Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method

We developed and validated a deep learning (DL)-based model using the segmentation method and assessed its ability to detect lung cancer on chest radiographs. Chest radiographs for use as a training dataset and a test dataset were collected separately from January 2006 to June 2018 at our hospital. The training dataset was used to train and validate the DL-based model with five-fold cross-validation. The model sensitivity and mean false positive indications per image (mFPI) were assessed with the independent test dataset. The training dataset included 629 radiographs with 652 nodules/masses and the test dataset included 151 radiographs with 159 nodules/masses. The DL-based model had a sensitivity of 0.73 with 0.13 mFPI in the test dataset. Sensitivity was lower in lung cancers that overlapped with blind spots such as pulmonary apices, pulmonary hila, chest wall, heart, and sub-diaphragmatic space (0.50–0.64) compared with those in non-overlapped locations (0.87). The dice coefficient for the 159 malignant lesions was on average 0.52. The DL-based model was able to detect lung cancers on chest radiographs, with low mFPI.

Targeting KRAS in Lung Cancer Beyond KRAS G12C Inhibitors: The Immune Regulatory Role of KRAS and Novel Therapeutic Strategies

Approximately 20% of lung adenocarcinomas harbor KRAS mutations, an oncogene that drives tumorigenesis and has the ability to alter the immune system and the tumor immune microenvironment. While KRAS was considered “undruggable” for decades, specific KRAS G12C covalent inhibitors have recently emerged, although their promising results are limited to a subset of patients. Several other drugs targeting KRAS activation and downstream signaling pathways are currently under investigation in early-phase clinical trials. In addition, KRAS mutations can co-exist with other mutations in significant genes in cancer (e.g., STK11 and KEAP1) which induces tumor heterogeneity and promotes different responses to therapies. This review describes the molecular characterization of KRAS mutant lung cancers from a biologic perspective to its clinical implications. We aim to summarize the tumor heterogeneity of KRAS mutant lung cancers and its immune-regulatory role, to report the efficacy achieved with current immunotherapies, and to overview the therapeutic approaches targeting KRAS mutations besides KRAS G12C inhibitors.

Case Report: A Novel Non-Reciprocal ALK Fusion: ALK-GCA and EML4-ALK Were Identified in Lung Adenocarcinoma, Which May Respond to Alectinib Adjuvant-Targeted Therapy

Anaplastic lymphoma kinase (ALK)-positive non-small-cell lung cancers (NSCLCs) have favorable and impressive response to ALK tyrosine kinase inhibitors (TKIs). However, ALK rearrangement had approximately 90 distinct fusion partners. Patients with different ALK fusions might have distinct responses to different-generation ALK-TKIs. In this case report, we identified a novel non-reciprocal ALK fusion: ALK-grancalcin (GCA) (A19: intragenic) and EML4-ALK (E20: A20) by next-generation sequencing (NGS) in a male lung adenocarcinoma patient who was staged as IIIB-N2 after surgery. After a multidisciplinary discussion, the patient received alectinib adjuvant targeted therapy and postoperative radiotherapy (PORT). He is currently in good condition, and disease-free survival (DFS) has been 20 months so far, which has been longer than the median survival time of IIIB NSCLC patients. Our study extended the spectrum of ALK fusion partners in ALK + NSCLC, and we reported a new ALK fusion: ALK-GCA and EML4-ALK and its sensitivity to alectinib firstly in lung cancer. It is vital for clinicians to detect fusion mutations of patients and report timely the newfound fusions and their response to guide treatment.

Putative Anticancer Compounds from Plant-Derived Endophytic Fungi: A Review

Endophytic fungi are microorganisms that exist almost ubiquitously inside the various tissues of living plants where they act as an important reservoir of diverse bioactive compounds. Recently, endophytic fungi have drawn tremendous attention from researchers; their isolation, culture, purification, and characterization have revealed the presence of around 200 important and diverse compounds including anticancer agents, antibiotics, antifungals, antivirals, immunosuppressants, and antimycotics. Many of these anticancer compounds, such as paclitaxel, camptothecin, vinblastine, vincristine, podophyllotoxin, and their derivatives, are currently being used clinically for the treatment of various cancers (e.g., ovarian, breast, prostate, lung cancers, and leukemias). By increasing the yield of specific compounds with genetic engineering and other biotechnologies, endophytic fungi could be a promising, prolific source of anticancer drugs. In the future, compounds derived from endophytic fungi could increase treatment availability and cost effectiveness. This comprehensive review includes the putative anticancer compounds from plant-derived endophytic fungi discovered from 1990 to 2020 with their source endophytic fungi and host plants as well as their antitumor activity against various cell lines.

Pulmonary Immune Dysregulation and Viral Persistence During HIV Infection

Despite the success of antiretroviral therapy (ART), people living with HIV continue to suffer from high burdens of respiratory infections, lung cancers and chronic lung disease at a higher rate than the general population. The lung mucosa, a previously neglected HIV reservoir site, is of particular importance in this phenomenon. Because ART does not eliminate the virus, residual levels of HIV that remain in deep tissues lead to chronic immune activation and pulmonary inflammatory pathologies. In turn, continuous pulmonary and systemic inflammation cause immune cell exhaustion and pulmonary immune dysregulation, creating a pro-inflammatory environment ideal for HIV reservoir persistence. Moreover, smoking, gut and lung dysbiosis and co-infections further fuel the vicious cycle of residual viral replication which, in turn, contributes to inflammation and immune cell proliferation, further maintaining the HIV reservoir. Herein, we discuss the recent evidence supporting the notion that the lungs serve as an HIV viral reservoir. We will explore how smoking, changes in the microbiome, and common co-infections seen in PLWH contribute to HIV persistence, pulmonary immune dysregulation, and high rates of infectious and non-infectious lung disease among these individuals.

Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China

BackgroundAbout 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China.MethodsA large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set.ResultsA total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk.ConclusionsWe developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.

Lung and Colon Cancer Classification Using Medical Imaging : A Feature Engineering Approach

Lung and colon cancers are the most common causes of death. Their simultaneous occurrence is uncommon, however, in the absence of early diagnosis, the metastasis of cancer cells is very high between these two organs. Currently, histopathological diagnosis and appropriate treatment are the only possibility to improve the chances of survival and reduce cancer mortality. Using artificial intelligence in the histopathological diagnosis of colon and lung cancer can provide significant help to specialists in identifying cases of colon and lung cancers with less effort, time and cost. The objective of this study is to set up a computer-aided diagnostic system that can accurately classify five types of colon and lung tissues (two classes for colon cancer and three classes for lung cancer) by analyzing their histopathological images. Using machine learning, features engineering and image processing techniques, the five models XGBoost, SVM, RF, LDA and MLP were used to perform the classification of histopathological images of lung and colon cancers that were acquired from the LC25000 dataset. The main advantage of using machine learning models is that they allow for better interpretability of the classification model since they are based on feature engineering; however, deep learning models are black box networks whose working is very difficult to understand due to the complex network design. The acquired experimental results show that machine learning models give satisfactory results and are very precise in identifying classes of lung and colon cancer subtypes. The XGBoost model gave the best performance with an accuracy of 99% and a F1-score of 98.8%. The implementation and the development of this model will help healthcare specialists identify types of colon and lung cancers. The code will be available upon request.

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions

BackgroundThe objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs).MethodsThis retrospective study enrolled 252 malignant pulmonary nodules with histopathologically confirmed SPLCs or PMs and randomly assigned them to a training or validation cohort. Clinical data were collected from the electronic medical records system. The imaging and radiomics features of each nodule were extracted from CT images.ResultsA rad-score was generated from the training cohort using the least absolute shrinkage and selection operator regression. A clinical and radiographic model was constructed using the clinical and imaging features selected by univariate and multivariate regression. A nomogram composed of clinical-radiographic factors and a rad-score were developed to validate the discriminative ability. The rad-scores differed significantly between the SPLC and PM groups. Sixteen radiomics features and four clinical-radiographic features were selected to build the final model to differentiate between SPLCs and PMs. The comprehensive clinical radiographic–radiomics model demonstrated good discriminative capacity with an area under the curve of the receiver operating characteristic curve of 0.9421 and 0.9041 in the respective training and validation cohorts. The decision curve analysis demonstrated that the comprehensive model showed a higher clinical value than the model without the rad-score.ConclusionThe proposed model based on clinical data, imaging features, and radiomics features could accurately discriminate SPLCs from PMs. The model thus has the potential to support clinicians in improving decision-making in a noninvasive manner.