scholarly journals Patient-Specific Registration of Pre-operative and Post-recurrence Brain Tumor MRI Scans

2019 ◽  
pp. 105-114
Author(s):  
Xu Han ◽  
Spyridon Bakas ◽  
Roland Kwitt ◽  
Stephen Aylward ◽  
Hamed Akbari ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Patient Specific ◽  
Mri Scans ◽  
Tumor Mri

scholarly journals Brain Tumor Resection in Elderly Patients: Potential Factors of Postoperative Worsening in a Predictive Outcome Model

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2320
Author(s):  
Paolo Ferroli ◽  
Ignazio Gaspare Vetrano ◽  
Silvia Schiavolin ◽  
Francesco Acerbi ◽  
Costanza Maria Zattra ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Postoperative Complications ◽  
Elderly Patients ◽  
Performance Status ◽  
Tumor Resection ◽  
Patient Specific ◽  
Invasive Treatment ◽  
Tertiary Referral Center ◽  
Surgical Complexity

The decision of whether to operate on elderly patients with brain tumors is complex, and influenced by pathology-related and patient-specific factors. This retrospective cohort study, based on a prospectively collected surgical database, aims at identifying possible factors predicting clinical worsening after elective neuro-oncological surgery in elderly patients. Therefore, all patients ≥65 years old who underwent BT resection at a tertiary referral center between 01/2018 and 12/2019 were included. Age, smoking, previous radiotherapy, hypertension, preoperative functional status, complications occurrence, surgical complexity and the presence of comorbidities were prospectively collected and analyzed at discharge and the 3-month follow-up. The series included 143 patients (mean 71 years, range 65–86). Sixty-five patients (46%) had at least one neurosurgical complication, whereas 48/65 (74%) complications did not require invasive treatment. Forty-two patients (29.4%) worsened at discharge; these patients had a greater number of complications compared to patients with unchanged/improved performance status. A persistent worsening at three months of follow-up was noted in 20.3% of patients; again, this subgroup presented more complications than patients who remained equal or improved. Therefore, postoperative complications and surgical complexity seem to influence significantly the early outcome in elderly patients undergoing brain tumor surgery. In contrast, postoperative complications alone are the only factor with an impact on the 3-month follow-up.

scholarly journals Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Angela M. Jarrett ◽  
David A. Hormuth ◽  
Vikram Adhikarla ◽  
Prativa Sahoo ◽  
Daniel Abler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Targeted Therapy ◽  
Treatment Response ◽  
Growth Factor Receptor ◽  
Patient Specific ◽  
Mathematical Framework ◽  
Breast Cancer Patients ◽  
Data Types ◽  
Her2 Breast Cancer ◽  
Mri Scans

AbstractWhile targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.

Predicting critical closing pressure in children with obstructive sleep apnea using fluid structure interaction simulations

2021 ◽  
Author(s):  
Goutham Mylavarapu ◽  
Ephraim Gutmark ◽  
Sally Shott ◽  
Robert J. Fleck ◽  
Mohamed Mahmoud ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Upper Airway ◽  
Dynamic Mri ◽  
Patient Specific ◽  
Apnea Hypopnea Index ◽  
Structure Interaction ◽  
Obstructive Sleep ◽  
Mri Scans ◽  
Closing Pressure

Surgical treatment of obstructive sleep apnea (OSA) in children requires knowledge of upper airway dynamics, including the closing pressure (Pcrit), a measure of airway collapsibility. We applied a Flow-Structure Interaction (FSI) computational model to estimate Pcrit in patient-specific upper airway models obtained from magnetic resonance imaging (MRI) scans. We sought to examine the agreement between measured and estimated Pcrit from FSI models in children with Down syndrome. We hypothesized that the estimated Pcrit would accurately reflect measured Pcrit during sleep and therefore reflect the severity of OSA as measured by the obstructive apnea hypopnea index (AHI). All participants (n=41) underwent polysomnography and sedated sleep MRI scans. We used Bland Altman Plots to examine the agreement between measured and estimated Pcrit. We determined associations between estimated Pcrit and OSA severity, as measured by AHI, using regression models. The agreement between passive and estimated Pcrit showed a fixed bias of -1.31 (CI=-2.78, 0.15) and a non-significant proportional bias. A weaker agreement with active Pcrit was observed. A model including AHI, gender, an interaction term for AHI and gender and neck circumference explained the largest variation (R2 = 0.61) in the relationship between AHI and estimated Pcrit. (P <0.0001). Overlap between the areas of the airway with lowest stiffness, and areas of collapse on dynamic MRI, was 77.4%±30% for the nasopharyngeal region and 78.6%±33% for the retroglossal region. The agreement between measured and estimated Pcrit and the significant association with AHI supports the validity of Pcrit estimates from the FSI model.

scholarly journals Brain Tumor Segmentation using K-Means Clustering and Detection using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 1452-1455
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Scans ◽  
Two Phases ◽  
Processing Techniques

This paper presents brain tumor detection and segmentation using image processing techniques. Convolutional neural networks can be applied for medical research in brain tumor analysis. The tumor in the MRI scans is segmented using the K-means clustering algorithm which is applied of every scan and the feed it to the convolutional neural network for training and testing. In our CNN we propose to use ReLU and Sigmoid activation functions to determine our end result. The training is done only using the CPU power and no GPU is used. The research is done in two phases, image processing and applying neural network.

scholarly journals Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI

2018 ◽  
Vol 20 ◽  
pp. 664-673 ◽  
Author(s):  
Stelios Angeli ◽  
Kyrre E. Emblem ◽  
Paulina Due-Tonnessen ◽  
Triantafyllos Stylianopoulos
Keyword(s):  
Brain Tumor ◽  
Tumor Growth ◽  
Patient Specific ◽  
Patient Specific Modeling ◽  
Specific Modeling

Advancements of MRI-Based Brain Tumor Segmentation from Traditional to Recent Trends- A Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Padmapriya Thiyagarajan ◽  
Sriramakrishnan Padmanaban ◽  
Kalaiselvi Thiruvenkadam ◽  
Somasundaram Karuppanagounder
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Human Head ◽  
Medical Community ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Learning Methods ◽  
Brain Tumor Segmentation ◽  
Mri Scans

Background: Among the brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is a very challenging task due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF). Introduction: The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques, to deep learning through machine learning on MRI of human head scans. Method: State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed. Results: The prime motivation of the paper is to instigate the young researchers towards the development of efficient brain tumor segmentation techniques using conventional and recent technologies. Conclusion: The proposed analysis concluded that the conventional and machine learning methods were mostly applied for brain tumor detection, whereas deep learning methods were good at tumor substructures segmentation.

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