Abstract:ObjectiveWe propose a Magnetic Resonance Imaging (MRI) guided approach to differential diagnosis of ovarian tumours based on morphological appearance.BackgroundCharacterization of ovarian lesions is of great importance in order to plan adequate therapeutic procedures, and may influence patient’s management. Optimal assessment of adnexal masses requires a multidisciplinary approach, based on physical examination, laboratory tests and imaging techniques. Primary ovarian tumours can be classified into three main … Show more
“…The preoperative qualitative diagnosis and accurate characterization of ovarian tumors are the key steps for an optimal clinical treatment strategy, which is also helpful for improving patient prognosis [2–4]. Fig.…”
BackgroundThe study aimed to investigate the utility of dynamic contrast enhanced MRI (DCE-MRI) in the differentiation of malignant, borderline, and benign complex ovarian tumors.MethodsDCE-MRI data of 102 consecutive complex ovarian tumors (benign 15, borderline 16, and malignant 71), confirmed by surgery and histopathology, were analyzed retrospectively. The patterns (I, II, and III) of time-signal intensity curve (TIC) and three semi-quantitative parameters, including enhancement amplitude (EA), maximal slope (MS), and time of half rising (THR), were evaluated and compared among benign, borderline, and malignant ovarian tumors. The types of TIC were compared by Pearson Chi-square χ
2 between malignant and benign, borderline tumors. The mean values of EA, MS, and THR were compared using one-way ANOVA or nonparametric Kruskal-Wallis test.ResultsFifty-nine of 71 (83%) malignant tumors showed a type-III TIC; 9 of 16 (56%) borderline tumors showed a type-II TIC, and 10 of 15 (67%) benign tumors showed a type-II TIC, with a statistically significant difference between malignant and benign tumors (P < 0.001) and between malignant and borderline tumors (P < 0.001). MS was significantly higher in malignant tumors than in benign tumors and in borderline than in benign tumors (P < 0.001, P = 0.013, respectively). THR was significantly lower in malignant tumors than in benign tumors and in borderline than in benign tumors (P < 0.001, P = 0.007, respectively). There was no statistically significant difference between malignant and borderline tumors in MS and THR (P = 0.19, 0.153) or among malignant, borderline, and benign tumors in EA (all P > 0.05).ConclusionsDCE-MRI is helpful for characterizing complex ovarian tumors; however, semi-quantitative parameters perform poorly when distinguishing malignant from borderline tumors.
“…The preoperative qualitative diagnosis and accurate characterization of ovarian tumors are the key steps for an optimal clinical treatment strategy, which is also helpful for improving patient prognosis [2–4]. Fig.…”
BackgroundThe study aimed to investigate the utility of dynamic contrast enhanced MRI (DCE-MRI) in the differentiation of malignant, borderline, and benign complex ovarian tumors.MethodsDCE-MRI data of 102 consecutive complex ovarian tumors (benign 15, borderline 16, and malignant 71), confirmed by surgery and histopathology, were analyzed retrospectively. The patterns (I, II, and III) of time-signal intensity curve (TIC) and three semi-quantitative parameters, including enhancement amplitude (EA), maximal slope (MS), and time of half rising (THR), were evaluated and compared among benign, borderline, and malignant ovarian tumors. The types of TIC were compared by Pearson Chi-square χ
2 between malignant and benign, borderline tumors. The mean values of EA, MS, and THR were compared using one-way ANOVA or nonparametric Kruskal-Wallis test.ResultsFifty-nine of 71 (83%) malignant tumors showed a type-III TIC; 9 of 16 (56%) borderline tumors showed a type-II TIC, and 10 of 15 (67%) benign tumors showed a type-II TIC, with a statistically significant difference between malignant and benign tumors (P < 0.001) and between malignant and borderline tumors (P < 0.001). MS was significantly higher in malignant tumors than in benign tumors and in borderline than in benign tumors (P < 0.001, P = 0.013, respectively). THR was significantly lower in malignant tumors than in benign tumors and in borderline than in benign tumors (P < 0.001, P = 0.007, respectively). There was no statistically significant difference between malignant and borderline tumors in MS and THR (P = 0.19, 0.153) or among malignant, borderline, and benign tumors in EA (all P > 0.05).ConclusionsDCE-MRI is helpful for characterizing complex ovarian tumors; however, semi-quantitative parameters perform poorly when distinguishing malignant from borderline tumors.
“…Only US is lower in cost than CT, but its accuracy is much lower than CT [8]. Although MRI may provide additional functional information, it has equivalent performance in detecting and staging abdominal-pelvic disease to CT, but the high-cost and longer examination times precludes the use of MRI for most ovarian cancer patients [9]. Thus, CT is the only imaging modality routinely used in current clinical practice for ovarian cancer diagnosis and treatment evaluation [8].…”
Objective
This study aimed to investigate the role of applying quantitative image features computed from CT images for early prediction of tumor response to chemotherapy in the clinical trials for treating ovarian cancer patients.
Materials and Methods
A dataset involving 91 patients was retrospectively assembled. Each patient had two sets of pre- and post-therapy CT images. A computer-aided detection scheme was applied to segment metastatic tumors previously tracked by radiologists on CT images and computed image features. Two initial feature pools were built using image features computed from pre-therapy CT images only and image feature difference computed from both pre- and post-therapy images. A feature selection method was applied to select optimal features and an equal-weighted fusion method was used to generate a new quantitative imaging marker from each pool to predict 6-month progression free survival. The prediction accuracy between quantitative imaging markers and RECIST criteria was also compared.
Results
The highest areas under ROC curve (AUC) are 0.684±0.056 and 0.771±0.050 when using a single image feature computed from pre-therapy CT images and feature difference computed from pre- and post-therapy CT images, respectively. Using two corresponding fusion-based image markers, AUCs significantly increased to 0.810±0.045 and 0.829±0.043 (p < 0.05), respectively. Overall prediction accuracy levels are 71.4%, 80.2%, and 74.7% when using two imaging markers and RECIST, respectively.
Conclusion
This study demonstrated the feasibility of predicting patients’ response to chemotherapy using quantitative imaging markers computed from pre-therapy CT images. However, using image feature difference computed between pre- and post-therapy CT images yielded higher prediction accuracy.
“…Ovarian thecoma-fibroma groups (OTFG) are uncommon sex cord-stromal neoplasms, constituting 1.0 to 4.0% of all ovarian tumors [1–4]. Most of them are benign, and often found in postmenopausal patients [5, 6].…”
BackgroundOvarian thecoma-fibroma groups (OTFG) are uncommon sex cord-stromal neoplasms. The objective of the study was to demonstrate clinical and sonographic features of OTFG and compare with surgical histopathology.MethodsA total of 61 patients with surgically proven OTFG were enrolled in this retrospective study to demonstrate its clinical and sonographic features and to compare with pathological findings. Gray scale and color Doppler sonography were performed presurgically with either transabdominal or transvaginal approach to image pelvic structures and lesions. The clinical findings and sonographic appearances were compared with the types of the OTFG tumors based on the histopathological diagnosis.ResultsThe mean patient age was 53.57 (range, 26–86) years. There were 63.93% (39/61) patients in postmenopausal and 63.93% (39/61) patients with no clinical symptoms. Ultrasound findings of OTFG revealed as solid tumors with a typical feature of well-demarcated hypoechoic masses in 70.49% (43/61), among which 74.41% (32/43) tumors were smaller than 5 cm in diameter. There were 17 mixed echogenic masses with calcification, hemorrhage, or cyst, among which 70.59% (12/17) lesions were larger than 5 cm in diameter. Acoustic attenuation of the tumor was presented in 44.26% (27/61) of the cases. Doppler flow signals within the tumors were found in 20 cases (32.79%), in which 80% (16/20) had minimal or moderate flow signals. Ascites was detected in 32.79% (20/61) of the cases, Megi’s syndrome was found in 1 case. Final pathology revealed 41 (67.21%) thecoma-fibromas, 15 (24.59%) fibromas, 4 (6.56%) thecomas and 1 (1.64%) fibrosarcoma. There were 58 patients underwent cancer antigen 125 (CA125) test, and 20.69% (12/58) showed an elevated level. The diameter of tumors was found to be significantly correlated with CA125 level (p < 0.01) and the amount of ascites fluid (p < 0.05).ConclusionsThe typical sonographic features of OTFG include adnexal hypoechoic masses with clear border and acoustic attenuation as well as minimal Doppler flow signals. All the aforementioned features could make ultrasound imaging as a assistent tool improve the preoperative diagnostic accuracy.
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