN, Semple SI, Gilbert FJ, et al. Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer[J]. Gynecol Oncol,2008,111(2):213-220.
[22]Le Bihan D, Breton E, Lallemand D, et al.MR imaging of intravoxel incoherent motions:application to diffusion and perfusion in neurologic disorders [J]. Radiology.1986,161(2):401-407.
[23]Tamura T, Usui S, Murakami S, et al. Comparisons of multi b-value DWI signal analysis with pathological specimen of breast cancer[J]. Magn Reson Med.2012,68(3):890-897.
[24]Chandarana H, Lee VS, Hecht E, et al. Comparison of biexponential and monoexponential model of diffusion weighted imaging in evaluation of renal lesions:preliminary experience[J]. Invest Radiol,2011,46(5):285-291.
[25]Niendorf T, Dijkhuizen RM, Norris DG, et al. Biexponential diffusion attenuation in various states of brain tissue:implications for diffusion-weighted imaging[J].Magn Reson Med,1996,36(6):847-857.
[26]Schwarcz A, Bogner P, Meric P, et al. The existence of biexponential signal decay in magnetic resonance diffusion-weighted imaging appears to be independent of compartmentalization[J]. Magn Reson Med,2004,51(2): 278-285.
[27]Le Bihan D. Intravoxel incoherent motion perfusion MR imaging:a wake-up call.Radiology[J].2008,249(3):748-752.
[28]Le Bihan D, Moonen CT, van Zijl PC, et al. Measuring random microscopic motion of water in tissues with MR imaging:a cat brain study[J]. J Comput Assist Tomogr,1991,15(1):19-25.
[29]Luciani A, Vignaud A, Cavet M, et al. Liver cirrhosis:intravoxel incoherent motion MR imaging--pilot study. Radiology[J],2008,249(3): 891-989.
[30]Hauser T, Essig M, Jensen A, et al. Characterization and therapy monitoring of head and neck carcinomas using diffusion-imaging-based intravoxel incoherent motion parameters preliminary results[J]. Neuroradiology,2013,55(5):527-536.
[31]Chandarana H, Lee VS, Hecht E, et al. Comparison of biexponential and monoexponential model of diffusion weighted imaging in evaluation of renal lesions:preliminary experience[J]. Invest Radiol,2011,46(5):285-291.
[32]Heusch P, Wittsack HJ, Heusner T, et al. Correlation of biexponential diffusion parameters with arterial spin-labeling perfusion MRI:results in transplanted kidneys[J]. Invest Radiol,2013,48(3):140-144.
[33]Yamashita Y, Baba T, Baba Y. Dynamic contrast-enhanced MR imaging of uterine cervical cancer:pharmacokinetic analysis with histopathologic correlation and its importance in predicting the outcome of radiation therapy[J]. Radiology,2000,216(3):803-809.
[34]Mayr NA, Huang Z, Wang JZ, et al. Characterizing tumor heterogeneity with functional imaging and quantifying high-risk tumor volume for early prediction of treatment outcome:cervical cancer as a model [J]. Int J Radiat Oncol Biol Phys,2012,83 (3):972-979.
[35]Vincens E, Balleyguier C, Rey A, et al. Accuracy of magnetic resonance imaging in predicting residual disease in patients treated for stage IB2/II cervical carcinoma with chemoradiation therapy correlation of radiologic findings with surgicopathologic results [J]. Cancer,2008,113(8):2158-2165.
[36]Seierstad T, R(?)e K, Olsen DR.Noninvasive monitoring of radiation-induced treatment response using proton magnetic resonance spectroscopy and diffusion-weighted magnetic resonance imaging in a colorectal tumor model[J]. Radiother Oncol,2007,85(2):187-194.
[1]Chen YB, Hu CM, Chen GL, et al[J]. Staging of uterine cervical carcinoma: whole-body diffusion-weighted magnetic resonance imaging. Abdom Imaging, 2011,36(5):619-626.
[2]Hoogendam JP, Klerkx WM, de Kort GA, et al. The influence of the b-value combination on apparent diffusion coefficient based differentiation between malignant and benign tissue in cervical cancer[J].J Magn Reson Imaging,2010,32(2):376-382.
[3]Charles-Edwards EM, Messiou C, Morgan VA, et al. Diffusion-weighted imaging in cervical cancer with an endovaginal technique:potential value for improving tumor detection in stage Ia and Ibl disease[J]. Radiology, 2008,249(2):541-550.
[4]Kundu S, Chopra S, Verma A, et al. Functional magnetic resonance imaging in cervical cancer:current evidence and future directions [J]. J Cancer Res Ther,2012,8(1):11-18.
[5]Liu Y, Bai R, Sun H, et al. Diffusion-weighted magnetic resonance imaging of uterine cervical cancer[J].J Comput Assist Tomogr,2009,33(6):858-862.
[6]Payne GS, Schmidt M, Morgan VA, et al. Evaluation of magnetic resonance diffusion and spectroscopy measurements as predictive biomarkers in stage 1 cervical cancer[J]. Gynecol Oncol,2010,116(2):246-252.
[7]Rezvani M, Shaaban A. Imaging of cervical pathology[J]. Clin Obstet Gynecol,2009,52(1):94-111.
[8]Rockall AG, Sohaib SA, Harisinghani MG, et al. Diagnostic performance of nanoparticle-enhanced magnetic resonance imaging in the diagnosis of lymphnode metastases in patients with endometrial and cervical cancer[J]. J Clin Oncol,2005,23(12):2813-2821.
[9]Choi HJ, Kim SH, Seo SS, et al. MRI for pretreatment lymph node staging in uterine cervical cancer[J].AJR Am J Roentgenol,2006,187(5):W538-543.
[10]Bipat S, Glas AS, van der Velden J, et al. Computed tomography and magnetic resonance imaging in staging of uterine cervical carcinoma:a systematic review[J]. Gynecol Oncol,2003,91(1):59-66.
[11]Chung HH, Kang KW, Cho JY, et al. Role of magnetic resonance imaging and positron emission tomography/computed tomography in preoperative lymph node detection of uterine cervical cancer[J]. Am J Obstet Gynecol, 2010,203(2):156.e1-5.
[12]Xue HD, Li S, Sun F, et al. Clinical application of body diffusion weighted MR imaging in the diagnosis and preoperative N staging of cervical cancer[J]. Chin Med Sci J,2008,23(3):133-137.
[13]Choi EK, Kim JK, Choi HJ, et al. Node-by-node correlation between MR and PET/CT in patients with uterine cervical cancer:diffusion-weighted imaging versus size-based criteria on T2WI[J]. EurRadiol,2009,19(8):2024-2032.
[14]Liu Y, Liu H, Bai X, et al. Differentiation of metastatic from non-metastatic lymph nodes in patients with uterine cervical cancer using diffusion-weighted imaging[J]. Gynecol Oncol,2011,122(1):19-24.
[15]Lin G, Ho KC, Wang JJ, et al. Detection of lymph node metastasis in cervical and uterine cancers by diffusion-weighted magnetic resonance imaging at 3T[J].J Magn Reson Imaging,2008,28(1):128-135.
[16]Kim JK, Kim KA, Park BW, et al. Feasibility of diffusion-weighted imaging in the differentiation of metastatic from nonmetastatic lymph nodes:early experience[J]. J Magn Reson Imaging,2008,28(3):714-719.
[17]Park SO, Kim JK, Kim KA, et al. Relative apparent diffusion coeffcient: determination of reference site and validation of benefit for detecting metastatic lymph nodes in uterine cervical cancer[J]. JMagn Reson Imaging 2009,29 (2):383-390.
[18]Chen YB, Liao J, Xie R, et al. Discrimination of metastatic from hyperplastic pelvic lymph nodes in patients with cervical cancer by diffusion-weighted magnetic resonance imaging[J].Abdom Imaging,2011, 36(1):102-109.
[19]Nakai G, Matsuki M, Inada Yn, et al. Detection and evaluation of pelvic lymph nodes in patients with gynecologic malignancie susing body diffusion-weighted magnetic resonance imaging[J].J Comput Assist Tomogr, 2008,32 (5):764-768.
[20]Klerkx WM, Veldhuis WB, Spijkerboer AM, et al. The value of 3.0 Tesla diffusion-weighted MRI for pelvic nodal staging in patients with early stage cervical cancer[J]. Eur J Cancer,2012,48(18):3414-3421.
[21]Zhang Y, Chen JY, Xie CM, et al. Diffusion-Weighted Magnetic Resonance Imaging for Prediction of Response of Advanced Cervical Cancer to Chemoradiation[J]. J Comput Assist Tomogr,2011,35(1):102-107.
[22]Liu Y, Bai R, Sun H, et al. Diffusion-weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation[J].Clin Radiol,2009,64(11):1067-1074.
[23]Kim HS, Kim CK, Park BK, et al. Evaluation of therapeutic response to concurrent chemoradiotherapy in patients with cervical cancer using diffusion-weighted MR imaging[J]. J Magn Reson Imaging,2013,37(1):187-193.
[24]Harry VN, Semple SI, Gilbert FJ, et al. Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer[J]. Gynecol Oncol,2008,111(2):213-220.
[25]Rizzo S, Summers P, Raimondi S, et al. Diffusion-weighted MR imaging in assessing cervical tumour response to nonsurgical therapy[J]. Radiol Med, 2011,116(5):766-780.
[26]Vincens E, Balleyguier C, Rey A, et al. Accuracy of magnetic resonance imaging in predicting residual disease in patients treated for stage IB2/Ⅱ cervical carcinoma with chemoradiation therapy:correlation of radiologic findings with surgicopathologic results[J]. Cancer,2008,113(8):2158-2165.
[27]Levy A, Caramella C, Chargari C, et al. Accuracy of diffusion-weighted echo-planar MR imaging and ADC mapping in the evaluation of residual cervical carcinoma after radiation therapy[J]. Gynecol Oncol,2011,123(1)::110-115.
[28]Cha S. Dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in pediatric patients[J].Neuroimaging Clin N Am,2006,16 (1):137-147.
[29]Seki H, Azumi R, Kimura M, et al.Stromal invasion by carcinoma of the cervix:assessment with dynamic MR imaging[J].AJR Am J Roentgenol,1997, 168(6):1579-1585.
[30]Nicolet V, Carignan L, Bourdon F, et al. MR imaging of cervical carcinoma: a practical staging approach[J]. Radiographics,2000,20(6):1539-1549.
[31]Iwata S, Joja I, Okuno K, et al. Cervical carcinoma with full-thickness strormal invasion:efficacy of dynamic MR imaging in the assessment of parametrial involvement[J]. Radiat Med,2002,20(5):247-255.
[32]Haider MA, Sitartchouk I, Roberts TP, et al. Correlations between dynamic contrast-enhanced magnetic resonance imaging-derived measures of tumour microvasculature and interstitial fluid pressure in patients with cervical cancer[J].J Magn Reson Imaging,2007,25(1):153-159.
[33]Harry VN. Novel imaging techniques as response bio-markers in cervical cancer[J]. Gynecol Oncol,2010,116(2):253-261.
[33]Esserman L, Hylton N, George T, Weidner N. Contrast-enhanced magnetic resonance imaging to assess tumor histopathology and angiogenesis in breastcarcinoma[J]. Breast J,1999,5(1):13-21.
[34]Hawighorst H, Knapstein PG, Knopp MV, et al. Cervical carcinoma:standard and pharmacokinetic analysis of time-intensity curves for assessment of tumor angiogenesis and patient survival[J]. MAGMA,1999,8(1):55-62.
[36]Buadu LD, Murakami J, Murayama S, et al. Breast lesions:correlation of contrast medium enhancement patterns on MR images with histopathologic findings and tumor angiogenesis[J]. Radiology,1996,200(3):639-649.
[37]Ellingsen C, Egeland TA, Gulliksrud K, et al. Assessment of hypoxia in human cervical carcinoma xenografts by dynamic contrast-enhanced magnetic resonance imaging[J]. Int J Radiat Oncol Biol Phys,2009,73(3):838-845.
[38]Seeber LM, Horree N, Vooijs MA, et al. The role of hypoxia inducible factor-lalpha in gynecological cancer [J]. Crit Rev Oncol Hematol,2011,78(3): 173-184.
[39]Donaldson SB, West CM, Davidson SE, et al. A comparison of tracer kinetic models for T1-weighted dynamic contrast-enhanced MRI:application in carcinoma of the cervix[J]. Magn Reson Med,2010,63(3):691-700.
[40]Zahra MA, Tan LT, Priest AN, et al. Semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging measurements predict radiation response in cervix cancer[J]. Int J Radiat Oncol Biol Phys, 2009,74(3):766-773.
[41]Andersen EK, Kristensen GB, Lyng H, et al. Pharmacokinetic analysis and k-means clustering of DCE MR images for radiotherapy outcome prediction of advanced cervical cancers[J]. ActaOncologica,2011,50 (6):859-865.
[42]Mayr NA, Yuh WT, Jajoura D, et al. Ultra-early predictive assay for treatment failure using functional magnetic resonance imaging and clinical prognostic parameters in cervical cancer[J]. Cancer,2010,116(4):903-912.
[43]Mayr NA, Huang Z, Wang JZ, et al. Characterizing tumor heterogeneity with functional imaging and quantifying high-risk tumor volume for early prediction of treatment outcome:cervical cancer as a model [J]. Int J Radiat Oncol Biol Phys,2012,83(3):972-979.
[44]Boss EA, Massuger LF, Pop LA, et al. Post-radiotherapy contrast enhancement changes in fast dynamic MRI of cervical carcinoma[J]. J Magn Reson Imaging,2001,13(4):600-606.
[45]Majos C, Alonso J, Aguilera C, et al. Proton magnetic resonance spectroscopy ((1)H MRS) of human brain tumours:assessment of differences between tumour types and its applicability in brain tumour categorization [J]. Eur Radiol,2003,13(3):582-591.
[46]Shah N, Sattar A, Benanti M, et al. Magnetic resonance spectroscopy as an imaging tool for cancer:a review of the literature[J]. J Am Osteopath Assoc,2006,106(1):23-27.
[47]Delikatny EJ, Russell P, Hunter JC, et al. Proton MR and human cervical neoplasia:ex vivo spectroscopy allows distinction of invasive carcinoma of the cervix from carcinoma in situ and other preinvasive lesions[J]. Radiology,1993,188(3):791-796.
[48]Mahon MM, Williams AD, Soutter WP, et al.1H magnetic resonance spectroscopy of invasive cervical cancer:an in vivo study with ex vivo corroboration[J]. Nmr in Biomedicine,2004,17(1):1-9.
[49]Mahon MM, Cox IJ, Dina R, et al.H-1 magnetic resonance spectroscopy of preinvasive and invasive cervical cancer:In vivo-ex vivo profiles and effect of tumor load[J].J Magn Reson Imaging,2004,19(3):356-364.
[50]Booth SJ, Pickles MD, Turnbull LW. In vivo magnetic resonance spectroscopy of gynaecological tumours at 3.0 Tesla[J]. BJOG,2009,116 (2): 300-303.
[51]Lee JH, Cho KS, Kim YM, et al. Localized in vivo 1H nuclear MR spectroscopy for evaluation of human uterine cervical carcinoma [J]. AJR Am J Roentgenol, 1998,170 (5):1279-1282.
[52]De Silva SS, Payne GS, Thomas V, et al. Investigation of metabolite changes in the transition from pre-invasive to invasive cervical cancer measured using (1)H and (31)P magic angle spinning MRS of intact tissue [J]. NMR Biomed,2009,22 (2):191-198.
[53]Allen JR, Prost RW, Griffith OW, et al. In vivo proton (H1) magnetic resonance spectroscopy for cervical carcinoma[J]. Am J Clin Oncol, 2001,24(5):522-529.
[54]deSouza NM, Soutter WP, Rustin G, et al. Use of neoadjuvant chemotherapy prior to radical hysterectomy in cervical cancer:monitoring tumour shrinkage and molecular profile on magnetic resonance and assessment of 3-year outcome[J]. Br J Cancer,2004,90(12):2326-2331.