Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 12 de 12
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Artif Intell Med ; 121: 102194, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34763809

RESUMO

Malignant epithelial ovarian tumors (MEOTs) are the most lethal gynecologic malignancies, accounting for 90% of ovarian cancer cases. By contrast, borderline epithelial ovarian tumors (BEOTs) have low malignant potential and are generally associated with a good prognosis. Accurate preoperative differentiation between BEOTs and MEOTs is crucial for determining the appropriate surgical strategies and improving the postoperative quality of life. Multimodal magnetic resonance imaging (MRI) is an essential diagnostic tool. Although state-of-the-art artificial intelligence technologies such as convolutional neural networks can be used for automated diagnoses, their application have been limited owing to their high demand for graphics processing unit memory and hardware resources when dealing with large 3D volumetric data. In this study, we used multimodal MRI with a multiple instance learning (MIL) method to differentiate between BEOT and MEOT. We proposed the use of MAC-Net, a multiple instance convolutional neural network (MICNN) with modality-based attention (MA) and contextual MIL pooling layer (C-MPL). The MA module can learn from the decision-making patterns of clinicians to automatically perceive the importance of different MRI modalities and achieve multimodal MRI feature fusion based on their importance. The C-MPL module uses strong prior knowledge of tumor distribution as an important reference and assesses contextual information between adjacent images, thus achieving a more accurate prediction. The performance of MAC-Net is superior, with an area under the receiver operating characteristic curve of 0.878, surpassing that of several known MICNN approaches. Therefore, it can be used to assist clinical differentiation between BEOTs and MEOTs.


Assuntos
Neoplasias Ovarianas , Qualidade de Vida , Inteligência Artificial , Atenção , Diagnóstico Diferencial , Feminino , Humanos , Redes Neurais de Computação , Neoplasias Ovarianas/diagnóstico por imagem , Estudos Retrospectivos
2.
Eur Radiol ; 31(10): 7855-7864, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-33864139

RESUMO

OBJECTIVES: To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). METHODS: In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. RESULTS: In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). CONCLUSIONS: The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. KEY POINTS: • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.


Assuntos
Nomogramas , Neoplasias Ovarianas , Feminino , Humanos , Metástase Linfática , Imageamento por Ressonância Magnética , Neoplasia Residual/diagnóstico por imagem , Neoplasias Ovarianas/diagnóstico por imagem , Estudos Retrospectivos
3.
Eur Radiol ; 31(1): 403-410, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32743768

RESUMO

OBJECTIVES: Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image-based radiomics model to differentiate between type I and type II EOC. METHODS: In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation. RESULTS: The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection. CONCLUSIONS: MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis. KEY POINTS: • Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.


Assuntos
Imageamento por Ressonância Magnética , Neoplasias Ovarianas , Carcinoma Epitelial do Ovário/diagnóstico por imagem , Feminino , Humanos , Neoplasias Ovarianas/diagnóstico por imagem , Curva ROC , Estudos Retrospectivos
4.
Acad Radiol ; 28(3): e77-e85, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-32061467

RESUMO

RATIONALE AND OBJECTIVES: To investigate the feasibility of apparent diffusion coefficient (ADC) histogram analysis of primary advanced high-grade serous ovarian cancer (HGSOC) to predict patient response to platinum-based chemotherapy. MATERIALS AND METHODS: A total of 70 patients with 102 advanced stage HGSOCs (International Federation of Gynecology and Obstetrics (FIGO) stages III-IV) who received standard treatment of primary debulking surgery followed by the first line of platinum-based chemotherapy were retrospectively enrolled. Patients were grouped as platinum-resistant and platinum-sensitive according to whether relapse occurred within 6 months. Clinical characteristics, including age, pretherapy CA125 level, International Federation of Gynecology and Obstetrics stage, residual tumor, and histogram parameters derived from whole tumor and solid component such as ADCmean; 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th percentiles; skewness and kurtosis, were compared between platinum-resistant and platinum-sensitive groups. RESULTS: No significantly different clinical characteristics were observed between platinum-sensitive and platinum-resistant patients. There were no significant differences in any whole-tumor histogram-derived parameters between the two groups. Significantly higher ADCmean and percentiles and significantly lower skewness and kurtosis from the solid-component histogram parameters were observed in the platinum-sensitive group when compared with the platinum-resistant group. ADCmean, skewness and kurtosis showed moderate prediction performances, with areas under the curve of 0.667, 0.733 and 0.616, respectively. Skewness was an independent risk factor for platinum resistance. CONCLUSION: Pretreatment ADC histogram analysis of primary tumors has the potential to allow prediction of response to platinum-based chemotherapy in patients with advanced HGSOC.


Assuntos
Neoplasias Ovarianas , Platina , Imagem de Difusão por Ressonância Magnética , Feminino , Humanos , Neoplasias Ovarianas/diagnóstico por imagem , Neoplasias Ovarianas/tratamento farmacológico , Estudos Retrospectivos
5.
Acta Radiol ; 62(1): 129-138, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32276553

RESUMO

BACKGROUND: Differentiation of borderline tumors from early ovarian cancer has recently received increasing attention, since borderline tumors often affect young women of childbearing age who desire to preserve fertility. However, previous studies have demonstrated that non-enhanced magnetic resonance imaging (MRI) sequences cannot sufficiently differentiate these tumors. PURPOSE: To investigate the value of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating serous borderline ovarian tumors (SBOT) from early serous ovarian cancers (eSOCA). MATERIAL AND METHODS: Twenty SBOT and 20 eSOCA rat models were performed with DCE-MRI and IVIM-DWI at 3.0-T MR scanner. Qualitative and quantitative parameters of DCE-MRI were acquired and compared between two groups and correlated with the microvessel density (MVD). The receiver operating characteristic (ROC) curve analyses were conducted to determine their differentiating performances. RESULTS: SBOTs presented significantly lower values of the initial area under the enhancement curve (iAUC), volume transfer constant (Ktrans), and extracellular extravascular volume fraction (ve) (P < 0.05) and a significantly higher value of true diffusion (D) (P = 0.001) compared with eSOCAs. The diagnostic effectiveness of ve combined with D was significantly better than that of ve or Ktrans alone (P ≤ 0.039). CONCLUSION: DCE-MRI may represent a promising tool for differentiating SBOTs from eSOCAs and may not be replaced by IVIM-DWI. Combining DCE-MRI with DWI may improve the diagnostic performance of ovarian tumors.


Assuntos
Imageamento por Ressonância Magnética/métodos , Neoplasias Ovarianas/diagnóstico por imagem , Animais , Meios de Contraste , Diagnóstico Diferencial , Modelos Animais de Doenças , Feminino , Aumento da Imagem , Ovário/diagnóstico por imagem , Ratos , Ratos Sprague-Dawley
6.
J Magn Reson Imaging ; 52(3): 897-904, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32045064

RESUMO

BACKGROUND: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results. PURPOSE: To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation. STUDY TYPE: Retrospective study of eight clinical centers. POPULATION: In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159). FIELD STRENGTH/SEQUENCE: Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T2 -weighted imaging (T2 WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T1 WI. ASSESSMENT: Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images. STATISTICAL TESTS: Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC). RESULTS: The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts. DATA CONCLUSION: Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women. LEVEL OF EVIDENCE: Level 4. TECHNICAL EFFICACY: Stage 2. J. Magn. Reson. Imaging 2020;52:897-904.


Assuntos
Imageamento por Ressonância Magnética , Neoplasias Ovarianas , Imagem de Difusão por Ressonância Magnética , Feminino , Humanos , Aprendizado de Máquina , Neoplasias Ovarianas/diagnóstico por imagem , Curva ROC , Estudos Retrospectivos
7.
Exp Anim ; 68(3): 257-265, 2019 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-30760660

RESUMO

Serous borderline ovarian tumors (SBOTs) behave between benign cystadenomas and carcinomas, and the effective detection and clinical management of SBOTs remain clinical challenges. Because it is difficult to isolate and enrich borderline tumor cells, a borderline animal model is in need. 7,12-dimethylbenz[a]anthracene (DMBA) is capable of inducing the initiation, promotion, and progression of serous ovarian tumors. This study aims to investigate the proper dosage and induction time of DMBA for rat models of SBOTs, and explore their morphological features demonstrated by magnetic resonance (MR) imaging and molecular genetic characteristics. Rats were randomly divided into six groups (1 mg/70 D, 2 mg/70 D, 3 mg/70 D, 2 mg/50 D, 2 mg/90 D, and 2 mg/110 D). The 3 mg/70 D group induced the most SBOTs (50.0%, 12/24). The micropapillary projections were shown on MR imaging, which was the characteristic of SBOTs. The Cyclin D1 characterizing an early pathogenetic event strongly expressed in induced serous benign tumors (SBTs). The immunoreactivity staining scores of P53 expression significantly increased from SBTs, SBOTs to serous ovarian carcinomas (SCAs), which elucidate that P53 might be a promising biomarker to grade serous ovarian tumors. Based on morphological and molecular genetic similarities, this rodent SBOT model was suitable for investigating the pathogenesis of serous ovarian tumors and developing an early detection strategy.


Assuntos
9,10-Dimetil-1,2-benzantraceno/farmacologia , Carcinógenos/farmacologia , Neoplasias Ovarianas/genética , Neoplasias Ovarianas/patologia , Ratos , Animais , Modelos Animais de Doenças , Relação Dose-Resposta a Droga , Feminino , Neoplasias Ovarianas/induzido quimicamente , Distribuição Aleatória , Ratos Sprague-Dawley , Fatores de Tempo
8.
J Magn Reson Imaging ; 49(6): 1684-1693, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-30353967

RESUMO

BACKGROUND: Due to the overlapping imaging appearances between borderline and malignant epithelial ovarian tumors (EOTs), borderline EOTs often represent a diagnostic challenge on conventional MRI. Proton magnetic resonance spectroscopy (1 H-MRS) might have potential to differentiate borderline from malignant tumors. PURPOSE: To investigate the ability of 1 H-MRS to differentiate borderline from malignant EOTs. STUDY TYPE: Prospective. POPULATION: In all, 278 patients with adnexal masses. FIELD STRENGTH/SEQUENCE: 1.5 T Siemens Avanto MRI system and 1 H-MRS using a point-resolved spectroscopy sequence (PRESS). ASSESSMENT: Resonance peak integrals of the most common metabolites were analyzed and compared between the two groups. STATISTICAL TESTS: The ratios of metabolites between borderline and malignant EOTs were compared with the Mann-Whitney U-test. A receiver operating characteristic (ROC) curve was used to determine their differential diagnosis performances. RESULTS: In the solid components of borderline and malignant EOTs, the mean Cho/Cr, NAA/Cr, and NAA/Cho ratios were 4.4 ± 1.1 and 9.9 ± 2.8; 10.4 ± 3.0 and 2.2 ± 1.0; and 2.4 ± 0.7 and 0.3 ± 0.1, respectively (all P < 0.001). The sensitivity, specificity, and area under the curve (AUC) were 91%, 100%, and 0.98 for the Cho/Cr ratio; 100%, 98%, and 0.99 for the NAA/Cr ratio; and 100%, 100%, and 1.00 for the NAA/Cho ratio, respectively. In the cystic components, the mean Cho/Cr, NAA/Cr, and NAA/Cho ratios were 3.2 ± 0.8 and 5.1 ± 1.2; 9.1 ± 3.4 and 2.3 ± 1.4; and 2.9 ± 1.2 and 0.5 ± 0.4, respectively (all P < 0.001). The sensitivity, specificity, and AUC were 84%, 82%, and 0.89 for the Cho/Cr ratio; 94%, 97%, and 0.99 for the NAA/Cr ratio; and 94%, 97%, and 0.99 for the NAA/Cho ratio, respectively. DATA CONCLUSION: The NAA/Cho ratio is a reliable biomarker for differentiating borderline from malignant EOTs. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:1684-1693.


Assuntos
Neoplasias Epiteliais e Glandulares/diagnóstico por imagem , Neoplasias/diagnóstico por imagem , Neoplasias Ovarianas/diagnóstico por imagem , Espectroscopia de Prótons por Ressonância Magnética , Adulto , Área Sob a Curva , Diagnóstico Diferencial , Diagnóstico por Imagem , Feminino , Humanos , Pessoa de Meia-Idade , Variações Dependentes do Observador , Estudos Prospectivos , Curva ROC
9.
Abdom Radiol (NY) ; 43(11): 3132-3141, 2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-29556691

RESUMO

PURPOSE: This study aimed to investigate the diagnostic performance of quantitative DCE-MRI for characterizing ovarian tumors. METHODS: We prospectively assessed the differences of quantitative DCE-MRI parameters (Ktrans, kep, and ve) among 15 benign, 28 borderline, and 66 malignant ovarian tumors; and between type I (n = 28) and type II (n = 29) of epithelial ovarian carcinomas (EOCs). DCE-MRI data were analyzed using whole solid tumor volume region of interest (ROI) method, and quantitative parameters were calculated based on a modified Tofts model. The non-parametric Kruskal-Wallis test, Mann-Whitney U test, Pearson's chi-square test, intraclass correlation coefficient (ICC), variance test, and receiver operating characteristic curves (ROC) were used for statistical analysis. RESULTS: The largest Ktrans and kep values were observed in ovarian malignant tumors, followed by borderline and benign tumors (all P < 0.001). Kep was the better parameter for differentiating benign tumors from borderline and malignant tumors, with a sensitivity of 89.3% and 95.5%, a specificity of 86.7% and 100%, an accuracy of 88.4% and 96.3%, and an area under the curve (AUC) of 0.94 and 0.992, respectively, whereas Ktrans was better for differentiating borderline from malignant tumors with a sensitivity of 60.7%, a specificity of 78.8%, an accuracy of 73.4%, and an AUC of 0.743. In addition, a combination with kep could further improve the sensitivity to 78.9%. The median Ktrans and kep values were significantly higher in type II than in type I EOCs. CONCLUSION: DCE-MRI with volume quantification is a technically feasible method, and can be used for the differentiation of ovarian tumors and for discriminating between type I and type II EOCs.


Assuntos
Imageamento por Ressonância Magnética/métodos , Neoplasias Ovarianas/diagnóstico por imagem , Neoplasias Ovarianas/patologia , Adolescente , Adulto , Idoso , Criança , Meios de Contraste , Diagnóstico Diferencial , Feminino , Humanos , Pessoa de Meia-Idade , Estudos Prospectivos , Sensibilidade e Especificidade
10.
Eur J Radiol ; 98: 136-142, 2018 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-29279152

RESUMO

PURPOSE: To identify the MRI features of borderline epithelial ovarian tumors (BEOTs) and to differentiate BEOTs from malignant epithelial ovarian tumors (MEOTs). MATERIALS AND METHODS: The clinical and MRI data of 89 patients with a BEOT and 109 patients with a MEOT proven by surgery and histopathology were retrospectively reviewed. MRI features, including bilaterality, size, shape, margin, cystic-solid interface, configuration, papillae or nodules, signal intensity, enhancement, presence of an ipsilateral ovary, peritoneal implants and ascites were analyzed and compared. Based on the odds ratio (OR) values, the significant risk features for BEOTs were scored as 3 (OR≈∞), 2 (5≤OR<∞) or 1 (OR<5). RESULTS: There were 89 BEOT patients with 113 tumors [mean size of (13±6.7)cm], with bilateral ovary involvement in 24 cases. There were 109 MEOT patients with 142 tumors [(9.3±4.2)cm] with bilateral ovary involvement in 33 cases. There were eight significant risk factors for BEOTs, including round or oval shape (OR=2.714), well-defined margins (OR=3.318), clear cystic-solid interfaces (OR=5.593), purely cystic (OR=15.206), predominantly cystic with papillae or nodules (OR=2.579), exophytic papillae or nodules (OR=5.351), branching papilla (OR≈∞) and the presence of an ipsilateral ovary (OR≈∞). Based on the scoring of the eight risk factors, a cut-off score of 3.5 yielded a differential sensitivity, specificity, and accuracy of 82%, 85% and 84%, respectively. CONCLUSION: In contrast to MEOTs, BEOTs frequently had the following features on MRI: round or oval, with well-defined margins and clear cystic-solid interfaces, purely cystic or predominantly cystic with papillae or nodules, branching or exophytic papillae, with the presence of an ipsilateral ovary. MRI can reveal the distinct morphological features of BEOTs and MEOTs and facilitate their discrimination.


Assuntos
Imageamento por Ressonância Magnética/métodos , Neoplasias Epiteliais e Glandulares/diagnóstico por imagem , Neoplasias Epiteliais e Glandulares/patologia , Neoplasias Ovarianas/diagnóstico por imagem , Neoplasias Ovarianas/patologia , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Diagnóstico Diferencial , Feminino , Humanos , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sensibilidade e Especificidade , Adulto Jovem
11.
Transl Oncol ; 10(3): 311-317, 2017 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-28325667

RESUMO

Lung cancer is the most common fatal malignancy for both men and women and adenocarcinoma is the most common histologic type. Early diagnosis of lung cancer can significantly improve the survival rate of patients. This study aimed to investigate the micro-computed tomography (micro-CT) manifestations of early lung adenocarcinoma (LAC) in mice and to provide a new perspective for early clinical diagnosis. Early LAC models in 10 mice were established by subcutaneously injecting 1-methyl-3-nitro-1-nitrosoguanidine (MNNG) solution. Micro-CT scan and multiple planar reconstruction (MPR) were used for mouse lungs. Micro-CT features of early LAC, especially the relationships between tumor and bronchus, were analyzed and correlated with pathology. Micro-CT findings of early LAC were divided into three types: non-solid (n = 8, 6%), partly solid (n = 85, 64%) and totally solid (n = 39, 30%). Tumor-bronchus relationships, which could be observed in 110 of 132(83%) LAC, were classified into four patterns: type I (n = 16, 15%), bronchus was truncated at the margin of the tumor; type II (n = 33, 30%), bronchus penetrated into the tumor with tapered narrowing and interruption; type III (n = 38, 35%), bronchus penetrated into the tumor with a patent and intact lumen; type IV (n = 99, 90%), bronchus ran at the border of the tumor with an intact or compressed lumen. Micro-CT manifestations of early LAC correlated well with pathological findings. Micro-CT can clearly demonstrate the features of mouse early LAC and bronchus-tumor relationships, and can also provide a new tool and perspective for the study of early LAC.

12.
J Magn Reson Imaging ; 46(5): 1499-1506, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-28295854

RESUMO

PURPOSE: To investigate the use of diffusion kurtosis imaging (DKI) in differentiating borderline from malignant epithelial ovarian tumors (MEOTs) and to correlate DKI parameters with Ki-67 expression. MATERIALS AND METHODS: Fifty-two consecutive patients with epithelial ovarian tumors (17 borderline epithelial ovarian tumors, BEOTs; 35 MEOTs) were prospectively evaluated using DKI with b values of 0, 500, 1000, 1500, 2000, and 2500 s/mm2 and standard diffusion-weighted imaging (DWI) with b values of 0 and 1000 s/mm2 using a 1.5T magnetic resonance imaging (MRI) unit. The kurtosis (K) and diffusion coefficient (D) from DKI and apparent diffusion coefficient (ADC) from standard DWI were measured, compared, and correlated with Ki-67 expression between the two groups. Statistical analyses were performed using the Mann-Whitney U-test, receiver operating characteristic (ROC) curves, and Spearman's correlation. RESULTS: The K value was significantly lower in BEOTs than in MEOTs (0.55 ± 0.09 vs. 0.9 ± 0.2), while the D and ADC values were significantly higher in BEOTs than in MEOTs (2.27 ± 0.35 vs. 1.39 ± 0.37 and 1.72 ± 0.36 vs. 1.1 ± 0.25, respectively) (P < 0.001). For differentiating between BEOTs and MEOTs, the sensitivity, specificity, and accuracy were 88.2%, 94.3%, and 92.3% for K value; 88.2%, 91.4%, and 90.4% for D value; and 88.2%, 88.6%, and 88.5% for ADC value, respectively. However, there were no differences in the diagnostic performances among the three parameters above (K vs. ADC, P = 0.203; D vs. ADC, P = 0.148; K vs. D, P = 0.904). The K value was positively correlated with Ki-67 expression (r = 0.699), while the D and ADC values were negatively correlated with Ki-67 expression (r = -0.680, -0.665, respectively). CONCLUSION: Preliminary findings demonstrate that DKI is an alternative tool for differentiating BEOTs from MEOTs, and is correlated with Ki-67 expression. However, no added value is found for DKI compared with standard DWI. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1499-1506.


Assuntos
Imagem de Difusão por Ressonância Magnética , Imagem de Tensor de Difusão , Processamento de Imagem Assistida por Computador , Antígeno Ki-67/metabolismo , Neoplasias Epiteliais e Glandulares/diagnóstico por imagem , Neoplasias Epiteliais e Glandulares/metabolismo , Neoplasias Ovarianas/diagnóstico por imagem , Neoplasias Ovarianas/metabolismo , Adulto , Idoso , Carcinoma Epitelial do Ovário , Feminino , Humanos , Imuno-Histoquímica , Pessoa de Meia-Idade , Variações Dependentes do Observador , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Adulto Jovem
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...