Imaging Pearls ❯ Neuroradiology ❯ Tumors
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- Skull Flap Implanted in Abdominal Wall
- On occasion, a portion of the skull may be emergently removed to relieve increased intracranial pressure (decompressive craniectomy)
- Mostly performed for acute subdural hemorrhage
- The remaining dura mater may be sewn together and the patient may wear a protective helmet
- Although other materials can be used to replace the bone flap, sometimes the patient's own skull flap is preserved for re-use - Skull Flap Implanted in Abdominal Wall
- The bone flap can be frozen, stored in sterile solutions or sewn into the subcutaneous tissue of the patient's abdomen where its viability is maintained by the body
- Advantages of storing the bone flap in the abdominal wall include sterility and continued nourishment that allows for the chondroblasts and osteoblasts to mature
- The flap is returned to the skull typically in 6-20 weeks after removal
- “Brain tumors are the most prominent neurologically malignant cancers with the highest injury and death rates worldwide. Glioma classification is crucial for the prognosis, assessment of prognostication and the planning of clinical guidelines before surgery. Herein, we introduce a novel stationary wavelet-based radiomics approach to classify the grade of glioma more accurately and in a non-invasive manner. The training dataset of Brain Tumor Segmentation (BraTS) Challenge 2018 is used for performance evaluation and calculation is done based on the radiomics features for three different regions of interest. The classifier, Random Forest, is trained on these features and predicted the grade of glioma. At last, the performance is val- idated by using five-fold cross-validation scheme. The state-of-the-art performance is achieved considering metric ⟨Acc, Sens, Spec, Score, MCC , AUC ⟩ ≡ ⟨97.54%, 97.62%, 97.33%, 98.3%, 94.12%, 97.48%⟩ with machine learning predictive model Random Forest (RF) for brain tumor patients’ classification. Considering the importance of glioma classification for the assessment of prognosis, our approach could be useful in the planning of clinical guidelines prior to surgery.”
CGHF: A Computational Decision Support System for Glioma Classification Using Hybrid Radiomics- and Stationary Wavelet-Based Features
Kumar R et al.
IEEE Access Digital Object Identifier 10.1109/ACCESS.2020.2989193 - In the proposed work, CGHF, a computationally efficient decision support system based on machine predictive model Random Forest is proposed for gliomas grading. The model is used to predict the instances in HGG or LGG category. The proposed system, CGHF, utilized the filters for radiomics feature extraction and several effective feature selection tech- niques over publicly available BraTS 2018 dataset and train the RF model for classification task. The LS and RFA are best feature selection methods for RF using R-, S- and RS-Extraction methods and ANOVA is second best, stable and suitable method for the proposed system, CGHF. As a future perspective, the multi-class classification of graded gliomas can be considered for prediction of brain tumors.
CGHF: A Computational Decision Support System for Glioma Classification Using Hybrid Radiomics- and Stationary Wavelet-Based Features
Kumar R et al.
IEEE Access Digital Object Identifier 10.1109/ACCESS.2020.2989193
- Skull Based Tumors: Differential Dx
Tumors (cont)- Metastases to skull base
- Chondrosarcoma
- Rhabdomyosarcoma
- Extension of nasopharyngeal tumor - Skull Based Tumors: Differential Dx
Infection
- Fungal disease
- Extension of paranasal sinus infection
- Radiation necrosis
Tumors
- Chordoma
- Meningioma
- Pituitary tumors