Lung Cancer and Radiological Imaging

Identification of CT Features to Differentiate Pulmonary Sarcoma from Carcinoma

Background  Primary lung sarcoma (PLS) differs in management protocols and prognosis from the more common primary lung carcinoma (PLC). It becomes imperative to raise a high index of suspicion on radiological and pathological features. Purpose  The aim of this study is to highlight the variable imaging appearances of PLS compared with PLC, which impacts radiologic - pathologic correlation. Materials and Methods  A retrospective observational study of 68 patients with biopsy-proven lung tumors who underwent baseline imaging at our tertiary care cancer hospital was conducted between January 2018 and March 2022. The patient details and imaging parameters of the mass on contrast-enhanced computed tomography (CECT) were recorded and analyzed for patients with PLS and compared with PLC. Follow-up imaging was available in 9/12 PLS and 52/56 PLC patients. Results  Among 12 patients with PLS, 5 patients had synovial sarcoma on histopathology. PLS was seen in patients with a mean age of 40.8 years; the mass showed a mean size of 13.2 cm, lower lobe (75%), parahilar (75%), hilar involvement (41.7%), oval shape (41.7%), circumscribed (25%) or lobulated (75%) margins, lower mean postcontrast attenuation of 57.3 HU, fissural extension (50%), calcification (50%), and no organ metastasis other than to the lung. PLC (56 patients) was seen in the elderly with a mean age of 54.8 years; the mass showed a mean size of 5.7 cm, irregular shape (83.9%), spiculated margins (73.2%), higher mean postcontrast attenuation (77.3 HU), chest wall infiltration (30.4%), and distant metastasis (58.9%) at baseline imaging. A statistically significant difference ( p  < 0.05) was seen between sarcoma and carcinoma in the mean age, size, site, shape, margins, postcontrast attenuation, presence of calcifications, fissural extension, and distant metastasis. Conclusion  The distinct imaging features of sarcoma help in differentiating it from carcinoma. This can also be used to corroborate with histopathology to achieve concordance and guide clinicians on further approach.

Comparing quantitative image parameters between animal and clinical CT-scanners: a translational phantom study analysis

Purpose

This study compares phantom-based variability of extracted radiomics features from scans on a photon counting CT (PCCT) and an experimental animal PET/CT-scanner (Albira II) to investigate the potential of radiomics for translation from animal models to human scans. While oncological basic research in animal PET/CT has allowed an intrinsic comparison between PET and CT, but no 1:1 translation to a human CT scanner due to resolution and noise limitations, Radiomics as a statistical and thus scale-independent method can potentially close the critical gap.

Methods

Two phantoms were scanned on a PCCT and animal PET/CT-scanner with different scan parameters and then the radiomics parameters were extracted. A Principal Component Analysis (PCA) was conducted. To overcome the limitation of a small dataset, a data augmentation technique was applied. A Ridge Classifier was trained and a Feature Importance- and Cluster analysis was performed.

Results

PCA and Cluster Analysis shows a clear differentiation between phantom types while emphasizing the comparability of both scanners. The Ridge Classifier exhibited a strong training performance with 93% accuracy, but faced challenges in generalization with a test accuracy of 62%.

Conclusion

These results show that radiomics has great potential as a translational tool between animal models and human routine diagnostics, especially using the novel photon counting technique. This is another crucial step towards integration of radiomics analysis into clinical practice.

Clinical application of computed tomographic volumetric imaging in postoperative lung function assessment in patients with lung cancer

BACKGROUND

To evaluate the effectiveness of the computed tomographic (CT) volumetric analysis in postoperative lung function assessment and the predicting value for postoperative complications in patients who had segmentectomy for lung cancer.

METHODS

CT scanning and pulmonary function examination were performed for 100 patients with lung cancer. CT volumetric analyses were performed by specific software, for the volume of the inspiratory phase (Vin), the mean inspiratory lung density (MLDin), the volume of expiratory phase (Vex), and the mean lung density at expiratory phase (MLDex). Pulmonary function examination results and CT volumetric analysis results were used to predict postoperative lung function. The concordance and correlations of these values were assessed by Bland-Altman analysis and Pearson correlation analysis, respectively. Multivariate binomial logistic regression analysis was executed to assess the associations of CT data with complication occurrence.

RESULTS

Correlations between CT scanning data and pulmonary function examination results were significant in both pre- and post-operation (0.8083 ≤ r ≤ 0.9390). Forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), and the ratio of FVC and FEV1 estimated by CT volumetric analyses showed high concordance with those detected by pulmonary function examination. Preoperative (Vin-Vex) and (MLDex- MLDin) values were identified as predictors for post-surgery complications, with hazard ratios of 5.378 and 6.524, respectively.

CONCLUSIONS

CT volumetric imaging analysis has the potential to determine the pre- and post-operative lung function, as well as to predict post-surgery complication occurrence in lung cancer patients with pulmonary lobectomy.

Development of an Engineered Single-Domain Antibody for Targeting MET in Non-Small Cell Lung Cancer

The Mesenchymal Epithelial Transition (MET) receptor tyrosine kinase is upregulated or mutated in 5% of non-small-cell lung cancer (NSCLC) patients and overexpressed in multiple other cancers. We sought to develop a novel single-domain camelid antibody with high affinity for MET that could be used to deliver conjugated payloads to MET expressing cancers. From a naïve camelid variable-heavy-heavy (VHH) domain phage display library, we identified a VHH clone termed 1E7 that displayed high affinity for human MET and was cross-reactive with MET across multiple species. When expressed as a bivalent human Fc fusion protein, 1E7-Fc was found to selectively bind to EBC-1 (MET amplified) and UW-Lung 21 (MET exon 14 mutated) cell lines by flow cytometry and immunofluorescence imaging. Next, we investigated the ability of [89Zr]Zr-1E7-Fc to detect MET expression in vivo by PET/CT imaging. [89Zr]Zr-1E7-Fc demonstrated rapid localization and high tumor uptake in both xenografts with a %ID/g of 6.4 and 5.8 for EBC-1 and UW-Lung 21 at 24 h, respectively. At the 24 h time point, clearance from secondary and nontarget tissues was also observed. Altogether, our data suggest that 1E7-Fc represents a platform technology that can be employed to potentially both image and treat MET-altered NSCLC.

On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans

Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.

Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions

Small cell lung cancer (SCLC) is characterized by rapid growth and high metastatic capacity. It has strong epidemiologic and biologic links to tobacco carcinogens. Although the majority of SCLCs exhibit neuroendocrine features, an important subset of tumors lacks these properties. Genomic profiling of SCLC reveals genetic instability, almost universal inactivation of the tumor suppressor genes TP53 and RB1, and a high mutation burden. Because of early metastasis, only a small fraction of patients are amenable to curative-intent lung resection, and these individuals require adjuvant platinum-etoposide chemotherapy. Therefore, the vast majority of patients are currently being treated with chemoradiation with or without immunotherapy. In patients with disease confined to the chest, standard therapy includes thoracic radiotherapy and concurrent platinum-etoposide chemotherapy. Patients with metastatic (extensive-stage) disease are treated with a combination of platinum-etoposide chemotherapy plus immunotherapy with an anti-programmed death-ligand 1 monoclonal antibody. Although SCLC is initially very responsive to platinum-based chemotherapy, these responses are transient because of the development of drug resistance. In recent years, the authors have witnessed an accelerating pace of biologic insights into the disease, leading to the redefinition of the SCLC classification scheme. This emerging knowledge of SCLC molecular subtypes has the potential to define unique therapeutic vulnerabilities. Synthesizing these new discoveries with the current knowledge of SCLC biology and clinical management may lead to unprecedented advances in SCLC patient care. Here, the authors present an overview of multimodal clinical approaches in SCLC, with a special focus on illuminating how recent advancements in SCLC research could accelerate clinical development.

Malignant Neoplasms Arising in the Cardiac Pacemaker Cavity: A Systematic Review

Cancer is the abnormal proliferation of physiologically inadequate cells. Studies have identified the cardiac pacemaker pocket as a site of rare neoplasms. To evaluate the clinical outcomes, treatment, prognosis, and individualized management of tumors originating in the cardiac pacemaker pocket, a systematic review was conducted using case reports and case series available in the PubMed/Medline, Science Direct, Cochrane Central, LILACS, and Scientific Electronic Library Online (Scielo) databases. Pacemaker pocket tumors affected patients with a mean age of 72.9 years, with a higher incidence in males (76.9%, n = 10). The average time for neoplasm development was 4.4 years (54.07 months). The most prevalent model was Medtronic (38.4%, n = 5), with titanium (83.3%) being the most common metal composition. Chemotherapy was the most performed procedure among patients (38.4%), followed by radiation therapy (38.4%) and surgical tumor resection (30.7%). Six analyzed cases (46.1%) resulted in death, and four patients (30.7%) achieved a cure. Patients with pacemakers should be routinely evaluated for the occurrence of malignant tumors at the site of device implantation.

Laterocervical lymph node metastases from suspected thyroidal primary site that turned out to be metastases of lung cancer: A case report

Incidental sonographic discovery of thyroid nodules is an increasingly common event in clinical practice. Less frequently, patients with cytological benign thyroid nodules have suspicious cervical lymph nodes detected by ultrasound examination or by cytological exam. Here, we discuss an intriguing case of cervical lymph node metastasis with a probable thyroid origin in a 65-year-old asymptomatic male smoker. He underwent thyroidectomy and unilateral cervical lymphadenectomy. Despite a negative chest X-ray, the postoperative histological examination revealed that the lymph node metastasis was actually from a lung carcinoma. Metastatic lesions in cervical lymph nodes from non-thyroidal origins must be excluded when evaluating lesions in the region, especially when thyroid nodules subjected to fine needle aspiration biopsy yield negative results, or lymph node cytological evaluations are inconsistent with thyroid cytological findings and sonographic features. Thyroid and lung adenocarcinomas share some epithelial and mesenchymal markers. Thyroglobulin helps differentiate primary thyroid tumors from lung ones, but in cases of poor differentiation, distinguishing metastatic lesions in the thyroid gland can be challenging. Lung cancer (LC) is the leading cause of cancer mortality worldwide, and survival rates have only marginally improved over the last several decades. The ongoing clinical challenge is detecting LC at earlier stages of the disease.

Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature

Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([¹⁸F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [¹⁸F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.

Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures currently in use, and evaluate the clinical relevance of these diagnostic imaging methods. A systematic search of PubMed, Web of Science, Cochrane, and Scopus databases was conducted in February 2023, encompassing the literature published up until December 2022. The review included nine studies, comprising five case-control studies, three retrospective cohort studies, and one prospective cohort study. Various ML architectures were analyzed, including artificial neural network (ANN), entropy degradation method (EDM), probabilistic neural network (PNN), support vector machine (SVM), partially observable Markov decision process (POMDP), and random forest neural network (RFNN). The ML architectures demonstrated promising results in detecting and classifying lung cancer across different lesion types. The sensitivity of the ML algorithms ranged from 0.81 to 0.99, while the specificity varied from 0.46 to 1.00. The accuracy of the ML algorithms ranged from 77.8% to 100%. The AI architectures were successful in differentiating between malignant and benign lesions and detecting small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This systematic review highlights the potential of ML AI architectures in the detection and classification of lung cancer, with varying levels of diagnostic accuracy. Further studies are needed to optimize and validate these AI algorithms, as well as to determine their clinical relevance and applicability in routine practice.

Role of γδ T Cells in Cancer Progression and Therapy

γδ T cells signify a foundational group of immune cells that infiltrate tumors early on, engaging in combat against cancer cells. The buildup of γδ T cells as cancer advances underscores their significance. Initially, these cells infiltrate and enact cytotoxic effects within the tumor tissue. However, in later stages, the predominant phenotype of γδ T cells undergoes changes in numerous cancers, fostering tumor growth and metastasis. Different mechanisms induced by cancer cell suppress effector action of γδ T cells and even sometimes promote cancer progression. In the early stages, stopping this mechanism clears this challenge and enables γδ T cells to effectively remove cancer cells. Given this context, it becomes imperative to delve into the mechanisms of how γδ T cells function in tumor microenvironment. This review discusses γδ T cells' role across different cancer types.

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.

Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey

Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks.

Application of Artificial Intelligence in Lung Cancer

Simple Summary

Lung cancer is the leading cause of malignancy-related mortality worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and decision making to prognosis prediction. AI could reduce the labor work of LDCT, CXR, and pathology slides reading. AI as a second reader in LDCT and CXR reading reduces the effort of radiologists and increases the accuracy of nodule detection. Introducing AI to WSI in digital pathology increases the Kappa value of the pathologist and help to predict molecular phenotypes with radiomics and H&E staining. By extracting radiomics from image data and WSI from the histopathology field, clinicians could use AI to predict tumor properties such as gene mutation and PD-L1 expression. Furthermore, AI could help clinicians in decision-making by predicting treatment response, side effects, and prognosis prediction in medical treatment, surgery, and radiotherapy. Integrating AI in the future clinical workflow would be promising.

Abstract

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

Polymer Nanoparticles and Nanomotors Modified by DNA/RNA Aptamers and Antibodies in Targeted Therapy of Cancer

Polymer nanoparticles and nano/micromotors are novel nanostructures that are of increased interest especially in the diagnosis and therapy of cancer. These structures are modified by antibodies or nucleic acid aptamers and can recognize the cancer markers at the membrane of the cancer cells or in the intracellular side. They can serve as a cargo for targeted transport of drugs or nucleic acids in chemo- immuno- or gene therapy. The various mechanisms, such as enzyme, ultrasound, magnetic, electrical, or light, served as a driving force for nano/micromotors, allowing their transport into the cells. This review is focused on the recent achievements in the development of polymer nanoparticles and nano/micromotors modified by antibodies and nucleic acid aptamers. The methods of preparation of polymer nanoparticles, their structure and properties are provided together with those for synthesis and the application of nano/micromotors. The various mechanisms of the driving of nano/micromotors such as chemical, light, ultrasound, electric and magnetic fields are explained. The targeting drug delivery is based on the modification of nanostructures by receptors such as nucleic acid aptamers and antibodies. Special focus is therefore on the method of selection aptamers for recognition cancer markers as well as on the comparison of the properties of nucleic acid aptamers and antibodies. The methods of immobilization of aptamers at the nanoparticles and nano/micromotors are provided. Examples of applications of polymer nanoparticles and nano/micromotors in targeted delivery and in controlled drug release are presented. The future perspectives of biomimetic nanostructures in personalized nanomedicine are also discussed.