CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma

Mukherjee, Pritam; Cintra, Murilo Bicudo; Chao, Huang; Zhou, Mu; Zhu, Shankuan; Colevas, A. Dimitrios; Fischbein, Nancy J.; Gevaert, Olivier

doi:10.1148/rycan.2020190039

Cited by 53 publications

(36 citation statements)

References 61 publications

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“…However, there is still a lack of large-scale multicenter validation in existing exploratory radiomics studies, and the vast majority of validation cohorts are still derived from retrospective data from a single independent unit. A data platform such as the cancer imaging archive (TCIA) has been created, but the quality of the data profile is mixed [67]. Although relatively reliable conclusions can be drawn from some of the mixed data by relying on big data techniques, however, differences in parameters during image acquisition or noise on the images can cause serious interference with the radiomics features extracted from them.…”

Section: Discussionmentioning

confidence: 99%

“…In HNSCC, radiological analysis was also used to design non-invasive biomarkers and to accurately distinguish well-differentiated from moderately differentiated and poorly differentiated HNSCC, with an AUC of 0.96 and an accuracy of 0.92. It has been reported that radiomics CT models have the potential to predict characteristics typically identified on pathologic assessment of HNSCC [67]. In a cohort of 96 papillary thyroid carcinoma (PTC) patients, a prospective study enrolled consecutive patients who underwent neck MR scans and subsequent thyroidectomy during the study interval.…”

Section: Pre-treatment Related Predictive Modelingmentioning

confidence: 99%

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Application of radiomics and machine learning in head and neck cancers

Peng¹,

Wang²,

Wang³

et al. 2021

Int. J. Biol. Sci.

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With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.

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Section: Discussionmentioning

confidence: 99%

Section: Pre-treatment Related Predictive Modelingmentioning

confidence: 99%

Application of radiomics and machine learning in head and neck cancers

Peng¹,

Wang²,

Wang³

et al. 2021

Int. J. Biol. Sci.

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“…Computed tomography (CT) can also be used to predict the histological classification before surgery by kernel principal component analysis (KPCA) and the random forest classifier ( Wu et al, 2019 ). Mukherjee et al (2020) performed principal component analysis and regularized regression to predict tumor grade, extracapsular spread, perineural invasion, lymphovascular invasion, and human papillomavirus infection status. The accuracy, sensitivity, and specificity of the model were 0.72, 0.83, and 0.48, respectively.…”

Section: Head and Neck Tumor Multiomics Analysismentioning

confidence: 99%

Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Wang

2021

Front. Genet.

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Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.

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“…The cancer characteristics investigated related to these features were tumor grade, perineural invasion, lymphovascular invasion, extracapsular spread, and HPV status (p16 expression) (43). For dimensionality reduction and classification of these features, principal component analysis, and regularized regression was applied, respectively (43). Results from this study indicated that the radiomic model produced by Mukherjee et al showed strong-to-moderate power in predictive prognosis for patients diagnosed with HNSCC, which was further validated in an external institutional testing cohort.…”

Section: Head and Neck Cancermentioning

confidence: 99%

“…The radiomic features are then integrated with other data sources for prognostic (7)(8)(9)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39), treatment response (40)(41)(42), histopathological (43)(44)(45)(46)(47)(48), or radiogenomic (11,(49)(50)(51) analyses using statistical or machine learning modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers

Gul¹,

Bonjoc²,

Gorlin³

et al. 2021

Front. Oncol.

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Radiomics is an emerging field in radiology that utilizes advanced statistical data characterizing algorithms to evaluate medical imaging and objectively quantify characteristics of a given disease. Due to morphologic heterogeneity and genetic variation intrinsic to neoplasms, radiomics have the potential to provide a unique insight into the underlying tumor and tumor microenvironment. Radiomics has been gaining popularity due to potential applications in disease quantification, predictive modeling, treatment planning, and response assessment – paving way for the advancement of personalized medicine. However, producing a reliable radiomic model requires careful evaluation and construction to be translated into clinical practices that have varying software and/or medical equipment. We aim to review the diagnostic utility of radiomics in otorhinolaryngology, including both cancers of the head and neck as well as the thyroid.

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CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma

Cited by 53 publications

References 61 publications

Application of radiomics and machine learning in head and neck cancers

Application of radiomics and machine learning in head and neck cancers

Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers

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