scholarly journals Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma

Neurosurgery ◽  
2016 ◽  
Vol 78 (4) ◽  
pp. 572-580 ◽  
Author(s):  
Hamed Akbari ◽  
Luke Macyszyn ◽  
Xiao Da ◽  
Michel Bilello ◽  
Ronald L. Wolf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Tumor Recurrence ◽  
Pattern Analysis ◽  
Early Recurrence ◽  
Magnetic Resonance Images ◽  
Tumor Infiltration ◽  
Multiparametric Imaging ◽  
Peritumoral Tissue ◽  
Spatial Maps

Abstract BACKGROUND: Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor. OBJECTIVE: To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. METHODS: Preoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. RESULTS: This technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. CONCLUSION: Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.

scholarly journals Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images

2019 ◽  
Vol 34 (10) ◽  
pp. 2210-2215 ◽  
Author(s):  
Romil F. Shah ◽  
Alejandro M. Martinez ◽  
Valentina Pedoia ◽  
Sharmila Majumdar ◽  
Thomas P. Vail ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Learning Algorithm ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithm ◽  
Knee Cartilage

scholarly journals Identification of potentially painful disc fissures in magnetic resonance images using machine-learning modelling

2021 ◽  
Author(s):  
Lagerstrand Kerstin ◽  
Hebelka Hanna ◽  
Brisby Helerna
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Diagnostic Accuracy ◽  
Negative Control ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Pain Model ◽  
Accuracy And Precision ◽  
Magnetic Resonance Imaging Mri ◽  
Very High

Abstract Purpose It is suggested that non-specific low back pain (LBP) can be related to nerve ingrowth along granulation tissue in disc fissures, extending into the outer layers of the annulus fibrosus. Present study aimed to investigate if machine-learning modelling of magnetic resonance imaging (MRI) data can classify such fissures as well as pain, provoked by discography, with plausible accuracy and precision. Methods The study was based on previously collected data from 30 LBP patients (age = 26–64 years, 11 males). Pressure-controlled discography was performed in 86 discs with pain-positive discograms, categorized as concordant pain-response at a pressure ≤ 50 psi and for each patient one negative control disc. The CT-discograms were used for categorization of fissures. MRI values and standard deviations were extracted from the midsagittal part and from 5 different sub-regions of the discs. Machine-learning algorithms were trained on the extracted MRI markers to classify discs with fissures extending into the outer annulus or not, as well as to classify discs as painful or non-painful. Results Discs with outer annular fissures were classified in MRI with very high precision (mean of 10 repeated testings: 99%) and accuracy (mean: 97%) using machine-learning modelling, but the pain model only demonstrated moderate diagnostic accuracy (mean accuracy: 69%; precision: 71%). Conclusion The present study showed that machine-learning modelling based on MRI can classify outer annular fissures with very high diagnostic accuracy and, hence, enable individualized diagnostics. However, the model only demonstrated moderate diagnostic accuracy regarding pain that could be assigned to either a non-sufficient model or the used pain reference.

An Intensity Variation Pattern Analysis Based Machine Learning Classifier for MRI Brain Tumor Detection

2019 ◽  
Vol 15 (6) ◽  
pp. 555-564
Author(s):  
Muthalakshmi Murugesan ◽  
Dhanasekaran Ragavan
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Pattern Analysis ◽  
Automatic Segmentation ◽  
Intensity Variation ◽  
Disease Diagnosis ◽  
Magnetic Resonance Images ◽  
Variation Pattern ◽  
Fully Automatic ◽  
Processing Techniques

Background: An accurate detection of tumor from the Magnetic Resonance Images (MRIs) is a critical and demanding task in medical image processing, due to the varying shape and structure of brain. So, different segmentation approaches such as manual, semi-automatic, and fully automatic are developed in the traditional works. Among them, the fully automatic segmentation techniques are increasingly used by the medical experts for an efficient disease diagnosis. But, it has the limitations of over segmentation, increased complexity, and time consumption. Objective: In order to solve these problems, this paper aims to develop an efficient segmentation and classification system by incorporating a novel image processing techniques. Methods: Here, the Distribution based Adaptive Median Filtering (DMAF) technique is employed for preprocessing the image. Then, skull removal is performed to extract the tumor portion from the filtered image. Further, the Neighborhood Differential Edge Detection (NDED) technique is implemented to cluster the tumor affected pixels, and it is segmented by the use of Intensity Variation Pattern Analysis (IVPA) technique. Finally, the normal and abnormal images are classified by using the Weighted Machine Learning (WML) technique. Results: During experiments, the results of the existing and proposed segmentation and classification techniques are evaluated based on different performance measures. To prove the superiority of the proposed technique, it is compared with the existing techniques. Conclusion: From the analysis, it is observed that the proposed IVPA-WML techniques provide the better results compared than the existing techniques.

scholarly journals Pattern analysis of lower limb magnetic resonance images in Korean patients with distal myopathy

2019 ◽  
Vol 21 (2) ◽  
pp. 79
Author(s):  
Hyung Jun Park ◽  
Ha Young Shin ◽  
Seung Min Kim ◽  
Kee Duk Park ◽  
Young-Chul Choi
Keyword(s):  
Magnetic Resonance ◽  
Lower Limb ◽  
Pattern Analysis ◽  
Magnetic Resonance Images ◽  
Distal Myopathy ◽  
Korean Patients
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