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  • Unbekannt  (9)
  • 2020-2024  (9)
  • 1
    Online-Ressource
    Online-Ressource
    NCCN Guidelines® Insights: Head and Neck Cancers, Version 1.2022 : Featured Updates to the NCCN Guidelines
    Harborside Press, LLC ; 2022
    In:  Journal of the National Comprehensive Cancer Network Vol. 20, No. 3 ( 2022-03), p. 224-234
    Details
    In: Journal of the National Comprehensive Cancer Network, Harborside Press, LLC, Vol. 20, No. 3 ( 2022-03), p. 224-234
    Kurzfassung: The NCCN Guidelines for Head and Neck Cancers address tumors arising in the oral cavity (including mucosal lip), pharynx, larynx, and paranasal sinuses. Occult primary cancer, salivary gland cancer, and mucosal melanoma (MM) are also addressed. The specific site of disease, stage, and pathologic findings guide treatment (eg, the appropriate surgical procedure, radiation targets, dose and fractionation of radiation, indications for systemic therapy). The NCCN Head and Neck Cancers Panel meets at least annually to review comments from reviewers within their institutions, examine relevant new data from publications and abstracts, and reevaluate and update their recommendations. These NCCN Guidelines Insights summarize the panel’s most recent recommendations regarding management of HPV-positive oropharynx cancer and ongoing research in this area.
    Materialart: Online-Ressource
    ISSN: 1540-1405 , 1540-1413
    URL: Article
    DOI: 10.6004/jnccn.2022.0016
    Sprache: Unbekannt
    Verlag: Harborside Press, LLC
    Publikationsdatum: 2022
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 2
    Online-Ressource
    Online-Ressource
    DataSheet_1.pdf
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-9-29)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-9-29)
    Kurzfassung: Accurate identifying head and neck squamous cell cancer (HNSCC) patients at high risk of local persistence/recurrence (P/R) is of importance for personalized patient management. Here we developed a multi-objective, multi-classifier radiomics model for early HNSCC local P/R prediction based on post-treatment PET/CT scans and clinical data. Materials and methods We retrospectively identified 328 individuals (69 patients have local P/R) with HNSCC treated with definitive radiation therapy at our institution. The median follow-up from treatment completion to the first surveillance PET/CT imaging was 114 days (range: 82-159 days). Post-treatment PET/CT scans were reviewed and contoured for all patients. For each imaging modality, we extracted 257 radiomic features to build a multi-objective radiomics model with sensitivity, specificity, and feature sparsity as objectives for model training. Multiple representative classifiers were combined to construct the predictive model. The output probabilities of models built with features from various modalities were fused together to make the final prediction. Results We built and evaluated three single-modality models and two multi-modality models. The combination of PET, CT, and clinical data in the multi-objective, multi-classifier radiomics model trended towards the best prediction performance, with a sensitivity of 93%, specificity of 83%, accuracy of 85%, and AUC of 0.94. Conclusion Our study demonstrates the feasibility of employing a multi-objective, multi-classifier radiomics model with PET/CT radiomic features and clinical data to predict outcomes for patients with HNSCC after radiation therapy. The proposed prediction model shows the potential to detect cancer local P/R early after radiation therapy.
    Materialart: Online-Ressource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2022.955712
    DOI: 10.3389/fonc.2022.955712.s001
    Sprache: Unbekannt
    Verlag: Frontiers Media SA
    Publikationsdatum: 2022
    ZDB Id: 2649216-7
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 3
    Online-Ressource
    Online-Ressource
    Alcohol Use Among Patients With Cancer and Survivors in the United States, 2000–2017
    Harborside Press, LLC ; 2020
    In:  Journal of the National Comprehensive Cancer Network Vol. 18, No. 1 ( 2020-01), p. 69-79
    Details
    In: Journal of the National Comprehensive Cancer Network, Harborside Press, LLC, Vol. 18, No. 1 ( 2020-01), p. 69-79
    Kurzfassung: Background: Alcohol use is an established risk factor for several malignancies and is associated with adverse oncologic outcomes among individuals diagnosed with cancer. The prevalence and patterns of alcohol use among cancer survivors are poorly described. Methods: We used the National Health Interview Survey from 2000 to 2017 to examine alcohol drinking prevalence and patterns among adults reporting a cancer diagnosis. Multivariable logistic regression was used to define the association between demographic and socioeconomic variables and odds of self-reporting as a current drinker, exceeding moderate drinking limits, and engaging in binge drinking. The association between specific cancer type and odds of drinking were assessed. Results: Among 34,080 survey participants with a known cancer diagnosis, 56.5% self-reported as current drinkers, including 34.9% who exceeded moderate drinking limits and 21.0% who engaged in binge drinking. Younger age, smoking history, and more recent survey period were associated with higher odds of current, exceeding moderate, and binge drinking ( P 〈 .001 for all, except P =.008 for excess drinking). Similar associations persisted when the cohort was limited to 20,828 cancer survivors diagnosed ≥5 years before survey administration. Diagnoses of melanoma and cervical, head and neck, and testicular cancers were associated with higher odds of binge drinking ( P 〈 .05 for all) compared with other cancer diagnoses. Conclusions: Most cancer survivors self-report as current alcohol drinkers, including a subset who seem to engage in excessive drinking behaviors. Given that alcohol intake has implications for cancer prevention and is a potentially modifiable risk factor for cancer-specific outcomes, the high prevalence of alcohol use among cancer survivors highlights the need for public health strategies aimed at the reduction of alcohol consumption.
    Materialart: Online-Ressource
    ISSN: 1540-1405 , 1540-1413
    URL: Article
    DOI: 10.6004/jnccn.2019.7341
    Sprache: Unbekannt
    Verlag: Harborside Press, LLC
    Publikationsdatum: 2020
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 4
    Online-Ressource
    Online-Ressource
    ARMO: automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer
    IOP Publishing ; 2023
    In:  Physics in Medicine & Biology Vol. 68, No. 9 ( 2023-05-07), p. 095012-
    Details
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 68, No. 9 ( 2023-05-07), p. 095012-
    Kurzfassung: Objective . Accurate diagnosis of lymph node metastasis (LNM) is critical in treatment management for patients with head and neck cancer. Positron emission tomography and computed tomography are routinely used for identifying LNM status. However, for small or less fluorodeoxyglucose (FDG) avid nodes, there are always uncertainties in LNM diagnosis. We are aiming to develop a reliable prediction model is for identifying LNM. Approach . In this study, a new automated and reliable multi-objective learning model (ARMO) is proposed. In ARMO, a multi-objective model is introduced to obtain balanced sensitivity and specificity. Meanwhile, confidence is calibrated by introducing individual reliability, whilst the model uncertainty is estimated by a newly defined overall reliability in ARMO. In the training stage, a Pareto-optimal model set is generated. Then all the Pareto-optimal models are used, and a reliable fusion strategy that introduces individual reliability is developed for calibrating the confidence of each output. The overall reliability is calculated to estimate the model uncertainty for each test sample. Main results . The experimental results demonstrated that ARMO obtained more promising results, which the area under the curve, accuracy, sensitivity and specificity can achieve 0.97, 0.93, 0.88 and 0.94, respectively. Meanwhile, based on calibrated confidence and overall reliability, clinicians could pay particular attention to highly uncertain predictions. Significance . In this study, we developed a unified model that can achieve balanced prediction, confidence calibration and uncertainty estimation simultaneously. The experimental results demonstrated that ARMO can obtain accurate and reliable prediction performance.
    Materialart: Online-Ressource
    ISSN: 0031-9155 , 1361-6560
    URL: Article
    DOI: 10.1088/1361-6560/acca5b
    RVK:
    WA 15000
    Sprache: Unbekannt
    Verlag: IOP Publishing
    Publikationsdatum: 2023
    ZDB Id: 1473501-5
    SSG: 12
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 5
    Online-Ressource
    Online-Ressource
    Locoregional recurrence prediction in head and neck cancer based on multi-modality and multi-view feature expansion
    IOP Publishing ; 2022
    In:  Physics in Medicine & Biology Vol. 67, No. 12 ( 2022-06-21), p. 125004-
    Details
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 67, No. 12 ( 2022-06-21), p. 125004-
    Kurzfassung: Objective. Locoregional recurrence (LRR) is one of the leading causes of treatment failure in head and neck (H & N) cancer. Accurately predicting LRR after radiotherapy is essential to achieving better treatment outcomes for patients with H & N cancer through developing personalized treatment strategies. We aim to develop an end-to-end multi-modality and multi-view feature extension method (MMFE) to predict LRR in H & N cancer. Approach. Deep learning (DL) has been widely used for building prediction models and has achieved great success. Nevertheless, 2D-based DL models inherently fail to utilize the contextual information from adjacent slices, while complicated 3D models have a substantially larger number of parameters, which require more training samples, memory and computing resources. In the proposed MMFE scheme, through the multi-view feature expansion and projection dimension reduction operations, we are able to reduce the model complexity while preserving volumetric information. Additionally, we designed a multi-modality convolutional neural network that can be trained in an end-to-end manner and can jointly optimize the use of deep features of CT, PET and clinical data to improve the model’s prediction ability. Main results. The dataset included 206 eligible patients, of which, 49 had LRR while 157 did not. The proposed MMFE method obtained a higher AUC value than the other four methods. The best prediction result was achieved when using all three modalities, which yielded an AUC value of 0.81. Significance. Comparison experiments demonstrated the superior performance of the MMFE as compared to other 2D/3D-DL-based methods. By combining CT, PET and clinical features, the MMFE could potentially identify H & N cancer patients at high risk for LRR such that personalized treatment strategy can be developed accordingly.
    Materialart: Online-Ressource
    ISSN: 0031-9155 , 1361-6560
    URL: Article
    DOI: 10.1088/1361-6560/ac72f0
    RVK:
    WA 15000
    Sprache: Unbekannt
    Verlag: IOP Publishing
    Publikationsdatum: 2022
    ZDB Id: 1473501-5
    SSG: 12
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 6
    Online-Ressource
    Online-Ressource
    Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features
    AME Publishing Company ; 2021
    In:  Quantitative Imaging in Medicine and Surgery Vol. 11, No. 12 ( 2021-12), p. 4781-4796
    Details
    In: Quantitative Imaging in Medicine and Surgery, AME Publishing Company, Vol. 11, No. 12 ( 2021-12), p. 4781-4796
    Materialart: Online-Ressource
    ISSN: 2223-4292 , 2223-4306
    URL: Journal
    URL: Article
    DOI: 10.21037/qims
    DOI: 10.21037/qims-21-274
    Sprache: Unbekannt
    Verlag: AME Publishing Company
    Publikationsdatum: 2021
    ZDB Id: 2653586-5
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
    Online-Ressource
    Online-Ressource
    Multifaceted radiomics for distant metastasis prediction in head & neck cancer
    IOP Publishing ; 2020
    In:  Physics in Medicine & Biology Vol. 65, No. 15 ( 2020-08-07), p. 155009-
    Details
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 65, No. 15 ( 2020-08-07), p. 155009-
    Materialart: Online-Ressource
    ISSN: 1361-6560
    URL: Article
    DOI: 10.1088/1361-6560/ab8956
    Sprache: Unbekannt
    Verlag: IOP Publishing
    Publikationsdatum: 2020
    ZDB Id: 1473501-5
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
    Online-Ressource
    Online-Ressource
    Predicting lymph node metastasis in patients with oropharyngeal cancer by using a convolutional neural network with associated epistemic and aleatoric uncertainty
    IOP Publishing ; 2020
    In:  Physics in Medicine & Biology Vol. 65, No. 22 ( 2020-11-21), p. 225002-
    Details
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 65, No. 22 ( 2020-11-21), p. 225002-
    Kurzfassung: There can be significant uncertainty when identifying cervical lymph node (LN) metastases in patients with oropharyngeal squamous cell carcinoma (OPSCC) despite the use of modern imaging modalities such as positron emission tomography (PET) and computed tomography (CT) scans. Grossly involved LNs are readily identifiable during routine imaging, but smaller and less PET-avid LNs are harder to classify. We trained a convolutional neural network (CNN) to detect malignant LNs in patients with OPSCC and used quantitative measures of uncertainty to identify the most reliable predictions. Our dataset consisted of images of 791 LNs from 129 patients with OPSCC who had preoperative PET/CT imaging and detailed pathological reports after neck dissections. These LNs were segmented on PET/CT imaging and then labeled according to the pathology reports. An AlexNet-like CNN was trained to classify LNs as malignant or benign. We estimated epistemic and aleatoric uncertainty by using dropout variational inference and test-time augmentation, respectively. CNN performance was stratified according to the median epistemic and aleatoric uncertainty values calculated using the validation cohort. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.99 on the testing dataset. Sensitivity and specificity were 0.94 and 0.90, respectively. Epistemic and aleatoric uncertainty values were statistically larger for false negative and false positive predictions than for true negative and true positive predictions (p 〈 0.001). Model sensitivity and specificity were 1.0 and 0.98, respectively, for cases with epistemic uncertainty lower than the median value of the incorrect predictions in the validation dataset. For cases with higher epistemic uncertainty, sensitivity and specificity were 0.67 and 0.41, respectively. Model sensitivity and specificity were 1.0 and 0.98, respectively, for cases with aleatoric uncertainty lower than the median value of the incorrect predictions in the validation dataset. For cases with higher aleatoric uncertainty, sensitivity and specificity were 0.67 and 0.37, respectively. We used a CNN to predict the malignant status of LNs in patients with OPSCC with high accuracy, and we showed that uncertainty can be used to quantify a prediction’s reliability. Assigning measures of uncertainty to predictions could improve the accuracy of LN classification by efficiently identifying instances where expert evaluation is needed to corroborate a model’s prediction.
    Materialart: Online-Ressource
    ISSN: 0031-9155 , 1361-6560
    URL: Article
    DOI: 10.1088/1361-6560/abb71c
    RVK:
    WA 15000
    Sprache: Unbekannt
    Verlag: IOP Publishing
    Publikationsdatum: 2020
    ZDB Id: 1473501-5
    SSG: 12
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 9
    Online-Ressource
    Online-Ressource
    Table_1.DOCX
    Frontiers Media SA ; 2022
    In:  Frontiers in Neurology Vol. 13 ( 2022-7-14)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 13 ( 2022-7-14)
    Kurzfassung: We report the genetic analysis of two consanguineous pedigrees of Pakistani ancestry in which two siblings in each family exhibited developmental delay, epilepsy, intellectual disability and aggressive behavior. Whole-genome sequencing was performed in Family 1, and we identified ~80,000 variants located in regions of homozygosity. Of these, 615 variants had a minor allele frequency ≤ 0.001, and 21 variants had CADD scores ≥ 15. Four homozygous exonic variants were identified in both affected siblings: PDZD7 (c.1348_1350delGAG, p.Glu450del), ALG6 (c.1033G & gt;C, p.Glu345Gln), RBM20 (c.1587C & gt;G, p.Ser529Arg), and CNTNAP2 (c.785G & gt;A, p.Gly228Arg). Sanger sequencing revealed co-segregation of the PDZD7, RBM20 , and CNTNAP2 variants with disease in Family 1. Pathogenic variants in PDZD7 and RBM20 are associated with autosomal recessive non-syndromic hearing loss and autosomal dominant dilated cardiomyopathy, respectively, suggesting that these variants are unlikely likely to contribute to the clinical presentation. Gene panel analysis was performed on the two affected siblings in Family 2, and they were found to also be homozygous for the p.Gly228Arg CNTNAP2 variant. Together these families provide a LOD score 2.9 toward p.Gly228Arg CNTNAP2 being a completely penetrant recessive cause of this disease. The clinical presentation of the affected siblings in both families is also consistent with previous reports from individuals with homozygous CNTNAP2 variants where at least one allele was a nonsense variant, frameshift or small deletion. Our data suggests that homozygous CNTNAP2 missense variants can also contribute to disease, thereby expanding the genetic landscape of CNTNAP2 dysfunction.
    Materialart: Online-Ressource
    ISSN: 1664-2295
    URL: Article
    URL: Article
    DOI: 10.3389/fneur.2022.918022
    DOI: 10.3389/fneur.2022.918022.s001
    Sprache: Unbekannt
    Verlag: Frontiers Media SA
    Publikationsdatum: 2022
    ZDB Id: 2564214-5
    Standort Signatur Einschränkungen Verfügbarkeit
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