Head and Neck Cancer Imaging and Image Analysis

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 28416

Special Issue Editor


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Guest Editor
1. Department of Oral & Maxillofacial Surgery, Medical University of Graz, Graz, Austria
2. Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria
3. Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen (UKE), Essen, Germany
Interests: head and neck cancer; head and neck tumor; 2D and 3D imaging; computed tomography; magnetic resonance imaging; positron emission tomography; computer vision; segmentation; registration; image-guided therapy; machine learning; deep learning; generative adversarial networks; radiomics; big data; visualization; navigation; surgery; clinical practice

Special Issue Information

Dear Colleagues,

In the last two decades, head and neck cancer treatment has undergone a remarkable rate of imaging and software-based technological innovation. Imaging modalities, such as computed tomography, magnetic resonance imaging and positron emission tomography, are, in general, the first step for cancer diagnosis and subsequent treatment decisions. An automatic generation and processing of these image datasets can aid clinicians in all therapy stages, from the data acquisition to a postinterventional follow-up monitoring and cancer prognosis. In this regard, this Special Issue targets the whole pipeline from image acquisition and medical image analysis up to epidemiology and demography studies in the field of head and neck cancer. Authors are invited to submit works in this field regarding imaging and medical image processing, such as segmentation, registration, deep learning, generative adversarial networks, radiomics, and image-guided therapies, but also regarding big data and clinical practice.

Prof. Dr. Jan Egger
Guest Editor

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Keywords

  • head and neck cancer
  • 2D and 3D imaging
  • magnetic resonance imaging
  • image-guided therapy
  • machine learning
  • generative adversarial networks
  • radiomics
  • big data
  • visualization
  • navigation
  • surgery
  • clinical practice

Published Papers (13 papers)

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13 pages, 2438 KiB  
Article
Deep Learning and Registration-Based Mapping for Analyzing the Distribution of Nodal Metastases in Head and Neck Cancer Cohorts: Informing Optimal Radiotherapy Target Volume Design
by Thomas Weissmann, Sina Mansoorian, Matthias Stefan May, Sebastian Lettmaier, Daniel Höfler, Lisa Deloch, Stefan Speer, Matthias Balk, Benjamin Frey, Udo S. Gaipl, Christoph Bert, Luitpold Valentin Distel, Franziska Walter, Claus Belka, Sabine Semrau, Heinrich Iro, Rainer Fietkau, Yixing Huang and Florian Putz
Cancers 2023, 15(18), 4620; https://doi.org/10.3390/cancers15184620 - 18 Sep 2023
Viewed by 1348
Abstract
We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N [...] Read more.
We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation. In the nonrigid-registration-based mapping method, LNs are mapped into a calculated template CT representing the cohort-average patient anatomy, and kernel density estimation is employed to estimate the underlying average 3D-LN probability distribution allowing for analysis and visualization without prespecified level definitions. Multireader assessment by three radio-oncologists with majority voting was used to evaluate the deep learning method and obtain the ground-truth distribution. For the mapping technique, the proportion of LNs predicted by the 3D probability distribution for each level was calculated and compared to the deep learning and ground-truth distributions. As determined by a multireader review with majority voting, the deep learning method correctly categorized all 449 LNs to their respective levels. Level 2 showed the highest LN involvement (59.0%). The level involvement predicted by the mapping technique was consistent with the ground-truth distribution (p for difference 0.915). Application of the proposed methods to multicenter cohorts with selected H/N tumor subtypes for informing optimal RT target volume design is promising. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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15 pages, 3247 KiB  
Article
Clinical-Radiomics Nomogram Based on Contrast-Enhanced Ultrasound for Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma
by Liqing Jiang, Zijian Zhang, Shiyan Guo, Yongfeng Zhao and Ping Zhou
Cancers 2023, 15(5), 1613; https://doi.org/10.3390/cancers15051613 - 5 Mar 2023
Cited by 7 | Viewed by 2436
Abstract
This study aimed to establish a new clinical-radiomics nomogram based on ultrasound (US) for cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC). We collected 211 patients with PTC between June 2018 and April 2020, then we randomly divided these patients into [...] Read more.
This study aimed to establish a new clinical-radiomics nomogram based on ultrasound (US) for cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC). We collected 211 patients with PTC between June 2018 and April 2020, then we randomly divided these patients into the training set (n = 148) and the validation set (n = 63). 837 radiomics features were extracted from B-mode ultrasound (BMUS) images and contrast-enhanced ultrasound (CEUS) images. The maximum relevance minimum redundancy (mRMR) algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and backward stepwise logistic regression (LR) were applied to select key features and establish a radiomics score (Radscore), including BMUS Radscore and CEUS Radscore. The clinical model and clinical-radiomics model were established using the univariate analysis and multivariate backward stepwise LR. The clinical-radiomics model was finally presented as a clinical-radiomics nomogram, the performance of which was evaluated by the receiver operating characteristic curves, Hosmer–Lemeshow test, calibration curves, and decision curve analysis (DCA). The results show that the clinical-radiomics nomogram was constructed by four predictors, including gender, age, US-reported LNM, and CEUS Radscore. The clinical-radiomics nomogram performed well in both the training set (AUC = 0.820) and the validation set (AUC = 0.814). The Hosmer–Lemeshow test and the calibration curves demonstrated good calibration. The DCA showed that the clinical-radiomics nomogram had satisfactory clinical utility. The clinical-radiomics nomogram constructed by CEUS Radscore and key clinical features can be used as an effective tool for individualized prediction of cervical LNM in PTC. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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10 pages, 2124 KiB  
Article
Intraoperative Assessment of Tumor Margins in Tissue Sections with Hyperspectral Imaging and Machine Learning
by David Pertzborn, Hoang-Ngan Nguyen, Katharina Hüttmann, Jonas Prengel, Günther Ernst, Orlando Guntinas-Lichius, Ferdinand von Eggeling and Franziska Hoffmann
Cancers 2023, 15(1), 213; https://doi.org/10.3390/cancers15010213 - 29 Dec 2022
Cited by 9 | Viewed by 1962
Abstract
The intraoperative assessment of tumor margins of head and neck cancer is crucial for complete tumor resection and patient outcome. The current standard is to take tumor biopsies during surgery for frozen section analysis by a pathologist after H&E staining. This evaluation is [...] Read more.
The intraoperative assessment of tumor margins of head and neck cancer is crucial for complete tumor resection and patient outcome. The current standard is to take tumor biopsies during surgery for frozen section analysis by a pathologist after H&E staining. This evaluation is time-consuming, subjective, methodologically limited and underlies a selection bias. Optical methods such as hyperspectral imaging (HSI) are therefore of high interest to overcome these limitations. We aimed to analyze the feasibility and accuracy of an intraoperative HSI assessment on unstained tissue sections taken from seven patients with oral squamous cell carcinoma. Afterwards, the tissue sections were subjected to standard histopathological processing and evaluation. We trained different machine learning models on the HSI data, including a supervised 3D convolutional neural network to perform tumor detection. The results were congruent with the histopathological annotations. Therefore, this approach enables the delineation of tumor margins with artificial HSI-based histopathological information during surgery with high speed and accuracy on par with traditional intraoperative tumor margin assessment (Accuracy: 0.76, Specificity: 0.89, Sensitivity: 0.48). With this, we introduce HSI in combination with ML hyperspectral imaging as a potential new tool for intraoperative tumor margin assessment. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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12 pages, 2501 KiB  
Article
Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?
by Rongli Zhang, Qi Yong H. Ai, Lun M. Wong, Christopher Green, Sahrish Qamar, Tiffany Y. So, Alexander C. Vlantis and Ann D. King
Cancers 2022, 14(23), 5804; https://doi.org/10.3390/cancers14235804 - 25 Nov 2022
Cited by 9 | Viewed by 1746
Abstract
The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and [...] Read more.
The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models’ performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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13 pages, 986 KiB  
Article
ADC Values of Cytologically Benign and Cytologically Malignant 18 F-FDG PET-Positive Lymph Nodes of Head and Neck Squamous Cell Carcinoma
by Petra K. de Koekkoek-Doll, Sander Roberti, Laura Smit, Wouter V. Vogel, Regina Beets-Tan, Michiel W. van den Brekel and Jonas Castelijns
Cancers 2022, 14(16), 4019; https://doi.org/10.3390/cancers14164019 - 20 Aug 2022
Viewed by 1384
Abstract
Nodal staging (N-staging) in head and neck squamous cell carcinoma (HNSCC) is essential for treatment planning and prognosis. 18F-fluordeoxyglucose positron emission tomography (FDG-PET) has high performance for N-staging, although the distinction between cytologically malignant and reactive PET-positive nodes, and consequently, the selection of [...] Read more.
Nodal staging (N-staging) in head and neck squamous cell carcinoma (HNSCC) is essential for treatment planning and prognosis. 18F-fluordeoxyglucose positron emission tomography (FDG-PET) has high performance for N-staging, although the distinction between cytologically malignant and reactive PET-positive nodes, and consequently, the selection of nodes for ultrasound-guided fine needle aspiration cytology (USgFNAC), is challenging. Diffusion-weighted magnetic resonance imaging (DW-MRI) can help to detect nodal metastases. We aim to investigate the potential of the apparent diffusion coefficient (ADC) as a metric to distinguish between cytologically reactive and malignant PET-positive nodes in order to improve node selection criteria for USgFNAC. PET-CT, real-time image-fused USgFNAC and DW-MRI to calculate ADC were available for 78 patients offered for routine N-staging. For 167 FDG-positive nodes, differences in the ADC between cytologically benign and malignant PET-positive nodes were evaluated, and both were compared to the ADC values of PET-negative reference nodes. Analyses were also performed in subsets of nodes regarding HPV status. A mild negative correlation between SUVmax and ADC was found. No significant differences in ADC values were observed between cytologically malignant and benign PET-positive nodes overall. Within the subset of non-HPV-related nodes, ADCb0-200-1000 was significantly lower in cytologically malignant PET-positive nodes when compared to benign PET-positive nodes. ADCb0-1000 and ADCb0-200-1000 were significantly lower (p = 0.018, 0.016, resp.) in PET-negative reference nodes than in PET-positive nodes. ADC was significantly higher in PET-negative reference nodes than in PET-positive nodes. The non-HPV-related subgroup showed significantly (p = 0.03) lower ADC values in cytologically malignant than in cytologically benign PET-positive nodes, which should help inform the node selection procedure for puncture. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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12 pages, 4824 KiB  
Article
Diagnostic Accuracy of High-Grade Intraepithelial Papillary Capillary Loops by Narrow Band Imaging for Early Detection of Oral Malignancy: A Cross-Sectional Clinicopathological Imaging Study
by Airi Ota, Ikuya Miyamoto, Yu Ohashi, Toshimi Chiba, Yasunori Takeda and Hiroyuki Yamada
Cancers 2022, 14(10), 2415; https://doi.org/10.3390/cancers14102415 - 13 May 2022
Cited by 5 | Viewed by 1953
Abstract
This study aimed to clarify the advantages and disadvantages of conventional visual inspection (CVI), endoscopic white light imaging (WLI), and narrow-band imaging (NBI) and to examine the diagnostic accuracy of intraepithelial papillary capillary loops (IPCL) for the detection of oral squamous cell carcinoma [...] Read more.
This study aimed to clarify the advantages and disadvantages of conventional visual inspection (CVI), endoscopic white light imaging (WLI), and narrow-band imaging (NBI) and to examine the diagnostic accuracy of intraepithelial papillary capillary loops (IPCL) for the detection of oral squamous cell carcinoma (OSCC). This cross-sectional study included 60 participants with oral mucosal diseases suspected of having oral potentially malignant disorders (OPMDs) or OSCC. The patients underwent CVI, WLI, NBI, and incisional biopsy. Images were evaluated to assess the lesion size, color, texture, and IPCL. Oral lichen planus (OLP) and oral leukoplakia lesions were observed in larger areas with NBI than with WLI; 75.0% were associated with low-grade (Type 0–II) IPCL. Various types of oral leukoplakia were seen; however, all OSCC cases showed high-grade (Type III–IV) IPCL. The diagnostic accuracy of high-grade IPCL for OSCC showed a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 100%, 80.9%, 59.1%, 100%, and 85.0%, respectively. A non-homogeneous lesion with high-grade IPCL strongly suggested malignancy. Overall, our results indicate that WLI and NBI are powerful tools for detecting precancerous and cancerous lesions using IPCL. However, NBI is influenced by mucosal thickness; therefore, image interpretation is important for accurate diagnosis. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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12 pages, 2104 KiB  
Article
Quantitative Multiparametric Ultrasound (mpUS) in the Assessment of Inconclusive Cervical Lymph Nodes
by Markus H. Lerchbaumer, Katharina Margherita Wakonig, Philipp Arens, Steffen Dommerich and Thomas Fischer
Cancers 2022, 14(7), 1597; https://doi.org/10.3390/cancers14071597 - 22 Mar 2022
Cited by 8 | Viewed by 2382
Abstract
Background: Enlarged cervical lymph nodes (CLN) are preferably examined by ultrasound (US) by using criteria such as size and echogenicity to assess benign and suspicious CLN, which should be histologically evaluated. This study aims to assess the differentiation of malign and benign CLN [...] Read more.
Background: Enlarged cervical lymph nodes (CLN) are preferably examined by ultrasound (US) by using criteria such as size and echogenicity to assess benign and suspicious CLN, which should be histologically evaluated. This study aims to assess the differentiation of malign and benign CLN by using multiparametric US applications (mpUS). Methods: 101 patients received a standardized US protocol prior to surgical intervention using B-mode–US, shear-wave elastography (SWE) and contrast-enhanced ultrasound (CEUS). SWE was assessed by 2D real-time SWE conducting a minimum of five measurements, CEUS parameters were assessed with post-processing perfusion software. Histopathological confirmation served as the gold standard. Results: B-mode–US and SWE analysis of 104 CLN (36 benign, 68 malignant) showed a significant difference between benign and malignant lesions, presenting a larger long axis and higher tissue stiffness (both p < 0.001). Moreover, tissue stiffness assessed by SWE was significantly higher in CLN with regular B-mode–US criteria (Solbiati Index > 2 and short-axis < 1 cm, p < 0.001). No perfusion parameter on CEUS showed a significant differentiation between benign and malignant CLN. Discussion: As the only multiparametric parameter, SWE showed higher tissue stiffness in malignant CLN, also in subgroups with regular B-mode criteria. This fast and easy application may be a promising noninvasive tool to US examination to ameliorate the sonographic differentiation of inconclusive CLN. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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12 pages, 1240 KiB  
Article
Preoperative Magnetic Resonance Image and Computerized Tomography Findings Predictive of Facial Nerve Invasion in Patients with Parotid Cancer without Preoperative Facial Weakness—A Retrospective Observational Study
by Won Ki Cho, Min Kyoung Lee, Young Jun Choi, Yoon Se Lee, Seung-Ho Choi, Soon Yuhl Nam and Sang Yoon Kim
Cancers 2022, 14(4), 1086; https://doi.org/10.3390/cancers14041086 - 21 Feb 2022
Viewed by 1836
Abstract
(1) Background: Facial nerve resection with reconstruction helps achieve optimal outcomes in the treatment of facial nerve invasion (FNI) of parotid cancer. Preoperative imaging is crucial to predict facial nerve reconstruction. The radiological findings of CT or MRI may predict FNI in the [...] Read more.
(1) Background: Facial nerve resection with reconstruction helps achieve optimal outcomes in the treatment of facial nerve invasion (FNI) of parotid cancer. Preoperative imaging is crucial to predict facial nerve reconstruction. The radiological findings of CT or MRI may predict FNI in the parotid cancer even without facial paralysis. Methods: We retrospectively reviewed the records of 151 patients without facial nerve paralysis before surgery who had undergone tumor resection. Previously untreated parotid cancers were included. (2) Results: The median follow-up duration was 62 months (range: 24–120 months). The FNI (+) group (n = 30) showed a significantly worse 5-year overall survival compared with the FNI (−) group (75.5 vs. 93.9%; hazard ratio = 4.19; 95% confidence interval: 1.74–10.08; p = 0.001). The tumor margin, tumor size, presence in the anterolateral parotid region (area 3), retromandibular vein involvement, distance from the stylomastoid foramen to the upper tumor margin, and a high tumor grade were significant factors related to FNI in the univariate analysis. A spiculated tumor margin, the tumor size (2.2 cm), and presence in area 3 were factors predicting FNI in the logistic regression model (p = 0.020, 0.005, and 0.050, respectively; odds ratio: 4.02, 6.40, and 8.16, respectively). (3) Conclusions: The tumor size (≥2.2 cm), spiculated margin, and presence in area 3 as presented in CT and MRI may help clinicians preoperatively predict FNI in patients with parotid cancer and establish an appropriate surgical plan. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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16 pages, 3229 KiB  
Article
Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification
by Zoltan R. Bardosi, Daniel Dejaco, Matthias Santer, Marcel Kloppenburg, Stephanie Mangesius, Gerlig Widmann, Ute Ganswindt, Gerhard Rumpold, Herbert Riechelmann and Wolfgang Freysinger
Cancers 2022, 14(3), 477; https://doi.org/10.3390/cancers14030477 - 18 Jan 2022
Cited by 6 | Viewed by 1904
Abstract
In head and neck squamous cell carcinoma (HNSCC) pathologic cervical lymph nodes (LN) remain important negative predictors. Current criteria for LN-classification in contrast-enhanced computed-tomography scans (contrast-CT) are shape-based; contrast-CT imagery allows extraction of additional quantitative data (“features”). The data-driven technique to extract, process, [...] Read more.
In head and neck squamous cell carcinoma (HNSCC) pathologic cervical lymph nodes (LN) remain important negative predictors. Current criteria for LN-classification in contrast-enhanced computed-tomography scans (contrast-CT) are shape-based; contrast-CT imagery allows extraction of additional quantitative data (“features”). The data-driven technique to extract, process, and analyze features from contrast-CTs is termed “radiomics”. Extracted features from contrast-CTs at various levels are typically redundant and correlated. Current sets of features for LN-classification are too complex for clinical application. Effective eliminative feature selection (EFS) is a crucial preprocessing step to reduce the complexity of sets identified. We aimed at exploring EFS-algorithms for their potential to identify sets of features, which were as small as feasible and yet retained as much accuracy as possible for LN-classification. In this retrospective cohort-study, which adhered to the STROBE guidelines, in total 252 LNs were classified as “non-pathologic” (n = 70), “pathologic” (n = 182) or “pathologic with extracapsular spread” (n = 52) by two experienced head-and-neck radiologists based on established criteria which served as a reference. The combination of sparse discriminant analysis and genetic optimization retained up to 90% of the classification accuracy with only 10% of the original numbers of features. From a clinical perspective, the selected features appeared plausible and potentially capable of correctly classifying LNs. Both the identified EFS-algorithm and the identified features need further exploration to assess their potential to prospectively classify LNs in HNSCC. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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17 pages, 1353 KiB  
Article
Effects of Pre-Operative Risk Factors on Intensive Care Unit Length of Stay (ICU-LOS) in Major Oral and Maxillofacial Cancer Surgery
by Juergen Wallner, Michael Schwaiger, Sarah-Jayne Edmondson, Irene Mischak, Jan Egger, Matthias Feichtinger, Wolfgang Zemann and Mauro Pau
Cancers 2021, 13(16), 3937; https://doi.org/10.3390/cancers13163937 - 4 Aug 2021
Cited by 2 | Viewed by 2178
Abstract
Objective: This study aimed to investigate the effect of certain pre-operative parameters directly on the post-operative intensive care unit (ICU)-length of stay (LOS), in order to identify at-risk patients that are expected to need prolonged intensive care management post-operatively. Material and Methods: Retrospectively, [...] Read more.
Objective: This study aimed to investigate the effect of certain pre-operative parameters directly on the post-operative intensive care unit (ICU)-length of stay (LOS), in order to identify at-risk patients that are expected to need prolonged intensive care management post-operatively. Material and Methods: Retrospectively, patients managed in an ICU after undergoing major oral and maxillofacial surgery were analyzed. Inclusion criteria entailed: age 18–90 years, major primary oral cancer surgery including tumor resection, neck dissection and microvascular free flap reconstruction, minimum operation time of 8 h. Exclusion criteria were: benign/borderline tumors, primary radiation, other defect reconstruction than microvascular, treatment at other centers. Separate parameters used within the clinical routine were set in correlation with ICU-LOS, by applying single testing calculations (t-tests, variance analysis, correlation coefficients, effect sizes) and a valid univariate linear regression model. The primary outcome of interest was ICU-LOS. Results: This study included a homogenous cohort of 122 patients. Mean surgery time was 11.4 (±2.2) h, mean ICU-LOS was 3.6 (±2.6) days. Patients with pre-operative renal dysfunction (p < 0.001), peripheral vascular disease-PVD (p = 0.01), increasing heart failure-NYHA stage categories (p = 0.009) and higher-grade categories of post-operative complications (p = 0.023) were identified as at-risk patients for a significantly prolonged post-operative ICU-LOS. Conclusions: At-risk patients are prone to need a significantly longer ICU-LOS than others. These patients are those with pre-operative severe renal dysfunction, PVD and/or high NYHA stage categories. Confounding parameters that contribute to a prolonged ICU-LOS in combination with other variables were identified as higher age, prolonged operative time, chronic obstructive pulmonary disease, and intra-operatively transfused blood. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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16 pages, 1968 KiB  
Article
Impact of Planning Method (Conventional versus Virtual) on Time to Therapy Initiation and Resection Margins: A Retrospective Analysis of 104 Immediate Jaw Reconstructions
by Michael Knitschke, Christina Bäcker, Daniel Schmermund, Sebastian Böttger, Philipp Streckbein, Hans-Peter Howaldt and Sameh Attia
Cancers 2021, 13(12), 3013; https://doi.org/10.3390/cancers13123013 - 16 Jun 2021
Cited by 15 | Viewed by 2262
Abstract
Virtual surgical planning (VSP) and patient-specific implants are currently increasing for immediate jaw reconstruction after ablative oncologic surgery. This technique contributes to more accurate and efficient preoperative planning and shorter operation time. The present retrospective, single-center study analyzes the influence of time delay [...] Read more.
Virtual surgical planning (VSP) and patient-specific implants are currently increasing for immediate jaw reconstruction after ablative oncologic surgery. This technique contributes to more accurate and efficient preoperative planning and shorter operation time. The present retrospective, single-center study analyzes the influence of time delay caused by VSP vs. conventional (non-VSP) reconstruction planning on the soft and hard tissue resection margins for necessary oncologic safety. A total number of 104 cases of immediate jaw reconstruction with free fibula flap are included in the present study. The selected method of reconstruction (conventionally, non-VSP: n = 63; digitally, VSP: n = 41) are analyzed in detail. The study reveals a statistically significant (p = 0.008) prolonged time to therapy initiation with a median of 42 days when the VSP method compared with non-VSP (31.0 days) is used. VSP did not significantly affect bony or soft tissue resection margin status. Apart from this observation, no significant differences concerning local tumor recurrence, lymph node, and distant metastases rates are found according to the reconstruction method, and affect soft or bone tissue resection margins. Thus, we conclude that VSP for immediate jaw reconstruction is safe for oncological purposes. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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13 pages, 1498 KiB  
Systematic Review
Diagnostic Performance of Various Ultrasound Risk Stratification Systems for Benign and Malignant Thyroid Nodules: A Meta-Analysis
by Ji-Sun Kim, Byung Guk Kim, Gulnaz Stybayeva and Se Hwan Hwang
Cancers 2023, 15(2), 424; https://doi.org/10.3390/cancers15020424 - 9 Jan 2023
Cited by 3 | Viewed by 1971
Abstract
Background: To evaluate the diagnostic performance of ultrasound risk-stratification systems for the discrimination of benign and malignant thyroid nodules and to determine the optimal cutoff values of individual risk-stratification systems. Methods: PubMed, Embase, SCOPUS, Web of Science, and Cochrane library databases were searched [...] Read more.
Background: To evaluate the diagnostic performance of ultrasound risk-stratification systems for the discrimination of benign and malignant thyroid nodules and to determine the optimal cutoff values of individual risk-stratification systems. Methods: PubMed, Embase, SCOPUS, Web of Science, and Cochrane library databases were searched up to August 2022. Sensitivity and specificity data were collected along with the characteristics of each study related to ultrasound risk stratification systems. Results: Sixty-seven studies involving 76,512 thyroid nodules were included in this research. The sensitivity, specificity, diagnostic odds ratios, and area under the curves by K-TIRADS (4), ACR-TIRADS (TR5), ATA (high suspicion), EU-TIRADS (5), and Kwak-TIRADS (4b) for malignancy risk stratification of thyroid nodules were 92.5%, 63.5%, 69.8%, 70.6%, and 95.8%, respectively; 62.8%, 89.6%, 87.2%, 83.9%, and 63.8%, respectively; 20.7111, 16.8442, 15.7398, 12.2986, and 38.0578, respectively; and 0.792, 0.882, 0.859, 0.843, and 0.929, respectively. Conclusion: All ultrasound-based risk-stratification systems had good diagnostic performance. Although this study determined the best cutoff values in individual risk-stratification systems based on statistical assessment, clinicians could adjust or alter cutoff values based on the clinical purpose of the ultrasound and the reciprocal changes in sensitivity and specificity. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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19 pages, 1137 KiB  
Systematic Review
Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature
by Eleonora Bicci, Cosimo Nardi, Leonardo Calamandrei, Michele Pietragalla, Edoardo Cavigli, Francesco Mungai, Luigi Bonasera and Vittorio Miele
Cancers 2022, 14(10), 2445; https://doi.org/10.3390/cancers14102445 - 16 May 2022
Cited by 9 | Viewed by 2493
Abstract
Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis [...] Read more.
Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis can provide structural information not perceptible to human eyes. A systematic literature search was performed on 16 February 2022 for studies with a focus on texture analysis in oropharyngeal cancers. We conducted the research on PubMed, Scopus, and Web of Science platforms. Studies were screened for inclusion according to the preferred reporting items for systematic reviews. Twenty-six studies were included in our review. Nineteen articles related specifically to the oropharynx and seven articles analysed the head and neck area with sections dedicated to the oropharynx. Six, thirteen, and seven articles used MRI, CT, and PET, respectively, as the imaging techniques by which texture analysis was performed. Regarding oropharyngeal tumours, this review delineates the applications of texture analysis in (1) the diagnosis, prognosis, and assessment of disease recurrence or persistence after therapy, (2) early differentiation of HPV-positive versus HPV-negative cancers, (3) the detection of cancers not visualised by imaging alone, and (4) the assessment of lymph node metastases from unknown primary carcinomas. Full article
(This article belongs to the Special Issue Head and Neck Cancer Imaging and Image Analysis)
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