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Objectives The 9th edition of the lung cancer tumor-node-metastasis (TNM) staging system downgrades certain non-small cell lung cancer (NSCLC) patients from stage IIIA (T1N2) to IIB(T1N2a). This study aimed to externally validate this stage adjustment. Methods Consecutive resected stage IIB and IIIA (the 9th edition of lung cancer TNM staging manual) NSCLC patients were included. Stage IIB was divided into groups A, B, and C according to lymph node involvement. Group A, patients who having single-station N2 without N1 involvement; Group B, patients who having single-station N2 with N1 involvements; Group C, patients who having station N1 involvement or N0. The stage IIIA patients divided into Group D. Overall survival (OS) and disease-free survival (DFS) were compared using the Kaplan-Meier method, with propensity score matching (PSM) employed to mitigate potential biases. COX regression models were utilized to assess prognostic differences. Results 224 stage IIB and 227 stage IIIA cases was included. There were 38, 66 and 120 patients in the Group A, B and C, respectively. Univariate COX analysis indicated comparable prognoses between the Group A and Group C patients, whereas Group B patients exhibited poorer outcomes. Upon combining the Group A and Group C patients, multivariate COX analysis demonstrated a significantly worse prognosis for Group B patients compared to those with Group A + C patients (OS, P  = 0.035; DFS, P  = 0.021). Further comparisons between Group B and Group D patients, following PSM analysis, indicated similar survivals (OS: P  = 0.390; DFS: P  = 0.210). Conclusion In the 9th edition of the lung cancer TNM staging system, the prognosis of stage IIB N2a2 patients was worse than that of remaining stage IIB patients but comparable to that of stage IIIA patients. We proposed that stage IIB N2a2 patients should be maintained as stage IIIA.

We propose an approach for characterizing structural heterogeneity of lung cancer nodules using Computed Tomography Texture Analysis (CTTA). Measures of heterogeneity were used to test the hypothesis that heterogeneity can be used as predictor of nodule malignancy and patient survival. To do this, we use the National Lung Screening Trial (NLST) dataset to determine if heterogeneity can represent differences between nodules in lung cancer and nodules in non-lung cancer patients. 253 participants are in the training set and 207 participants in the test set. To discriminate cancerous from non-cancerous nodules at the time of diagnosis, a combination of heterogeneity and radiomic features were evaluated to produce the best area under receiver operating characteristic curve (AUROC) of 0.85 and accuracy 81.64%. Second, we tested the hypothesis that heterogeneity can predict patient survival. We analyzed 40 patients diagnosed with lung adenocarcinoma (20 short-term and 20 long-term survival patients) using a leave-one-out cross validation approach for performance evaluation. A combination of heterogeneity features and radiomic features produce an AUROC of 0.9 and an accuracy of 85% to discriminate long- and short-term survivors.

Endobronchial ultrasound (EBUS) and electromagnetic navigation bronchoscopy (ENB) have increased the diagnostic yield of bronchoscopic diagnosis of peripheral lung lesions. However, the role of combining these modalities to overcome each individual technique's limitations and, consequently, to further increase the diagnostic yield remains untested. A prospective randomized controlled trial involving three diagnostic arms: EBUS only, ENB only, and a combined procedure. All procedures were performed via flexible bronchoscopy and transbronchial forceps biopsies were obtained without fluoroscopic guidance. In the combined group, after electromagnetic navigation, the ultrasound probe was passed through an extended working channel to visualize the lesion. Biopsies were taken if ultrasound visualization showed that the extended working channel was within the target. Primary outcome was diagnostic yield. The reference \"gold standard\" was a surgical biopsy if bronchoscopic biopsy did not reveal a definite histological diagnosis compatible with the clinical presentation. Secondary outcomes were yields by size, lobar distribution, and lesion pathology. Complication rates were also documented. Of the 120 patients recruited, 118 had a definitive histological diagnosis and were included in the final analysis. The diagnostic yield of the combined procedure (88%) was greater than EBUS (69%) or ENB alone (59%; p = 0.02). The combined procedure's yield was independent of lesion size or lobar distribution. The pneumothorax rates ranged from 5 to 8%, with no significant differences between the groups. Combined EBUS and ENB improves the diagnostic yield of flexible bronchoscopy in peripheral lung lesions without compromising safety.

Recent advances in the technology of helical multidetector CT allow precise evaluations of nodule hemodynamics. In addition, the efficacy of tissue characterization has improved, and now sensitivity and specificity of >90% are achieved. Moreover, the efficacy of PET for the tissue characterization of solitary pulmonary nodules (SPNs) has also become of importance. The purpose of this study was to compare the diagnostic accuracy of helical dynamic (HD) CT (HDCT) and integrated PET/CT for pulmonary nodule characterization. One hundred nineteen patients with an SPN underwent both HDCT (unenhanced scans, followed by series of images at 30, 60, 90, 120 s and at 5 and 15 min after intravenous injection of contrast medium) and integrated PET/CT. On HDCT, a nodule was regarded as malignant with a net enhancement of > or =25 Hounsfield units (HU) and a washout of 5-31 HU. On integrated PET/CT, nodules were considered malignant with a > or =3.5 maximum standardized uptake value and an 18F-FDG uptake greater than that of mediastinal structures. The sensitivity, specificity, and accuracy of the 2 modalities for malignancy were compared using the McNemar test. There were 79 malignant and 40 benign nodules. The sensitivity, specificity, and accuracy for malignancy on HDCT were 81% (64/79 nodules), 93% (37/40), and 85% (101/119), respectively, whereas those on integrated PET/CT were 96% (76/79), 88% (35/40), and 93% (111/119), respectively (P = 0.008, 0.727, and 0.011, respectively). All malignant nodules were interpreted correctly on either HDCT or PET/CT. Integrated PET/CT is more sensitive and accurate than HDCT for the malignant nodule characterization; therefore, PET/CT may be performed as the first-line evaluation tool for SPN characterization. Because HDCT has high specificity and acceptable sensitivity and accuracy, it may be a reasonable alternative for nodule characterization when PET/CT is unavailable.

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and they play fundamental roles in immune regulation. Here we introduce an integrated algorithm, ImmLnc, for identifying lncRNA regulators of immune-related pathways. We comprehensively chart the landscape of lncRNA regulation in the immunome across 33 cancer types and show that cancers with similar tissue origin are likely to share lncRNA immune regulators. Moreover, the immune-related lncRNAs are likely to show expression perturbation in cancer and are significantly correlated with immune cell infiltration. ImmLnc can help prioritize cancer-related lncRNAs and further identify three molecular subtypes (proliferative, intermediate, and immunological) of non-small cell lung cancer. These subtypes are characterized by differences in mutation burden, immune cell infiltration, expression of immunomodulatory genes, response to chemotherapy, and prognosis. In summary, the ImmLnc pipeline and the resulting data serve as a valuable resource for understanding lncRNA function and to advance identification of immunotherapy targets. In cancer, long noncoding RNAs (lncRNAs) can regulate immune-related pathways. Here, the authors present ImmLnc, an algorithm that can help prioritise immune-related lncRNAs in cancer immunotherapy research

Background Bronchoscopy is frequently used for the evaluation of suspicious pulmonary lesions found on computed tomography, but its sensitivity for detecting lung cancer is limited. Recently, a bronchial genomic classifier was validated to improve the sensitivity of bronchoscopy for lung cancer detection, demonstrating a high sensitivity and negative predictive value among patients at intermediate risk (10–60 %) for lung cancer with an inconclusive bronchoscopy. Our objective for this study was to determine if a negative genomic classifier result that down-classifies a patient from intermediate risk to low risk (<10 %) for lung cancer would reduce the rate that physicians recommend more invasive testing among patients with an inconclusive bronchoscopy. Methods We conducted a randomized, prospective, decision impact survey study assessing pulmonologist recommendations in patients undergoing workup for lung cancer who had an inconclusive bronchoscopy. Cases with an intermediate pretest risk for lung cancer were selected from the AEGIS trials and presented in a randomized fashion to pulmonologists either with or without the patient’s bronchial genomic classifier result to determine how the classifier results impacted physician decisions. Results Two hundred two physicians provided 1523 case evaluations on 36 patients. Invasive procedure recommendations were reduced from 57 % without the classifier result to 18 % with a negative (low risk) classifier result ( p  < 0.001). Invasive procedure recommendations increased from 50 to 65 % with a positive (intermediate risk) classifier result ( p  < 0.001). When stratifying by ultimate disease diagnosis, there was an overall reduction in invasive procedure recommendations in patients with benign disease when classifier results were reported (54 to 41 %, p  < 0.001). For patients ultimately diagnosed with malignant disease, there was an overall increase in invasive procedure recommendations when the classifier results were reported (50 to 64 %, p  = 0.003). Conclusions Our findings suggest that a negative (low risk) bronchial genomic classifier result reduces invasive procedure recommendations following an inconclusive bronchoscopy and that the classifier overall reduces invasive procedure recommendations among patients ultimately diagnosed with benign disease. These results support the potential clinical utility of the classifier to improve management of patients undergoing bronchoscopy for suspect lung cancer by reducing additional invasive procedures in the setting of benign disease.

Background: Recent research suggests that the Bayesian paradigm may be useful for modeling biases in epidemiological studies, such as those due to misclassification and missing data. We used Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to the potential effect of these two important sources of bias. Methods: We used data from a study of the joint associations of radiotherapy and smoking with primary lung cancer among breast cancer survivors. We used Bayesian methods to provide an operational way to combine both validation data and expert opinion to account for misclassification of the two risk factors and missing data. For comparative purposes we considered a “full model” that allowed for both misclassification and missing data, along with alternative models that considered only misclassification or missing data, and the naïve model that ignored both sources of bias. Results: We identified noticeable differences between the four models with respect to the posterior distributions of the odds ratios that described the joint associations of radiotherapy and smoking with primary lung cancer. Despite those differences we found that the general conclusions regarding the pattern of associations were the same regardless of the model used. Overall our results indicate a nonsignificantly decreased lung cancer risk due to radiotherapy among nonsmokers, and a mildly increased risk among smokers. Conclusions: We described easy to implement Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to misclassification and missing data.

The conventional way of classifying metastatic cancers according to their organ of origin is denying people access to drugs that could help them. The conventional way of classifying metastatic cancers according to their organ of origin is denying people access to drugs that could help them. A radiologist and an assistant wearing face masks check a screen during cryotherapy treatment on a patient with kidney cancer

Malignant mesothelioma (MM) is an aggressive cancer primarily diagnosed on the basis of histological criteria 1 . The 2015 World Health Organization classification subdivides mesothelioma tumors into three histological types: epithelioid, biphasic and sarcomatoid MM. MM is a highly complex and heterogeneous disease, rendering its diagnosis and histological typing difficult and leading to suboptimal patient care and decisions regarding treatment modalities 2 . Here we have developed a new approach—based on deep convolutional neural networks—called MesoNet to accurately predict the overall survival of mesothelioma patients from whole-slide digitized images, without any pathologist-provided locally annotated regions. We validated MesoNet on both an internal validation cohort from the French MESOBANK and an independent cohort from The Cancer Genome Atlas (TCGA). We also demonstrated that the model was more accurate in predicting patient survival than using current pathology practices. Furthermore, unlike classical black-box deep learning methods, MesoNet identified regions contributing to patient outcome prediction. Strikingly, we found that these regions are mainly located in the stroma and are histological features associated with inflammation, cellular diversity and vacuolization. These findings suggest that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries. Deep convolutional neural networks predict survival of mesothelioma patients and identify histological features associated with outcome that transcend current histological classifications.

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.