Case Study on Oropharyngeal Cancer Prediction and Diagnosis and Management Based upon MRI, CT Scan Imaging Techniques

Bhosle, Pallavi; Pathan, Hujeb; Tapadiya, Ganesh G.; Alam, Irshad

doi:10.1201/9781003272694-8

Cited by 3 publications

(3 citation statements)

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“…A patient must remain still while positioned within a large magnet to produce an MRI image. MRIs offer images of the same body parts as CT scans but with greater depth, providing a more detailed examination of specific structures [31,32].…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Deep Learning Medical Image Segmentation Methods: A Survey

مختار,

عبد الجليل,

خوريبه

2024

النشرة المعلوماتية في الحاسبات والمعلومات

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Medical image segmentation is essential for detecting and localizing tumors in medical image analysis. Image segmentation involves the identification of anatomical structures in images. Medical image segmentation starts with manual segmentation using Atlas methods, then auto-segmentation, facilitated by deep learning algorithms. Deep learning-based medical image segmentation retains a significant pledge in reducing treatment planning, radiation-related toxicities, and side effects. This study provides a complete overview of deep-learning medical image segmentation models. We review various deep-learning models and architectures applied to medical image segmentation, including fully convolutional networks, U-Net, and attentionbased models. This literature review discusses using different loss functions, data augmentation techniques, and transfer learning in deep learning-based medical image segmentation and several types of medical image modality. Evaluation analysis encloses benchmark datasets for human body organs such as the brain, lungs, chest, and liver. Finally, we summarize the challenges and future directions of deep learning for medical image segmentation.

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Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Deep Learning Medical Image Segmentation Methods: A Survey

مختار,

عبد الجليل,

خوريبه

2024

النشرة المعلوماتية في الحاسبات والمعلومات

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“…[11,12] The primary diagnostic methods for oral cancer involve clinical examination, biopsy, imaging studies (X-rays, CT scans, and MRI scans), and endoscopy. [13][14][15] Clinical examination entails visual inspection by a healthcare professional to identify visible abnormalities. [16] Biopsy is essential for confirming the presence of cancerous cells and determining cancer type and stage.…”

Section: Introductionmentioning

confidence: 99%

High Sensitivity Detection of Oral Leukoplakia Comprehensive Analysis of P90 Biomarker Expression in Human Saliva and Tissue Samples

Wan,

Zhu,

Chiang

et al. 2024

Preprint

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Oral cancer represents a significant global public health challenge, contributing substantially to cancer incidence and mortality. Despite established risk factors such as tobacco use and alcohol consumption, early detection remains crucial for effective treatment. This study introduces a novel approach using a transistor-based biosensor system for detecting the P90 (CIP2A) protein. We have tested the presence of CIP2A in human leukoplakia samples that can rise into oral squamous cell carcinoma. The method employs commercially available glucose test strips functionalized with P90 antibodies, providing high sensitivity and a low limit of detection, which is five orders lower than commercial ELISA kits. A specially designed printed circuit board (PCB) facilitates accurate measurements, and the device's performance is optimized through characteristic tests. Human sample testing validates the biosensor's effectiveness in distinguishing samples after cell lysis. This study contributes to advancing accurate and cost-effective diagnostic approaches for oral pre-cancer and cancer tissues.

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“…Early detection of oral cancer is crucial for successful treatment [ 11 , 12 ]. The primary diagnostic methods for oral cancer involve clinical examinations, biopsies, imaging studies (X-rays, CT scans, and MRI scans), and endoscopy [ 13 , 14 , 15 ]. Clinical examination entails visual inspection by a healthcare professional to identify visible abnormalities [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Sensitive Detection of Oral Leukoplakia: Analyzing P90 Biomarkers in Saliva and Tissue

Wan,

Zhu,

Chiang

et al. 2024

Biosensors

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Oral cancer represents a significant global public health challenge, contributing substantially to the incidence and mortality of cancer. Despite established risk factors such as tobacco use and alcohol consumption, early detection remains crucial for effective treatment. This study introduces a novel approach using a transistor-based biosensor system for detecting the P90 (CIP2A) protein. We tested the presence of CIP2A in human leukoplakia samples, which can undergo malignant conversion into aggressive oral squamous cell carcinoma. The method used commercially available glucose test strips functionalized with P90 antibodies, providing high sensitivity and a low limit of detection which was five orders lower than that of commercial ELISA kits. A specially designed printed circuit board (PCB) facilitated accurate measurements, and the device’s performance was optimized through characteristic tests. Human sample testing validated the biosensor’s effectiveness in distinguishing samples after cell lysis. This study contributes to advancing accurate and cost-effective diagnostic approaches for oral pre-cancer and cancer tissues.

show abstract

Case Study on Oropharyngeal Cancer Prediction and Diagnosis and Management Based upon MRI, CT Scan Imaging Techniques

Cited by 3 publications

References 0 publications

Deep Learning Medical Image Segmentation Methods: A Survey

Deep Learning Medical Image Segmentation Methods: A Survey

High Sensitivity Detection of Oral Leukoplakia Comprehensive Analysis of P90 Biomarker Expression in Human Saliva and Tissue Samples

Sensitive Detection of Oral Leukoplakia: Analyzing P90 Biomarkers in Saliva and Tissue

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