Combined subjective and quantitative analysis of magnetic resonance images could improve the diagnostic performance of deep myometrial invasion in endometrial cancer

2017 ◽  
Vol 43 ◽  
pp. 69-73 ◽  
Author(s):  
Lei Deng ◽  
Qiu-ping Wang ◽  
Rui Yan ◽  
Nan Yu ◽  
Lu Bai ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Quantitative Analysis ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Myometrial Invasion ◽  
Magnetic Resonance Images ◽  
Deep Myometrial Invasion

scholarly journals Tumoral volume measured preoperatively by magnetic resonance imaging is related to survival in endometrial cancer

2021 ◽  
Vol 55 (1) ◽  
pp. 35-41
Author(s):  
Pluvio J. Coronado ◽  
Javier de Santiago-López ◽  
Javier de Santiago-García ◽  
Ramiro Méndez ◽  
Maria Fasero ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Endometrial Cancer ◽  
Magnetic Resonance ◽  
Auxiliary Information ◽  
Myometrial Invasion ◽  
Figo Stage ◽  
Mri Imaging ◽  
Resonance Imaging ◽  
Deep Myometrial Invasion ◽  
Grade 3

AbstractBackgroundThe aim of the study was to determine if the endometrial tumor volume (TV) measured by magnetic resonance imaging (MRI-TV) is associated with survival in endometrial cancer and lymph nodes metastases (LN+).Patients and methodsWe evaluated the MRI imaging and records of 341 women with endometrial cancer and preoperative MRI from 2008 to 2018. The MRI-TV was calculated using the ellipsoid formula measuring three perpendicular tumor diameters. Tumor myometrial invasion was also analyzed.ResultsHigher MRI-TV was associated with age ≥ 65y, non-endometrioid tumors, grade-3, deep-myometrial invasion, LN+ and advanced FIGO stage. There were 37 patients with LN+ (8.8%). Non-endometrioid tumors, deep-myometrial invasion, grade-3 and MRI-TV ≥ 10 cm3 were the factors associated with LN+. Using a receiver operating characteristic [ROC] curve, the MRI-TV cut-off for survival was 10 cm3 (area under curve [AUC] = 0.70; 95% CI: 0.61–0.73). 5 years disease-free (DFS) and overall survival (OS) was significantly lower in MRI-TV ≥ 10 cm3 (69.3% vs. 84.5%, and 75.4% vs. 96.1%, respectively). MRI-TV was considered an independent factor of DFS (HR: 2.20, 95% CI: 1.09–4.45, p = 0.029) and OS (HR: 3.88, 95% CI: 1.34–11.24, p = 0.012) in multivariate analysis.ConclusionsMRI-TV was associated with LN+, and MRI-TV ≥ 10 cm3 was an independent prognostic factor of lower DFS and OS. The MRI-TV can be auxiliary information to plan the surgery strategy and predict the adjuvant treatment in women with endometrial cancer.

scholarly journals Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study

2020 ◽  
Vol 17 (16) ◽  
pp. 5993
Author(s):  
Hsiang-Chun Dong ◽  
Hsiang-Kai Dong ◽  
Mu-Hsien Yu ◽  
Yi-Hsin Lin ◽  
Cheng-Chang Chang
Keyword(s):  
Endometrial Cancer ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Myometrial Invasion ◽  
Magnetic Resonance Images ◽  
Stage I ◽  
Invasion Depth ◽  
Accuracy Rate ◽  
Neural Network Approach ◽  
Contrast Enhanced

Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-enhanced T1-weighted images (T1w) and T2-weighted images (T2w) from 72 patients who were diagnosed with surgico-pathological stage I endometrial carcinoma. We used the images from 24 patients (33.3%) to train the AI. The images from the remaining 48 patients (66.7%) were used to evaluate the accuracy of the model. The AI then interpreted each of the cases and sorted them into stage IA or IB. Compared with the accuracy rate of radiologists’ diagnoses (77.8%), the accuracy rate of AI interpretation in contrast-enhanced T1w was higher (79.2%), whereas that in T2w was lower (70.8%). The diagnostic accuracy was not significantly different between radiologists and AI for both T1w and T2w. However, AI was more likely to provide incorrect interpretations in patients with coexisting benign leiomyomas or polypoid tumors. Currently, the ability of this AI technology to make an accurate diagnosis has limitations. However, in hospitals with limited resources, AI may be able to assist in reading magnetic resonance images. We believe that AI has the potential to assist radiologists or serve as a reasonable alternative for pre-operative evaluation of the myometrial invasion depth of stage I endometrial cancers.

Assessment of Myometrial Invasion in Endometrial Cancer by Transvaginal Sonography, Doppler Ultrasonography, Magnetic Resonance Imaging and Frozen Section

2009 ◽  
Vol 19 (6) ◽  
pp. 1085-1090 ◽  
Author(s):  
Suna Özdemir ◽  
Çetin Çelik ◽  
Dilek Emlik ◽  
Demet Kiresi ◽  
Hasan Esen
Keyword(s):  
Magnetic Resonance Imaging ◽  
Blood Flow ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Doppler Ultrasonography ◽  
Frozen Section ◽  
Myometrial Invasion ◽  
Transvaginal Sonography ◽  
Resonance Imaging ◽  
Deep Myometrial Invasion

Objective:We aimed to compare the diagnostic performance of transvaginal sonography (TVS), magnetic resonance imaging (MRI), and intraoperative frozen section in the assessment of myometrial invasion and to evaluate intratumoral blood flow in any myometrial invasion with transvaginal Color Doppler ultrasonography (TV-CDU).Methods:This prospective study included 64 women consecutively diagnosed with endometrial carcinoma. The subjects were evaluated by TVS, MRI, and TV-CDU by 2 radiologists with a special training in gynecology. Intraoperatively, a frozen section was obtained and processed for interpretation by a blinded pathologist. Sensitivity, specificity, negative, and positive predictive values were calculated for each imaging modality and frozen section with regard to assessment of myometrial invasion. The intratumoral blood flow was evaluated by TV-CDU.Results:Transvaginal sonography, MRI, and frozen section showed no statistical significant differences in overall diagnostic performance for the preoperative and intraoperative assessment of any myometrial invasion, although frozen section seemed to be slightly superior to the imaging techniques. The positive rate of intratumoral blood flow was higher in deep myometrial invasion, but statistical significance was not obtained. The mean value of resistance index was significantly lower in the cases with deep myometrial invasion.Conclusions:Transvaginal sonography with concomitant TV-UCD is low-cost, easily performed, and repeated technique for particularly deep myometrial invasion. Because of its high costs and time-consuming, MRI may be recommended in the cases with poor quality of TVS. Because depending solely on imaging methods could lead to insufficient treatment schedules, intraoperative frozen section should also be performed for myometrial assessment.

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

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