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Computer vision techniques for Upper Aero-Digestive Tract tumor grading classification - Addressing pathological challenges
تکنیک های بینایی ماشین برای طبقه بندی تومورهای دستگاه هضم دستگاه گوارش فوقانی - پرداختن به چالش های آسیب شناختی-2021 Oral cancer is one of the common cancer types which scales higher in death rate every year. The con- nectivity of two different cavities like oral cavity and nasal cavity is known as Upper Aero-Digestive Tract (UADT). Both oral and nasal cavities consist of thirteen connecting sites from mouth to upper stomach. The traditional pathological analysis like manual microscopic review brings out major intra and inter- observer variability problem. A new automated system is proposed using computer vision techniques to focus and analyse major pathological problems like intra and interobserver variability problem and mis- classification of dysplasia type of tumours. The morphological behaviour of biopsy tissue samples are analysed digitally with different sites of UADT and different cancerous and non-cancerous stages. The proposed technique will play a major role in assisting the manual pathology procedure for analysing the morphology of dysplasia type of tumours and classification of tumour gradings. A method is proposed which integrates an alternate process to find the morphology of dysplasia type tumours using different image processing techniques. A state-of-the-art Force Reconstructed Particle Swarm Optimization Based SVM is proposed for UADT oral cancer classification for ten different oral cavity sites. The proposed clas- sification technique achieved 94 % accuracy.© 2021 Elsevier B.V. All rights reserved. Keywords: FR-PSO | SVM | Classification | Cancer | UADT | Machine Learning |
مقاله انگلیسی |
2 |
Oral Cancer
سرطان دهان-2020 Oral squamous cell carcinoma (OSCC), a distinct subtype of head and neck squamous
cell carcinoma, is typically human papillomavirus-negative and harbors TP53 loss-offunction
mutations.
OSCC is thought to begin with cancer initiating cells that are able to self-renew and
generate heterogeneous clones of neoplastic cells to comprise the tumor (ie, tumor
heterogeneity).
Carcinogenesis is a multistep process, which involves an accumulation of both genetic
and epigenetic alterations in oncogenes and/or tumor suppressor genes.
Metastasis is one of the major prognostic indicators in OSCC. Both epithelial-tomesenchymal
transition and interactions between OSCC cells and the tumor microenvironment
play significant roles in this complex process.
The integration of omics technologies, bioinformatics, and molecular biology uncovers
complex, clinically meaningful information that greatly improves our understanding of
the disease process. KEYWORDS : Oral cancer | Oral squamous cell carcinoma | Malignant transformation | Epigenetics | Omics technology | Big data | Personalized medicine | Precision medicine |
مقاله انگلیسی |
3 |
Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma
یادگیری ماشین برای پیش بینی متاستاز گره غشایی در کارسینوم سلول سنگفرشی اولیه دهان-2019 Objectives: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative
oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a
model based on tumor depth of invasion (DOI).
Materials and methods: Patients who underwent primary tumor extirpation and elective neck dissection from
2007 to 2013 for clinical T1-2N0 OCSCC were identified from the National Cancer Database (NCDB). Multiple
machine learning algorithms were developed to predict pathologic nodal metastasis using clinicopathologic data
from 782 patients. The algorithm was internally validated using test data from 654 patients in NCDB and was
then externally validated using data from 71 patients treated at a single academic institution. Performance was
measured using area under the receiver operating characteristic (ROC) curve (AUC). Machine learning and DOI
model performance were compared using Delong’s test for two correlated ROC curves.
Results: The best classification performance was achieved with a decision forest algorithm (AUC=0.840). When
applied to the single-institution data, the predictive performance of machine learning exceeded that of the DOI
model (AUC=0.657, p=0.007). Compared to the DOI model, machine learning reduced the number of neck
dissections recommended while simultaneously improving sensitivity and specificity.
Conclusion: Machine learning improves prediction of pathologic nodal metastasis in patients with clinical T1-
2N0 OCSCC compared to methods based on DOI. Improved predictive algorithms are needed to ensure that
patients with occult nodal disease are adequately treated while avoiding the cost and morbidity of neck dissection
in patients without pathologic nodal disease. Keywords: Oral cancer | Squamous cell carcinoma | Machine learning | Artificial intelligence |
مقاله انگلیسی |