scholarly journals EASE: Clinical Implementation of Automated Tumor Segmentation and Volume Quantification for Adult Low-Grade Glioma

2021 ◽  
Vol 8 ◽  
Author(s):  
Karin A. van Garderen ◽  
Sebastian R. van der Voort ◽  
Adriaan Versteeg ◽  
Marcel Koek ◽  
Andrea Gutierrez ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Automatic Segmentation ◽  
Low Grade Glioma ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Clinical Implementation ◽  
Volumetric Assessment ◽  
Ensure Patient Safety ◽  
Volume Measurements ◽  
The Individual

The growth rate of non-enhancing low-grade glioma has prognostic value for both malignant progression and survival, but quantification of growth is difficult due to the irregular shape of the tumor. Volumetric assessment could provide a reliable quantification of tumor growth, but is only feasible if fully automated. Recent advances in automated tumor segmentation have made such a volume quantification possible, and this work describes the clinical implementation of automated volume quantification in an application named EASE: Erasmus Automated SEgmentation. The visual quality control of segmentations by the radiologist is an important step in this process, as errors in the segmentation are still possible. Additionally, to ensure patient safety and quality of care, protocols were established for the usage of volume measurements in clinical diagnosis and for future updates to the algorithm. Upon the introduction of EASE into clinical practice, we evaluated the individual segmentation success rate and impact on diagnosis. In its first 3 months of usage, it was applied to a total of 55 patients, and in 36 of those the radiologist was able to make a volume-based diagnosis using three successful consecutive measurements from EASE. In all cases the volume-based diagnosis was in line with the conventional visual diagnosis. This first cautious introduction of EASE in our clinic is a valuable step in the translation of automatic segmentation methods to clinical practice.

scholarly journals Automatic segmentation of low-grade glioma in MRI image based on UNet++ model

2020 ◽  
Vol 1693 ◽  
pp. 012135
Author(s):  
Dan Xu ◽  
Xidong Zhou ◽  
Xuefen Niu ◽  
Junwei Wang
Keyword(s):  
Automatic Segmentation ◽  
Low Grade Glioma ◽  
Low Grade ◽  
Mri Image

Automated Early Detection of Astrocytomas Tumor Based on Optimized Adaptive Cluster with Super Pixel Model

2021 ◽  
Vol 30 (06n08) ◽  
Author(s):  
V. K. Deepak ◽  
R. Sarath
Keyword(s):  
Brain Tumor ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Grey Wolf ◽  
Dice Coefficient ◽  
Grey Wolf Optimization ◽  
Brain Tumor Segmentation ◽  
Better Than

In the medical image-processing field brain tumor segmentation is aquintessential task. Thereby early diagnosis gives us a chance of increasing survival rate. It will be way much complex and time consuming when comes to processing large amount of MRI images manually, so for that we need an automatic way of brain tumor image segmentation process. This paper aims to gives a comparative study of brain tumor segmentation, which are MRI-based. So recent methods of automatic segmentation along with advanced techniques gives us an improved result and can solve issue better than any other methods. Therefore, this paper brings comparative analysis of three models such as Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP) and Grey Wolf Optimization based ACSP (GWO_ACSP) and these are tested on CANCER IMAGE ACHRCHIEVE which is a preparation information base containing High Grade and Low-Grade astrocytoma tumors. Here boundaries including Accuracy, Dice coefficient, Jaccard score and MCC are assessed and along these lines produce the outcomes. From this examination the test consequences of Grey Wolf Optimization based ACSP (GWO_ACSP) gives better answer for mind tumor division issue.

scholarly journals Delivery of a nutritional prescription by enteral tube feeding in children with chronic kidney disease stages 2–5 and on dialysis—clinical practice recommendations from the Pediatric Renal Nutrition Taskforce

2020 ◽  
Vol 36 (1) ◽  
pp. 187-204
Author(s):  
Lesley Rees ◽  
◽  
Vanessa Shaw ◽  
Leila Qizalbash ◽  
Caroline Anderson ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Clinical Practice ◽  
Kidney Disease ◽  
Growth Parameters ◽  
Tube Feeding ◽  
Low Grade ◽  
Enteral Tube Feeding ◽  
Practice Recommendations ◽  
The Individual ◽  
Disease Stages

AbstractThe nutritional prescription (whether in the form of food or liquid formulas) may be taken orally when a child has the capacity for spontaneous intake by mouth, but may need to be administered partially or completely by nasogastric tube or gastrostomy device (“enteral tube feeding”). The relative use of each of these methods varies both within and between countries. The Pediatric Renal Nutrition Taskforce (PRNT), an international team of pediatric renal dietitians and pediatric nephrologists, has developed clinical practice recommendations (CPRs) based on evidence where available, or on the expert opinion of the Taskforce members, using a Delphi process to seek consensus from the wider community of experts in the field. We present CPRs for delivery of the nutritional prescription via enteral tube feeding to children with chronic kidney disease stages 2–5 and on dialysis. We address the types of enteral feeding tubes, when they should be used, placement techniques, recommendations and contraindications for their use, and evidence for their effects on growth parameters. Statements with a low grade of evidence, or based on opinion, must be considered and adapted for the individual patient by the treating physician and dietitian according to their clinical judgement. Research recommendations have been suggested. The CPRs will be regularly audited and updated by the PRNT.

scholarly journals MRI Modality-based Brain Tumor Segmentation Using Deep Neural Networks

2021 ◽  
Author(s):  
Pankaj Eknath Kasar ◽  
Shivajirao M. Jadhav ◽  
Vineet Kansal
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Clinical Practice ◽  
Brain Tumors ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation

Abstract The tumor detection is major challenging task in brain tumor quantitative evaluation. In recent years, owing to non-invasive and strong soft tissue comparison, Magnetic Resonance Imaging (MRI) has gained great interest. MRI is a commonly used image modality technique to locate brain tumors. An immense amount of data is produced by the MRI. Heterogeneity, isointense and hypointense tumor properties restrict manual segmentation in a fair period of time, thus restricting the use of reliable quantitative measures in clinical practice. In the clinical practice manual segmentation task is quite time consuming and their performance is highly depended on the operator’s experience. Accurate and automated tumor segmentation techniques are also needed; however, the severe spatial and structural heterogeneity of brain tumors makes automatic segmentation a difficult job. This paper proposes fully automatic segmentation of brain tumors using encoder-decoder based convolutional neural networks. The paper focuses on well-known semantic segmentation deep neural networks i.e., UNET and SEGNET for segmenting tumors from Brain MRI images. The networks are trained and tested using freely accessible standard dataset, with Dice Similarity Coefficient (DSC) as metric for whole predicted image i.e., including tumor and background. UNET’s average DSC on test dataset is 0.76 whereas for SEGNET we got average DSC 0.67. The evaluation of results proves that UNET is having better performance than SEGNET.

scholarly journals One year of procarbazine lomustine and vincristine is poorly tolerated in low grade glioma: a real world experience in a national neuro-oncology centre

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rachel J. Keogh ◽  
Razia Aslam ◽  
Maeve A. Hennessy ◽  
Zac Coyne ◽  
Bryan T. Hennessy ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Local Therapy ◽  
Dose Intensity ◽  
Low Grade Glioma ◽  
Randomised Control Trial ◽  
Low Grade ◽  
Oncology Centre ◽  
Dose Modifications ◽  
Experienced Grade ◽  
Grade Iii

Abstract Background Following optimal local therapy, adjuvant Procarbazine, Lomustine and Vincristine (PCV) improves overall survival (OS) in low-grade glioma (LGG). However, 1 year of PCV is associated with significant toxicities. In the pivotal RTOG 9802 randomised control trial, approximately half of the patients discontinued treatment after 6 months. As patients on clinical trials may be fitter, we aimed to further explore the tolerability of PCV chemotherapy in routine clinical practice. Methods We conducted a retrospective study between 2014 and 2018 at a National Neuro-Oncology centre. Patients who had received PCV during this time period were included. The primary objective was to assess tolerability of treatment. Secondary objectives included evaluation of treatment delays, dose modifications and toxicities. Results Overall, 41 patients were included, 24 (58%) were male and 21 (51%) aged ≥40 years. 38 (93%) underwent surgical resection and all patients received adjuvant radiotherapy prior to chemotherapy. The median number of cycles completed was 3,2,4 for procarbazine, lomustine and vincristine respectively. Only 4 (10%) completed all 6 cycles of PCV without dose modifications. There was a universal decline in dose intensity as cycles of chemotherapy progressed. Dose intensity for cycle 1 versus cycle 6 respectively: procarbazine (98% versus 46%), lomustine (94% versus 48%) and vincristine (93% versus 50%). Haematological toxicities were common. Six (14%) patients experienced Grade III-IV thrombocytopaenia and 13 (31%) experienced Grade III-IV neutropaenia. Conclusion Toxicities are frequently observed with the PCV regimen in clinical practice. It might be preferable to adjust doses from the start of chemotherapy to improve tolerability or consider alternative chemotherapy, particularly in older patients with LGG.

scholarly journals Deep neural networks allow expert-level brain meningioma detection, segmentation and improvement of current clinical practice

2021 ◽  
Author(s):  
Alessandro Boaro ◽  
Jakub Kaczmarzyk ◽  
Vasileios K Kavouridis ◽  
Maya Harary ◽  
Marco Mammi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Practice ◽  
Prospective Trial ◽  
Volume Estimation ◽  
Response To Treatment ◽  
Estimation Accuracy ◽  
Tumor Segmentation ◽  
Current Clinical Practice ◽  
Volumetric Assessment ◽  
3D Cnn

Background. Accurate brain meningioma detection, segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma detection and segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Methods. We developed a three-dimensional convolutional neural network (3D-CNN) to perform expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The algorithm tumor-labeling performance was assessed with standard metrics of tumor segmentation performance (i.e., Dice score). To evaluate clinical applicability, we compared volume estimation accuracy and segmentation time based on current practice versus the use of our automated algorithm. Findings. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6% - 91.6%). Compared to current workflows, the use of the algorithm reduced processing time by 99% and produced tumor volume calculations with an almost perfect correlation with the expert manual segmentations (r=0.98, p<0.001), significantly more accurate compared to volume estimation techniques used in practice. Conclusions. We demonstrate through a prospective trial conducted in a simulated setting that a deep learning approach to meningioma segmentation is feasible, highly accurate and can substantially improve current clinical practice.

scholarly journals Energy and protein requirements for children with CKD stages 2-5 and on dialysis–clinical practice recommendations from the Pediatric Renal Nutrition Taskforce

2019 ◽  
Vol 35 (3) ◽  
pp. 519-531 ◽  
Author(s):  
Vanessa Shaw ◽  
Nonnie Polderman ◽  
José Renken-Terhaerdt ◽  
Fabio Paglialonga ◽  
Michiel Oosterveld ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Clinical Judgment ◽  
Pediatric Nephrology ◽  
Low Grade ◽  
Poor Growth ◽  
Protein Requirements ◽  
Practice Recommendations ◽  
Additional Protein ◽  
The Individual ◽  
Research Recommendations

AbstractDietary management in pediatric chronic kidney disease (CKD) is an area fraught with uncertainties and wide variations in practice. Even in tertiary pediatric nephrology centers, expert dietetic input is often lacking. The Pediatric Renal Nutrition Taskforce (PRNT), an international team of pediatric renal dietitians and pediatric nephrologists, was established to develop clinical practice recommendations (CPRs) to address these challenges and to serve as a resource for nutritional care. We present CPRs for energy and protein requirements for children with CKD stages 2–5 and those on dialysis (CKD2–5D). We address energy requirements in the context of poor growth, obesity, and different levels of physical activity, together with the additional protein needs to compensate for dialysate losses. We describe how to achieve the dietary prescription for energy and protein using breastmilk, formulas, food, and dietary supplements, which can be incorporated into everyday practice. Statements with a low grade of evidence, or based on opinion, must be considered and adapted for the individual patient by the treating physician and dietitian according to their clinical judgment. Research recommendations have been suggested. The CPRs will be regularly audited and updated by the PRNT.

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