Lung cancer identification: a review on detection and classification

Recent advances in the therapeutic potential of nobiletin against respiratory diseases

BACKGROUND

Nobiletin is a natural polymethoxylated flavonoid widely present in citrus fruit peels. It has been demonstrated to exert the effects of anti-tumor, anti-inflammation, anti-oxidative, anti-apoptotic and improve cardiovascular function. Increasing evidences suggest that nobiletin plays an important role in respiratory diseases (RDs) treatment.

OBJECTIVE

This review aimed to investigate the therapeutic potential of nobiletin against RDs, such as lung cancer, COPD, pulmonary fibrosis, asthma, pulmonary infection, acute lung injury, coronavirus disease 2019, and pulmonary arterial hypertension.

METHODS

We retrieved extensive literature of relevant literatures in English until June 26, 2023 from the database of PubMed, Web of Science, and Scopus databases. The keywords of "nobiletin and lung", "nobiletin and respiratory disease", "nobiletin and chronic respiratory diseases", "nobiletin and metabolites", "nobiletin and pharmacokinetics", "nobiletin and toxicity" were searched in pairs. A total of 298 literatures were retrieved from the above database. After excluding the duplicates and reviews, 53 were included in the current review.

RESULTS

We found that the therapeutic mechanisms are based on different signaling pathways. Firstly, nobiletin inhibited the proliferation and suppressed the invasion and migration of cancer cells by regulating the related pathway or key target, like Bcl-2, PD-L1, PARP, and Akt/GSK3β/β-catenin in lung cancer treatment. Secondly, nobiletin treats COPD and ALI by targeting classical signaling pathway mediating inflammation. Besides, the available findings show that nobiletin exerts the effect of PF treatment via regulating mTOR pathway.

CONCLUSIONS

With the wide range of pharmacological activities, high efficiency and low toxicity, nobiletin can be used as a potential agent for preventing and treating RDs. These findings will contribute to further research on the molecular mechanisms of nobiletin and facilitate in-depth studies on nobiletin at both preclinical and clinical levels for the treatment of RDs.

A ferroptosis-related signature predicts the clinical diagnosis and prognosis, and associates with the immune microenvironment of lung cancer

Targeting ferroptosis-related pathway is a potential strategy for treatment of lung cancer (LC). Consequently, exploration of ferroptosis-related markers is important for treating LC. We collected LC clinical data and mRNA expression profiles from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained through FerrDB database. Expression analysis was performed to obtain differentially expressed FRGs. Diagnostic and prognostic models were constructed based on FRGs by LASSO regression, univariate, and multivariate Cox regression analysis, respectively. External verification cohorts GSE72094 and GSE157011 were used for validation. The interrelationship between prognostic risk scores based on FRGs and the tumor immune microenvironment was analyzed. Immunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighteen FRGs were used for diagnostic models, 8 FRGs were used for prognostic models. The diagnostic model distinguished well between LC and normal samples in training and validation cohorts of TCGA. The prognostic models for TCGA, GSE72094, and GSE157011 cohorts significantly confirmed lower overall survival (OS) in high-risk group, which demonstrated excellent predictive properties of the survival model. Multivariate Cox regression analysis further confirmed risk score was an independent risk factor related with OS. Immunoassays revealed that in high-risk group, a significantly higher proportion of Macrophages_M0, Neutrophils, resting Natural killer cells and activated Mast cells and the level of B7H3, CD112, CD155, B7H5, and ICOSL were increased. In conclusion, diagnostic and prognostic models provided superior diagnostic and predictive power for LC and revealed a potential link between ferroptosis and TIME.

Advancing Nonsmall Cell Lung Cancer Diagnosis Accuracy via Dual Detection Fluorescent Nanoprobes

Among the primary threats to human health worldwide, nonsmall cell lung cancer (NSCLC) remains a significant factor and is a leading cause of cancer-related deaths. Due to subtle early symptoms, NSCLC patients are diagnosed at advanced stages, resulting in low survival rates. Herein, novel Au-Se bond nanoprobes (NPs) designed for the specific detection of Calpain-2 (CAPN2) and Human Neutrophil Elastase (HNE), pivotal biomarkers in NSCLC, were developed. The NPs demonstrated exceptional specificity and sensitivity toward CAPN2 and HNE, enabling dual-color fluorescence imaging to distinguish between NSCLC cells and normal lung cells effectively. The NPs' performance was consistent across a wide pH range (6.2 to 8.0), and it exhibited remarkable resistance to biological thiol interference, indicating its robustness in complex physiological environments. These findings suggest the nanoprobe is a promising tool for early NSCLC diagnosis, offering a novel approach for enhancing the accuracy of cancer detection.

The application of Raman spectroscopy for the diagnosis and monitoring of lung tumors

Raman spectroscopy is an optical technique that uses inelastic light scattering in response to vibrating molecules to produce chemical fingerprints of tissues, cells, and biofluids. Raman spectroscopy strategies produce high levels of chemical specificity without requiring extensive sample preparation, allowing for the use of advanced optical tools such as microscopes, fiber optics, and lasers that operate in the visible and near-infrared spectral range, making them increasingly suitable for a wide range of medical diagnostic applications. Metal nanoparticles and nonlinear optical effects can improve Raman signals, and optimized fiber optic Raman probes can make real-time, in vivo, single-point observations. Furthermore, diagnostic speed and spatial accuracy can be improved through the multimodal integration of Raman measurements and other technologies. Recent studies have significantly contributed to the improvement of diagnostic speed and accuracy, making them suitable for clinical application. Lung cancer is a prevalent type of respiratory malignancy. However, the use of computed tomography for detection and screening frequently reveals numerous smaller lung nodules, which makes the diagnostic process more challenging from a clinical perspective. While the majority of small nodules detected are benign, there are currently no direct methods for identifying which nodules represent very early-stage lung cancer. Positron emission tomography and other auxiliary diagnostic methods for non-surgical biopsy samples from these small nodules yield low detection rates, which might result in significant expenses and the possibility of complications for patients. While certain subsets of patients can undergo curative treatment, other individuals have a less favorable prognosis and need alternative therapeutic interventions. With the emergence of new methods for treating cancer, such as immunotherapies, which can potentially extend patient survival and even lead to a complete cure in certain instances, it is crucial to determine the most suitable biomarkers and metrics for assessing the effectiveness of these novel compounds. This will ensure that significant treatment outcomes are accurately measured. This review provides a comprehensive overview of the prospects of Raman spectroscopy and its applications in the diagnosis and analysis of lung tumors.

Long non-coding RNA CCAT1 acts as an oncogene to promote radiation resistance in lung adenocarcinoma: an epigenomics-based investigation

Long non-coding RNAs (lncRNAs) appear to be the crucial modulators in various processes and critically influence the oncogenesis. As one of the LncRNAs, LncRNA CCAT1 has been reported to be closely associated with the progression multiple cancers, but its role in modulating the radioresistance of lung adenocarcinoma (LUAD) remains unclear. In our present study, we screened the potential radioresistance related LncRNAs in LUAD based on the data from The Cancer Genome Atlas (TCGA) database. Data suggested that CCAT1 was abundantly expressed in LUAD and CCAT1 was significantly associated with poor prognosis and radioresistance. Moreover, our in vitro experiments showed that radiation treatment could trigger elevated expression of CCAT1 in the human LUAD cell lines. Further loss/gain-of-function investigations indicated that CCAT1 knockdown significantly inhibited cell proliferation, migration and promoted cell apoptosis in NCI-H1299 cells under irradiation, whereas CCAT1 overexpression in A549 cells yield the opposite effects. In summary, we identified the promoting role of CCAT1 in radioresistance of LUAD, which may provide a theoretical basis for radiotherapy sensitization of LUAD.

A challenge and solution for automatic thin slice thickness measurements on images of the Catphan phantom

Purpose. The use of the Hough transform for angle detection is quite accurate for relatively wide slice thickness. However, the Hough transform fails to accurately detect the angle for thin slice thickness. This study proposes a method for automatically measuring the thickness of thin slices on images of a Catphan phantom.Methods. In the proposed method, the angle of the phantom's orientation was determined based on the relative coordinates of the four hole objects in the phantom. After the angles of the wires were determined, the profiles of pixel values across the wire objects were constructed. Finally, their full widths at half maximum (FWHMs) were determined and multiplied bytan23° to obtain the slice thicknesses of the images. The results of the proposed method were compared to a previous method, which used the Hough transform to obtain the phantom's orientation. We used slice thicknesses ranging from 0.8 mm to 5.0 mm, and phantom angles from 0° to 10°.Results. Our proposed method detected the angle of the phantom accurately for thin slices, whereas a previous method did not accurately detect the angle. The results of the slice thickness using this current method were slightly higher (within 7.9%) compared to the previous method. However, the results of the two methods did not differ significantly (p-value > 0.05). Using different angles, the current method detected all the angles more accurately. Again, the slice thicknesses were not significantly different from the previous method (p-value > 0.05).Conclusion. The proposed method for measuring the thickness of thin slices in an image of a Catphan phantom, based on the relative coordinates of the four hole objects in the phantom, outperformed a previous method based on the Hough transform.

Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. Medical image analysis and Nature appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade.

KIAA1429-mediated RXFP1 attenuates non-small cell lung cancer tumorigenesis via N6-methyladenosine modification

BACKGROUND

N6-methyladenosine (m6A) modification has been associated with non-small cell lung cancer (NSCLC) tumorigenesis.

OBJECTIVES

This study aimed to determine the functions of Vir-like m6A methyltransferase-associated (KIAA1429) and relaxin family peptide receptor 1 (RXFP1) in NSCLC.

METHODS

A quantitative real-time polymerase chain reaction was used to analyze the mRNA levels of KIAA1429 and RXFP1 in NSCLC. After silencing KIAA1429 or RXFP1 in NSCLC cells, changes in the malignant phenotypes of NSCLC cells were assessed using cell counting kit-8, colony formation, and transwell assays. Finally, the m6A modification of RXFP1 mediated by KIAA1429 was confirmed using luciferase, methylated RNA immunoprecipitation, and western blot assays.

RESULTS

KIAA1429 and RXFP1 were upregulated and downregulated in NSCLC, respectively. Silencing of KIAA1429 attenuated the viability, migration, and invasion of NSCLC cells, whereas silencing of RXFP1 showed the opposite function in NSCLC cells. Moreover, RXFP1 expression was inhibited by KIAA1429 via m6A-modification. Therefore, silencing RXFP1 reversed the inhibitory effect of KIAA1429 knockdown in NSCLC cells.

CONCLUSION

Our findings confirmed that the KIAA1429/RXFP1 axis promotes NSCLC tumorigenesis. This is the first study to reveal the inhibitory function of RXFP1 in NSCLC via KIAA1429-mediated m6A-modification. These findings may help identify new biomarkers for targeted NSCLC therapy.

Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network

Lung cancer is one of the leading causes of death worldwide and early detection is crucial to reduce the mortality. A reliable computer-aided diagnosis (CAD) system can help facilitate early detection of malignant nodules. Although existing methods provide adequate classification accuracy, there is still room for further improvement. This study is dedicated to investigating a new CAD scheme for predicting the malignant likelihood of lung nodules in computed tomography (CT) images in light of a deep learning strategy. Conceived from the residual learning and selective kernel, we investigated an efficient residual selective kernel (RSK) block to handle the diversity of lung nodules with various shapes and obscure structures. Founded on this RSK block, we established a multiview RSK network (MRSKNet), to which three anatomical planes in the axial, coronal, and sagittal directions were fed. To reinforce the classification efficiency, seven handcrafted texture features with a filter-like computation strategy were explored, among which the homogeneity (HOM) feature maps are combined with the corresponding intensity CT images for concatenation input, leading to an improved network architecture. Evaluated on the public benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) challenge database with ten-fold cross validation of binary classification, our experimental results indicated high area under receiver operating characteristic (AUC) and accuracy scores. A better compromise between recall and specificity was struck using the suggested concatenation strategy comparing to many state-of-the-art approaches. The proposed pulmonary nodule classification framework exhibited great efficacy and achieved a higher AUC of 0.9711. The association of handcrafted texture features with deep learning models is promising in advancing the classification performance. The developed pulmonary nodule CAD network architecture is of potential in facilitating the diagnosis of lung cancer for further image processing applications.

Enhancing intracellular mRNA precise imaging-guided photothermal therapy with a nucleic acid-based polydopamine nanoprobe

Despite significant advancements in cancer research, real-time monitoring and effective treatment of cancer through non-invasive techniques remain a challenge. Herein, a novel polydopamine (PDA) nucleic acid nanoprobe has been developed for imaging signal amplification of intracellular mRNA and precise photothermal therapy guidance in cancer cells. The PDA nucleic acid nanoprobe (PDA@DNA) is constructed by assembling an aptamer hairpin (H1) labeled with the Cy5 fluorophore and another nucleic acid recognition hairpin (H2) onto PDA nanoparticles (PDA NPs), which have exceptionally high fluorescence quenching ability and excellent photothermal conversion properties. The nanoprobe could facilitate cellular uptake of DNA molecules and their protection from nuclease degradation. Upon recognition and binding to the intracellular mRNA target, a catalytic hairpin assembly (CHA) reaction occurs. The stem of H1 unfolds upon binding, allowing the exposed H1 to hybridize with H2, forming a flat and sturdy DNA double-stranded structure that detaches from the surface of PDA NPs. At the same time, the target mRNA is displaced and engages in a new cyclic reaction, resulting in the recovery and significant amplification of Cy5 fluorescence. Using thymidine kinase1 (TK1) mRNA as a model mRNA, this nanoprobe enables the analysis of TK1 mRNA with a detection limit of 9.34 pM, which is at least two orders of magnitude lower than that of a non-amplifying imaging nucleic acid probe. Moreover, with its outstanding performance for in vitro detection, this nanoprobe excels in precisely imaging tumor cells. Through live-cell TK1 mRNA imaging, it can accurately distinguish between tumor cells and normal cells. Furthermore, when exposed to 808-nm laser irradiation, the nanoprobe fully harnesses exceptional photothermal conversion properties of PDA NPs. This results in a localized temperature increase within tumor cells, which ultimately triggers apoptosis in these tumor cells. The integration of PDA@DNA presents innovative prospects for tumor diagnosis and image-guided tumor therapy, offering the potential for high-precision diagnosis and treatment of tumors.

Sevoflurane Confers Protection Against the Malignant Phenotypes of Lung Cancer Cells via the microRNA-153-3p/HIF1α/KDM2B Axis

Sevoflurane is shown to curtail lung cancer (LC) development. Herein, this research sought to investigate the underlying mechanism of sevoflurane in regard to its repressive effects on LC. Expression levels of microRNA (miR)-153-3p, HIF1α, and KDM2B in LC tissues and cells were determined with qRT-PCR. Following sevoflurane pretreatment and/or ectopic expression and knockdown experiments, the malignant phenotypes, and levels of miR-153-3p, HIF1α, and KDM2B in LC A549 cells were detected using Transwell, scratch, EdU, CCK-8, Western blot, and qRT-PCR assays. Relationship between HIF1α and miR-153-3p was verified with a dual-luciferase reporter assay. The interaction between HIF1α and KDM2B was verified with a ChIP assay. LC tissues and cells presented low miR-153-3p expression and high HIF1α and KDM2B expression. Sevoflurane pretreatment, miR-153-3p upregulation, HIF1α downregulation, or KDM2B downregulation impeded the malignant phenotypes of A549 cells. Sevoflurane pretreatment augmented miR-153-3p expression, while miR-153-3p negatively targeted HIF1α. HIF1α bound to the KDM2B promoter to upregulate KDM2B. HIF1α or KDM2B overexpression counteracted the inhibitory effects of sevoflurane pretreatment on A549 cell malignant behaviors. Sevoflurane decreased HIF1α expression through upregulation of miR-153-3p, thereby reducing KDM2B transcription to restrict the malignant phenotypes of LC A549 cells.

The classification of benign and malignant lung nodules based on CT radiomics: a systematic review, quality score assessment, and meta-analysis

Radiomics methods are increasingly used to identify benign and malignant lung nodules, and early monitoring is essential in prognosis and treatment strategy formulation. To evaluate the diagnostic performance of computed tomography (CT)-based radiomics for distinguishing between benign and malignant lung nodules by performing a meta-analysis. Between January 2000 and December 2021, we searched the PubMed and Embase electronic databases for studies in English. Studies were included if they demonstrated the sensitivity and specificity of CT-based radiomics for diagnosing benign and malignant lung nodules. The studies were evaluated using the QUADAS-2 and radiomics quality scores (RQS). The inhomogeneity of the data and publishing bias were also evaluated. Some subgroup analyses were performed to investigate the impact of diagnostic efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Guidelines were followed for this meta-analysis. A total of 20 studies involving 3793 patients were included. The combined sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve based on CT radiomics diagnosis of benign and malignant lung nodules were 0.81, 0.86, 27.00, and 0.91, respectively. Deek's funnel plot asymmetry test confirmed no significant publication bias in all studies. Fagan nomograms showed a 40% increase in post-test probability among pretest-positive patients. Current evidence shows that CT-based radiomics has high accuracy in the diagnosis of benign and malignant lung nodules.

Novel roles of PIWI proteins and PIWI-interacting RNAs in human health and diseases

Non-coding RNA has aroused great research interest recently, they play a wide range of biological functions, such as regulating cell cycle, cell proliferation, and intracellular substance metabolism. Piwi-interacting RNAs (piRNAs) are emerging small non-coding RNAs that are 24-31 nucleotides in length. Previous studies on piRNAs were mainly limited to evaluating the binding to the PIWI protein family to play the biological role. However, recent studies have shed more lights on piRNA functions; aberrant piRNAs play unique roles in many human diseases, including diverse lethal cancers. Therefore, understanding the mechanism of piRNAs expression and the specific functional roles of piRNAs in human diseases is crucial for developing its clinical applications. Presently, research on piRNAs mainly focuses on their cancer-specific functions but lacks investigation of their expressions and epigenetic modifications. This review discusses piRNA's biogenesis and functional roles and the recent progress of functions of piRNA/PIWI protein complexes in human diseases. Video Abstract.

A novel fusion algorithm for benign-malignant lung nodule classification on CT images

The accurate recognition of malignant lung nodules on CT images is critical in lung cancer screening, which can offer patients the best chance of cure and significant reductions in mortality from lung cancer. Convolutional Neural Network (CNN) has been proven as a powerful method in medical image analysis. Radiomics which is believed to be of interest based on expert opinion can describe high-throughput extraction from CT images. Graph Convolutional Network explores the global context and makes the inference on both graph node features and relational structures. In this paper, we propose a novel fusion algorithm, RGD, for benign-malignant lung nodule classification by incorporating Radiomics study and Graph learning into the multiple Deep CNNs to form a more complete and distinctive feature representation, and ensemble the predictions for robust decision-making. The proposed method was conducted on the publicly available LIDC-IDRI dataset in a 10-fold cross-validation experiment and it obtained an average accuracy of 93.25%, a sensitivity of 89.22%, a specificity of 95.82%, precision of 92.46%, F1 Score of 0.9114 and AUC of 0.9629. Experimental results illustrate that the RGD model achieves superior performance compared with the state-of-the-art methods. Moreover, the effectiveness of the fusion strategy has been confirmed by extensive ablation studies. In the future, the proposed model which performs well on the pulmonary nodule classification on CT images will be applied to increase confidence in the clinical diagnosis of lung cancer.

Identification of exosome protein panels as predictive biomarkers for non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related deaths worldwide, primarily due to its propensity for metastasis. Patients diagnosed with localized primary cancer have higher survival rates than those with metastasis. Thus, it is imperative to discover biomarkers for the early detection of NSCLC and the timely prediction of tumor metastasis to improve patient outcomes.

METHODS

Here, we utilized an integrated approach to isolate and characterize plasma exosomes from NSCLC patients as well as healthy individuals. We then conducted proteomics analysis and parallel reaction monitoring to identify and validate the top-ranked proteins of plasma exosomes.

RESULTS

Our study revealed that the proteome in exosomes from NSCLC patients with metastasis was distinctly different from that from healthy individuals. The former had larger diameters and lower concentrations of exosomes than the latter. Furthermore, among the 1220 identified exosomal proteins, we identified two distinct panels of biomarkers. The first panel of biomarkers (FGB, FGG, and VWF) showed potential for early NSCLC diagnosis and demonstrated a direct correlation with the survival duration of NSCLC patients. The second panel of biomarkers (CFHR5, C9, and MBL2) emerged as potential biomarkers for assessing NSCLC metastasis, of which CFHR5 alone was significantly associated with the overall survival of NSCLC patients.

CONCLUSIONS

These findings underscore the potential of plasma exosomal biomarkers for early NSCLC diagnosis and metastasis prediction. Notably, CFHR5 stands out as a promising prognostic indicator for NSCLC patients. The clinical utility of exosomal biomarkers offers the potential to enhance the management of NSCLC.

Ugonin P inhibits lung cancer motility by suppressing DPP-4 expression via promoting the synthesis of miR-130b-5p

Lung cancer is the leading cause of cancer-related death worldwide, and the survival rate of metastatic lung cancer is exceedingly low. Helminthostatchys Zeylanica (H. Zeylanica) is a Chinese herbal medicine renowned for its anti-inflammatory, immunomodulatory, and anti-cancer activities in various cellular and animal studies. The current study evaluated the effects of H. Zeylanica derivatives on lung cancer cells. We determined that dipeptidyl peptidase-4 (DPP-4) expression levels were higher in lung cancer tissues than in normal tissues. We also determined that DPP-4 expression levels were higher in the metastatic stage and strongly correlated with lung cancer survival rates. An H. Zeylanica derivative (ugonin P) was shown to inhibit DPP-4 mRNA and protein expression in two lung cancer cell lines in a dose-dependent manner. Ugonin P was shown to decrease migration and invasion activities in lung cancer cells while promoting the synthesis of miR-130b-5p, which was found to negatively regulate DPP-4 protein expression and cell motility in lung cancer. We determined that ugonin P suppresses the DPP-4-dependent migration and invasion of lung cancer cells by downregulating the RAF/MEK/ERK signalling pathway and enhancing the expression of miR-130b-5p. This study provides compelling evidence that ugonin P could be used to develop novel therapeutic agents for the treatment of lung cancer.

Delta-like ligand 3 in small cell lung cancer: Potential mechanism and treatment progress

Small cell lung cancer (SCLC) is one of a pathological type of lung cancer, and it is characterized by invasiveness, high malignancy and refractoriness. The mortality rate of SCLC is significantly higher than other types of lung cancer, and the treatment options for SCLC patients are limited. Delta-like ligand 3 (DLL3) is a Notch signaling ligand that plays a role in regulating the proliferation, development and metastasis of SCLC cells. Mnay studies have shown that DLL3 is overexpressed on the surface of SCLC cells, suggesting that DLL3 is a potential target for SCLC patients. A series of drug trials targeting DLL3 are underway. The Phase III clinical trials of Rova-T, a drug targeting DLL3, have not yielded the expected results. However, other drugs that target DLL3, such as AMG119, AMG757 and DLL3-targeted NIR-PIT, bring new ideas for SCLC treatment. Overall, DLL3 remains a valuable target for SCLC.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

 Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

 The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

 AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

  · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

Diagnosing Orthotopic Lung Tumor Using a NTR-Activatable Near-Infrared Fluorescent Probe by Tracheal Inhalation

Nitroreductase (NTR) is an enzyme that is upregulated under tumor-depleted oxygen conditions. The majority of studies have been conducted on NTR, but many existing fluorescent imaging tools for monitoring NTR inevitably suffer from weak targeting, low sensitivity, and simple tumor models. Research on diagnosing lung tumors has been very popular in recent years, but targeting assays in orthotopic lung tumors is still of great research value, as such models better mimic the reality of cancer in the organism. Here, we developed a novel near-infrared (NIR) fluorescent probe IR-ABS that jointly targets NTR and carbonic anhydrase IX (CAIX). IR-ABS has excellent sensitivity and selectivity and shows exceptional NTR response in spectroscopic tests. The measurements ensured that this probe has good biosafety in both cells and mice. A better NTR response was found in hypoxic tumor cells at the cellular level, distinguishing tumor cells from normal cells. In vivo experiments demonstrated that IR-ABS achieves a hypoxic response at the zebrafish level and enables rapid and accurate tumor margin distinguishment in different mouse tumor models. More importantly, we successfully applied IR-ABS for NTR detection in orthotopic lung tumor models, further combined with tracheal inhalation drug delivery to improve targeting. To the best of our knowledge, we present for the first time a near-infrared imaging method for targeting lung cancerous tumor in situ via tracheal inhalation drug delivery, in contrast to the reported literature. This NIR fluorescence diagnostic strategy for targeting orthotopic lung cancer holds exciting potential for clinical aid in cancer diagnosis.

Hyperparameter optimization and development of an advanced CNN-based technique for lung nodule assessment

Objective. This paper aims to propose an advanced methodology for assessing lung nodules using automated techniques with computed tomography (CT) images to detect lung cancer at an early stage.Approach. The proposed methodology utilizes a fixed-size 3 × 3 kernel in a convolution neural network (CNN) for relevant feature extraction. The network architecture comprises 13 layers, including six convolution layers for deep local and global feature extraction. The nodule detection architecture is enhanced by incorporating a transfer learning-based EfficientNetV_2 network (TLEV2N) to improve training performance. The classification of nodules is achieved by integrating the EfficientNet_V2 architecture of CNN for more accurate benign and malignant classification. The network architecture is fine-tuned to extract relevant features using a deep network while maintaining performance through suitable hyperparameters.Main results. The proposed method significantly reduces the false-negative rate, with the network achieving an accuracy of 97.56% and a specificity of 98.4%. Using the 3 × 3 kernel provides valuable insights into minute pixel variation and enables the extraction of information at a broader morphological level. The continuous responsiveness of the network to fine-tune initial values allows for further optimization possibilities, leading to the design of a standardized system capable of assessing diversified thoracic CT datasets.Significance. This paper highlights the potential of non-invasive techniques for the early detection of lung cancer through the analysis of low-dose CT images. The proposed methodology offers improved accuracy in detecting lung nodules and has the potential to enhance the overall performance of early lung cancer detection. By reconfiguring the proposed method, further advancements can be made to optimize outcomes and contribute to developing a standardized system for assessing diverse thoracic CT datasets.

An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier

INTRODUCTION

A critical step to ameliorate diagnosis and extend patient survival is Benign-malignant Pulmonary Nodule (PN) classification at earlier detection. On account of the noise of Computed Tomography (CT) images, the prevailing Lung Nodule (LN) detection techniques exhibit broad variation in accurate prediction.

METHODS

Thus, a novel Nodule Detection along with Classification algorithm for early diagnosis of Lung Cancer (LC) has been proposed. Initially, employing the Adaptive Mode Ostu Binarization (AMOB) technique, the Lung Volumes (LVs) isextortedas of the image together with the extracted lung regions is pre-processed. Then, detection of LNs takes place, and utilizing Geodesic Fuzzy C-Means Clustering (GFCM) Segmentation Algorithm, it is segmented.Next, the vital features are extracted, and the Nodules are classified by utilizing Logarithmic Layer Xception Neural Network (LLXcepNN) Classifier grounded on the extracted feature.

RESULTS

The nodules are classified as Benign Nodules (BN) and Malignant Nodules (MN) by the proposed classifier. Lastly, the Lung CT images are scrutinized.

DISCUSSION

Thus, when weighed against the prevailing techniques, the proposed systems' acquired outcomes exhibit that the rate of accuracy of classification is enhanced.

Multi-class Tissue Segmentation of CT images using an Ensemble Deep Learning method

Microwave ablation (MWA) therapy is a well-known technique for locally destroying lung tumors with the help of computed tomography (CT) images. However, tumor recurrence occurs because of insufficient ablation of the tumor. In order to perform an accurate treatment of lung cancer, there is a demand to determine the tumor area precisely. To address the problem at hand, which involves accurately segmenting organs and tumors in CT images obtained during MWA therapy, physicians could benefit from a semantic segmentation method. However, such a method typically requires a large number of images to achieve optimal results through deep learning techniques. To overcome this challenge, our team developed four different (multiple) U-Net based semantic segmentation models that work in conjunction with one another to produce a more precise segmented image, even when working with a relatively small dataset. By combining the highest weight value of segmentation from multiple methods into a single output, we can achieve a more reliable and accurate segmentation outcome. Our approach proved successful in segmenting four different tissue structures, including lungs, lung tumors, and ablated tissues in CT medical images. The Intersection over Union (IoU) is employed to quantitatively evaluate the proposed method. The method shows the highest average IoU, with 0.99 for the background, 0.98 for the lung, 0.77 for the ablated, and 0.54 for the tumor tissue. The results show that employing multiple DL methods is superior to that of individual base-learner models for all four different tissue structures, even in the presence of the relatively small dataset.Clinical relevance- An essential issue of tumor ablation therapy is to know when the entire tumor tissue has completely been destroyed. However, as it is difficult to distinguish between destroyed and living tumor, this is hardly reliable in clinical practice during MWA therapy, especially when working with a small dataset. Improved AI segmentation methods can help to improve performance to reduce recurrence.

Functional imaging for assessing regional lung ventilation in preclinical and clinical research

Dynamic heterogeneity in lung ventilation is an important measure of pulmonary function and may be characteristic of early pulmonary disease. While standard indices like spirometry, body plethysmography, and blood gases have been utilized to assess lung function, they do not provide adequate information on regional ventilatory distribution nor function assessments of ventilation during the respiratory cycle. Emerging technologies such as xenon CT, volumetric CT, functional MRI and X-ray velocimetry can assess regional ventilation using non-invasive radiographic methods that may complement current methods of assessing lung function. As a supplement to current modalities of pulmonary function assessment, functional lung imaging has the potential to identify respiratory disease phenotypes with distinct natural histories. Moreover, these novel technologies may offer an optimal strategy to evaluate the effectiveness of novel therapies and therapies targeting localized small airways disease in preclinical and clinical research. In this review, we aim to discuss the features of functional lung imaging, as well as its potential application and limitations to adoption in research.

Bioinformatics analysis of the genes associated with co-occurrence of heart failure and lung cancer

Deaths of non-cardiac causes in patients with heart failure (HF) are on the rise, including lung cancer (LC). However, the common mechanisms behind the two diseases need to be further explored. This study aimed to improve understanding on the co-occurrence of LC and HF. In this study, gene expression profiles of HF (GSE57338) and LC (GSE151101) were comprehensively analyzed using the Gene Expression Omnibus database. Functional annotation, protein-protein interaction network, hub gene identification, and co-expression analysis were proceeded when the co-differentially expressed genes in HF and LC were identified. Among 44 common differentially expressed genes, 17 hub genes were identified to be associated with the co-occurrence of LC and HF; the hub genes were verified in 2 other data sets. Nine genes, including ALOX5, FPR1, ADAMTS15, ALOX5AP, ANPEP, SULF1, C1orf162, VSIG4, and LYVE1 were selected after screening. Functional analysis was performed with particular emphasis on extracellular matrix organization and regulation of leukocyte activation. Our findings suggest that disorders of the immune system could cause the co-occurrence of HF and LC. They also suggest that abnormal activation of extracellular matrix organization, inflammatory response, and other immune signaling pathways are essential in disorders of the immune system. The validated genes provide new perspectives on the common underlying pathophysiology of HF and LC, and may aid further investigation in this field.

Zengshengping improves lung cancer by regulating the intestinal barrier and intestinal microbiota

Lung cancer is a common malignant tumor in clinical practice, and its morbidity and mortality are in the forefront of malignant tumors. Radiotherapy, chemotherapy, and surgical treatment play an important role in the treatment of lung cancer, however, radiotherapy has many complications and even causes partial loss of function, the recurrence rate after surgical resection is high, and the toxic and side effects of chemotherapy drugs are strong. Traditional Chinese medicine has played a huge role in the prognosis and improvement of lung cancer, among them, Zengshengping (ZSP) has the effect of preventing and treating lung cancer. Based on the “gut-lung axis” and from the perspective of “treating the lung from the intestine”, the purpose of this study was to research the effect of Zengshengping on the intestinal physical, biological, and immune barriers, and explore its role in the prevention and treatment of lung cancer. The Lewis lung cancer and urethane-induced lung cancer models were established in C57BL/6 mice. The tumor, spleen, and thymus were weighed, and the inhibition rate, splenic and thymus indexes analyzed. Inflammatory factors and immunological indexes were detected by enzyme-linked immunosorbent assay. Collecting lung and colon tissues, hematoxylin and eosin staining was performed on lung, colon tissues to observe histopathological damage. Immunohistochemistry and Western blotting were carried out to detect tight junction protein expression in colon tissues and expression of Ki67 and p53 proteins in tumor tissues. Finally, the feces of mice were collected to investigate the changes in intestinal microbiota using 16SrDNA high-throughput sequencing technology. ZSP significantly reduced tumor weight and increased the splenic and thymus indexes. It decreased expression of Ki67 protein and increased expression of p53 protein. Compared with Model group, ZSP group reduced the serum levels of interleukin (IL)-1β, IL-6, tumor necrosis factor α (TNF-α), and ZSP group increased the concentration of secretory immunoglobulin A (sIgA) in the colon and the bronchoalveolar lavage fluid (BALF). ZSPH significantly increased the level of tight junction proteins such as ZO-1, Occludin and Claudin-1. Model group significantly reduced the relative abundance of Akkermansia (p < 0.05) and significantly promoted the amount of norank_f_Muribaculaceae, norank_f_Lachnospiraceae (p < 0.05) compared with that in the Normal group. However, ZSP groups increased in probiotic strains (Akkermansia) and decreased in pathogens (norank_f_Muribaculaceae, norank_f_Lachnospiraceae). Compared with the urethane-induced lung cancer mice, the results showed that ZSP significantly increased the diversity and richness of the intestinal microbiota in the Lewis lung cancer mice. ZSP played an important role in the prevention and treatment of lung cancer by enhancing immunity, protecting the intestinal mucosa and regulating the intestinal microbiota.

Biomedically-relevant metal organic framework-hydrogel composites

Metal organic frameworks (MOFs) are incredibly versatile three-dimensional porous materials with a wide range of applications that arise from their well-defined coordination structures, high surface areas and porosities, as well as ease of structural tunability due to diverse compositions achievable. In recent years, following advances in synthetic strategies, development of water-stable MOFs and surface functionalisation techniques, these porous materials have found increasing biomedical applications. In particular, the combination of MOFs with polymeric hydrogels creates a class of new composite materials that marries the high water content, tissue mimicry and biocompatibility of hydrogels with the inherent structural tunability of MOFs in various biomedical contexts. Additionally, the MOF-hydrogel composites can transcend each individual component such as by providing added stimuli-responsiveness, enhancing mechanical properties and improving the release profile of loaded drugs. In this review, we discuss the recent key advances in the design and applications of MOF-hydrogel composite materials. Following a summary of their synthetic methodologies and characterisation, we discuss the state-of-the-art in MOF-hydrogels for biomedical use - cases including drug delivery, sensing, wound treatment and biocatalysis. Through these examples, we aim to demonstrate the immense potential of MOF-hydrogel composites for biomedical applications, whilst inspiring further innovations in this exciting field.

Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software

AIM

To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD).

MATERIALS AND METHODS

A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference.

RESULTS

Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively.

CONCLUSION

Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.

Tumor Lung Visualization and Localization through Virtual Reality and Thermal Feedback Interface

The World Health Organization estimates that there were around 10 million deaths due to cancer in 2020, and lung cancer was the most common type of cancer, with over 2.2 million new cases and 1.8 million deaths. While there have been advances in the diagnosis and prediction of lung cancer, there is still a need for new, intelligent methods or diagnostic tools to help medical professionals detect the disease. Since it is currently unable to detect at an early stage, speedy detection and identification are crucial because they can increase a patient's chances of survival. This article focuses on developing a new tool for diagnosing lung tumors and providing thermal touch feedback using virtual reality visualization and thermal technology. This tool is intended to help identify and locate tumors and measure the size and temperature of the tumor surface. The tool uses data from CT scans to create a virtual reality visualization of the lung tissue and includes a thermal display incorporated into a haptic device. The tool is also tested by touching virtual tumors in a virtual reality application. On the other hand, thermal feedback could be used as a sensory substitute or adjunct for visual or tactile feedback. The experimental results are evaluated with the performance comparison of different algorithms and demonstrate that the proposed thermal model is effective. The results also show that the tool can estimate the characteristics of tumors accurately and that it has the potential to be used in a virtual reality application to "touch" virtual tumors. In other words, the results support the use of the tool for diagnosing lung tumors and providing thermal touch feedback using virtual reality visualization, force, and thermal technology.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

· Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

CITATION FORMAT

· Feuerecker B, Heimer M, Geyer T et al. Artificial Intelligence in Oncological Hybrid Imaging. Fortschr Röntgenstr 2023; 195: 105 - 114.

Hyaluronic Acid-modified Liposomes for Ursolic Acid-targeted Delivery Treat Lung Cancer Based on p53/ARTS-mediated Mitochondrial Apoptosis

Background

Chemotherapy drugs can cause drug resistance and other problems when treating lung cancer, which leads to treatment failure. Ursolic acid (UA) is used in formulations based on traditional Chinese medicine. UA has excellent anti-tumor effects, but they are limited by solubility and non-specificity to tumor cells.

Objectives

To overcome these issues, we created a novel hyaluronic acid (HA)-targeted liposome system for delivering UA (HA-Lipo/UA) to explore the targeting and anti-tumor effects of UA.

Methods

We constructed the HA-Lipo/UA delivery system by the thin film hydration method. The uptake and localization of UA were detected by flow cytometry and microscope. Cell proliferation of A549 cells was detected by MTT assays. Apoptosis and reactive oxygen species (ROS) expression of A549 cells were also evaluated after being treated with HA-Lipo/UA. Western blot analysis evaluated the anti-tumor mechanism of HA-Lipo/UA.

Results

HA-Lipo/UA exhibited favorable targeting of the cluster of differentiation (CD)44-overexpressing A549 cells. HA-Lipo/UA exhibited significant inhibition of the proliferation of A549 cells and induced their apoptosis compared with the corresponding monotherapies. HA-Lipo/UA induced overexpression of reactive oxygen species and upregulated expression of p53 and apoptosis-related protein in the transforming growth factor-β signaling (ARTS) pathway, which induced cytochrome-c release, activation of caspase-3, and promoted mitochondrial apoptosis in A549 cells.

Conclusions

Taken together, these data suggested that HA-Lipo/UA could be used to target tumor cells.

The automated measurement of CT number linearity using an ACR accreditation phantom

We developed a software to automatically measure the linearity between the CT numbers and densities of objects using an ACR 464 CT phantom, and investigated the CT number linearity of 16 different CT scanners. The software included a segmentation-rotation method. After segmenting five objects within the phantom image, the software computed the mean CT number of each object and plotted a graph between the CT numbers and densities of the objects. Linear regression and coefficients of regression, R², were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed at 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (R²> 0.99) between the CT numbers and densities of the five phantom materials. Two scanners (Siemens Emotion 16) had the strongest linear correlation withR²= 0.999, and two Hitachi Eclos scanners had the weakest linear correlation withR²< 0.99.

A circadian rhythm-related biomarker for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma

Lung adenocarcinoma (LUAD) remains a major reason of cancer-associated mortality globally, and there exists a lack of indicators for survival in LUAD patients. Therefore, it is clinically required to obtain a novel prognostically indicator for guiding clinical management. In this study, we established a circadian rhythm (CR) related signature by a combinative investigation of multiple datasets. The newly-established signature showed an acceptable ability to predict survival and could serve as an independent indicator for prognosis. Moreover, the newly-established signature was critically associated with tumor malignancy, including proliferation, invasion, EMT and metastasis. The newly-established signature was predictive of response to immune checkpoint blockade. Collectively, we established a CR-related gene signature that could forecast survival, tumor malignancy and therapeutic response; our findings could help guiding clinical management.

Changes in Treatment Patterns and Costs for Lung Cancer Have Not Resulted in Relevant Improvements in Survival: A Population-Based Observational Study in Catalonia

OBJECTIVE

Few published studies have described multidisciplinary therapeutic strategies for lung cancer. This study aims to describe the different approaches used for treating lung cancer in Catalonia in 2014 and 2018 and to assess the associated cost and impact on patient survival.

METHODS

A retrospective observational cohort study using data of patients with lung cancer from health care registries in Catalonia was carried out. We analyzed change in treatment patterns, costs and survival according to the year of treatment initiation (2014 vs. 2018). The Kaplan-Meier method was used to estimate survival, with the follow-up until 2021.

RESULTS

From 2014 to 2018, the proportion of patients undergoing surgery increased and treatments for unresectable tumors decreased, mainly in younger patients. Immunotherapy increased by up to 9% by 2018. No differences in patient survival were observed within treatment patterns. The mean cost per patient in the first year of treatment increased from EUR 14,123 (standard deviation [SD] 4327) to EUR 14,550 (SD 3880) in surgical patients, from EUR 4655 (SD 3540) to EUR 5873 (SD 6455) in patients receiving curative radiotherapy and from EUR 4723 (SD 7003) to EUR 6458 (SD 10,116) in those treated for unresectable disease.

CONCLUSIONS

From 2014 to 2018, surgical approaches increased in younger patients. The mean cost of treating patients increased, especially in pharmaceutical expenditure, mainly related to the use of several biomarker-targeted treatments. While no differences in overall patient survival were observed, it seems reasonable to expect improvements in this outcome in upcoming years as more patients receive innovative treatments.

Quantitative proteomics identified circulating biomarkers in lung adenocarcinoma diagnosis

BACKGROUND

Lung cancer (LC) is a common malignant tumor with a high incidence and poor prognosis. Early LC could be cured, but the 5-year-survival rate for patients advanced is extremely low. Early screening of tumor biomarkers through plasma could allow more LC to be detected at an early stage, leading to a earlier treatment and a better prognosis.

METHODS

This study was based on total proteomic analysis and parallel reaction monitoring validation of peripheral blood from 20 lung adenocarcinoma patients and 20 healthy individuals. Furthermore, differentially expressed proteins closely related to prognosis were analysed using Kaplan-Meier Plotter and receiver operating characteristic curve (ROC) curve analysis.

RESULTS

The candidate proteins GAPDH and RAC1 showed the highest connectivity with other differentially expressed proteins between the lung adenocarcinoma group and the healthy group using STRING. Kaplan-Meier Plotter analysis showed that lung adenocarcinoma patients with positive ATCR2, FHL1, RAB27B, and RAP1B expression had observably longer overall survival than patients with negative expression (P < 0.05). The high expression of ARPC2, PFKP, PNP, RAC1 was observably negatively correlated with prognosis (P < 0.05). 17 out of 27 proteins showed a high area under the curve (> 0.80) between the lung adenocarcinoma and healthy plasma groups. Among those proteins, UQCRC1 had an area under the curve of 0.960, and 5 proteins had an area under the curve from 0.90 to 0.95, suggesting that these hub proteins might have discriminatory potential in lung adenocarcinoma, P < 0.05.

CONCLUSIONS

These findings provide UQCRC1, GAPDH, RAC1, PFKP have potential as novel biomarkers for the early screening of lung adenocarcinoma.

The Roles of EXO1 and RPA1 Polymorphisms in Prognosis of Lung Cancer Patients Treated with Platinum-Based Chemotherapy

Background. Lung cancer is one of the major causes of cancer-related mortality worldwide. DNA repair and damage response contribute to genomic instability that accompanies tumor progression. In this study, we focus on evaluating association between DNA repair polymorphisms of EXO1, RPA1, and prognosis in lung cancer patients whom received platinum-based chemotherapy. Methods. 593 lung cancer patients were recruited in this study. We performed genotyping of 19 single nucleotide polymorphisms (SNPs) by Sequenom MassARRAY. Cox regression analysis was used to assess overall survival (OS) and progression-free survival (PFS) among SNP genotypes. Results. Significant differences in PFS and OS were observed in RPA1 rs5030740, EXO1 rs1776148, and rs1047840. Results showed that patients with CC genotype in rs5030740 (recessive model: $P=0.034$ ) had a better PFS. Patients with AA or/and AG genotypes in rs1776148 (additive model: $P=0.004$ ; dominant model: $P=0.048$ ) and AA genotype in rs1047840 (recessive model: $P=0.023$ ) had longer OS. We also demonstrated differences in subgroup analysis between rs5030740, rs1776148, rs1047840, and prognosis. Conclusions. Our results indicated that EXO1 rs1776148, rs1047840, and RPA1 rs5030740 were significantly associated with prognosis of lung cancer. Rs1776148, rs1047840, and rs5030740 may act as prognosis markers in lung cancer patients with platinum-based chemotherapy.

State of the Art: Lung Cancer Staging Using Updated Imaging Modalities

Lung cancer is among the most common mortality causes worldwide. This scientific article is a comprehensive review of current knowledge regarding screening, subtyping, imaging, staging, and management of treatment response for lung cancer. The traditional imaging modality for screening and initial lung cancer diagnosis is computed tomography (CT). Recently, a dual-energy CT was proven to enhance the categorization of variable pulmonary lesions. The National Comprehensive Cancer Network (NCCN) recommends usage of fluorodeoxyglucose positron emission tomography (FDG PET) in concert with CT to properly stage lung cancer and to prevent fruitless thoracotomies. Diffusion MR is an alternative to FDG PET/CT that is radiation-free and has a comparable diagnostic performance. For response evaluation after treatment, FDG PET/CT is a potent modality which predicts survival better than CT. Updated knowledge of lung cancer genomic abnormalities and treatment regimens helps to improve the radiologists’ skills. Incorporating the radiologic experience is crucial for precise diagnosis, therapy planning, and surveillance of lung cancer.

Clinical efficacy and safety of NSCLC ancillary treatment with compound Kushen injection through immunocompetence regulation: A systematic review and meta-analysis

BACKGROUND

Compound Kushen injection (CKI) is a Chinese patented medicine that improves the immunity level of cancer patients and inhibits tumor cell proliferation and metastasis. Clinically, CKI is widely used in combination with platinum-based chemotherapy (PBC) for non-small cell lung cancer (NSCLC) treatment. This study attempted to systemically evaluate the efficacy and safety of a combination of CKI and PBC for NSCLC treatment by modulating the immune function.

PURPOSE

To evaluate the clinical efficacy and safety of CKI in combination with PBC for NSCLC.

MATERIALS AND METHODS

English and Chinese databases were retrieved for randomized controlled trials (RCTs) of NSCLC treatment using a combination of CKI and PBC, and the changes of peripheral blood T lymphocytes (such as CD3⁺ T cells, CD4⁺ T cells, CD8⁺ T cells), and CD4⁺/CD8⁺ T cell ratio among NSCLC patients were detected before and after treatment using CKI with PBC. The search deadline was set as November 2021. The systemic evaluation was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The methodology and quality of each study included in the systemic evaluation were assessed. Review Manager 5.4, Stata12.0, and trial sequential analysis (TSA) were used for data analysis. The outcome indicators were qualified using GRADEprofiler software.

RESULTS

A total of 25 RCTs involving 2460 cases of patients were included. The results showed that the combination of CKI with PBC effectively increased the objective response rate (ORR) [relative risk (RR) = 1.31, 95% confidence interval (CI) (1.19, 1.44)] and disease control rate (DCR) [RR = 1.16, 95%CI (1.09,1.23)], regulated the expression of peripheral blood T lymphocytes (such as CD3⁺T cells, CD4⁺T cells, CD8⁺T cells, and CD4⁺/CD8⁺T cell ratio), upregulated the level of serum immunoglobulins (such as IgA, IgG, and IgM), and reduced the frequency of gastrointestinal reaction, marrow inhibition, hepatorenal toxicity, reduction of white blood cells and blood platelets, baldness, infection, neutrophilic granulocyte counts, diarrhea, or constipation. According to subgroup analysis results, chemotherapy cycles (1-2) had a more significant effect on DCR. A combination of CKI and GP regimens had better effects on improving CD3⁺T cell levels, and there were no significant changes among other chemotherapies regiments.

CONCLUSION

A combination of CKI and PBC had a marked effect in improving tumor response, priming immune function, and decreasing the frequency of adverse reactions, which was safe for NSCLC treatment.

miR-199a: A Tumor Suppressor with Noncoding RNA Network and Therapeutic Candidate in Lung Cancer

Lung cancer is the leading cause of cancer death worldwide. miR-199a, which has two mature molecules: miR-199a-3p and miR-199a-5p, plays an important biological role in the genesis and development of tumors. We collected recent research results on lung cancer and miR-199a from Google Scholar and PubMed databases. The biological functions of miR-199a in lung cancer are reviewed in detail, and its potential roles in lung cancer diagnosis and treatment are discussed. With miR-199a as the core point and a divergence outward, the interplay between miR-199a and other ncRNAs is reviewed, and a regulatory network covering various cancers is depicted, which can help us to better understand the mechanism of cancer occurrence and provide a means for developing novel therapeutic strategies. In addition, the current methods of diagnosis and treatment of lung cancer are reviewed. Finally, a conclusion was drawn: miR-199a inhibits the development of lung cancer, especially by inhibiting the proliferation, infiltration, and migration of lung cancer cells, inhibiting tumor angiogenesis, increasing the apoptosis of lung cancer cells, and affecting the drug resistance of lung cancer cells. This review aims to provide new insights into lung cancer therapy and prevention.

Identification of genes and pathways associated with sex in Non-smoking lung cancer population

INTRODUCTION

Women represent a higher proportion than men among those with lung cancer in nonsmokers compared to smokers. The reason for this abnormally higher proportion is not yet clear, but sex differences suggest there may be a genetic component at play.

MATERIALS AND METHODS

The gene expression determined by Illumina RNA Sequencing and the relevant clinical information of lung cancer patients was download from TCGA. The differentially expressed genes (DEGs) were screened between males and females in both nonsmoking and smoking populations. The top 50 validated DEGs are represented with heatmaps. Based on the DEGs, GO functional and KEGG pathway enrichment analyses were performed. PPI networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect lung cancer patient prognosis.

RESULTS

In non-smoking patients, there were significantly more females than males (female 73.0% vs male 27.0%, P < 0.001). Such difference was not found in smoking patients (female 50.7% vs male 49.3%, P = 0.770). A total of 898 DEGs were identified in the non-smoking population, while a total of 992 DEGs were identified in the smoking population. Of these, only 122 genes were shared by both populations. Some pathways were enriched specifical in non-smoking population, such as cAMP signaling pathway and ovarian steroidogenesis. Several proteins related to estrogen function and MAPK/PI3K signaling, such as KRT16, ERBB4 and NTF4, showed differential effects on the lung adenocarcinoma progression in non-smoking males or females.

CONCLUSIONS

Some genetic differences between male and female in non-smoking lung adenocarcinoma patients have been identified. Potentially, ER signaling and MAPK/PI3K signaling partially participated in this discrepancy.

Cloud-YLung for Non-Small Cell Lung Cancer Histology Classification from 3D Computed Tomography Whole-Lung Scans

Non-Small Cell Lung Cancer (NSCLC) represents up to 85% of all malignant lung nodules. Adenocarcinoma and squamous cell carcinoma account for 90% of all NSCLC histotypes. The standard diagnostic procedure for NSCLC histotype characterization implies cooperation of 3D Computed Tomography (CT), especially in the form of low-dose CT, and lung biopsy. Since lung biopsy is invasive and challenging (especially for deeply-located lung cancers and for those close to blood vessels or airways), there is the necessity to develop non-invasive procedures for NSCLC histology classification. Thus, this study aims to propose Cloud-YLung for NSCLC histology classification directly from 3D CT whole-lung scans. With this aim, data were selected from the openly-accessible NSCLC-Radiomics dataset and a modular pipeline was designed. Automatic feature extraction and classification were accomplished by means of a Convolutional Long Short-Term Memory (ConvLSTM)-based neural network trained from scratch on a scalable GPU cloud service to ensure a machine-independent reproducibility of the entire framework. Results show that Cloud- YLung performs well in discriminating both NSCLC histotypes, achieving a test accuracy of 75% and AUC of 84%. Cloud-YLung is not only lung nodule segmentation free but also the first that makes use of a ConvLSTM-based neural network to automatically extract high-throughput features from 3D CT whole-lung scans and classify them. Clinical relevance- Cloud-YLung is a promising framework to non-invasively classify NSCLC histotypes. Preserving the lung anatomy, its application could be extended to other pulmonary pathologies using 3D CT whole-lung scans.

Pulmonary CT scans of white rabbits using the selective photon shield technique of the third-generation dual-source CT

This study aims to optimise the protocol for the low-dose pulmonary computed tomography (CT) scanning of infants by studying the effects of the selective photon shield (SPS) technique of the third-generation dual-source CT (DSCT) on the image quality and radiation dose of a chest CT in white rabbits under different tube currents. Twelve white rabbits of a similar weight to an infant were selected and randomly divided into an experimental group and a control group. The experimental groups (A1-A5) were scanned at low dose by the third-generation DSCT using SPS under different tube current × time (60, 50, 40, 30, and 20 mAs). The control group (B) was scanned under a conventional tube voltage (100 kV) and current × time (20 mAs). Advanced model iterative reconstruction at strength three was used for the objective and subjective evaluation of the image quality and radiation dose of the lung and mediastinal windows. With the standard deviation of the air in the trachea as image noise, the signal-to-noise ratio (SNR), contrast-to-noise ratio, and CT values of each site were evaluated. Radiation doses were compared using the volume CT dose index, dose length product, and effective dose. The differences in subjective image quality between groups A2 and B were not statistically significant (P= 0.34). The differences in the SNRs of the lung and mediastinal windows between groups A2 and B were not statistically significant (P> 0.05). The radiation dose of group A2 was 83.2% lower than that of group B. The SPS of the third-generation DSCT under 50 mAs might be applied in the pulmonary CT examination of infants.

The Combined Clinical Efficacy and Safety Analysis of Adoptive Immunotherapy with Radiotherapy and Chemotherapy in Non-Small-Cell Lung Cancer: Systematic Review and Meta-Analysis

Objective

To explore the differential efficacy of chemoradiotherapy combined with adoptive immunotherapy and radiochemotherapy alone in patients with non-small-cell lung cancer (NSCLC).

Methods

Qualified randomized controlled trial (randomized controlled trial, RCT), or nonrandomized concurrent controlled trial (NRCCT), published in various databases, including PubMed, EMBASE, Chinese journal full-text database, Medline, Cochrane database, and VIP Chinese database, and the Revman5. 0 software performed the data analysis.

Results

We found the significantly different curative effect between the experimental and control groups (OR = 1.94, 95% CI (1.46, 2.58), P < 0.001, I 2 = 0%, Z = 4.59), effect of adoptive immunotherapy on the progression of disease (OR = 1.80, 95% CI (1.38, 2.35), P < 0.001, I 2 = 0%, Z = 4.33), adoptive immunotherapy on overall survival (OR = 2.19, 95% CI (1.60, 2.99), P < 0.001, I 2 = 0%, Z = 4.91), and adverse effects of adoptive immunotherapy (OR = 1.76, 95% CI (1.25, 2.48), P = 0.001, I 2 = 0%, Z = 3.26).

Conclusion

Adoptive immunotherapy combined with microradiotherapy can decrease the recurrence of NSCLC and improve patient survival, as well as early patients can be benefited more significantly from immunotherapy.

Chromobox Homologue 7 Acts as a Tumor Suppressor in Both Lung Adenocarcinoma and Lung Squamous Cell Carcinoma via Inhibiting ERK/MAPK Signaling Pathway

Chromobox homologue 7 (CBX7) is a member of the polycomb group family that plays a pivotal role in regulating cellular processes in human cancers. This study aims to explore the function and underlying molecular mechanisms of CBX7 in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). The expression of CBX7 in LUAD and LUSC tissues was analyzed by UALCAN and GEPIA based on the TCGA database. Cell viability and apoptosis were measured by CCK-8 and flow cytometry assays, respectively. Cell migration and invasion were detected by transwell assay. The functions of downregulated genes in LUAD were enriched via GO and KEGG pathway analyses. The mRNA expression of CBX7, ERK1/2, and p38 was determined by qRT-PCR, and the protein levels of CBX7, ERK1/2, p-ERK1/2, p38, and p-p38 were measured by Western blotting. Tumor xenograft model was established to validate the antitumor effect of CBX7. The expression of CBX7 and Ki-67 in tumor tissues was detected by immunohistochemistry. CBX7 was downregulated in the tissues and cells of both LUAD and LUSC. Low CBX7 expression was associated with a poor overall survival rate in LUAD patients. CBX7 overexpression inhibited the viability, migration, and invasion and promoted the apoptosis of LUAD and LUSC cells. In addition, the downregulated genes in LUAD were enriched in MAPK cascade (GO) and MAPK signaling pathway (KEGG). ERK/MAPK pathway was then determined as a downstream target of CBX7, which was inhibited by CBX7 overexpression in LUAD and LUSC cells. The overexpression of CBX7 inhibited the malignant progression of LUAD and LUSC cells probably via suppressing the ERK/MAPK signaling pathway in vitro and in vivo.

Hsa_circ_0006692 Promotes Lung Cancer Progression via miR-205-5p/CDK19 Axis

Circular RNA (CircRNA) is related to tumor development. Nevertheless, the regulation and function of hsa_circ_0006692 and its interactions with miR-205-5p and CDK19 in the development of non-small-cell lung cancer (NSCLC) were un-explored. The correlations of expression levels of hsa_circ_0006692 in NSCLC specimens and cells with pathological characteristics were studied. The interactions of hsa_circ_0006692 with miR-205-5p and CDK19 were assessed with real-time PCR, RNA-binding protein immunoprecipitation (RIP), luciferase reporter, RNA pull-down, and fluorescence in situ hybridization (FISH). The roles of hsa_circ_0006692 on cell growth, invasion, and migration in vitro and metastasis in vivo were evaluated. Hsa_circ_0006692 was over-expressed in 60 cases of NSCLC specimens and cells, which was positively correlated with TNM stage, tumor size, and invasion of the lung basal layer. The results of the in vitro and in vivo studies revealed that the over-expression of hsa_circ_0006692 facilitated NSCLC cell growth, migration, and invasion, cell cycle arrest at the S phase, and the activation of BCL-2, CCND1, and PCNA. The results of the dual-luciferase reporter assay, RNA immunoprecipitation, and pull-down assays indicated that hsa_circ_0006692 sponged miR-205-5p, which targeted CDK19 and facilitated the malignant behaviors of lung cancer cells. Hsa_circ_0006692 modulated EMT of lung cancer cells via the stimulation of CDH1, CDH2, VIMENTIN, and MMP7. This study revealed that hsa_circ_0006692 promoted NSCLC progression via enhancing cell growth, invasion, and metastasis through sponging mir-205-5p, up-regulating the downstream oncogene CDK19 and modulating EMT of lung cancer cells. The circ-0006692/mir-205-5p/CDK19 axis might serve as a prognosis biomarker and target for drugs aimed against NSCLC.

Marked safety and high diagnostic yield of freehand ultrasound-guided core-needle biopsies performed by pulmonologists

Background

Adequate tissue sampling is fundamental for establishing a definitive diagnosis, assessing prognosis and tailoring therapy. Each of the methods for obtaining tissue (e.g., endoscopic, image guidance and surgical biopsies) results in a different diagnostic yield and complication rate profile.

Objectives

Present feasibility, and assess safety and efficacy of freehand transthoracic ultrasound‐guided core‐needle biopsies (USGNB) of thoracic lesions performed by pulmonologist.

Methods

A retrospective analysis study of ultrasound‐guided core‐needle biopsies of thoracic lesions performed at the Pulmonary Institute of Rabin Medical Center was conducted from September 2020 to October 2021. All core‐needle biopsies were performed under local anesthesia with guidance of Mindray TE7 2019 US system. Procedural variables including complications and pathological diagnostic yield were the primary end point. IRB 0671‐21‐RMC.

Results

In total 91 biopsy procedures were analyzed in38 females and 53 males, average age 71.1 years. Twenty‐three (25.3%) cases were lung lesions, 7 (7.7%) – mediastinal, 13 (14.3%) – chest wall, 27 (29.7%) – pleural, and 21 (23.1%) supraclavicular lesions. Average lesion size was 51.6 mm, the largest in the mediastinum and the smallest in supraclavicular locations (97.7mm and 28.0 mm, respectively). Overall pathological diagnostic yield was 90%, highest success in chest wall (100%) and lowest in mediastinal biopsies (71.4%). We had only one complication –hemothorax resolved by chest tube drainage‐ accounting for only 1.1% complication rate.

Conclusion

Safety and efficacy were demonstrated in freehand US‐guided core‐needle biopsy of thoracic lesions performed by pulmonologists. We suggest thoracic ultrasound and USG‐CNB be part of training and clinical practice in interventional pulmonology.

Study on Identification Method of Pulmonary Nodules: Improved Random Walk Pulmonary Parenchyma Segmentation and Fusion Multi-Feature VGG16 Nodule Classification

Purpose

The purpose of this study was to realize automatic segmentation of lung parenchyma based on random walk algorithm to ensure the accuracy of lung parenchyma segmentation. The explicable features of pulmonary nodules were added into VGG16 neural network to improve the classification accuracy of pulmonary nodules.

Materials and Methods

LIDC-IDRI, a public dataset containing lung Computed Tomography images/pulmonary nodules, was used as experimental data. In lung parenchyma segmentation, the maximum Between-Class Variance method (OTSU), corrosion and expansion methods were used to automatically obtain the foreground and background seed points of random walk algorithm in lung parenchyma region. The shortest distance between point sets was added as one of the criteria of prospect probability in the calculation of random walk weight function to achieve accurate segmentation of pulmonary parenchyma. According to the location of the nodules marked by the doctor, the nodules were extracted. The texture features and grayscale features were extracted by Volume Local Direction Ternary Pattern (VLDTP) method and gray histogram. The explicable features were input into VGG16 network in series mode and fused with depth features to achieve accurate classification of nodules. Intersection of Union (IOU) and false positive rate (FPR) were used to measure the segmentation results. Accuracy, Sensitivity, Specificity, Accuracy and F1 score were used to evaluate the results of nodule classification.

Results

The automatic random walk algorithm is effective in lung parenchyma segmentation, and its segmentation efficiency is improved obviously. In VGG16 network, the accuracy of nodular classification is 0.045 higher than that of single depth feature classification.

Conclusion

The method proposed in this paper can effectively and accurately achieve automatic segmentation of lung parenchyma. In addition, the fusion of multi-feature VGG16 network is effective in the classification of pulmonary nodules, which can improve the accuracy of nodular classification.

Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images

Lung cancer is the leading cause of cancer deaths worldwide. Accurately diagnosing the malignancy of suspected lung nodules is of paramount clinical importance. However, to date, the pathologically-proven lung nodule dataset is largely limited and is highly imbalanced in benign and malignant distributions. In this study, we proposed a Semi-supervised Deep Transfer Learning (SDTL) framework for benign-malignant pulmonary nodule diagnosis. First, we utilize a transfer learning strategy by adopting a pre-trained classification network that is used to differentiate pulmonary nodules from nodule-like tissues. Second, since the size of samples with pathological-proven is small, an iterated feature-matching-based semi-supervised method is proposed to take advantage of a large available dataset with no pathological results. Specifically, a similarity metric function is adopted in the network semantic representation space for gradually including a small subset of samples with no pathological results to iteratively optimize the classification network. In this study, a total of 3,038 pulmonary nodules (from 2,853 subjects) with pathologically-proven benign or malignant labels and 14,735 unlabeled nodules (from 4,391 subjects) were retrospectively collected. Experimental results demonstrate that our proposed SDTL framework achieves superior diagnosis performance, with accuracy = 88.3%, AUC = 91.0% in the main dataset, and accuracy = 74.5%, AUC = 79.5% in the independent testing dataset. Furthermore, ablation study shows that the use of transfer learning provides 2% accuracy improvement, and the use of semi-supervised learning further contributes 2.9% accuracy improvement. Results implicate that our proposed classification network could provide an effective diagnostic tool for suspected lung nodules, and might have a promising application in clinical practice.

Prognostic Significance of PTTG1 and Its Methylation in Lung Adenocarcinoma

Pituitary tumor-transforming gene-1 (PTTG1), one type of DNA repair-related gene, has been reported to be dysregulated in several tumors and serve as a tumor promotor. Previously, the oncogenic roles of PTTG1 were also reported in lung adenocarcinoma (LUAD). However, the prognostic values of PTTG1 in LUAD and the possible mechanism of its dysregulation have not been clarified. We analyzed TCGA datasets and reported that PTTG1 expression showed a distinct increase within LUAD specimens in comparison with nontumor specimens. Further survival study revealed that patients containing a great PTTG1 level had noticeably less overall survival and progression-free survival as compared with patients containing a low PTTG1 level. Multivariate analyses confirmed that PTTG1 expression was a factor of prognosis that is independent in terms of LUAD patients. Besides, PTTG1 methylation had a negative regulation on PTTG1, so PTTG1 had a high expressing level in LUAD tissues. However, the relation between hypermethylation and overall survival was not demonstrated using TCGA datasets. In addition, we observed that LUAD specimens with advanced stages exhibited a higher level of PTTG1. Finally, the dysregulated genes related to PTTG1 expression were screened, and KEGG assays revealed that the above genes were involved in the p53 signaling pathway, indicating the possible regulatory function of PTTG1 in the p53 signaling pathway. Overall, our findings suggest that PTTG1 may serve as an efficient clinical biomarker and a therapeutic target for patients suffering from LUAD.

Lanthanide-based metal-organic frameworks solidified by gelatin-methacryloyl hydrogels for improving the accuracy of localization and excision of small pulmonary nodules

The localization of invisible and impalpable small pulmonary nodules has become an important concern during surgery, since current widely used techniques for localization have a number of limitations, such as invasive features of hookwires and microcoils, and rapid diffusion after injection of indocyanine green (ICG). Lanthanide-based metal–organic frameworks (MOFs) have been proven as potential fluorescent agents because of their prominent luminescent characteristics, including large Stokes shifts, high quantum yields, long decay lifetimes, and undisturbed emissive energies. In addition, lanthanides, such as Eu, can efficiently absorb X-rays for CT imaging. In this study, we synthesized Eu-UiO-67-bpy (UiO = University of Oslo, bpy = 2,2'-bipyridyl) as a fluorescent dye with a gelatin-methacryloyl (GelMA) hydrogel as a liquid carrier. The prepared complex exhibits constant fluorescence emission owing to the luminescent characteristics of Eu and the stable structure of UiO-67-bpy with restricted fluorescence diffusion attributed to the photocured GelMA. Furthermore, the hydrogel provides stiffness to make the injection site tactile and improve the accuracy of localization and excision. Finally, our complex enables fluorescence-CT dual-modal imaging of the localization site.

Diagnosis of Lung Cancer by FTIR Spectroscopy Combined With Raman Spectroscopy Based on Data Fusion and Wavelet Transform

Lung cancer is a fatal tumor threatening human health. It is of great significance to explore a diagnostic method with wide application range, high specificity, and high sensitivity for the detection of lung cancer. In this study, data fusion and wavelet transform were used in combination with Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy to study the serum samples of patients with lung cancer and healthy people. The Raman spectra of serum samples can provide more biological information than the FTIR spectra of serum samples. After selecting the optimal wavelet parameters for wavelet threshold denoising (WTD) of spectral data, the partial least squares–discriminant analysis (PLS-DA) model showed 93.41% accuracy, 96.08% specificity, and 90% sensitivity for the fusion data processed by WTD in the prediction set. The results showed that the combination of FTIR spectroscopy and Raman spectroscopy based on data fusion and wavelet transform can effectively diagnose patients with lung cancer, and it is expected to be applied to clinical screening and diagnosis in the future.