Generalization of tumor identification algorithms

2019 ◽  
Author(s):  
Muhammad Khalid Khan Niazi ◽  
Thomas E. Tavolara ◽  
Caglar Senaras ◽  
Gary Tozbikian ◽  
Douglas J. Hartman ◽  
...  
Keyword(s):  
Tumor Identification

scholarly journals Translation of c-Met Targeted Image-Guided Surgery Solutions in Oral Cavity Cancer—Initial Proof of Concept Data

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2674
Author(s):  
Tessa Buckle ◽  
Maarten van Alphen ◽  
Matthias N. van Oosterom ◽  
Florian van Beurden ◽  
Nina Heimburger ◽  
...  
Keyword(s):  
Oral Cavity ◽  
Oral Cavity Cancer ◽  
Ex Vivo ◽  
Intermediate Step ◽  
Guided Surgery ◽  
Fluorescence Guided Surgery ◽  
Tumor Identification ◽  
Imaging Results ◽  
Pathological Evaluation ◽  
Fluorescence Camera

Intraoperative tumor identification (extension/margins/metastases) via receptor-specific targeting is one of the ultimate promises of fluorescence-guided surgery. The translation of fluorescent tracers that enable tumor visualization forms a critical component in the realization of this approach. Ex vivo assessment of surgical specimens after topical tracer application could help provide an intermediate step between preclinical evaluation and first-in-human trials. Here, the suitability of the c-Met receptor as a potential surgical target in oral cavity cancer was explored via topical ex vivo application of the fluorescent tracer EMI-137. Freshly excised tumor specimens obtained from ten patients with squamous cell carcinoma of the tongue were incubated with EMI-137 and imaged with a clinical-grade Cy5 prototype fluorescence camera. In-house developed image processing software allowed video-rate assessment of the tumor-to-background ratio (TBR). Fluorescence imaging results were related to standard pathological evaluation and c-MET immunohistochemistry. After incubation with EMI-137, 9/10 tumors were fluorescently illuminated. Immunohistochemistry revealed c-Met expression in all ten specimens. Non-visualization could be linked to a more deeply situated lesion. Tumor assessment was improved via video representation of the TBR (median TBR: 2.5 (range 1.8–3.1)). Ex vivo evaluation of tumor specimens suggests that c-Met is a possible candidate for fluorescence-guided surgery in oral cavity cancer.

Serum SELDI-TOF MS analysis model applied to benign and malignant ovarian tumor identification

2016 ◽  
Vol 8 (1) ◽  
pp. 183-188 ◽  
Author(s):  
Yankun Li ◽  
Xiangchao Zeng
Keyword(s):  
Ovarian Tumor ◽  
Analysis Model ◽  
Pattern Recognition Technique ◽  
Serum Peptide ◽  
Tumor Identification ◽  
Benign Ovarian Tumor ◽  
Malignant Ovarian Tumor ◽  
Peptide Profiles ◽  
Recognition Technique ◽  
Tof Ms

SELDI-TOF MS serum peptide profiles of malignant and benign ovarian tumor samples were studied using a pattern recognition technique.

scholarly journals SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4487 ◽  
Author(s):  
Himar Fabelo ◽  
Samuel Ortega ◽  
Elizabeth Casselden ◽  
Jane Loh ◽  
Harry Bulstrode ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Human Brain ◽  
Spectroscopic Data ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Human Brain Tissue ◽  
Extensive Analysis ◽  
Tumor Identification ◽  
Infrared Spectroscopic ◽  
Spectroscopic Signatures

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200–3500 cm−1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.

scholarly journals Brain Tumor Detection from MR Images using Image Process Techniques and Tools in Matlab Software

2021 ◽  
Vol 1 (4) ◽  
pp. 1-4
Author(s):  
Faisal Rehman ◽  
◽  
Syed Sheeraz Ali ◽  
Hamadullah Panhwar ◽  
Dr. Akhtar Hussain Phul ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Image Quality ◽  
Research Work ◽  
Cost Effective ◽  
Image Process ◽  
Medical Sciences ◽  
Mr Images ◽  
Research Areas ◽  
Tumor Identification ◽  
The Brain

In the medical era the Brain tumor is one of the most important research areas in the field of medical sciences. Researcher are trying to find the reliable and cost effective medical equipment’s for the cancer and its type for the diagnosed, especially tumor has deferent kinds but the major two type are discussed in this research paper. Which are the benign and Pre-Malignant, this research work is proposed for these factors such as the accuracy of the MRI image for the tumor identification and actual placing were taken into consideration. In this study, an algorithm is proposed to detect the brain tumor from magnetic resonance image (MRI) data simple. As enhance the image quality for the easiness the tumor treatments and diagnosed for the patients. The proposed algorithm enhances the MR image quality and detects the Brain tumor which helps the Physician to diagnose the tumor easily. As well this algorithm automatically calculates the area of tumor, size and location of the tumor where it is present for diagnostic the Patient.

scholarly journals Voxel-Based Analysis of Dual-Time-Point 18F-FDG PET Images for Brain Tumor Identification and Delineation

2011 ◽  
Vol 52 (6) ◽  
pp. 865-872 ◽  
Author(s):  
E. Prieto ◽  
J. M. Marti-Climent ◽  
I. Dominguez-Prado ◽  
P. Garrastachu ◽  
R. Diez-Valle ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Fdg Pet ◽  
Tumor Identification ◽  
18F Fdg ◽  
Dual Time Point ◽  
Time Point

Phosphorous 32 Eye Tumor Identification Test

JAMA ◽  
1976 ◽  
Vol 236 (14) ◽  
pp. 1617 ◽  
Author(s):  
James H. Larose
Keyword(s):  
Tumor Identification ◽  
Identification Test
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