scholarly journals Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiang Liu ◽  
Zhaonan Sun ◽  
Chao Han ◽  
Yingpu Cui ◽  
Jiahao Huang ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Average Distance ◽  
Model Development ◽  
Area Under The Curve ◽  
True Positive Rate ◽  
Validation Dataset ◽  
Diffusion Weighted Images ◽  
Diffusion Weighted ◽  
Expert Radiologist ◽  
Temporal Validation

Abstract Background The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images. Methods A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for temporal validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS), Hausdorff distance (HD), the Average distance (AVD), and the Mahalanobis distance (MHD) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an hold-out test dataset between the model and radiologist was assessed using Cohen’s kappa coefficient. Results In the testing set used for model development, the Dice score, TPR, PPV, VS, HD, AVD and MHD values for the segmentation of suspicious LNs were 0.85, 0.82, 0.80, 0.86, 2.02 (mm), 2.01 (mm), and 1.54 (mm) respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the temporal validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892–0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist. Conclusion The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.

Development and External Validation of the 3D U-Net Algorithm for Segmentation of Pelvic Lymph Nodes On Diffusion-Weighted Images

2021 ◽  
Author(s):  
Xiang Liu ◽  
Zhaonan Sun ◽  
Chao Han ◽  
Yingpu Cui ◽  
Jiahao Huang ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
External Validation ◽  
Model Development ◽  
Area Under The Curve ◽  
True Positive Rate ◽  
Validation Dataset ◽  
Diffusion Weighted Images ◽  
Diffusion Weighted ◽  
Expert Radiologist ◽  
High Consistency

Abstract Background: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images.Methods: A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for external validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an external dataset between the model and radiologist was assessed using Cohen’s kappa coefficient.Results: In the testing set used for model development, the Dice score, TPR, PPV, and VS for the segmentation of suspicious LNs were 0.85, 0.82, 0.80 and 0.86, respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the external validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892-0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist.Conclusion: The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.

Combining tumor size and diffusion-weighted imaging to diagnose normal-sized metastatic pelvic lymph nodes in cervical cancers

2018 ◽  
Vol 60 (3) ◽  
pp. 388-395 ◽  
Author(s):  
Jiacheng Song ◽  
Qiming Hu ◽  
Junwen Huang ◽  
Zhanlong Ma ◽  
Ting Chen
Keyword(s):  
Cervical Cancer ◽  
Lymph Nodes ◽  
Cancer Patients ◽  
Tumor Size ◽  
Diffusion Weighted Imaging ◽  
Area Under The Curve ◽  
Adc Value ◽  
Cervical Cancers ◽  
Pelvic Lymph Nodes ◽  
Diffusion Weighted

Background Detecting normal-sized metastatic pelvic lymph nodes (LNs) in cervical cancers, although difficult, is of vital importance. Purpose To investigate the value of diffusion-weighted-imaging (DWI), tumor size, and LN shape in predicting metastases in normal-sized pelvic LNs in cervical cancers. Material and Methods Pathology confirmed cervical cancer patients with complete magnetic resonance imaging (MRI) were documented from 2011 to 2016. A total of 121 cervical cancer patients showed small pelvic LNs (<5 mm) and 92 showed normal-sized (5–10 mm) pelvic LNs (39 patients with 55 nodes that were histologically metastatic, 53 patients with 71 nodes that were histologically benign). Preoperative clinical and MRI variables were analyzed and compared between the metastatic and benign groups. Results LN apparent diffusion coefficient (ADC) values and short-to-long axis ratios were not significantly different between metastatic and benign normal-sized LNs (0.98 ± 0.15 × 10−3 vs. 1.00 ± 0.18 × 10−3 mm2/s, P = 0.45; 0.65 ± 0.16 vs. 0.64 ± 0.16, P = 0.60, respectively). Tumor ADC value of the metastatic LNs was significantly lower than the benign LNs (0.98 ± 0.12 × 10−3 vs. 1.07 ± 0.21 × 10−3 mm2/s, P = 0.01). Tumor size (height) was significantly higher in the metastatic LN group (27.59 ± 9.18 mm vs. 21.36 ± 10.40 mm, P < 0.00). Spiculated border rate was higher in the metastatic LN group (9 [16.4%] vs. 3 [4.2%], P = 0.03). Tumor (height) combined with tumor ADC value showed the highest area under the curve of 0.702 ( P < 0.00) in detecting metastatic pelvic nodes, with a sensitivity of 59.1% and specificity of 78.8%. Conclusions Tumor DWI combined with tumor height were superior to LN DWI and shape in predicting the metastatic state of normal-sized pelvic LNs in cervical cancer patients.

scholarly journals Blastocyst Prediction of Day-3 Cleavage-Stage Embryos Using Machine Learning

2021 ◽  
pp. 1-6
Author(s):  
Dung P. Nguyen ◽  
Quan T. Pham ◽  
Thanh L. Tran ◽  
Lan N. Vuong ◽  
Tuong M. Ho
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Blastocyst Formation ◽  
Final Model ◽  
Vitro Fertilization ◽  
Day 3 Embryo

Background:Embryo selection plays an important role in the success of in vitro fertilization (IVF). However, morphological embryo assessment has a number of limitations, including the time required, lack of accuracy, and inconsistency. This study determined whether a machine learning-based model could predict blastocyst formation using day-3 embryo images. Methods:Day-3 embryo images from IVF/intracytoplasmic sperm injection (ICSI) cycles performed at My Duc Phu Nhuan Hospital between August 2018 and June 2019 were retrospectively analyzed to inform model development. Day-3 embryo images derived from two-pronuclear (2PN) zygotes with known blastocyst formation data were extracted from the CCM-iBIS time-lapse incubator (Astec, Japan) at 67 hours post ICSI, and labeled as blastocyst/non-blastocyst based on results at 116 hours post ICSI. Images were used as the input dataset to train (85%) and validate (15%) the convolutional neural network (CNN) model, then model accuracy was determined using the training and validation dataset. The performance of 13 experienced embryologists for predicting blastocyst formation based on 100 day-3 embryo images was also evaluated. Results:A total of 1,135 images were allocated into training ([Formula: see text] = 967) and validation ([Formula: see text] = 168) sets, with an even distribution for blastocyst formation outcome. The accuracy of the final model for blastocyst formation was 97.72% in the training dataset and 76.19% in the validation dataset. The final model predicted blastocyst formation from day-3 embryo images in the validation dataset with an area under the curve of 0.75 (95% confidence interval [CI] 0.69–0.81). Embryologists predicted blastocyst formation with the accuracy of 70.07% (95% CI 68.12%–72.03%), sensitivity of 87.04% (95% CI 82.56%–91.52%), and specificity of 30.93% (95% CI 29.35%–32.51%). Conclusions:The CNN-based machine learning model using day-3 embryo images predicted blastocyst formation more accurately than experienced embryologists. The CNN-based model is a potential tool to predict additional IVF outcomes.

Early Ischemic Stroke: Comparison of Diffusion Weighted Images and ADC maps

1998 ◽  
Vol 39 (1) ◽  
pp. 21
Author(s):  
Soo Jung Choi ◽  
Choong Gon Choi ◽  
Jae Kyun Kim ◽  
Jung Hoon Kim ◽  
Jae Hong Lee ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Diffusion Weighted Images ◽  
Diffusion Weighted

Ascertaining an efficient eligibility cut-off for extended Medicare items for eating disorders

2021 ◽  
pp. 103985622110286
Author(s):  
Tracey Wade ◽  
Jamie-Lee Pennesi ◽  
Yuan Zhou
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Rate Sensitivity ◽  
True Positive Rate ◽  
Eating Disorder Examination Questionnaire ◽  
Eating Disorder Examination ◽  
Positive Rate ◽  
The Relationship

Objective: Currently eligibility for expanded Medicare items for eating disorders (excluding anorexia nervosa) require a score ⩾ 3 on the 22-item Eating Disorder Examination-Questionnaire (EDE-Q). We compared these EDE-Q “cases” with continuous scores on a validated 7-item version of the EDE-Q (EDE-Q7) to identify an EDE-Q7 cut-off commensurate to 3 on the EDE-Q. Methods: We utilised EDE-Q scores of female university students ( N = 337) at risk of developing an eating disorder. We used a receiver operating characteristic (ROC) curve to assess the relationship between the true-positive rate (sensitivity) and the false-positive rate (1-specificity) of cases ⩾ 3. Results: The area under the curve showed outstanding discrimination of 0.94 (95% CI: .92–.97). We examined two specific cut-off points on the EDE-Q7, which included 100% and 87% of true cases, respectively. Conclusion: Given the EDE-Q cut-off for Medicare is used in conjunction with other criteria, we suggest using the more permissive EDE-Q7 cut-off (⩾2.5) to replace use of the EDE-Q cut-off (⩾3) in eligibility assessments.

Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI

2021 ◽  
pp. 028418512110258
Author(s):  
Lan Li ◽  
Tao Yu ◽  
Jianqing Sun ◽  
Shixi Jiang ◽  
Daihong Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Nodes ◽  
Area Under The Curve ◽  
Test Group ◽  
Predictive Performance ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Delayed Phase

Background The number of metastatic axillary lymph nodes (ALNs) play a crucial role in the staging, prognosis and therapy of patients with breast cancer. Purpose To predict the number of metastatic ALNs in breast cancer via radiomics. Material and Methods We enrolled 197 patients with breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A total of 3386 radiomic features were extracted from the early- and delayed-phase subtraction images. To classify the number of metastatic ALNs, logistic regression was used to develop a radiomic signature and nomogram. Results The radiomic signature were constructed to distinguish the N0 group from the N+ (metastatic ALNs ≥ 1) group, which yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and test group, respectively. Based on the radiomic signature and BI-RADS category, a nomogram was further developed and showed excellent predictive performance with AUC values of 0.85 and 0.89 in the training and test groups, respectively. Another radiomic signature was constructed to distinguish the N1 (1–3 ALNs) group from the N2–3 (≥4 metastatic ALNs) group and showed encouraging performance with AUC values of 0.94 and 0.84 in training and test group, respectively. Conclusions We developed a nomogram and a radiomic signature that can be used to predict ALN metastasis and distinguish the N1 from the N2-3 group. Both nomogram and radiomic signature may be potential tools to assist clinicians in assessing ALN metastasis in patients with breast cancer.

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