On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans.

Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.

Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey.

Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks.

Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-small-Cell Lung Cancer.

Background: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study, we investigated the association between radiomics features and the tumor histological subtypes, and we aimed to establish a nomogram for the classification of small cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). Methods: This was a retrospective single center study. In total, 468 cases including 202 patients with SCLC and 266 patients with NSCLC were enrolled in our study, and were randomly divided into a training set (n = 327) and a validation set (n = 141) in a 7:3 ratio. The clinical data of the patients, including age, sex, smoking history, tumor maximum diameter, clinical stage, and serum tumor markers, were collected. All patients underwent enhanced computed tomography (CT) scans, and all lesions were pathologically confirmed. A radiomics signature was generated from the training set using the least absolute shrinkage and selection operator algorithm. Independent risk factors were identified by multivariate logistic regression analysis, and a radiomics nomogram based on the radiomics signature and clinical features was constructed. The capability of the nomogram was evaluated in the training set and validated in the validation set. Results: Fourteen of 396 radiomics parameters were screened as important factors for establishing the radiomics model. The radiomics signature performed well in differentiating SCLC and NSCLC, with an area under the curve (AUC) of 0.86 (95% CI: 0.82-0.90) in the training set and 0.82 (95% CI: 0.75-0.89) in the validation set. The radiomics nomogram had better predictive performance [AUC = 0.94 (95% CI: 0.90-0.98) in the validation set] than the clinical model [AUC = 0.86 (95% CI: 0.80-0.93)] and the radiomics signature [AUC = 0.82 (95% CI: 0.75-0.89)], and the accuracy was 86.2% (95% CI: 0.79-0.92) in the validation set. Conclusion: The enhanced CT radiomics signature performed well in the classification of SCLC and NSCLC. The nomogram based on the radiomics signature and clinical factors has better diagnostic performance for the classification of SCLC and NSCLC than the simple application of the radiomics signature.

Survival disparities following surgery among patients with different histological types of non-small cell lung cancer.

OBJECTIVES: Clinical decisions for NSCLC patients are often based on TNM stage, which does not account for different histological subtype. Whether histological subtype affects survival still remains unclear. The main objective of this study was to determine the extent to which the survival outcomes of patients with early-stage NSCLC differ by histological subtype. MATERIAL AND METHODS: Retrospective cohort study of SEER data base. Patients with stage IA and IB NSCLC that underwent surgery with lymph node dissection were included. The primary outcome was the time to death. Cox proportional hazards models were used to identify risk factors associated with overall survival (OS). The secondary outcome was the time to death from lung cancer. A Cox model and a Fine-Gray subdistribution hazards model in which death from causes other than lung cancer was considered a competing risk event were used to identify risk factors for death from lung cancer. RESULTS: Analysis of the SEER database identified 28,584 NSCLC patients, of whom 19,750 (69 %) had adenocarcinoma and 8834 (31 %) had squamous cell carcinoma. In the multivariate for OS, older age (p < 0.001), male gender (p < 0.001), pneumonectomy (p < 0.001), larger tumor size (p < 0.001), squamous cell carcinoma (p < 0.001) not being Hispanic or Asian were associated with increased risk of death. In the competing risk model, older age (p < 0.001), male gender (p < 0.001), pneumonectomy (p < 0.001), larger tumor size (p < 0.001), and squamous cell carcinoma (p < 0.001) were was associated with an increased risk of death from lung cancer. CONCLUSION: This study suggests that among patients with stage I NSCLC, those with squamous histology have a higher risk of mortality than those with adenocarcinoma histology taking into account competing risks.

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.

BACKGROUND: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell carcinoma). Furthermore, in order to predict histologic subtypes, we employed machine-learning methods and independently evaluated their prediction performance. METHODS: Two independent radiomic cohorts with a combined size of 350 patients were included in our analysis. A total of 440 radiomic features were extracted from the segmented tumor volumes of pretreatment CT images. These radiomic features quantify tumor phenotypic characteristics on medical images using tumor shape and size, intensity statistics, and texture. Univariate analysis was performed to assess each feature's association with the histological subtypes. In our multivariate analysis, we investigated 24 feature selection methods and 3 classification methods for histology prediction. Multivariate models were trained on the training cohort and their performance was evaluated on the independent validation cohort using the area under ROC curve (AUC). Histology was determined from surgical specimen. RESULTS: In our univariate analysis, we observed that fifty-three radiomic features were significantly associated with tumor histology. In multivariate analysis, feature selection methods ReliefF and its variants showed higher prediction accuracy as compared to other methods. We found that Naive Baye's classifier outperforms other classifiers and achieved the highest AUC (0.72; p-value = 2.3 × 10(-7)) with five features: Stats_min, Wavelet_HLL_rlgl_lowGrayLevelRunEmphasis, Wavelet_HHL_stats_median, Wavelet_HLL_stats_skewness, and Wavelet_HLH_glcm_clusShade. CONCLUSION: Histological subtypes can influence the choice of a treatment/therapy for lung cancer patients. We observed that radiomic features show significant association with the lung tumor histology. Moreover, radiomics-based multivariate classifiers were independently validated for the prediction of histological subtypes. Despite achieving lower than optimal prediction accuracy (AUC 0.72), our analysis highlights the impressive potential of non-invasive and cost-effective radiomics for precision medicine. Further research in this direction could lead us to optimal performance and therefore to clinical applicability, which could enhance the efficiency and efficacy of cancer care.

Active and passive smoking in relation to lung cancer incidence in the Women's Health Initiative Observational Study prospective cohort.

BACKGROUND: Lung cancer is the leading cause of worldwide cancer deaths. While smoking is its leading risk factor, few prospective cohort studies have reported on the association of lung cancer with both active and passive smoking. This study aimed to determine the relationship between lung cancer incidence with both active and passive smoking (childhood, adult at home, and at work). PATIENTS AND METHODS: The Women's Health Initiative Observational Study (WHI-OS) was a prospective cohort study conducted at 40 US centers that enrolled postmenopausal women from 1993 to 1999. Among 93 676 multiethnic participants aged 50-79, 76 304 women with complete smoking and covariate data comprised the analytic cohort. Lung cancer incidence was calculated by Cox proportional hazards models, stratified by smoking status. RESULTS: Over 10.5 mean follow-up years, 901 lung cancer cases were identified. Compared with never smokers (NS), lung cancer incidence was much higher in current [hazard ratio (HR) 13.44, 95% confidence interval (CI) 10.80-16.75] and former smokers (FS; HR 4.20, 95% CI 3.48-5.08) in a dose-dependent manner. Current and FS had significantly increased risk for all lung cancer subtypes, particularly small-cell and squamous cell carcinoma. Among NS, any passive smoking exposure did not significantly increase lung cancer risk (HR 0.88, 95% CI 0.52-1.49). However, risk tended to be increased in NS with adult home passive smoking exposure ≥30 years, compared with NS with no adult home exposure (HR 1.61, 95% CI 1.00-2.58). CONCLUSIONS: In this prospective cohort of postmenopausal women, active smoking significantly increased risk of all lung cancer subtypes; current smokers had significantly increased risk compared with FS. Among NS, prolonged passive adult home exposure tended to increase lung cancer risk. These data support continued need for smoking prevention and cessation interventions, passive smoking research, and further study of lung cancer risk factors in addition to smoking. CLINICALTRIALS.GOV: NCT00000611.