Computer Aided Diagnosis of Parotid Gland Lesions Using Ultrasonic Multi-Feature Tissue Characterization

Siebers, S.; Zenk, Johannes; Bozzato, Alessandro; Klintworth, Nils; Iro, Heinrich; Ermert, H.

doi:10.1016/j.ultrasmedbio.2010.06.009

Cited by 11 publications

(8 citation statements)

References 46 publications

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“…The sensitivity and specificity of RULES imaging were 93% and 89%, respectively, for detection of breast cancer. Similar results have been obtained for detection of prostate cancer 16 , 19 and parotid gland lesions 22 …”

Section: Discussionsupporting

confidence: 86%

Clinical Value of Radiofrequency Ultrasonic Local Estimators in Classifying Breast Lesions

Wang

Chen

Liu

et al. 2013

Journal of Ultrasound in Medicine

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reast cancer is the most frequent female malignancy, and its incidence is second overall, constituting 10.9% of cancers. 1 Although the incidence of breast cancer is higher in the United States and northern Europe than in Asia, 2-4 economic development and lifestyle changes in China have substantially increased its incidence, 5 and breast cancer is now the most common malignancy in China. The cure rate for early-stage breast cancer can be up to 97%, although the cure rate for later-stage breast cancer is only 40%. 6 In addition, because the clinical manifestations of early-stage breast cancer are not typical, therapeutic efforts are greatly aided by early detection, diagnosis, and treatment. 6 Jian Wang, MD, PhD, Chunsong Kang, MD, PhD, Xiaofang Liu, MD, Tingting Li, MD, Yu Wang, MD, Tinghua Feng, MD, Zhaojun Li, MD, PhD, Jiping Xue, MD, Kailing Shi, MD Received March 16, 2012, ORIGINAL RESEARCHObjectives-We sought to summarize the features of radiofrequency ultrasonic local estimator (RULES) images of benign and malignant masses and to explore the diagnostic value of RULES scores to identify breast lumps.Methods-A total of 81 women with a mean age ± SD of 41.33 ± 12.03 years (range, 19-68 years) with 82 lesions seen at our hospital were included in this study. Inclusion criteria were Breast Imaging Reporting and Data System grade 2 to 5 breast lesions, preoperative 2-dimensional (2D) ultrasound (US) examinations and RULES image acquisition, no treatment before the US examinations, surgical resection in our hospital, and histopathologic results. Each RULES characteristic was scored on the basis of expected values for malignant characteristics, and this RULES scoring system was assessed by a receiver operating characteristic curve.Results-Of the 82 lesions, 45 were benign, and 37 were malignant. Malignancy was associated with multiple colors, red as the main color, colors distributed in 3 or more locations, aggregated colors, and more than half of the area filled with colors. A RULES score of 7 had the highest sum of sensitivity (67.6%) and specificity (95.6%) and the highest accuracy (82.9%) for diagnosis of malignancy. When 2D US imaging a Breast Imaging Reporting and Data System category of 4 was combined with a RULES score of 4 to detect breast cancer, the sensitivity was 83.8%; the specificity was 93.3%; and the accuracy increased to 89.0%.Conclusions-The use of RULES images and characteristics is helpful in differentiating benign and malignant breast lesions. Diagnostic accuracy can be improved by combining 2D US imaging and RULES.

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Section: Discussionsupporting

confidence: 86%

Clinical Value of Radiofrequency Ultrasonic Local Estimators in Classifying Breast Lesions

Wang

Chen

Liu

et al. 2013

Journal of Ultrasound in Medicine

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“…Some differences in flow kinetics have been observed between pleomorphic adenomas and Warthin's tumours when contrast agents are used [11,20]. In addition, approaches using computer-aided tissue characterisation are regularly able to differentiate between benign and malignant tumours [13,21]. However, using these approaches in routine clinical work does not appear feasible yet.…”

Section: Introductionmentioning

confidence: 99%

Sonoelastography of parotid gland tumours: initial experience and identification of characteristic patterns

et al. 2012

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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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Computer Aided Diagnosis of Parotid Gland Lesions Using Ultrasonic Multi-Feature Tissue Characterization

Cited by 11 publications

References 46 publications

Clinical Value of Radiofrequency Ultrasonic Local Estimators in Classifying Breast Lesions

Clinical Value of Radiofrequency Ultrasonic Local Estimators in Classifying Breast Lesions

Sonoelastography of parotid gland tumours: initial experience and identification of characteristic patterns

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

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