Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine

Wu, Bangxian; Khong, Pek‐Lan; Chan, Tao

doi:10.1007/s11548-011-0669-y

Cited by 30 publications

(17 citation statements)

References 26 publications

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“…Amongst these, nine studies focussed on the assessment of oral squamous cell carcinoma (OSCC) and seven studies focussed on the evaluation of, or differentiation between, oral potentially malignant disorders (OPMD) and OSCC. The remaining studies focussed on assessment of nasopharyngeal SCC ( n = 3), 29 – 31 laryngeal SCC ( n = 2), 32 , 33 oropharyngeal SCC ( n = 3), 34 – 36 parotid gland neoplasms ( n = 2) 37 , 38 and differentiation between sinonasal SCC from inverted papilloma ( n = 1). 39 In four studies, 40 – 42 tissue sections of HNC from various different sites (tongue, floor of mouth, soft palate, mandible, gingivae, alveolar ridge, supraglottis, maxillary sinus, nose, thyroid and parotid gland) were evaluated.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

et al. 2021

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Background This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis. Methods Electronic database searches using MEDLINE via OVID, EMBASE and Google Scholar were conducted to retrieve articles using AI/ML for diagnostic evaluation of HNC (2009–2020). No restrictions were placed on the AI/ML method or imaging modality used. Results In total, 32 articles were identified. HNC sites included oral cavity (n = 16), nasopharynx (n = 3), oropharynx (n = 3), larynx (n = 2), salivary glands (n = 2), sinonasal (n = 1) and in five studies multiple sites were studied. Imaging modalities included histological (n = 9), radiological (n = 8), hyperspectral (n = 6), endoscopic/clinical (n = 5), infrared thermal (n = 1) and optical (n = 1). Clinicopathologic/genomic data were used in two studies. Traditional ML methods were employed in 22 studies (69%), deep learning (DL) in eight studies (25%) and a combination of these methods in two studies (6%). Conclusions There is an increasing volume of studies exploring the role of AI/ML to aid HNC detection using a range of imaging modalities. These methods can achieve high degrees of accuracy that can exceed the abilities of human judgement in making data predictions. Large-scale multi-centric prospective studies are required to aid deployment into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

et al. 2021

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“…Meanwhile, support vector machine (SVM) and neural network have been widely utilized in learning-based methods. Wu et al [13] developed an automatic algorithm for detecting NPC lesions on PET-CT images with SVM. Mohammed et al [14] implemented automatic segmentation and identification of NPC by using artificial neural network in microscopy images to identify preliminary NPC cases.…”

Section: Introductionmentioning

confidence: 99%

Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images

Zhao

Jiang

et al. 2019

J Digit Imaging

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Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most efficient means to treat this malignant tumor. Positron emission tomography-computed tomography (PET-CT) is a suitable imaging technique to assess this disease. However, the large amount of data produced by numerous patients causes traditional manual delineation of tumor contour, a basic step for radiotherapy, to become time-consuming and labor-intensive. Thus, the demand for automatic and credible segmentation methods to alleviate the workload of radiologists is increasing. This paper presents a method that uses fully convolutional networks with auxiliary paths to achieve automatic segmentation of NPC on PET-CT images. This work is the first to segment NPC using dual-modality PET-CT images. This technique is identical to what is used in clinical practice and offers considerable convenience for subsequent radiotherapy. The deep supervision introduced by auxiliary paths can explicitly guide the training of lower layers, thus enabling these layers to learn more representative features and improve the discriminative capability of the model. Results of threefold cross-validation with a mean dice score of 87.47% demonstrate the efficiency and robustness of the proposed method. The method remarkably outperforms state-of-the-art methods in NPC segmentation. We also validated by experiments that the registration process among different subjects and the auxiliary paths strategy are considerably useful techniques for learning discriminative features and improving segmentation performance.

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“…Alternative studies exist which tackle the challenge of automatically discriminating between normal and pathological tissues in PET. Unfortunately, a full comparison of such algorithms is not possible as the relative studies often concern different body district and specific types of abnormality, e.g., lung tumours [40][41][42], oesophageal tumours [43], and nasopharyngeal tumours [44]. Studies on discrimination of pathological structures in whole-body PET have been conducted as well, and some preliminary results have shown that different anatomical areas pose different challenges [45,46].…”

Section: Introductionmentioning

confidence: 99%

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans

Comelli

Stefano

Russo

et al. 2018

Computers in Biology and Medicine

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Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automatically stops when a "cancer-free" slice is identified. User intervention is limited at drawing an initial rough contour around the cancer region. By design, the algorithm performs the segmentation minimizing any dependence from the initial input, so that the final result is extremely repeatable. To assess the performances under different conditions, our system is evaluated on a dataset comprising five synthetic experiments and fifty oncological lesions located in different anatomical regions (i.e. lung, head and neck, and brain) using PET studies with 18F-fluoro-2-deoxy-d-glucose and 11C-labeled Methionine radio-tracers. Results on synthetic lesions demonstrate enhanced performances when compared against the most common PET segmentation methods. In clinical cases, the proposed system produces accurate segmentations (average dice similarity coefficient: 85.36 ± 2.94%, 85.98 ± 3.40%, 88.02 ± 2.75% in the lung, head and neck, and brain district, respectively) with high agreement with the gold standard (determination coefficient R = 0.98). We believe that the proposed system could be efficiently used in the everyday clinical routine as a medical decision tool, and to provide the clinicians with additional information, derived from PET, which can be of use in radiation therapy, treatment, and planning.

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Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine

Cited by 30 publications

References 26 publications

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans

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