Survival prediction of squamous cell head and neck cancer patients based on radiomic features selected from lung cancer patients using artificial neural network

2018 ◽  
Author(s):  
Hidemi Kamezawa ◽  
Hidetaka Arimura ◽  
Mazen Soufi
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Squamous Cell ◽  
Survival Prediction ◽  
Lung Cancer Patients

SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

2014 ◽  
Vol 41 (6Part14) ◽  
pp. 270-270
Author(s):  
Daniel D Cho ◽  
A Gabriella Wernicke ◽  
Dattatreyudu Nori ◽  
KSC Chao ◽  
Bhupesh Parashar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Radiotherapy Toxicity

scholarly journals ЦИТОГЕНЕТИЧНІ ПОШКОДЖЕННЯ ХРОМАТИДНОГО ТИПУ ТА ГЕНОМНІ ПОРУШЕННЯ У ЛІМФОЦИТАХ ОНКОЛОГІЧНИХ ПАЦІЄНТІВ З РІЗНОЮ ЛОКАЛІЗАЦІЄЮ ПУХЛИН ПРИ ПРОМЕНЕВІЙ ТЕРАПІЇ

2019 ◽  
pp. 110-120
Author(s):  
Т. С. Сипко
Keyword(s):  
Lung Cancer ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Tumor Localization ◽  
Damage Level ◽  
Lung Cancer Patients ◽  
Significant Difference ◽  
Chromatid Aberrations

The article showed the study of chromatid type aberrations and genome abnormalities in 65 cancer patients at the stages of radiotherapy depending on tumor localization. Оncogynecological patients (with cancer in female reproductive system), lung cancer patients and head and neck cancer patients were examined before treatment, in the middle and at the end of the radiotherapy course. The over-spontaneous level of chromatid type aberrations and genomic abnormalities in cancer patients before the radiotherapy start was noted. The highest level of chromatid type aberrations before treatment was observed in lung cancer patients. No significant changes in the level of chromatid aberrations in oncogynecological patients during the whole radiotherapy course were detected. In the middle of treatment there was a significant frequency increase of chromatid type aberrations in head and neck cancer patients compared with pre-radiotherapy values of these parameters. This increase disappeared at the end of the radiotherapy course. In contrast to oncogynecological cancer patients and head and neck cancer patients in the group of lung cancer patients there was a significant increase of chromatid type damage level from the beginning to the end of the radiotherapy. The accumulation of radiation-non-specific rearrangements was mainly due to chromatid fragments, and the level of chromatid exchanges remained unchanged during the radiotherapy. The frequency variations of genome abnormalities, such as hyperploids and endoreplications, fluctuated in all patient groups. Concerning the polyploid cells, a significant difference at all stages of the study was observed in oncogynecological patients. The research of chromatid type aberrations and genome abnormalities showed some different features in changes of these parameters depending on tumor localization. The obtained data complemented the knowledge about the general cytogenetic status of cancer patients and are important for determining the influence of such a factor as tumor localization on the formation and dynamics of radiation-non-specific chromatid type lesions and genomic abnormalities during a radiotherapy course.

scholarly journals A guided self-help intervention targeting psychological distress among head and neck cancer and lung cancer patients: motivation to start, experiences and perceived outcomes

2016 ◽  
Vol 25 (1) ◽  
pp. 127-135 ◽  
Author(s):  
Anne-Marie H Krebber ◽  
Cornelia F van Uden-Kraan ◽  
Heleen C Melissant ◽  
Pim Cuijpers ◽  
Annemieke van Straten ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Head And Neck Cancer ◽  
Psychological Distress ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Lung Cancer Patients ◽  
Self Help ◽  
Perceived Outcomes

scholarly journals Reproducibility test of radiomics using network analysis and Wasserstein K-means algorithm

2019 ◽  
Author(s):  
Jung Hun Oh ◽  
Aditya P. Apte ◽  
Evangelia Katsoulakis ◽  
Nadeem Riaz ◽  
Vaios Hatzoglou ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Partial Correlation ◽  
Wasserstein Distance ◽  
Lung Cancer Patients ◽  
Cancer Data ◽  
Phantom Data

ABSTRACTPurposeTo construct robust and validated radiomic predictive models, the development of a reliable method that can identify reproducible radiomic features robust to varying image acquisition methods and other scanner parameters should be preceded with rigorous validation. Due to the property of high correlation present between radiomic features, we hypothesize that reproducible radiomic features across different datasets that are obtained from different image acquisition settings preserve some level of connectivity between features in the form of a network.MethodsWe propose a regularized partial correlation network to identify robust and reproducible radiomic features. This approach was tested on two radiomic feature sets generated with two different reconstruction methods from a cohort of 47 lung cancer patients. The commonality of the resulting two networks was assessed. A largest common network component from the two networks was tested on phantom data consisting of 5 cancer samples. We further propose a novel K-means algorithm coupled with the optimal mass transport (OMT) theory to cluster samples. This approach following the regularized partial correlation analysis was tested on computed tomography (CT) scans from 77 head and neck cancer patients that were downloaded from The Cancer Imaging Archive (TCIA) and validated on CT scans from 83 head and neck cancer patients treated at our institution.ResultsCommon radiomic features were found in relatively large network components between the resulting two partial correlation networks from a cohort of 47 lung cancer patients. The similarity of network components in terms of the common number of radiomic features was statistically significant. For phantom data, the Wasserstein distance on a largest common network component from the lung cancer data was much smaller than the Wasserstein distance on the same network using random radiomic features, implying the reliability of those radiomic features present in the network. Further analysis using the proposed Wasserstein K-means algorithm on TCIA head and neck cancer data showed that the resulting clusters separate tumor subsites and this was validated on our institution data.ConclusionsWe showed that a network-based analysis enables identifying reproducible radiomic features. This was validated using phantom data and external data via the Wasserstein distance metric and the proposed Wasserstein K-means method.

scholarly journals Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Wei Guo ◽  
Guoyun Gao ◽  
Jun Dai ◽  
Qiming Sun
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Cancer Patients ◽  
Network Model ◽  
Neural Network Model ◽  
Palliative Chemotherapy ◽  
Lung Infection ◽  
Lung Cancer Patients

Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the prediction model of infection in patients with lung cancer during chemotherapy by an artificial neural network (ANN). Based on the data of historical cases in our hospital, the variables were screened, and the prediction model was established. A logistic regression (LR) model was used to screen the data. The indexes with statistical significance were selected, and the LR model and back propagation neural network model were established. A total of 80 cases of advanced lung cancer patients with palliative chemotherapy were predicted, and the prediction performance of different model was evaluated by the receiver operating characteristic curve (ROC). It was found that age ≧ 60 years, length of stay ≧ 14  d, surgery history, combined chemotherapy, myelosuppression, diabetes, and hormone application were risk factors of infection in lung cancer patients during chemotherapy. The area under the ROC curve of the LR model for prediction lung infection was 0.729 ± 0.084 , which was less than that of the ANN model ( 0.897 ± 0.045 ). The results concluded that the neural network model is better than the LR model in predicting lung infection of lung cancer patients during chemotherapy.

Osteoprotegerin and Bone Mass in Squamous Cell Head and Neck Cancer Patients

2006 ◽  
Vol 78 (6) ◽  
pp. 343-347 ◽  
Author(s):  
C. Valero ◽  
J. M. Olmos ◽  
F. Rivera ◽  
J. L. Hernández ◽  
M. E. Vega ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Bone Mass ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Squamous Cell

scholarly journals Oesophageal squamous cell neoplasia in head and neck cancer patients: upregulation of COX-2 during carcinogenesis

2003 ◽  
Vol 88 (8) ◽  
pp. 1217-1222 ◽  
Author(s):  
K Maaser ◽  
P Däubler ◽  
B Barthel ◽  
B Heine ◽  
B von Lampe ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Squamous Cell ◽  
Cox 2

A historical review of head and neck cancer in celebrities

2006 ◽  
Vol 121 (6) ◽  
pp. 511-520 ◽  
Author(s):  
B J Folz ◽  
A Ferlito ◽  
N Weir ◽  
L W Pratt ◽  
A Rinaldo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Medical Literature ◽  
Public Awareness ◽  
Historical Review ◽  
Term Survival

Introduction: The illnesses of celebrity patients always receive more attention from the general public than those of ordinary patients. With regard to cancer, this fact has helped to spread information about the four major malignancies: breast cancer, prostatic cancer, lung cancer and colorectal cancer. Head and neck cancer, on the other hand, is still not well recognised by the lay public, although the risk factors are similar to those of lung cancer. It was the objective of this analysis to identify cases of celebrity patients, the description of which could help to increase awareness of head and neck cancer, its symptoms and risk factors.Methods: The Internet and medical literature databases were searched for celebrity patients who had suffered from head and neck cancer.Results: The search revealed numerous famous head and neck cancer patients. However, only seven cases were documented well in the medical literature. Among the identified persons were one emperor, two United States presidents, a legendary composer, a world-renowned medical doctor, an outstanding athlete and an extraordinary entertainer. In spite of their exclusive position in society, these patients did not have a better prognosis compared with ordinary patients of their time. Only two of the group experienced long term survival and only one was cured. None of these influential figures used their influence to fund research or to promote knowledge about their respective diseases.Conclusion: The identified cases could help increase public awareness of head and neck cancer. Similar to activities in other oncologic fields, current celebrity head and neck cancer patients should be encouraged to discuss their diseases openly, which could have a positive effect on public health.

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