Incidence, prevalence, and mortality associated with head and neck cancer in India: Protocol for a systematic review

Poddar, Aayush; Aranha, Ritchlynn; Royam, Madhav Madurantakam; Gothandam, Kodiveri Muthukaliannan; Nachimuthu, Ramesh; Jayaraj, Rama

doi:10.4103/ijc.ijc_416_18

Cited by 19 publications

(9 citation statements)

References 14 publications

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“…The incidence is high due to the use of chewing tobacco. [ 11 13 32 ] Most of the patients (71%) were from T2–T3, unlike the two cohorts where there was almost equal distribution of patients from stages T1 to T4. No patient was included in stage I, 16% stage II TNM grouping were included in this study in contrast to 19% and 8% Stage 1 and 8% and 19% Stage II in HNC1 in HNC2, respectively.…”

Section: Discussionmentioning

confidence: 96%

“…In this study, we focussed on head-and-neck cancers (HNC) since it contributes about 25%–30% of all cancers in India as opposed to 3%–4% in the Western world[ 10 11 12 ] and with >20,000 new cases being reported yearly since 2018. [ 13 ] These cancers are different with respect to the patient characteristics, disease presentation, and etiological differences compared to the West. Some of the differences include that Indian patients often present themselves at a very advanced stage of disease, lower incidence of HPV related cancers affecting the oropharynx, unusual presentations of HNC due to oral/chewable tobacco consumption with increased risk due to poor oral hygiene among certain sections (individuals with low educational status, farmers or manual labourers) and higher incidence in younger patients (<40 years) compared to the West (55–60 years).…”

Section: Introductionmentioning

confidence: 99%

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Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers

Devadhas

Sunny

Sasidharan

et al. 2021

Journal of Medical Physics

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Context: Cancer Radiomics is an emerging field in medical imaging and refers to the process of converting routine radiological images that are typically qualitatively interpreted to quantifiable descriptions of the tumor phenotypes and when combined with statistical analytics can improve the accuracy of clinical outcome prediction models. However, to understand the radiomic features and their correlation to molecular changes in the tumor, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models which is often limited in low- and middle-income countries (LMIC). Aims: The aim is to build a framework for machine learning of radiomic features of planning computed tomography (CT) and positron emission tomography (PET) using open source radiomics and data analytics platforms to make it widely accessible to clinical groups. The framework is tested in a small cohort to predict local disease failure following radiation treatment for head-and-neck cancer (HNC). The predictors were also compared with the existing Aerts HNC radiomics signature. Settings and Design: Retrospective analysis of patients with locally advanced HNC between 2017 and 2018 and 31 patients with both pre- and post-radiation CT and evaluation PET were selected. Subjects and Methods: Tumor volumes were delineated on baseline PET using the semi-automatic adaptive-threshold algorithm and propagated to CT; PyRadiomics features (total of 110 under shape/intensity/texture classes) were extracted. Two feature-selection methods were tested for model stability. Models were built based on least absolute shrinkage and selection operator-logistic and Ridge regression of the top pretreatment radiomic features and compared to Aerts' HNC-signature. Average model performance across all internal validation test folds was summarized by the area under the receiver operator curve (ROC). Results: Both feature selection methods selected CT features MCC (GLCM), SumEntropy (GLCM) and Sphericity (Shape) that could predict the binary failure status in the cross-validated group and achieved an AUC >0.7. However, models using Aerts' signature features (Energy, Compactness, GLRLM-GrayLevelNonUniformity and GrayLevelNonUniformity-HLH wavelet) could not achieve a clear separation between outcomes (AUC = 0.51–0.54). Conclusions: Radiomics pipeline included open-source workflows which makes it adoptable in LMIC countries. Additional independent validation of data is crucial for the implementation of radiomic models for clinical risk stratification.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers

Devadhas

Sunny

Sasidharan

et al. 2021

Journal of Medical Physics

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“…More than 90% of HNSCC cases are associated with patients with OSCC [4]. South Asian countries including India [5], Bangladesh (Collaboration, 2019) and Pakistan [6] have higher OSCC incidence rates than other parts of the world on average (Collaboration, 2019). Oncogenic viruses such as the Human Papillomavirus (HPV) [7], excessive alcohol use, chewing tobacco usage and cigarette smoking are some of the known risk factors for HNSCC [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction and Survival Analysis of Head and Neck Cancer in Patients Using Epigenomics Data and Advanced Machine Learning Methods

Passi

2024

SBB

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Epigenomics is the field of biology dealing with modifications of the phenotype that do not cause any alteration in the sequence of cell DNA. Epigenomics adds something to the top of DNA to change the properties, which eventually prohibits certain DNA behavior from being performed. Such modifications occur in cancer cells and are the sole cause of cancer. The main objective of this research is to perform prediction and survival analysis of Head and Neck Squamous Cell Carcinoma (HNSCC) which is one of the biggest reasons of death and accounts for more than 650,000 cases and 330,000 deaths annually worldwide. Tobacco use, alcohol consumption, Human Papillomavirus (HPV) infection (for oropharyngeal cancer) and Epstein-Barr Virus (EBV) infection are the main risk factors associated with head and neck cancer (for nasopharyngeal cancer). Males, with a proportion ranging from 2:1 to 4:1, are slightly more affected than females. Four different types of data are used in this research to predict HNSCC in patients. The data includes methylation, histone, human genome and RNA-Sequences. The data is accessed through open-source technologies in R and Python programming languages. The data is processed to create features and with the help of statistical analysis and advanced machine learning techniques, the prediction of HNSCC is obtained from the fine-tuned model. The optimal model was determined to be ResNet50 utilizing the Sobel feature selection method for image data and Relief F-based feature selection for clinical features, achieving a test accuracy of 97.9%. The model's precision score was 0.929, its recall score was 0.930 and its F1 score was 0.930. Additionally, the ResNet101 model demonstrated the best performance using the Histogram of Gradients feature selection method for image data and mutual informationbased feature selection for clinical features, yielding a test accuracy of 96.1%. Its precision score, recall score and F1 score were identical to the aforementioned ResNet50 model. The research also utilized Kaplan-Meier survival analysis to investigate the survival rates of patients based on various factors, including age, gender, smoking status, tumor size and location of site. The results obtained from this analysis yielded the effectiveness of the method in providing valuable insights for risk assessment.

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“…More than 90% of HNSCC are associated with OSCC patients (Vigneswaran & Williams, 2014). The incidence rates (mainly OSCC (Collaboration, 2019)) are higher in South Asian countries such as India (Poddar et al, 2019), Bangladesh (Collaboration, 2019), and Pakistan (Akhtar et al, 2016) as compared to other parts of the world. There are several known risk factors of HNSCC such as chewing tobacco, smoking cigarettes, excessive alcohol consumption (Stenson, Brockstein & Ross, 2016) and oncogenic virus such as Human papillomavirus (HPV) (Marur et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)

Kumar

Patnaik

Dixit

2020

PeerJ

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Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.

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Incidence, prevalence, and mortality associated with head and neck cancer in India: Protocol for a systematic review

Cited by 19 publications

References 14 publications

Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers

Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers

Prediction and Survival Analysis of Head and Neck Cancer in Patients Using Epigenomics Data and Advanced Machine Learning Methods

Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)

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