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Cancers
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Advances in Diagnosis and Differentiation in Head and Neck and...
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Advances in Diagnosis and Differentiation in Head and Neck and Neuro Oncology

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Pathophysiology".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 9120

Special Issue Editor

Dr. Qi-Yong Hemis Ai

E-Mail Website
Guest Editor
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong
Interests: head and neck imaging; radiomics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tumours in the head and neck and nervous system comprise a wide range of histological types arising from different tissues. Due to the complex anatomical structure in the and head and neck region and the nervous system, diagnosis and differentiation of the nature of neoplasms in these areas could be challenging in clinical practice. In the last decade, the speedy development in bioinformatics and medical radiology brings more possible advanced techniques that potentially improve the diagnostic performance to many areas in medical research. These techniques may also be applied to head and neck and neuro oncology and potentially bring a new era to the diagnosis and differentiation of head and neck and neuro tumours.

This Special Issue aims to collect and publish studies that investigate the applications of advanced techniques in bioinformatics and medical radiology in diagnosis and differentiation in head and neck and neuro oncology.

Dr. Qi-Yong Hemis Ai
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • head and neck oncology
  • neuro oncology
  • cancer diagnosis
  • cancer differentiation
  • bioinformatics
  • artificial intelligence
  • radiomics analysis
  • medical radiology
  • functional imaging

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Published Papers (6 papers)

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Research

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13 pages, 1467 KiB  
Article
B1 Power Modification for Amide Proton Transfer Imaging in Parotid Glands: A Strategy for Image Quality Accommodation and Evaluation of Tumor Detection Feasibility
by Xiaoqian Wu, Tong Su, Yu Chen, Zhentan Xu, Xiaoqi Wang, Geli Hu, Yunting Wang, Lun M. Wong, Zhuhua Zhang, Tao Zhang and Zhengyu Jin
Cancers 2024, 16(5), 888; https://doi.org/10.3390/cancers16050888 - 22 Feb 2024
Viewed by 1044
Abstract
Background: In the application of APTw protocols for evaluating tumors and parotid glands, inhomogeneity and hyperintensity artifacts have remained an obstacle. This study aimed to improve APTw imaging quality and evaluate the feasibility of difference B1 values to detect parotid tumors. Methods: A [...] Read more.
Background: In the application of APTw protocols for evaluating tumors and parotid glands, inhomogeneity and hyperintensity artifacts have remained an obstacle. This study aimed to improve APTw imaging quality and evaluate the feasibility of difference B1 values to detect parotid tumors. Methods: A total of 31 patients received three APTw sequences to acquire 32 lesions and 30 parotid glands (one patient had lesions on both sides). Patients received T2WI and 3D turbo-spin-echo (TSE) APTw imaging on a 3.0 T scanner for three sequences (B1 = 2 μT, 1 μT, and 0.7 μT in APTw 1, 2, and 3, respectively). APTw image quality was evaluated using four-point Likert scales in terms of integrity and hyperintensity artifacts. Image quality was compared between the three sequences. An evaluable group and a trustable group were obtained for APTmean value comparison. Results: Tumors in both APT2 and APT3 had fewer hyperintensity artifacts than in APT1. With B1 values decreasing, tumors had less integrity in APTw imaging. APTmean values of tumors were higher than parotid glands in traditional APT1 sequence though not significant, while the APTmean subtraction value was significantly different. Conclusions: Applying a lower B1 value could remove hyperintensity but could also compromise its integrity. Combing different APTw sequences might increase the feasibility of tumor detection. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Differentiation in Head and Neck and Neuro Oncology)
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13 pages, 2167 KiB  
Article
Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading
by Zongyou Cai, Lun M. Wong, Ye Heng Wong, Hok Lam Lee, Kam Yau Li and Tiffany Y. So
Cancers 2023, 15(22), 5459; https://doi.org/10.3390/cancers15225459 - 17 Nov 2023
Viewed by 1275
Abstract
Background: Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics [...] Read more.
Background: Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). Methods: This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative multisequence MRI imaging. A dual-level augmentation strategy combining IA and FA was applied and evaluated in 100 repetitions in 3-, 5-, and 10-fold cross-validation. Results: The best area under the receiver operating characteristics curve of our method in 100 repetitions was ≥0.78 in all cross-validations. The corresponding cross-validation sensitivities (cross-validation specificity) were 0.72 (0.69), 0.76 (0.71), and 0.63 (0.82) in 3-, 5-, and 10-fold cross-validation, respectively. The proposed method achieved significantly better performance and distribution of results, outperforming single-level augmentation (IA or FA) or no augmentation in each cross-validation. Conclusions: The dual-level augmentation strategy using IA and FA significantly improves the performance of the radiomics model for meningioma grading on MRI, allowing better radiomics-based preoperative stratification and individualized treatment. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Differentiation in Head and Neck and Neuro Oncology)
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15 pages, 2559 KiB  
Article
Impact of Region-of-Interest Size on the Diagnostic Performance of Shear Wave Elastography in Differentiating Thyroid Nodules
by Kai-Lun Cheng, Pin-Hsien Lai, Chun-Lang Su, Jung Hwan Baek and Hsiang-Lin Lee
Cancers 2023, 15(21), 5214; https://doi.org/10.3390/cancers15215214 - 30 Oct 2023
Viewed by 1479
Abstract
This study investigated the impact of different region-of-interest (ROI) sizes (Max, 1 mm, and 2 mm) on shear wave elastography (SWE) in differentiating between malignant and benign thyroid nodules. The study cohort comprised 129 thyroid nodules (50 malignant, 79 benign) and 78 normal [...] Read more.
This study investigated the impact of different region-of-interest (ROI) sizes (Max, 1 mm, and 2 mm) on shear wave elastography (SWE) in differentiating between malignant and benign thyroid nodules. The study cohort comprised 129 thyroid nodules (50 malignant, 79 benign) and 78 normal subjects. Diagnostic efficacy was assessed through pairwise comparisons of area under the curve (AUC) values in receiver operating characteristic analysis by using DeLong’s test. Our results indicated significant differences in all SWE elasticity metrics between the groups, with malignant nodules exhibiting higher values than benign nodules (p < 0.05). Smaller ROIs (1 and 2 mm) were found to outperform the max ROI in terms of diagnostic accuracy, particularly for the Emax and Emin elasticity metrics. Emax(1mm) had the highest diagnostic accuracy, with an AUC of 0.883, sensitivity of 74.0%, and specificity of 86.1%. This study underscores the significant influence of ROI size selection on the diagnostic performance of SWE, offering valuable insights for future research and clinical applications in thyroid nodule assessment. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Differentiation in Head and Neck and Neuro Oncology)
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12 pages, 3099 KiB  
Article
Free-Breathing StarVIBE Sequence for the Detection of Extranodal Extension in Head and Neck Cancer: An Image Quality and Diagnostic Performance Study
by Jiangming Qu, Tong Su, Boju Pan, Tao Zhang, Xingming Chen, Xiaoli Zhu, Yu Chen, Zhuhua Zhang and Zhengyu Jin
Cancers 2023, 15(20), 4992; https://doi.org/10.3390/cancers15204992 - 15 Oct 2023
Cited by 1 | Viewed by 1355
Abstract
(1) Background: This study aims to evaluate the image quality of abnormal cervical lymph nodes in head and neck cancer and the diagnostic performance of detecting extranodal extension (ENE) using free-breathing StarVIBE. (2) Methods: In this retrospective analysis, 80 consecutive head and neck [...] Read more.
(1) Background: This study aims to evaluate the image quality of abnormal cervical lymph nodes in head and neck cancer and the diagnostic performance of detecting extranodal extension (ENE) using free-breathing StarVIBE. (2) Methods: In this retrospective analysis, 80 consecutive head and neck cancer patients underwent StarVIBE before neck dissection at an academic center. Image quality was compared with conventional VIBE available for 28 of these patients. A total of 73 suspicious metastatic lymph nodes from 40 patients were found based on morphology and enhancement pattern on StarVIBE. Sensitivity (SN), specificity (SP), and odds ratios were calculated for each MR feature from StarVIBE to predict pathologic ENE. (3) Results: StarVIBE showed significantly superior image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for enlarged lymph nodes compared to VIBE. The MR findings of “invading adjacent planes” (SN, 0.54; SP, 1.00) and “matted nodes” (SN, 0.72; SP, 0.89) emerged as notable observations. The highest diagnostic performance was attained by combining these two features (SN, 0.93; SP, 0.89). (4) Conclusions: This study confirms that StarVIBE offers superior image quality for abnormal lymph nodes compared to VIBE, and it can accurately diagnose ENE by utilizing a composite MR criterion in head and neck cancer. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Differentiation in Head and Neck and Neuro Oncology)
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15 pages, 4927 KiB  
Article
High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development
by Jakub Malinowski, Wioletta Pietruszewska, Konrad Stawiski, Magdalena Kowalczyk, Magda Barańska, Aleksander Rycerz and Ewa Niebudek-Bogusz
Cancers 2023, 15(14), 3716; https://doi.org/10.3390/cancers15143716 - 22 Jul 2023
Cited by 2 | Viewed by 1471
Abstract
The aim of the study was to utilize a quantitative assessment of the vibratory characteristics of vocal folds in diagnosing benign and malignant lesions of the glottis using high-speed videolaryngoscopy (HSV). Methods: Case-control study including 100 patients with unilateral vocal fold lesions in [...] Read more.
The aim of the study was to utilize a quantitative assessment of the vibratory characteristics of vocal folds in diagnosing benign and malignant lesions of the glottis using high-speed videolaryngoscopy (HSV). Methods: Case-control study including 100 patients with unilateral vocal fold lesions in comparison to 38 normophonic subjects. Quantitative assessment with the determination of vocal fold oscillation parameters was performed based on HSV kymography. Machine-learning predictive models were developed and validated. Results: All calculated parameters differed significantly between healthy subjects and patients with organic lesions. The first predictive model distinguishing any organic lesion patients from healthy subjects reached an area under the curve (AUC) equal to 0.983 and presented with 89.3% accuracy, 97.0% sensitivity, and 71.4% specificity on the testing set. The second model identifying malignancy among organic lesions reached an AUC equal to 0.85 and presented with 80.6% accuracy, 100% sensitivity, and 71.1% specificity on the training set. Important predictive factors for the models were frequency perturbation measures. Conclusions: The standard protocol for distinguishing between benign and malignant lesions continues to be clinical evaluation by an experienced ENT specialist and confirmed by histopathological examination. Our findings did suggest that advanced machine learning models, which consider the complex interactions present in HSV data, could potentially indicate a heightened risk of malignancy. Therefore, this technology could prove pivotal in aiding in early cancer detection, thereby emphasizing the need for further investigation and validation. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Differentiation in Head and Neck and Neuro Oncology)
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Other

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17 pages, 1266 KiB  
Systematic Review
Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review
by Kaijing Mao, Lun M. Wong, Rongli Zhang, Tiffany Y. So, Zhiyi Shan, Kuo Feng Hung and Qi Yong H. Ai
Cancers 2023, 15(20), 4918; https://doi.org/10.3390/cancers15204918 - 10 Oct 2023
Cited by 5 | Viewed by 1620
Abstract
Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on [...] Read more.
Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23–334 benign and 8–56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin’s tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Differentiation in Head and Neck and Neuro Oncology)
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