Survival Rates in Lung Cancer Patients with and without Bronchial Asthma

1993 ◽  
Vol 32 (5) ◽  
pp. 517-520 ◽  
Author(s):  
Erkki Vesterinen ◽  
Sakari Karjalainen ◽  
Tuomo Timonen ◽  
Eero Pukkala ◽  
Timo Hakulinen
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Bronchial Asthma ◽  
Survival Rates ◽  
Lung Cancer Patients

scholarly journals P2.16-14 Survival Rates in Non-Small Cell Lung Cancer Patients in the Brazilian Private Health System: A Cohort Study

2019 ◽  
Vol 14 (10) ◽  
pp. S870
Author(s):  
E. Cerqueira ◽  
C.G. Ferreira ◽  
R. Buzzatti Peixoto ◽  
P. Mendoça Batista ◽  
M. Datz Abadi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cohort Study ◽  
Health System ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Survival Rates ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancer Patients ◽  
System A

scholarly journals Personalized drug-response prediction model for lung cancer patients using machine learning

2020 ◽  
Author(s):  
Rizwan Qureshi
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Targeted Therapies ◽  
Drug Response ◽  
Kinase Inhibitors ◽  
Binding Free Energy ◽  
Survival Rates ◽  
Drug Binding ◽  
Geometrical Properties ◽  
Lung Cancer Patients

Lung cancer caused by mutations in the epidermal growth factor receptor (EGFR) is a major cause of cancer deaths worldwide. EGFR Tyrosine kinase inhibitors (TKIs) have been developed, and have shown increased survival rates and quality of life in clinical studies. However, drug resistance is a major issue, and treatment efficacy is lost after about an year. Therefore, predicting the response to targeted therapies for lung cancer patients is a significant research problem. In this work, we address this issue and propose a personalized model to predict the drug-response of lung cancer patients. This model uses clinical information, geometrical properties of the drug binding site, and the binding free energy of the drug-protein complex. The proposed model achieves state of the art performance with 97.5% accuracy, 100% recall, 95% precision, and 96.3% F1-score with a random forest classifier. This model can also be tested on other types of cancer and diseases, and we believe that it may help in taking optimal clinical decisions for treating patients with targeted therapies

scholarly journals The prognostic value of PET/CT in clinical stage I lung cancer patients: A propensity-match analysis.

2019 ◽  
Author(s):  
Bing-Yen Wang ◽  
Wei-Heng Hung ◽  
Jing-Yang Huang ◽  
Heng-Chung Chen ◽  
Ching-Hsiung Lin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Clinical Stage ◽  
Survival Rates ◽  
Small Cell ◽  
Stage I ◽  
Small Cell Lung ◽  
Lung Cancer Patients ◽  
Pet Ct ◽  
Stage I Lung Cancer

Abstract Introduction The application of PET/CT directly improved treatment choice and management in 25% of non-small cell lung cancer patients and 29% of small cell lung cancer patients. However, the long-term outcome of altering the management of these patients remains unclear. The aim of this study was to compare the 5-year overall survival rates of two groups of clinical stage I lung cancer patients: those who received PET/CT and those who did not.Methods Data were obtained from the Taiwan Society of Cancer Registry. There were 6,587 clinical stage I lung cancer patients analyzed between 2009 and 2014 in this retrospective study. We performed propensity matching to reduce the bias; it resulted in both groups having 2,649 patients. We measured the 1, 3, and 5-year survival rates of all clinical stage I lung cancer patients and the survival rates of pathological I, II and III lung cancer patients and compared the survival rates between clinical stage I lung cancer patients with PET/CT scans and patients without PET/CT scans.Results The 1, 3, and 5-year survival rates of all clinical stage I lung cancer patients are 97.2%, 88.2% and 79.0%, respectively. The 1, 3, and 5-year survival rates are 97.0%, 88.2% and 79.8% in the PET/CT group and 97.5%, 88.1% and 78.2% in the no PET/CT group; there was no statistical difference (p= 0.6528).Conclusion Although stage I lung cancer patients who received PET/CT had their management strategies modified and avoided any unnecessary thoracotomies, our data showed that there was no 5-year survival benefit for these patients.

Clinical outcome of chemoradiotherapy in elderly patients with non-small cell lung cancer at G.V Montgomery Veterans Affairs Medical Center

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 19683-19683
Author(s):  
M. Choi ◽  
W. Chan ◽  
J. Jaiwatana ◽  
T. Khansur
Keyword(s):  
Lung Cancer ◽  
Radiation Therapy ◽  
Elderly Patients ◽  
Cancer Patients ◽  
Median Survival ◽  
Concurrent Chemoradiotherapy ◽  
Medical Center ◽  
Survival Rates ◽  
The United States ◽  
Lung Cancer Patients

19683 Background: More than 2/3 of lung cancer patients are age = 65 and the proportion of elderly patients are expected to rise in the United States. However data on the use of optimal chemoradiotherapy in this group of patients are limited. Methods: All lung cancer patients = 65 years, who received both chemotherapy and radiation therapy at the G. V. Montgomery VAMC between Jan 2000 to Dec 2005, were analyzed from tumor registry and computerized medical records. Patients who only received palliative radiation therapy for bone and brain metastasis were excluded. Results: Among 652 lung cancer patients diagnose, 46 patients = 65 years received both chemotherapy and radiation therapy over the 6 year study period. The median age was 72 (range:65–84) and 70% of the patients were = 70 years. All patients were male with 65% white and 35% black population. The majority of patients were stage III (85%) while there was one stage I and six stage II patients. 41 patients (89%) were able to complete the planned radiation therapy and median dose delivered was 6140 cGy. The chemotherapy regimen was carboplatin and paclitaxel either weekly during radiation therapy and every three weeks in sequential treatment. There was only 1 treatment related mortality and only 15% of patients survived less than 6 months. The 1, 2, 3 year survival rates were 67%, 24%, and 15% respectively and median survival was 15.3 months. 15 patients were treated sequentially(S) with chemotherapy (median cycle-3) followed by radiation therapy and 31 patients concurrently (C) with chemoradiotherapy. (median- 5 weekly treatment) The survival among the two groups did not differ statistically. (median survival-19.1 month (S) vs. 14 month (C) p=0.78) Conclusions: Both sequential and concurrent chemoradiotherapy is feasible and beneficial in elderly patients with lung cancer. Sequential treatments might be as effective as concurrent chemoradiotherapy in elderly VA patient population. No significant financial relationships to disclose.

Survival rates of older lung cancer patients: Analysis from the SEER database.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. e19683-e19683
Author(s):  
M. Prabhakar ◽  
B. Kanapuru ◽  
A. Jemal ◽  
W. B. Ershler ◽  
J. W. Yates
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Survival Rates ◽  
Seer Database ◽  
Lung Cancer Patients

Clinical profile, pathological characteristics, mutational profile and disease outcome of non-small cell lung cancer patients treated at the Benavides Cancer Institute-University of Santo Tomas Hospital : A five-year study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e20580-e20580
Author(s):  
Maria Diana Aileen Chua Bautista ◽  
Priscilla B. Caguioa
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Survival Rates ◽  
Small Cell ◽  
Smoking History ◽  
Small Cell Lung ◽  
Lung Cancer Patients

e20580 Background: Lung cancer is the most commonly diagnosed cancer worldwide and its incidence continues to grow. Lung cancer mortality and survival rates vary markedly by race and ethnicity resulting into a public health burden that differs by population subgroups. This study determined the association of the clinical profile, pathological characteristics and mutational profile with the disease outcome of Non Small Cell Lung Cancer patients at the Benavides Cancer Institute – University of Santo Tomas Hospital from January 1, 2007 to December 31, 2011. Methods: This is a retrospective descriptive study. Medical records of all of patients diagnosed with non-small cell lung cancer were included. Results: A total of 80 patients with non small cell lung cancer were treated, 78.75% (63) were adenocarcinoma and 13.75% (11) were squamous cell carcinoma. Overall 5-year survival rate was at 5%. The 1 year, 2-year, 3-year survival rates were 38.75%, 21.25% and 12.5% respectively with the mean survival time of 1.6 years. The mean survival time of patients who received adjuvant treatment was 26 months, while for metastatic patients was 18 months. Only the stage at time of diagnosis was correlated with overall survival. Age, gender, family history, smoking history, number of pack years smoking history, histologic subtype, response after first line of treatment and sites of metastases were not associated with overall survival. EGFR mutational analysis was not performed in this study since it was not the standard of care and had limited availability in low to middle income countries at the time of the study period. Conclusions: Similar to global incidence, adenocarcinoma remains the most common type of lung cancer in our center. Stage at diagnosis predicts overall survival among lung cancer patients. There is a need for molecular immunotyping to further characterize patients and response to treatment.

scholarly journals Thirty-day mortality and five-year survival in thoracic surgery: “real-world” assessment of outcomes from a single-institution audit

2018 ◽  
Vol 104 (2) ◽  
pp. 96-104 ◽  
Author(s):  
Anna Cantarutti ◽  
Carlotta Galeone ◽  
Giovanni Leuzzi ◽  
Elena Bertocchi ◽  
Giovanna Pomponi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Thoracic Surgery ◽  
Cancer Patients ◽  
Real World ◽  
Surgical Procedure ◽  
Lung Metastases ◽  
Survival Rates ◽  
Single Institution ◽  
Lung Cancer Patients ◽  
Extent Of Surgery

Background: Accurate measurement of outcomes is essential to monitor the effectiveness of public health policies. In Italy, the Ministry of Health has chosen 30-day mortality after major surgical or medical procedures as the main outcome measure, pooling all pulmonary resections for malignancy in a single category. The present audit evaluated all pulmonary resections performed over a 13-year period in a single institution to assess the immediate (30-day mortality) and long-term (5-year survival) outcomes according to type and stage of disease and extent of surgery. Methods: We analyzed the results of 4,234 first pulmonary resections performed from 2003 to 2015 for lung cancer (2,636), lung metastases (1,080), other primary cancers (259) and benign diseases (259). The median follow-up of cancer patients was 4.1 years. Results: Overall 30-day mortality was 1.1%, being 1.2% for lung cancer, 0.3% for lung metastases, 3.5% for pneumonectomies, 1% for lobectomies, and 0.5% for sublobar resections. Among lung cancer patients, 30-day mortality was 0.7% for simple anatomical resections, 2.8% for complex resections, 0.7% for stage I, and 1.6% for higher stages. Overall 5-year survival was 56% for lung cancer, 49% for lung metastases, and 53% for other primary cancers (p = 0.03). According to the surgical procedure for lung cancer, 5-year survival was 60%, 55% and 36% for lobectomies, segmentectomies and pneumonectomies, respectively (p<0.0001). Conclusions: For better monitoring of thoracic surgery outcomes in a real-world setting, we suggest evaluating lung cancer separately from other thoracic malignancies, and including 5-year survival rates stratified by resection volume and surgical procedure complexity.

scholarly journals Personalized drug-response prediction model for lung cancer patients using machine learning

2020 ◽  
Author(s):  
Rizwan Qureshi
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Targeted Therapies ◽  
Drug Response ◽  
Kinase Inhibitors ◽  
Binding Free Energy ◽  
Survival Rates ◽  
Drug Binding ◽  
Geometrical Properties ◽  
Lung Cancer Patients

Lung cancer caused by mutations in the epidermal growth factor receptor (EGFR) is a major cause of cancer deaths worldwide. EGFR Tyrosine kinase inhibitors (TKIs) have been developed, and have shown increased survival rates and quality of life in clinical studies. However, drug resistance is a major issue, and treatment efficacy is lost after about an year. Therefore, predicting the response to targeted therapies for lung cancer patients is a significant research problem. In this work, we address this issue and propose a personalized model to predict the drug-response of lung cancer patients. This model uses clinical information, geometrical properties of the drug binding site, and the binding free energy of the drug-protein complex. The proposed model achieves state of the art performance with 97.5% accuracy, 100% recall, 95% precision, and 96.3% F1-score with a random forest classifier. This model can also be tested on other types of cancer and diseases, and we believe that it may help in taking optimal clinical decisions for treating patients with targeted therapies

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