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Lymphangitic carcinomatosis and pleural metastases in 26-year old male non-smoker with ROS1-positive non-small cell lung cancer. A: Pre-treatment computed tomography (CT) shows a dominant right lower lobe mass with diffuse bilateral, right greater than left, lymphangitic carcinomatosis characterized by nodular interstitial thickening and ground glass opacities; B: Pre-treatment CT (mediastinal window) shows right pleural nodular thickening and pleural effusion (arrowheads) consisted with pleural metastasis; C: Initial post-treatment CT shows marked interval response to targeted therapy with near complete resolution of right lower lobe mass and of lymphangitic carcinomatosis. Increased frequencies of lymphangitic carcinomatosis and pleural metastases have also been described in ALK-positive non-small cell lung cancer (Figure 2).
Source publication
Lung cancer remains the leading cause of cancer-related deaths worldwide. The treatment of non-small cell lung cancer (NSCLC), which accounts for a vast majority of lung cancers, has shifted to personalized, targeted therapy following discoveries of several targetable oncogenic mutations. Targeting of specific mutations has improved outcomes in man...
Contexts in source publication
Context 1
... NSCLC, on the other hand, has been associated with lymphangitic carcinomatosis ( Figure 3B and 4B) in comparison to EGFR-mutant NSCLC [65][66][67]78] . More recently, ROS1-positive NSCLC has also been associated with predilection for lymphangitic carcinomatosis ( Figure 5A) [71] . On imaging, lymphangitic carcinomatosis is characterized by nodular thickening of the axial and peripheral, subpleural interstitium, with relative sparing of the intralobular interstitium [79] . ...
Context 2
... carcinomatosis is associated with worse prognosis in various extrapulmonary malignancies, but its prognostic impact in the setting of primary lung malignancies remains unclear du to paucity of data [80] . While it may appear intuitive to that lymphangitic carcinomatosis is suggestive of more advanced disease, a concurrent targetable mutation with either ALK or ROS1 may improve outcomes in these patients ( Figure 5C). ...
Context 3
... addition to increased frequency of lymphangitic carcinomatosis, ALK-positive NSCLC has also been associated with increased frequencies of both pleural ( Figure 3C) and pericardial metastases [65] , and ROS1-positive NSCLC has also been associated with pleural metastases ( Figure 5B) [71] . The mechanism behind these potential differences in metastatic tropisms among the different genotypes remains to be determined. ...
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... In a parallel vein, Zhou et al. [133] found that when radiomic features are amalgamated with machine learning methodologies, there is a powerful discriminatory capacity to distinguish between primary lung lesions and metastatic lung lesions in the brain. Intriguingly, emerging datasets also indicate that NSCLCs harboring different targetable oncogenic driver mutations (e.g., ALK) present unique imaging features, both in the primary tumor and in metastatic tumors [134,135]. Specifically, the utility of contrast-enhanced T1-weighted (CET1W) images in predicting EGFR mutation status in small-sized brain metastases (<10 mm) from lung cancer has been highlighted [136]. Moreover, our group has made pivotal contributions in elucidating the synergistic potential of combining radiomic imaging biomarkers with conventional molecular markers. ...
Brain metastases represent a significant clinical challenge in the treatment of non-small-cell lung cancer (NSCLC), often leading to a severe decline in patient prognosis and survival. Recent advances in imaging and systemic treatments have increased the detection rates of brain metastases, yet clinical outcomes remain dismal due to the complexity of the metastatic tumor microenvironment (TME) and the lack of specific biomarkers for early detection and targeted therapy. The intricate interplay between NSCLC tumor cells and the surrounding TME in brain metastases is pivotal, influencing tumor progression, immune evasion, and response to therapy. This underscores the necessity for a deeper understanding of the molecular underpinnings of brain metastases, tumor microenvironment, and the identification of actionable biomarkers that can inform multimodal treatment approaches. The goal of this review is to synthesize current insights into the TME and elucidate molecular mechanisms in NSCLC brain metastases. Furthermore, we will explore the promising horizon of emerging biomarkers, both tissue- and liquid-based, that hold the potential to radically transform the treatment strategies and the enhancement of patient outcomes.
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Background:
Lung cancer is the principal cause of cancer-related deaths worldwide. Early detection of lung cancer with screening is indispensable to reduce the high morbidity and mortality rates. Artificial intelligence (AI) is widely utilised in healthcare, including in the assessment of medical images. A growing number of reviews studied the application of AI in lung cancer screening, but no overarching meta-analysis has examined the diagnostic test accuracy (DTA) of AI-based imaging for lung cancer screening.
Objective:
To systematically review the DTA of AI-based imaging for lung cancer screening.
Methods:
PubMed, EMBASE, Cochrane Library, CINAHL, IEEE Xplore, Web of Science, ACM Digital Library, Scopus, PsycINFO, and ProQuest Dissertations and Theses were searched from inception to date. Studies that were published in English and that evaluated the performance of AI-based imaging for lung cancer screening were included. Two independent reviewers screened titles and abstracts and used the Quality Assessment of Diagnostic Accuracy Studies-2 tool to appraise the quality of selected studies. Grading of Recommendations Assessment, Development, and Evaluation to diagnostic tests was used to assess the certainty of evidence.
Results:
Twenty-six studies with 150,721 imaging data were included. Hierarchical summary receiver-operating characteristic model used for meta-analysis demonstrated that the pooled sensitivity for AI-based imaging for lung cancer screening was 94.6 % (95 % CI: 91.4 % to 96.7 %) and specificity was 93.6 % (95 % CI: 88.5 % to 96.6 %). Subgroup analyses revealed that similar results were found among different types of AI, region, data source, and year of publication, but the overall quality of evidence was very low.
Conclusion:
AI-based imaging could effectively detect lung cancer and be incorporated into lung cancer screening programs. Further high-quality DTA studies on large lung cancer screening populations are required to validate AI's role in early lung cancer detection.
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Background: Lung cancer has the highest mortality rates and one of the lowest 5-year survival rates amongst cancer types in the world. Although there are constant advancements in treatment, the overall prognosis for lung cancer continues to be poor. In order to achieve early detection and personalized targeted treatment, an effective method is needed to make prognostic and treatment decisions. Methods: A thorough literature search was conducted to identify tumor tissue, blood, and expired breath markers that have been discovered in lung cancer. Articles were chosen by determining main markers holding promise for future clinical use. Results: Data suggests significance in using tumor tissue markers as promising diagnostic, prognostic and predictive of treatment response and outcome. Epidermal Growth Factor Receptor (EGFR) and Anaplastic Lymphoma Kinase (ALK) and ROS-1 biomarkers can be used to decide to treat with EGFR-TKI and ALK-TKI, respectively. KRAS and p53 mutations suggest a likelihood of developing EGFR-TKI resistance. And c-MET is showing pertinence in predicting disease recurrence. Blood and expired breath markers are two more novel sources for biomarkers that is gaining more ground in lung cancer research. Circulating tumor cells (CTC) and DNA (ctDNA) were shown to be important markers in lung cancer prognosis and treatment response prediction. Circulating tumor cells suggest negative prognosis and increased likelihood of recurrence, while ctDNA data indicates use in treatment monitoring to help make decisions without keeping patients on disagreeable therapies. Volatile organic compounds (VOC) are the least studied, but investigators have noticed changes in VOC profiles between healthy and lung cancer patients. Blood and expired breath markers continue to be studied as these would be a welcome alternative to invasive biopsies. Recently there had been interest in using specific tumor biomarkers for imaging to localize tumors and determine disease progression. Conclusions: Years of research have elucidated multiple candidates as biomarkers found in tumor tissues, circulation, and even in exhaled air. Although more studies need to be performed on some markers mentioned in this review, such as EGFR, KRAS, ALK, and ROS-1, there is enough evidence for some use of these biomarkers to guide decisions in clinic, as well as evidence for promising future developments.
... Lung cancer is the leading cause of death in many countries around the word [1]. Non-small-cell lung cancer (NSCLC) is the most widespread, accounting for approximately 85% of lung cancers, with a five-year survival rate of approximately 5% [2]. Many studies have been done and several methods have been developed to fight this disease but the main obstacle that they face is the development of drug resistance or the late detection of the disease [3]. ...
Cancer is one of the leading causes of death in many countries, and this continues to be the case because of the lack of sufficient treatment. One of the most common types is non-small-cell lung cancer (NSCLC). The increasingly large and diverse public datasets about NSCLC constitute a rich source of data on which several analyses can be performed so as to find candidate oncogenic drivers or therapeutic targets. The aim of this study is to reanalyze an existing NSCLC NCBI GEO Dataset (accession = GSE19804) in order to see if novel involved genes can be found. For this, we used microarray technology for preprocessing and, based on random forest, support vector machine and C5.0 decision tree models, made a comparison of the 10 most important genes recorded. This study was realized with R-Studio 4.0.2 and Bioconductor 3.11. In conclusion, the EFNA4 gene and other genes, namely KANK3, GRK5, CLIC5, SH3GL3, ACACB, LIN7A, JCAD, and NEDD1, are thought to be potential genes that may play a role in NSCLC and it is recommended that researchers working in the wet laboratory should focus on these genes.
... Molecular markers have become increasingly important in the era of personalized medicine (67). Lung cancers with certain biomarkers have been found to respond favorably to targeted therapies, thereby directing treatment strategies (68). However, biomarker profiles and targeted therapies are beyond the scope of this chapter as they play a lesser role in the management of small lung nodules, most of which will be surgically treated. ...
Non-small cell lung cancer is the most common type of lung cancer. Identification of genes associated with this disease may contribute to the treatment of the disease. Therefore, a lot of work is being done. In some of these studies, genetic data is obtained by microarray analysis and shared publicly in databases such as NCBI Gene Expression Omnibus. In today’s big data era, machine learning algorithms are frequently used to access valuable information from data stacks. Within the scope of this study, all (6 pieces) microarray datasets related to NSCLC and drug resistance in the NCBI GEO database were analyzed by R Studio. With support vector machine, k nearest neighbor, naïve Bayes, random forest, C5.0 decision tree, multilayer perceptron, and artificial neural network algorithms with principal component step, the datasets were analyzed separately and related genes were determined through the caret package, and the top 10 genes for each algorithm were given in the findings section in order of importance. In this resulting gene table, ELOVL7, HMGA2, SAT1, RRM1, IER3, SLC7A11, and U2AF1 genes are included in at least 2 different datasets. These identified genes are recommended to researchers working in a wet laboratory environment to be validated experimentally.
