Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis

Denewar, Fatmaelzahraa Abdelfattah; Takeuchi, Mitsuru; Urano, Misugi; Kamishima, Yuki; Kawai, Tatsuya; Takahashi, Naoki; Takeuchi, Moe; Kobayashi, Susumu; Honda, Junichi; Shibamoto, Yuta

doi:10.1016/j.ejrad.2017.04.001

Cited by 30 publications

(29 citation statements)

References 24 publications

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“…Denewar et al reported that the minimum ADC value was useful to distinguish between BOTs and MOTs [9]. In the present study as well as the previous reports [7][8][9], both average and minimum ADC values were significantly lower in MOTs than that in BOTs. One of the limitations in ADC mapping for diagnosing ovarian tumors is the difficulty in evaluation due to small solid part [7].…”

Section: Discussionsupporting

confidence: 77%

“…Subsequently, Mimura et al demonstrated that ADC histograms of the solid tumor component can be helpful to distinguish between BOTs and MOTs, and 10 th percentile ADC values appeared to be the best parameter for the differentiation of tumor types [8]. Denewar et al reported that the minimum ADC value was useful to distinguish between BOTs and MOTs [9]. In the present study as well as the previous reports [7][8][9], both average and minimum ADC values were significantly lower in MOTs than that in BOTs.…”

Section: Discussionmentioning

confidence: 99%

“…One of the limitations in ADC mapping for diagnosing ovarian tumors is the difficulty in evaluation due to small solid part [7]. According to Denewar et al, the minimum ADC value has higher sensitivity and specificity than the average ADC value and more accurately reflects the aggressiveness of the tumor [9]. Mimura et al reported that the 10th percentile ADC value had the highest p value, but the minimum ADC value also showed a significant difference where its sensitivity was much higher [8].…”

Section: Discussionmentioning

confidence: 99%

“…A threshold value of 1.039 × 10 −3 mm 2 /s using a 1.5T Siemens scanner was reported to permit the distinction with 97.0% sensitivity, 92.2% specificity, and 96.4% accuracy [7]. More recently, using 1.5 T Philips, Siemens, or GE scanner, or 3 T Siemens or Philips scanner, Denewar et al revealed that a cutoff value of minimum ADC; 1.41 × 10 −3 mm 2 /s for indicators of MOTs with an AUC, sensitivity, and specificity of 0.86, 81%, and 84%, respectively [9]. These cutoff values previously reported widely varied by the institutions and MRI scanners.…”

Section: Discussionmentioning

confidence: 99%

“…If the accurate diagnosis of BOTs could be preoperatively obtained, fertility preservation would be easily prepared. Recently, the apparent diffusion coefficient (ADC) value in MRI diffusion-weighted imaging (DWI), which has the potential to replace contrast-enhanced sequences, is increasingly used for the differential diagnosis between BOT and MOT [7][8][9]. The ADC value may be an index that indirectly captures the properties of the tumor [10].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Differentiation between stage I ovarian cancer and borderline epithelial ovarian tumor by apparent diffusion coefficient value

Masamoto¹,

Mekaru²,

Nakasone³

et al. 2018

Is It All Just an Akt - You’d Be SMAD to Believe It! Role of TGFβ1 in Oral Cancer Metastasis

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To clarify usefulness of apparent diffusion coefficient (ADC) value in the differential diagnosis of stage I malignant epithelial ovarian tumor (MOT) and borderline epithelial ovarian tumor (BOT), the ADC value of the solid portion or thickened wall of ovarian tumors was evaluated by a 1.5-T magnetic resonance imaging system in 18 cases of BOT and MOT. The median of average ADC value was 1.668 × 10 −3 mm 2 /s for BOT and 1.021 × 10 −3 mm 2 /s for MOT (p = 0.0005), and the median of minimum ADC value was 1.394 × 10 −3 mm 2 /s for BOT and 0.812× 10 −3 mm 2 /s for MOT (p = 0.0006), both of which were significantly lower in MOT than that in BOT. So the ADC value is useful and especially the minimum ADC value is useful in that it can easily measure the ROI so that it contains solid portion or thickened wall.

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Section: Discussionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Differentiation between stage I ovarian cancer and borderline epithelial ovarian tumor by apparent diffusion coefficient value

Masamoto¹,

Mekaru²,

Nakasone³

et al. 2018

Is It All Just an Akt - You’d Be SMAD to Believe It! Role of TGFβ1 in Oral Cancer Metastasis

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MRI‐Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study

Jian

Pickhardt

et al. 2020

Magnetic Resonance Imaging

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BackgroundPreoperative 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.PurposeTo 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 TypeRetrospective study of eight clinical centers.PopulationIn 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/SequencePreoperative 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 (T2WI), fat saturation (FS), diffusion‐weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast‐enhanced (CE)‐T1WI.AssessmentDiagnostic 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 TestsMann–Whitney U‐test: significance of the clinical characteristics; chi‐square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC).ResultsThe 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 ConclusionPerformance 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 EvidenceLevel 4.Technical EfficacyStage 2. J. Magn. Reson. Imaging 2020;52:897–904.

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Differentiation of borderline tumors from type I ovarian epithelial cancers on CT and MR imaging

et al. 2020

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Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis

Cited by 30 publications

References 24 publications

Differentiation between stage I ovarian cancer and borderline epithelial ovarian tumor by apparent diffusion coefficient value

Differentiation between stage I ovarian cancer and borderline epithelial ovarian tumor by apparent diffusion coefficient value

MRI‐Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study

Differentiation of borderline tumors from type I ovarian epithelial cancers on CT and MR imaging

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