scholarly journals Tumor Margin Contains Prognostic Information: Radiomic Margin Characteristics Analysis in Lung Adenocarcinoma Patients

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1676
Author(s):  
Geewon Lee ◽  
Hyunjin Park ◽  
Ho Yun Lee ◽  
Joong Hyun Ahn ◽  
Insuk Sohn ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
External Validation ◽  
Quantitative Computed Tomography ◽  
Prognostic Information ◽  
Tumor Margin ◽  
Clinical Variables ◽  
Characteristics Analysis ◽  
Selection Operator ◽  
Lung Adenocarcinomas

We aimed to investigate the relationship between tumor radiomic margin characteristics and prognosis in patients with lung cancer. We enrolled 334 patients who underwent complete resection for lung adenocarcinoma. A quantitative computed tomography analysis was performed, and 76 radiomic margin characteristics were extracted. The radiomic margin characteristics were correlated with overall survival. The selected clinical variables and radiomic margin characteristics were used to calculate a prognostic model with subsequent internal and external validation. Nearly all of the radiomic margin characteristics showed excellent reproducibility. The least absolute shrinkage and selection operator (LASSO) method was used to select eight radiomic margin characteristics. When compared to the model with clinical variables only (C-index = 0.738), the model incorporating clinical variables and radiomic margin characteristics (C-index = 0.753) demonstrated a higher C-index for predicting overall survival. In the model integrating both clinical variables and radiomic margin characteristics, convexity, a Laplace of Gaussian (LoG) kurtosis of 3, and the roundness factor were each independently predictive of overall survival. In addition, radiomic margin characteristics were also correlated with the micropapillary subtype, and the sphericity value was able to predict the presence of the micropapillary subtype. In conclusion, our study showed that radiomic margin characteristics helped predict overall survival in patients with lung adenocarcinomas, thus implying that the tumor margin contains prognostic information.

scholarly journals Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Suyu Wang ◽  
Yue Yu ◽  
Wenting Xu ◽  
Xin Lv ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
External Validation ◽  
Predictive Performance ◽  
Information Criterion ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Concordance Index ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis

Abstract Background The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram. Methods 25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ2 test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis. Results NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ2 test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability. Conclusions We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

Prognostic phenotypes of early-stage lung adenocarcinoma

2021 ◽  
pp. 2101674
Author(s):  
Anne-Sophie Lamort ◽  
Jan Christian Kaiser ◽  
Mario A.A. Pepe ◽  
Ioannis Lilis ◽  
Giannoula Ntaliarda ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Prognostic Information ◽  
Tissue Samples ◽  
Cancer Hallmarks ◽  
Patient Subgroups ◽  
Prognostic Power

BackgroundSurvival after curative resection of early-stage lung adenocarcinoma (LUAD) varies and prognostic biomarkers are urgently needed.MethodsLarge-format tissue samples from a prospective cohort of 200 patients with resected LUAD were immunophenotyped for cancer hallmarks TP53, NF1, CD45, PD-1, PCNA, TUNEL, and FVIII, and were followed for median (95%CI)=2.34 (1.71–3.49) years.ResultsUnsupervised hierarchical clustering revealed two patient subgroups with similar clinicopathologic features and genotype, but with markedly different survival: “proliferative” patients (60%) with elevated TP53, NF1, CD45, and PCNA expression had 50% 5-year overall survival while “apoptotic” patients (40%) with high TUNEL had 70% 5-year survival [HR95%CI=2.23 (1.33–3.80); p=0.0069]. Cox regression and machine learning algorithms including random forests built clinically useful models: a score to predict overall survival and a formula and nomogram to predict tumour phenotype. The distinct LUAD phenotypes were validated in TCGA and KMplotter data and showed prognostic power supplementary to IASLC TNM stage and WHO histologic classification.ConclusionsTwo molecular subtypes of LUAD exist and their identification provides important prognostic information.

scholarly journals Comparison of Diagnostic Performance of Spread Through Airspaces of Lung Adenocarcinoma Based on Morphological Analysis and Perinodular and Intranodular Radiomic Features on Chest CT Images

2021 ◽  
Vol 11 ◽  
Author(s):  
Lin Qi ◽  
Xiaohu Li ◽  
Linyang He ◽  
Guohua Cheng ◽  
Yongjun Cai ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Morphological Analysis ◽  
External Validation ◽  
Free Survival ◽  
Poor Differentiation ◽  
Validation Set ◽  
Tumor Edge ◽  
Contrast Ct ◽  
Lung Adenocarcinomas ◽  
Radiologic Features

ObjectSTAS is associated with poor differentiation, KRAS mutation and poor recurrence-free survival. The aims of this study are to evaluate the ability of intra- and perinodular radiomic features to distinguish STAS at non-contrast CT.Patients and MethodsThis retrospective study included 216 patients with pathologically confirmed lung adenocarcinoma (STAS+, n = 56; STAS−, n = 160). Texture-based features were extracted from intra- and perinodular regions of 2, 4, 6, 8, 10, and 20 mm distances from the tumor edge using an erosion and expansion algorithm. Traditional radiologic features were also analyzed including size, consolidation tumor ratio (CTR), density, shape, vascular change, cystic airspaces, tumor–lung interface, lobulation, spiculation, and satellite sign. Nine radiomic models were established by using the eight separate models and a total of the eight VOIs (eight-VOI model). Then the prediction efficiencies of the nine radiomic models were compared to predict STAS of lung adenocarcinomas.ResultsAmong the traditional radiologic features, CTR, unclear tumor–lung interface, and satellite sign were found to be associated with STAS significantly, and the AUCs were 0.796, 0.677, and 0.606, respectively. Radiomic model of combined tumor bodies and all the distances of perinodular areas (eight-VOI model) had better predictive efficiency for predicting STAS+ lung adenocarcinoma. The AUCs of the eight-VOI model in the training and verification sets were 0.907 (95%CI, 0.862–0.947) in the training set, and 0.897 (95%CI, 0.784–0.985) in the testing set, and 0.909 (95%CI, 0.863–0.949) in the external validation set, and the diagnostic accuracy in the external validation set was 0.849.ConclusionRadiomic features from intra- and perinodular regions of nodules can best distinguish STAS of lung adenocarcinoma.

scholarly journals Clinical Characteristics of Patients With Lung Adenocarcinomas Harboring BRAF Mutations

2011 ◽  
Vol 29 (15) ◽  
pp. 2046-2051 ◽  
Author(s):  
Paul K. Paik ◽  
Maria E. Arcila ◽  
Michael Fara ◽  
Camelia S. Sima ◽  
Vincent A. Miller ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Clinical Characteristics ◽  
Median Overall Survival ◽  
Smoking History ◽  
Egfr Mutations ◽  
Braf Mutations ◽  
Former Smokers ◽  
Lung Adenocarcinomas

Purpose BRAF mutations occur in non–small-cell lung cancer. Therapies targeting BRAF mutant tumors have recently been identified. We undertook this study to determine the clinical characteristics of patients with lung adenocarcinomas harboring BRAF mutations. Patients and Methods We reviewed data from consecutive patients with lung adenocarcinoma whose tumors underwent BRAF, EGFR, and KRAS mutation testing as well as fluorescence in situ hybridization for ALK rearrangements. Patient characteristics including age, sex, race, performance status, smoking history, stage, treatment history, and overall survival were collected. Results Among 697 patients with lung adenocarcinoma, BRAF mutations were present in 18 patients (3%; 95% CI, 2% to 4%). The BRAF mutations identified were V600E (50%), G469A (39%), and D594G (11%). Mutations in EGFR were present in 24%, KRAS in 25%, and ALK translocations in 6%. In contrast to patients with EGFR mutations and ALK rearrangements who were mostly never smokers, all patients with BRAF mutations were current or former smokers (P < .001). The median overall survival of advanced-stage patients with BRAF mutations was not reached. In comparison, the median overall survival of patients with EGFR mutations was 37 months (P = .73), with KRAS mutations was 18 months (P = .12), and with ALK rearrangements was not reached (P = .64). Conclusion BRAF mutations occur in 3% of patients with lung adenocarcinoma and occur more commonly in current and former smokers. The incidence of BRAF mutations other than V600E is significantly higher in lung cancer than in melanoma.

The tyrosine kinase TEK is differentially expressed in non-small cell lung adenocarcinomas.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
United States ◽  
Tyrosine Kinase ◽  
Lung Adenocarcinoma ◽  
Microarray Data ◽  
The United States ◽  
Small Cell ◽  
Small Cell Lung ◽  
Significant Differential Expression ◽  
Lung Cancers ◽  
Lung Adenocarcinomas

Non-small cell lung adenocarcinoma (NSCLC) is a leading cause of death in the United States and worldwide (1, 2). We mined published microarray data (3, 4, 5) to discover genes associated with NSCLC. We identified significant differential expression of the tyrosine kinase TEK in tumors from patients with NSCLC. TEK may be of relevance to the initiation, progression or maintenance of non-small cell lung cancers.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

scholarly journals Serum potassium levels provide prognostic information in symptomatic heart failure beyond traditional clinical variables

2021 ◽  
Author(s):  
Camila Cristiane Toledo ◽  
Pedro Vellosa Schwartzmann ◽  
Luis Miguel Silva ◽  
Gabriel Silva Ferreira ◽  
Fernando Bianchini Cardoso ◽  
...  
Keyword(s):  
Heart Failure ◽  
Serum Potassium ◽  
Prognostic Information ◽  
Symptomatic Heart Failure ◽  
Clinical Variables

scholarly journals Functions and clinical significance of KLRG1 in the development of lung adenocarcinoma and immunotherapy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaodong Yang ◽  
Yuexin Zheng ◽  
Zhihai Han ◽  
Xiliang Zhang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Killer Cell ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Normal Lung ◽  
Using Data ◽  
Low Expression

Abstract Background As a marker of differentiation, Killer cell lectin like receptor G1 (KLRG1) plays an inhibitory role in human NK cells and T cells. However, its clinical role remains inexplicit. This work intended to investigate the predictive ability of KLRG1 on the efficacy of immune-checkpoint inhibitor in the treatment of lung adenocarcinoma (LUAD), as well as contribute to the possible molecular mechanisms of KLRG1 on LUAD development. Methods Using data from the Gene Expression Omnibus, the Cancer Genome Atlas and the Genotype-Tissue Expression, we compared the expression of KLRG1 and its related genes Bruton tyrosine kinase (BTK), C-C motif chemokine receptor 2 (CCR2), Scm polycomb group protein like 4 (SCML4) in LUAD and normal lung tissues. We also established stable LUAD cell lines with KLRG1 gene knockdown and investigated the effect of KLRG1 knockdown on tumor cell proliferation. We further studied the prognostic value of the four factors in terms of overall survival (OS) in LUAD. Using data from the Gene Expression Omnibus, we further investigated the expression of KLRG1 in the patients with different responses after immunotherapy. Results The expression of KLRG1, BTK, CCR2 and SCML4 was significantly downregulated in LUAD tissues compared to normal controls. Knockdown of KLRG1 promoted the proliferation of A549 and H1299 tumor cells. And low expression of these four factors was associated with unfavorable overall survival in patients with LUAD. Furthermore, low expression of KLRG1 also correlated with poor responses to immunotherapy in LUAD patients. Conclusion Based on these findings, we inferred that KLRG1 had significant correlation with immunotherapy response. Meanwhile, KLRG1, BTK, CCR2 and SCML4 might serve as valuable prognostic biomarkers in LUAD.

scholarly journals Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chunlei Wu ◽  
Quanteng Hu ◽  
Dehua Ma
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Pathological Subtype

AbstractLung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

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