Current Advances in Computational biology of Lung Cancer

Lung cancer is the main basis of cancer death amongst men and women, making up almost 25% of the world’s total cancer deaths. Lung cancer described for nearly 1.6 million deaths in 2012 and 1.80 million deaths in 2020. Small cell lung cancer and non-small-cell lung cancer are the two key categories of Lung cancer. The signs of lung cancer include hemoptysis, weight loss, shortness of breath and chest pain. Lung cancer treated by chemotherapy, surgery and CT scan. In this review paper, one of the most crucial zones aiming lung cancer diagnosis has been discussed. Computer-aided diagnosis (CAD) systems adapted for lung cancer can increase the patients’ survival chances. A typical CAD system for lung cancer functions in the fields of lung segmentation, detecting lung nodules and the diagnosis of the nodules as benign or malignant. CAD systems for lung cancer are examined in a huge number of research case studies. CAD system steps and outlining of inhibitor genes at molecular level is being discussed. An insight into multi-omics and molecular dynamics simulations is also given in this paper.

Smoking cessation after lung cancer diagnosis improves disease prognosis

The presented clinical and epidemiological study is the world»s first large prospective study of the effect of smoking cessation after lung cancer (LC) diagnosis on the prognosis. Follow‑up of 517 patients with NSCLC for 7 years in average showed that continued smoking after diagnosis is a serious negative prognostic factor. At the same time smoking cessation improves OS and PFS by 22,6 months and specific cancer mortality by 22,8 months; reduces the risk of all‑cause mortality by 33 %, the risk of progression by 30 % and the risk of specific cancer mortality by 25 %. Almost 60 % of patients in our study continued smoking after diagnosis. Consequently, they had avoidable excess mortality which eventually reduced their life by 2 years.The positive effect of smoking cessation after diagnosis found in our study significantly exceeds the «meaningful benefit» (improvement in median overall survival by 2,5–6 months) for antineoplastic agents proposed by the American Society of Clinical Oncology (ASCO). Moreover, the study suggests that the benefits of smoking cessation after LC diagnosis are at least equal or superior to the significant results obtained in clinical studies of the effectiveness of innovative treatments.We hope that the results of our study will contribute to the inclusion of smoking cessation in clinical guidelines for the treatment of NSCLC and other cancers. The treatment program for cancer patients should include evidence‑based methods of smoking cessation presented in the form of «Clinical Guidelines for Smoking Cessation for Cancer Patients».Treating smoking in cancer patients is cost‑effective for the health care system, especially when compared to other treat‑ments. Conversely, continuing smoking after diagnosis significantly increases treatment costs.The introduction of recommendations on smoking cessation and treatment of nicotine addiction into the practice will improve the overall mortality rate by 30–35 % in more than 60,000 patients annually diagnosed with lung cancer in Russia. The clinical value of this method is obvious, since it has been proven to be highly efficient in improving the life expectancy of patients, and, ultimately, in reducing cancer mortality in Russia.

Advances in lung cancer diagnosis and treatment - a review

Lung cancer is the second and third most common cancer in Iceland for females and males, respectively. Although the incidence is declining, lung cancer still has the highest mortality of all cancers in Iceland. Symptoms of lung cancer can be specific and localized to the lungs, but more commonly they are unspecific and result in significant diagnostic delay. Therefore, majority of lung cancer patients are diagnosed with non-localized disease. In recent years, major developments have been made in the diagnosis and treatment of lung cancer. Positive emission scanning (PET) and both transbroncial (EBUS) or transesophageal ultrasound (EUS) biopsy techniques have resulted in improved mediastinal staging of the disease and minimal invasive video-assisted thoracic surgery (VATS) has lowered postoperative complications and shortened hospital stay. Technical developments in radiotherapy have benefitted those patients who are not candidates for curative surgery. Finally, and most importantly, recent advances in targeted chemotherapeutics and development of immunomodulating agents have made individual tailoring of treatment possible. Recent screening-trials with low-dose computed tomography show promising results in lowering mortality. This evidence-based review focuses on the most important developments in the diagnosis and treatment of lung cancer, and includes Icelandic studies in the field.

Influence of Psychological Factors in Breast and Lung Cancer Risk – A Systematic Review

Introduction: In 2020, according to the Global Cancer Observatory, nearly 10 million people died of cancer. Amongst all cancers, breast cancer had the highest number of new cases and lung cancer had the highest number of deaths. Even though the literatures suggest a possible connection between psychological factors and cancer risk, their association throughout studies remains inconclusive. The present systematic review studied the connection between psychological factors and the risk of breast and lung cancer, prior to a cancer diagnosis. The psychological factors of trauma, grief, and depression were studied.Methods: The current systematic review was carried out across multiple databases in two phases, an initial exploratory research in June 2020, refined with a second electronic research in December 2020. The inclusion criteria included studies describing the association between trauma, posttraumatic stress disorder (PTSD), grief, and depression with breast and lung cancer risk. The psychological data collection must have been carried out prior to a confirmed breast or lung cancer diagnosis, and accessed through self-report measures, questionnaires, clinical interviews, or clinical diagnoses. Study reports had to contain information about the incidence of cancer and effect size. The exclusion criteria were studies in which psychological factors were collected after cancer diagnosis.Results and Conclusion: A total of 26 studies were included. Although non-consensual, the findings from the present systematic review suggest that, in addition to the known risk factors, psychological factors may play an important role in the etiology of both breast and lung cancer. To include psychological factors as a variable that affects cancer development may be fundamental to opening new avenues for prevention and intervention.Systematic Review Registration: [www.ClinicalTrials.gov], identifier [CRD42020209161].

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.

DETECTION OF LUNG CANCER USING IMAGE PROCESSING

This project constructs and assesses an image processing approach for lung cancer diagnosis in this study. Image processing techniques are frequently utilized for picture improvement in the detection phase to enable early medical therapy in a variety of medical issues. We suggested a lung cancer detection approach based on picture segmentation in this study. Image segmentation is a level of image processing that is intermediate. To segment a CT scan image, a marker control watershed and region growth technique is applied. Following the detection phases, picture augmentation with the Gabor filter, image segmentation, and feature extraction is performed. We discovered the efficiency of our strategy based on the experimental results. The results demonstrate that the watershed with the masking method, which has great accuracy and robustness, is the best strategy for detecting major features. Keywords: Lung cancer, MATLAB, CT images, Distortion removal, Segmentation, Mortality rate.

Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics

The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.

Opting for Local Region in SHOX2 Promoter as a DNA Methylation Biomarker for Lung Cancer Diagnosis

Epigenetic alterations play a main role in the initiation and progression of lung cancer. CpG methylation in the promoter of the Short Stature Homeobox 2 (SHOX2) gene has been evaluated and validated at different stages of this malignant disease. Several approaches for measuring DNA methylation have been established, including quantitative methylation-specific PCR (qMSP). This is a simple, fast, and cost-effective technique that can be easily applied to clinical practice. In this study, formalin-fixed, paraffin-embedded (FFPE) tissue samples were collected from 30 lung cancer patients and 30 patients suffering from non-cancerous pulmonary diseases. The methylation level of SHOX2 was evaluated in two CpG-riched regions by using qMSP and one of them could be conferred as a potential biomarker to lung cancer.