scholarly journals Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest

2020 ◽  
Vol 10 (18) ◽  
pp. 6425
Author(s):  
Natascha Claudia D’Amico ◽  
Rosa Sicilia ◽  
Ermanno Cordelli ◽  
Lorenzo Tronchin ◽  
Carlo Greco ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Small Cell Lung Cancer ◽  
State Of The Art ◽  
Local Binary Patterns ◽  
Research Field ◽  
Small Cell ◽  
The Other ◽  
Small Cell Lung

Lung cancer accounts for the largest amount of deaths worldwide with respect to the other oncological pathologies. To guarantee the most effective cure to patients for such aggressive tumours, radiomics is increasing as a novel and promising research field that aims at extracting knowledge from data in terms of quantitative measures that are computed from diagnostic images, with prognostic and predictive ends. This knowledge could be used to optimize current treatments and to maximize their efficacy. To this end, we hereby study the use of such quantitative biomarkers computed from CT images of patients affected by Non-Small Cell Lung Cancer to predict Overall Survival. The main contributions of this work are two: first, we consider different volumes of interest for the same patient to find out whether the volume surrounding the visible lesions can provide useful information; second, we introduce 3D Local Binary Patterns, which are texture measures scarcely explored in radiomics. As further validation, we show that the proposed signature outperforms not only the features automatically computed by a deep learning-based approach, but also another signature at the state-of-the-art using other handcrafted features.

scholarly journals Use tumor suppressor genes as biomarkers for diagnosis of non-small cell lung cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chuantao Zhang ◽  
Man Jiang ◽  
Na Zhou ◽  
Helei Hou ◽  
Tianjun Li ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Mortality Rate ◽  
Tumor Suppressor ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Network Effects ◽  
Small Cell ◽  
The Other ◽  
Small Cell Lung ◽  
High Efficient

AbstractLung cancer is the leading cause of death worldwide. Especially, non-small cell lung cancer (NSCLC) has higher mortality rate than the other cancers. The high mortality rate is partially due to lack of efficient biomarkers for detection, diagnosis and prognosis. To find high efficient biomarkers for clinical diagnosis of NSCLC patients, we used gene differential expression and gene ontology (GO) to define a set of 26 tumor suppressor (TS) genes. The 26 TS genes were down-expressed in tumor samples in cohorts GSE18842, GSE40419, and GSE21933 and at stages 2 and 3 in GSE19804, and 15 TS genes were significantly down-expressed in tumor samples of stage 1. We used S-scores and N-scores defined in correlation networks to evaluate positive and negative influences of these 26 TS genes on expression of other functional genes in the four independent cohorts and found that SASH1, STARD13, CBFA2T3 and RECK were strong TS genes that have strong accordant/discordant effects and network effects globally impacting the other genes in expression and hence can be used as specific biomarkers for diagnosis of NSCLC cancer. Weak TS genes EXT1, PTCH1, KLK10 and APC that are associated with a few genes in function or work in a special pathway were not detected to be differentially expressed and had very small S-scores and N-scores in all collected datasets and can be used as sensitive biomarkers for diagnosis of early cancer. Our findings are well consistent with functions of these TS genes. GSEA analysis found that these 26 TS genes as a gene set had high enrichment scores at stages 1, 2, 3 and all stages.

scholarly journals Impact of neutrophil-to-lymphocyte ratio throughout the course of chemoradiotherapy on overall survival and distant failure in unresectable stage III non-small cell lung cancer

2021 ◽  
Author(s):  
Hiromitsu Kanzaki ◽  
Yasushi Hamamoto ◽  
Kei Nagasaki ◽  
Toshiyuki Kozuki
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Small Cell ◽  
Stage Iii ◽  
Small Cell Lung ◽  
Related Factors ◽  
Distant Failure ◽  
Unresectable Stage ◽  
Significant Factors

Abstract Purpose Neutrophil-to-lymphocyte ratio (NLR) has been reported to be associated with treatment outcomes in various cancers; however, the optimal timing to measure NLR is unclear. In this study, “average-NLR” was newly devised, which reflects the NLR throughout the course of radiotherapy, and its usefulness was assessed for stage III non-small cell lung cancer (NSCLC) patients treated with chemoradiotherapy. Materials and methods A total of 111 patients who received definitive chemoradiotherapy for unresectable stage III NSCLC were reviewed. Patient/tumor-related factors, treatment-related, and NLR-related factors (average-NLR, pre- and post-radiotherapy NLR, NLR-nadir, NLR-maximum) were assessed using univariate and multivariate analyses. Results The median follow-up period was 43.8 months among the survivors. In the multivariate analysis, average-NLR and post-radiotherapy NLR were significant factors for the overall survival (OS) (p = 0.016 and 0.028) and distant failure (DF) (p = 0.008 and 0.040). For the patients with low, intermediate, and high average-NLR, the median OS was 41.2, 37.7, and 14.8 months, respectively, and the median DF free time was 52.5, 13.5, and 8.9 months, respectively. Conclusion Average-NLR and post-radiotherapy NLR were significant factors for the OS and DF. Average-NLR, which was available immediately after the completion of chemoradiotherapy, seemed to be helpful for treatment decisions.

Impact of analgesia on cancer recurrence and mortality within 5 years after stage I non-small cell lung cancer resection

2021 ◽  
pp. 030089162110200
Author(s):  
Giulio Luca Rosboch ◽  
Edoardo Ceraolo ◽  
Ilaria De Domenici ◽  
Francesco Guerrera ◽  
Eleonora Balzani ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Propensity Score ◽  
Small Cell Lung Cancer ◽  
Cumulative Incidence ◽  
Cancer Recurrence ◽  
Small Cell ◽  
Stage I ◽  
Small Cell Lung ◽  
Perioperative Analgesia

Objective: The choice of analgesia after cancer surgery may play a role in the onset of cancer recurrence. Particularly opioids seem to promote cancer cell proliferation and migration. Based on this consideration, we assessed the impact of perioperative analgesia choice on cancer recurrence after curative surgery for stage I non-small cell lung cancer (NSCLC). Methods: We retrospectively reviewed the records of all patients undergoing lung resection for stage I NSCLC between January 2005 and December 2012. Patients received analgesia either by peridural (PERI group) or intravenous analgesia with opioids (EV group). Follow-up was concluded in August 2019. Five-year cumulative incidence of recurrence and overall survival were evaluated and adjusted using a propensity score matching method. Results: A total of 382 patients were evaluated, 312 belonging to the PERI group (81.7%) and 70 to the EV group (18.3%). There was no statistically significant difference between the two groups in 5-year cumulative incidence of recurrence ( p = 0.679) or overall survival rates ( p = 0.767). These results were confirmed after adjustment for propensity score matching for cumulative incidence of recurrence ( p = 0.925) or overall survival ( p = 0.663). Conclusions: We found no evidence suggesting an association between perioperative analgesia choice and recurrence-free survival or overall survival in patients undergoing surgical resection of stage I NSCLC.

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