scholarly journals Computer-Aided Segmentation and Machine Learning of Integrated Clinical and Diffusion-Weighted Imaging Parameters for Predicting Lymph Node Metastasis in Endometrial Cancer

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1406
Author(s):  
Lan-Yan Yang ◽  
Tiing Yee Siow ◽  
Yu-Chun Lin ◽  
Ren-Chin Wu ◽  
Hsin-Ying Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Endometrial Cancer ◽  
Lymph Node ◽  
Decision Tree ◽  
Diffusion Weighted Imaging ◽  
Short Axis ◽  
Adc Value ◽  
Diffusion Weighted ◽  
Computer Aided ◽  
And Diffusion

Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortality. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging before surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10−3 mm2/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10−2), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p < 0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance surpassing the conventional ADC and size criteria.

Observer variation of magnetic resonance imaging and diffusion weighted imaging in pelvic lymph node detection

2011 ◽  
Vol 78 (1) ◽  
pp. 71-74 ◽  
Author(s):  
Wenche M. Klerkx ◽  
Willem M. Mali ◽  
A. Peter Heintz ◽  
Gerard A. de Kort ◽  
Taro Takahara ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Lymph Node ◽  
Magnetic Resonance ◽  
Diffusion Weighted Imaging ◽  
Observer Variation ◽  
Pelvic Lymph Node ◽  
Resonance Imaging ◽  
Diffusion Weighted ◽  
And Diffusion

Differentiation of cystic lesions in the jaw by conventional magnetic resonance imaging and diffusion-weighted imaging

2021 ◽  
pp. 20210212
Author(s):  
Natnicha Wamasing ◽  
Hiroshi Watanabe ◽  
Junichiro Sakamoto ◽  
Hiroshi Tomisato ◽  
Tohru Kurabayashi
Keyword(s):  
Decision Tree ◽  
Diffusion Weighted Imaging ◽  
Cystic Lesions ◽  
Characteristic Analysis ◽  
Discrimination Power ◽  
Adc Value ◽  
Diffusion Weighted ◽  
Imaging Characteristics ◽  
Dentigerous Cysts ◽  
Adc Values

Objectives: This study aimed to determine the discrimination power of apparent diffusion coefficient (ADC) for cystic lesions in the jaw using MRI. Methods: We selected 127 cystic lesions, comprising dentigerous cysts (DCs), odontogenic keratocysts (OKCs), and unicystic ameloblastomas (UABs), from our MRI database examined by 3T MRI, including diffusion-weighted imaging sequences, and we reviewed their imaging characteristics. We attempted to discriminate the three types of lesions by ADC values with receiver operator characteristic analysis; however, satisfactory results were not obtained for differentiation between DC and OKC. Therefore, we performed a decision tree analysis. Results: The imaging characteristics of the lesions were significantly different according to Fisher’s exact test, except for differences in sex. The ADC values statistically discriminated the lesions of DC and UAB, OKC and UAB, but not DC and OKC. Thus, differentiation was performed by a decision tree for DC and OKC by evaluating the following points: the attached tooth condition, signal intensity on the T1 weighted image (T1SI), ADC value, and the cyst site. However, cases showing hypo- or isointense T1SI with an ADC value under 1.168 × 10–3 mm2/s were difficult to differentiate. Conclusion: The ADC value helped distinguish UAB from both DC and OKC, but not DC from OKC. However, the decision tree based on ADC value, tooth contact status, and T1SI helped differentiate DC and OKC to some extent.

Value of MRI and diffusion weighted imaging in diagnosing normal-sized pelvic lymph nodes metastases in patients with cervical cancer

2020 ◽  
pp. 20200203
Author(s):  
Qingling Song ◽  
Yanyan Yu ◽  
Xiaomiao Zhang ◽  
Yanmei Zhu ◽  
Yahong Luo ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Decision Tree ◽  
Lymph Nodes ◽  
Diffusion Weighted Imaging ◽  
Short Axis ◽  
Metastatic Lymph Nodes ◽  
Pelvic Lymph Nodes ◽  
Metastatic Lymph ◽  
And Diffusion ◽  
Lymph Nodes Metastases

Objectives: To investigate the value of conventional magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) in diagnosing normal-sized pelvic lymph nodes metastases in patients with cervical cancer. Methods: 102 patients with cervical cancer who underwent MRI and DWI scan were included. 137 lymph nodes were analyzed, including 44 metastatic lymph nodes(MLNs) and 93 non-metastatic lymph nodes(non-MLNs).The morphology and apparent diffusion coefficient (ADC) value of lymph nodes were measured including short-axis diameter (DS), long-axis diameter (DL), ratio of short to long-axis diameter (DR), fatty hilum, asymmetry, ADCmax, ADCmean and ADCmin. The Mann-Whitney U test, independent sample t test and Chi-square test were employed to compare the difference of all criteria between MLNs and non-MLNs. Logistic regression and decision tree were used to develop the combined diagnostic model. ROC analyses were used to evaluate the diagnostic performance. Results: The DS and DR of MLNs were significantly higher than those of non-MLNs (p < 0.05), the ADCmax, ADCmean and ADCmin of MLNs were significantly lower than those of non-MLNs (p < 0.05). Presence of fatty hilum and asymmetric lymph nodes between MLNs and non-MLNs were significantly different (p＜0.05). Combined measurement of ADCmin, DS and DR had the highest AUC 0.937 with 90.9% sensitivity and 87.1% specificity. The accuracy of decision tree was 88.3%. Conclusion: MRI with DWI had potential in diagnosing normal-sized pelvic lymph nodes metastases in patients with cervical cancer. The combined evaluation of DS, DR and ADCmin of lymph nodes and decision tree of the combined measure showed better diagnostic performances than sole criteria. Advances in knowledge: The short-axis diameter, ratio of short to long-axis diameter and ADCmin of lymph nodes have moderate value in the diagnosis of the metastases of the normal-sized lymph nodes for the patient with cervical cancer as the sole indices. The combined evaluation of DS, DR and ADCmin is much more valuable in the detection of metastatic lymph nodes.

scholarly journals Endometrial Cancer: Combined MR Volumetry and Diffusion-weighted Imaging for Assessment of Myometrial and Lymphovascular Invasion and Tumor Grade

Radiology ◽  
2015 ◽  
Vol 276 (3) ◽  
pp. 797-808 ◽  
Author(s):  
Stephanie Nougaret ◽  
Caroline Reinhold ◽  
Shaza S. Alsharif ◽  
Helen Addley ◽  
Jocelyne Arceneau ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Diffusion Weighted Imaging ◽  
Lymphovascular Invasion ◽  
Tumor Grade ◽  
Diffusion Weighted ◽  
Mr Volumetry ◽  
And Diffusion

scholarly journals Role of Quantitative Diffusion-Weighted Imaging in Differentiating Benign and Malignant Orbital Masses

2021 ◽  
Author(s):  
Preeti Mundhada ◽  
Sudarshan Rawat ◽  
Ullas Acharya ◽  
Dhananjay Raje
Keyword(s):  
Roc Curve ◽  
Diffusion Weighted Imaging ◽  
Care Hospital ◽  
Adc Value ◽  
Diffusion Weighted ◽  
Significant Difference ◽  
Orbital Masses ◽  
Adc Values ◽  
Orbital Lesions

Abstract Aim To determine the role of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) values in differentiating benign and malignant orbital masses. Materials and Methods After obtaining institutional ethical board approval and informed consent from all patients, an observational study was done for a period of 24 months in the radiology department of a tertiary care hospital in South India. Conventional magnetic resonance imaging and DWI using a 3T scanner was done for all patients with suspected orbital mass lesion. ADC value and clinicohistopathological correlation were studied for every patient. Chi-square test was used to compare the signal characteristics of DWI and ADC maps between benign and malignant lesions. A comparison of mean ADC values for benign and malignant masses was performed using Student’s t-test for independent samples. The cut-off value for ADC was obtained using the receiver operating characteristic (ROC) curve. Results Of 44 patients with orbital lesions, 70% were benign and 30% were malignant. There was a significant difference in the mean ADC values of benign and malignant orbital masses. Using ROC curve analysis, an optimal ADC threshold of 1.26 × 10−3 mm2/s was calculated for the prediction of malignancy with 100% sensitivity, 80.65% specificity, and 86.36% accuracy (95% confidence interval: 0.872, 1.00, p < 0.0001). Two ADC thresholds were used to characterize the orbital masses with more than 90% confidence. Conclusion Quantitative assessment of ADC is a useful noninvasive diagnostic tool for differentiating benign and malignant orbital masses. Malignant orbital lesions demonstrate significantly lower ADC values as compared with benign lesions.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

2021 ◽  
pp. 1-8
Author(s):  
Haimei Cao ◽  
Xiang Xiao ◽  
Jun Hua ◽  
Guanglong Huang ◽  
Wenle He ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Roc Curve ◽  
Diffusion Weighted Imaging ◽  
Low Grade ◽  
High Grade ◽  
Mr Images ◽  
Vascular Space ◽  
Diffusion Weighted ◽  
High Grade Gliomas ◽  
And Diffusion

Objectives: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. Methods: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. Results: Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10−3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. Conclusion: The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

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