The role of computed tomography in the diagnosis of lung cancer - a case report

Introduction Lung cancer has been the main oncological problem in the world for years. It is extremely important to use appropriate diagnostic methods that enable its detection and implementation of appropriate treatment. Aim The presented case shows the advantage of computed tomography over chest X-ray (X-ray) in visualizing neoplastic changes in the lungs. Case Study The paper presents a description of a patient diagnosed with centrally located advanced lung adenocarcinoma with a strong expression of PD-L1 qualified for treatment with pembrolizumab. Results and Discussion Presented case confirms that X-ray is less sensitive, especially in the case of centrally located tumors. Therefore, the emergence of a new cough in a smoker or ex-smoker should raise concerns related to lung cancer despite a normal X-ray image. The central location of the tumor may cause dramatic course of the symptoms. In the presented case, a sudden significant deterioration of the condition was observed due to atelectasis of the entire lung. Haemoptysis observed during hospitalization was another symptom of centraly located tumor mass. Conclusions In conclusion, the history of cigarette smoking, presence of typical symptoms should provide an in-depth diagnosis of lung cancer, despite normal X-ray. Diagnostic procedures include computed tomography in the first place. The course of centrally localized disease may change rapidly during on first cycle of treatment. Due to the possibility of serious complications of the ongoing neoplastic disease, the patient should be under constant medical supervision.

Development, validation, and evaluation of prediction models to identify individuals at high risk of lung cancer for screening in the English primary care population using the QResearch database: research protocol and statistical analysis plan

Background and research aim: Lung cancer is a research priority in the UK. Early diagnosis of lung cancer can improve patients' survival outcomes. The DART-QResearch project is part of a larger academic-industrial collaborative initiative, using big data and artificial intelligence to improve patient outcomes with thoracic diseases. There are two general research aims in the DART-QResearch project: (1) to understand the natural history of lung cancer, (2) to develop, validate, and evaluate risk prediction models to select patients at high risk for lung cancer screening. Methods: This population-based cohort study uses the QResearch database (version 45) and includes patients aged between 25 and 84 years old and without a diagnosis of lung cancer at cohort entry (study period: 1 January 2005 to 31 December 2020). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. The following statistical techniques will be used for different research objectives, including descriptive statistics, multi-level modelling, multiple imputation for missing data, fractional polynomials to explore non-linear relationships between continuous variables and the outcome, and Cox regression for the prediction model. We will update our QCancer (lung, 10-year risk) algorithm, and compare it with the other two mainstream models (LLP and PLCOM2012) for lung cancer screening using the same dataset. We will evaluate the discrimination, calibration, and clinical usefulness of the prediction models, and recommend the best one for lung cancer screening for the English primary care population. Discussion: The DART-QResearch project focuses on both symptomatic presentation and asymptomatic patients in the lung cancer care pathway. A better understanding of the patterns, trajectories, and phenotypes of symptomatic presentation may help GPs consider lung cancer earlier. Screening asymptomatic patients at high risk is another route to achieve earlier diagnosis of lung cancer. The strengths of this study include using large-scale representative population-based clinical data, robust methodology, and a transparent research process. This project has great potential to contribute to the national cancer strategic plan and yields substantial public and societal benefits through earlier diagnosis of lung cancer.

Analysis on the Effects of CT- and Ultrasound-Guided Percutaneous Transthoracic Needle Biopsy Combined with Serum CA125 and CEA on the Diagnosis of Lung Cancer

The number of patients with lung cancer is difficultly diagnosed in the early stage. The purpose of the study was to investigate the effects of CT- and ultrasound-guided percutaneous transthoracic needle biopsy combined with serum CA125 and CEA on the diagnosis of lung cancer. 120 patients with suspected lung cancer admitted to our hospital from January 2019 to January 2020 were selected and divided into an ultrasound group (n = 60) and CT group (n = 60), according to different percutaneous transthoracic needle biopsy modalities. All patients received serum tumor markers detection, so as to compare the CT- and ultrasound-guided percutaneous transthoracic needle biopsy results and pathology results, levels of serum tumor markers among all patients and the patients with different lung cancer types, and diagnostic efficacy of tumor markers, as well as complication rate (CR) in patients. The sensitivity and specificity of ultrasound-guided percutaneous transthoracic needle biopsy were 0.880 and 0.800, respectively, while those of CT-guided percutaneous transthoracic needle biopsy were 0.909 and 0.625, respectively; the CA125 and CEA levels in the lung cancer group were higher than those in the benign group ( P < 0.001 ); the CA125 and CEA levels of the patients with adenocarcinoma were higher than those with squamous carcinoma, and the CEA levels of the patients with small-cell carcinoma were lower than those with adenocarcinoma ( P < 0.05 ); the sensitivity, specificity, and Youden indexes of CA125 were 0.638, 0.833, and 0.471, respectively, while those of CEA were 0.766, 0.778, and 0.544, respectively; there were no significant differences in CR between the two groups ( P > 0.05 ). CT- and ultrasound-guided percutaneous transthoracic needle biopsy is a safe and feasible diagnostic modality for lung cancer, and its combination with serum CA125 and CEA can significantly improve the accuracy of the detection results, which is worthy of promotion and application in clinical practice.

A Novel Approach of Lung Tumor Segmentation Using a 3D Deep Convolutional Neural Network

Cancer is one of the most life-threatening diseases in the world, and lung cancer is the leading cause of death worldwide. If not detected at an early stage, the survival rate of lung cancer patients can be very low. To treat patients in later stages, one needs to analyze the tumour region. For accurate diagnosis of lung cancer, the first step is to detect and segment the tumor. In this chapter, an approach for segmentation of a lung tumour is presented. For pre-processing of lung CT images, simple image processing like morphological operations is used, and for tumour segmentation task, a 3D convolutional neural network (CNN) is used. The CNN architecture consists of a 3D encoder block followed by 3D decoder block just like U-Net but with deformable convolution blocks. For this study, two datasets have been used; one is the online-available NSCLC Radiomics dataset, and the other is collected from an Indian local hospital. The approach proposed in this chapter is evaluated in terms of dice coefficient. This approach is able to give significant results with a dice coefficient of 77.23%.

Autoantibody Signatures Discovered by HuProt Protein Microarray to Enhance the Diagnosis of Lung Cancer

Background: This study aims to comprehensively discover novel autoantibodies (TAAbs) against tumor-associated antigens (TAAs) and establish diagnostic models for assisting in the diagnosis of lung cancer (LC) and discrimination of pulmonary nodules (PN).Methods: HuProt human microarray was used to discover the candidate TAAs and Enzyme-linked immunosorbent assay (ELISA) was performed to detect the level of TAAbs in 634 participants of two independent validation cohorts. Logistic regression analysis was used to construct models. Receiver operating characteristic curve (ROC) analysis was utilized to assess the diagnostic value of models.Results: Eleven TAAs were discovered by means of protein microarray and data analysis. The level of ten TAAbs (anti-SARS, anti-ZPR1, anti-FAM131A, anti-GGA3, anti-PRKCZ, anti-HDAC1, anti-GOLPH3, anti-NSG1, anti-CD84 and anti-EEA1) was higher in LC patients than that in NC of validation cohort 1 (P<0.05). The model 1 comprising 4 TAAbs (anti-ZPR1, anti-PRKCZ, anti-NSG1 and anti-CD84) and CEA reached an AUC of 0.813 (95%CI: 0.762-0.864) for diagnosing LC from normal individuals. 5 of 10 TAAbs (anti-SARS, anti-GOLPH3, anti-NSG1, anti-CD84 and anti-EEA1) existed a significant difference between malignant pulmonary nodules (MPN) and benign pulmonary nodules (BPN) patients in validation cohort 2 (P<0.05). Model 2 consisting of anti-EEA1, traditional biomarkers (CEA, CYFRA211 and CA125) and 3 CT characteristics (vascular notch sign, lobulation sign, mediastinal lymph node enlargement) could distinguish MPN from BPN patients with an AUC of 0.845 (sensitivity: 58.3%, specificity: 96.6%).Conclusions: High-throughput protein microarray is an efficient approach to discovering novel TAAbs which could increase the accuracy of lung cancer diagnosis in the clinic.

Risk Factors, Treatment and Prognosis of Patients with Lung Cancer after Heart Transplantation

Long-term survival after heart transplantation (HTX) is impacted by adverse effects of immunosuppressive pharmacotherapy, and post-transplant lung cancer is a common occurrence. This study aimed to examine the risk factors, treatment, and prognosis of patients with post-transplant lung cancer. We included 625 adult patients who received HTX at Heidelberg Heart Center between 1989 and 2018. Patients were stratified by diagnosis and staging of lung cancer after HTX. Analysis comprised donor and recipient characteristics, medications including immunosuppressive drugs, and survival after diagnosis of lung cancer. A total of 41 patients (6.6%) were diagnosed with lung cancer after HTX, 13 patients received curative care and 28 patients had palliative care. Mean time from HTX until diagnosis of lung cancer was 8.6 ± 4.0 years and 1.8 ± 2.7 years from diagnosis of lung cancer until last follow-up. Twenty-four patients (58.5%) were switched to an mTOR-inhibitor after diagnosis of lung cancer. Multivariate analysis showed recipient age (HR: 1.05; CI: 1.01–1.10; p = 0.02), COPD (HR: 3.72; CI: 1.88–7.37; p < 0.01), and history of smoking (HR: 20.39; CI: 2.73–152.13; p < 0.01) as risk factors for post-transplant lung cancer. Patients in stages I and II had a significantly better 1-year (100.0% versus 3.6%), 2-year (69.2% versus 0.0%), and 5-year survival (53.8% versus 0.0%) than patients in stages III and IV (p < 0.01). Given the poor prognosis of late-stage post-transplant lung cancer, routine reassessment of current smoking status, providing smoking cessation support, and intensified lung cancer screening in high-risk HTX recipients are advisable.