MA13.02 Incidence of Venous Thromboembolism at the Time of Lung Cancer Diagnosis: A Multicenter, Prospective Observational Trial (Rising-VTE/NEJ037)

Introduction: The relationship between cancer and thromboembolic events has been known for a long time. Lung and venous thromboembolism are frequent complications of lung cancer and its treatment, being a great cause of morbidity and mortality. We pretend to establish the relationship between lung and venous thromboembolism and lung cancer, describe patient characteristics and analyze the impact in the survival and prognosis.Material and Methods: It was a retrospective study. All research subjects were selected from lung cancer patients with a newly diagnosed lung and venous thromboembolism event admitted to Hospital S. João, between January 2008 and December 2013 and were followed until December 2014. Statistical analysis was performed with SPSS.Results: From the search, we obtained 113 patients. The majority was male, smokers or ex-smokers, and adenocarcinoma was the most frequent histologic type, being diagnosed mostly in advanced stages. We noticed that the median time between lung cancer diagnosis and lung venous thromboembolism was 2.9 months. In 24 patients (21.4%), the lung cancer diagnosis occurred after the lung and venous thromboembolism event and in 86 patients (76.8%), it occurred before the event. After a median follow up of 1.4 months, 107 (94.7%) patients died, 1 (0.9%) was lost to follow-up and 5 (4.4%) were still alive. The median survival rate was 1.5 months.Discussion: The diagnosis of lung and venous thromboembolism in patients with lung cancer is associated with bad prognosis. It occurs most frequently in patients with advanced disease, in the first months after lung cancer diagnosis and after beginning chemotherapy.Conclusion: Disease progression is an independent predictor with negative impact in overall survival.

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Incidence of venous thromboembolism in advanced lung cancer and efficacy and safety of direct oral anticoagulant: A multicenter, prospective, observational study (Rising-VTE/NEJ037 Study).

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.12021 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 12021-12021

Author(s):

Yukari Tsubata ◽

Takamasa Hotta ◽

Kosuke Hamai ◽

Naoki Furuya ◽

Toshihide Yokoyama ◽

...

Keyword(s):

Lung Cancer ◽

Venous Thromboembolism ◽

Treatment Group ◽

Observational Study ◽

Cancer Diagnosis ◽

Prospective Observational Study ◽

Small Cell ◽

Small Cell Lung ◽

Lung Cancer Diagnosis ◽

Multicenter Prospective Observational Study

12021 Background: Venous thromboembolism (VTE) is a well-known kind of cancer-associated thrombosis and a common complication of malignancy. However, little is known about the incidence of VTE and the effectiveness of direct oral anticoagulants (DOACs) associated with lung cancer chemotherapy. Methods: The Rising-VTE/NEJ037 study was a multicenter, prospective, observational study with 40 participating Japanese institutions. A total of 1,021 patients diagnosed with lung cancer that was unsuitable for radical resection or radiation were enrolled and followed up for two years. The diagnosis of VTE was confirmed through a central review by two radiologists. Patients with VTE at the time of lung cancer diagnosis started treatment with edoxaban. The primary endpoint of this trial was the rate of newly diagnosed VTE after enrollment or the recurrence rate 6 months after the start of treatment with edoxaban. Results: Of the 1,021 enrolled patients, data were available for 1,008 patients. The median age was 70 years (range: 30-94 years), and 70.8% were males. Eighty-six percent of patients had non-small cell lung cancer, and 13.6% had small cell lung cancer. Histological types included adenocarcinoma (N = 641, 63.6%), squamous cell carcinoma (N = 187, 18.6%), and others (N = 42, 4.2%). Sixty-two patients (6.2%) had VTE at the time of lung cancer diagnosis, and 42 patients (4.2%) developed VTE during two years follow-up, making a total of 104 patients (10.3%). No cases of VTE recurrence were found 6 months after the start of treatment with edoxaban. Major and minor bleeding occurred in 95 patients (9.4%) and increased to 23% in the edoxaban treatment group. The two-year survival probability was 0.43 in the non-VTE group and 0.48 in the VTE with edoxaban treatment group, showing no difference. Conclusions: This study shows a high cumulative incidence of VTE, suggesting that attention should be paid to VTE during treatment for lung cancer. Treatment with edoxaban was highly effective in preventing recurrence of VTE, and there was no difference in survival with or without VTE, but treatment should be considered more carefully because of the high bleeding rate associated with DOAC. Clinical trial information: jRCTs061180025.

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Beyond the Lung Cancer Diagnosis: Leveraging the Oncology Clinic Setting for Actively Smoking Family Members

Case Medical Research ◽

10.31525/ct1-nct03966807 ◽

2019 ◽

Author(s):

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Family Members ◽

Clinic Setting ◽

Lung Cancer Diagnosis

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Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

Journal of Computer-Aided Design & Computer Graphics ◽

10.3724/sp.j.1089.2018.16919 ◽

2018 ◽

Vol 30 (1) ◽

pp. 90 ◽

Cited By ~ 3

Author(s):

Peng Zhang ◽

Xinnan Xu ◽

Hongwei Wang ◽

Yuanli Feng ◽

Haozhe Feng ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Cancer Diagnosis ◽

Lung Cancer Diagnosis ◽

Computer Aided

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Integration of Anatomic and Pathogenetic Bases for Early Lung Cancer Diagnosis

10.21236/ada468685 ◽

2007 ◽

Author(s):

Wei Qian

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Lung Cancer Diagnosis ◽

Early Lung Cancer

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Biased Lung Cancer Risk Perceptions: Smokers are Misinformed

Jahrbücher für Nationalökonomie und Statistik ◽

10.1515/jbnst-2018-0017 ◽

2018 ◽

Vol 238 (5) ◽

pp. 395-421 ◽

Cited By ~ 1

Author(s):

Nicolas R. Ziebarth

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Cancer Diagnosis ◽

Mortality Risk ◽

Risk Perceptions ◽

Lung Cancer Risk ◽

Lifetime Risk ◽

Lung Cancer Diagnosis ◽

Cancer Risk Perceptions ◽

Overall Mortality

Abstract This paper empirically investigates biased beliefs about the risks of smoking. First, it confirms the established tendency of people to overestimate the lifetime risk of a smoker to contract lung cancer. In this paper’s survey, almost half of all respondents overestimate this risk. However, 80% underestimate lung cancer deadliness. In reality, less than one in five patients survive five years after a lung cancer diagnosis. Due to the broad underestimation of the lung cancer deadliness, the lifetime risk of a smoker to die of lung cancer is underestimated by almost half of all respondents. Smokers who do not plan to quit are significantly more likely to underestimate this overall mortality risk.

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AKT question relating to lung cancer diagnosis

InnovAiT Education and inspiration for general practice ◽

10.1177/17557380211014008s ◽

2021 ◽

Vol 14 (7) ◽

pp. e74-e74

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Lung Cancer Diagnosis

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An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽

10.3390/math9131457 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1457

Author(s):

Muazzam Maqsood ◽

Sadaf Yasmin ◽

Irfan Mehmood ◽

Maryam Bukhari ◽

Mucheol Kim

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Ct Scan ◽

Cancer Diagnosis ◽

Lung Nodule ◽

Search Space ◽

Lung Nodules ◽

Suggested Model ◽

Lung Cancer Diagnosis ◽

Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

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