Primary tumor location in lung cancer: the evaluation and administration

Xie, Xuemei; Li, Xin; Tang, Wenjie; Xie, Peng; Tan, Xuefen

doi:10.1097/cm9.0000000000001802

Cited by 32 publications

(18 citation statements)

References 122 publications

(264 reference statements)

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“…Clinical symptoms of nodular lesions in the lung may appear later than imaging symptoms. Several recent studies have attempted to correlate different CT features of peripheral lung cancer with specific pathological subtypes ( 32 , 33 ). Ground glass nodules (GGN) on chest CT could be a key imaging biomarker for early primary ADC in diagnosis, which had been observed at our institution ( 23 , 34 ).…”

Section: Discussionmentioning

confidence: 99%

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

et al. 2023

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PurposeExploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis.MethodsThis retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models.ResultsFive clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical–radiographic model (P=0.049).ConclusionsThe proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.

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Section: Discussionmentioning

confidence: 99%

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

et al. 2023

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“…For instance, exposure to tobacco carcinogens leads to centrally located NSCLC tumors with higher accumulation of alterations in suppressor genes [ 78 ]. On the other hand, oncogenic alterations such as EGFR-activating mutations are prone to occur in the peripheral location that is more common to LUAD [ 79 , 80 ]. Our results indicate that central locations of NSCLC (high neighborhood of bronchi) decrease the probability to detect RET alterations (Fig.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

et al. 2022

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Background Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world – lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice. Methods Here, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations. Results We achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification. Conclusions We presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

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“…SCLC arises from the central area and peripheric regions of the lungs [31]. It accounts for 15% of LC cases and is characterized by its aggressiveness, poor prognosis, low fiveyear survival rate (3.5%), and early metastasis to distinct organs, such as the brain and liver [32,33].…”

Section: Small Cell Lung Cancer (Sclc)mentioning

confidence: 99%

Activities against Lung Cancer of Biosynthesized Silver Nanoparticles: A Review

2023

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Nanomedicine is an interdisciplinary field where nanostructured objects are applied to treat or diagnose disease. Nanoparticles (NPs) are a special class of materials at nanometric scale that can be prepared from lipids, polymers, or noble metals through bottom-up approaches. Biological synthesis is a reliable, sustainable, and non-toxic bottom-up method that uses phytochemicals, microorganisms, and enzymes to induce the reduction of metal ions into NPs. Silver (Ag) NPs exhibit potent therapeutic properties that can be exploited to overcome the limitations of current treatment modalities for human health issues such as lung cancer (LC). Here, we review the preparation of AgNPs using biological synthesis and their application against LC using in vitro and in vivo models. An overview of the staging, diagnosis, genetic mutations, and treatment of LC, as well as its main subtypes, is presented. A summary of the reaction mechanisms of AgNPs using microbial cell cultures, plant extracts, phytochemicals, and amino acids is included. The use of capping agents in the biosynthesis of AgNPs with anticancer activity is also detailed. The history and biological activities of metal-based nanostructures synthesized with gold, copper, palladium, and platinum are considered. The possible anticancer mechanisms of AgNPs against LC models are covered. Our perspective about the future of AgNPs in LC treatment and nanomedicine is added.

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Primary tumor location in lung cancer: the evaluation and administration

Cited by 32 publications

References 122 publications

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

Activities against Lung Cancer of Biosynthesized Silver Nanoparticles: A Review

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