Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning

Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model

According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model's training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods.

Measurement of mouse head and neck tumors by automated analysis of CBCT images

Animal experiments are often used to determine effects of drugs and other biological conditions on cancer progression, but poor accuracy and reproducibility of established tumor measurement methods make results unreliable. In orthotopic mouse models of head and neck cancer, tumor volumes approximated from caliper measurements are conventionally used to compare groups, but geometrical challenges make the procedure imprecise. To address this, we developed software to better measure these tumors by automated analysis of cone-beam computed tomography (CBCT) scans. This allows for analyses of tumor shape and growth dynamics that would otherwise be too inaccurate to provide biological insight. Monitoring tumor growth by calipers and imaging in parallel, we find that caliper measurements of small tumors are weakly correlated with actual tumor volume and highly susceptible to experimenter bias. The method presented provides a unique window to sources of error in a foundational aspect of preclinical head and neck cancer research and a valuable tool to mitigate them.

The National Cancer Institute's Co-Clinical Quantitative Imaging Research Resources for Precision Medicine in Preclinical and Clinical Settings

Genetically engineered mouse models (GEMMs) and patient-derived xenograft mouse models (PDXs) can recapitulate important biological features of cancer. They are often part of precision medicine studies in a co-clinical setting, in which therapeutic investigations are conducted in patients and in parallel (or sequentially) in cohorts of GEMMs or PDXs. Employing radiology-based quantitative imaging in these studies allows in vivo assessment of disease response in real time, providing an important opportunity to bridge precision medicine from the bench to the bedside. The Co-Clinical Imaging Research Resource Program (CIRP) of the National Cancer Institute focuses on the optimization of quantitative imaging methods to improve co-clinical trials. The CIRP supports 10 different co-clinical trial projects, spanning diverse tumor types, therapeutic interventions, and imaging modalities. Each CIRP project is tasked to deliver a unique web resource to support the cancer community with the necessary methods and tools to conduct co-clinical quantitative imaging studies. This review provides an update of the CIRP web resources, network consensus, technology advances, and a perspective on the future of the CIRP. The presentations in this special issue of Tomography were contributed by the CIRP working groups, teams, and associate members.

Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective

Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.

Airway remodeling heterogeneity in asthma and its relationship to disease outcomes

Asthma affects an estimated 262 million people worldwide and caused over 461,000 deaths in 2019. The disease is characterized by chronic airway inflammation, reversible bronchoconstriction, and airway remodeling. Longitudinal studies have shown that current treatments for asthma (inhaled bronchodilators and corticosteroids) can reduce the frequency of exacerbations, but do not modify disease outcomes over time. Further, longitudinal studies in children to adulthood have shown that these treatments do not improve asthma severity or fixed airflow obstruction over time. In asthma, fixed airflow obstruction is caused by remodeling of the airway wall, but such airway remodeling also significantly contributes to airway closure during bronchoconstriction in acute asthmatic episodes. The goal of the current review is to understand what is known about the heterogeneity of airway remodeling in asthma and how this contributes to the disease process. We provide an overview of the existing knowledge on airway remodeling features observed in asthma, including loss of epithelial integrity, mucous cell metaplasia, extracellular matrix remodeling in both the airways and vessels, angiogenesis, and increased smooth muscle mass. While such studies have provided extensive knowledge on different aspects of airway remodeling, they have relied on biopsy sampling or pathological assessment of lungs from fatal asthma patients, which have limitations for understanding airway heterogeneity and the entire asthma syndrome. To further understand the heterogeneity of airway remodeling in asthma, we highlight the potential of in vivo imaging tools such as computed tomography and magnetic resonance imaging. Such volumetric imaging tools provide the opportunity to assess the heterogeneity of airway remodeling within the whole lung and have led to the novel identification of heterogenous gas trapping and mucus plugging as important predictors of patient outcomes. Lastly, we summarize the current knowledge of modification of airway remodeling with available asthma therapeutics to highlight the need for future studies that use in vivo imaging tools to assess airway remodeling outcomes.

Neoadjuvant Radiation Therapy and Surgery Improves Metastasis-Free Survival over Surgery Alone in a Primary Mouse Model of Soft Tissue Sarcoma

This study aims to investigate whether adding neoadjuvant radiotherapy (RT), anti-programmed cell death protein-1 (PD-1) antibody (anti-PD-1), or RT + anti-PD-1 to surgical resection improves disease-free survival for mice with soft tissue sarcomas (STS). We generated a high mutational load primary mouse model of STS by intramuscular injection of adenovirus expressing Cas9 and guide RNA targeting Trp53 and intramuscular injection of 3-methylcholanthrene (MCA) into the gastrocnemius muscle of wild-type mice (p53/MCA model). We randomized tumor-bearing mice to receive isotype control or anti-PD-1 antibody with or without radiotherapy (20 Gy), followed by hind limb amputation. We used micro-CT to detect lung metastases with high spatial resolution, which was confirmed by histology. We investigated whether sarcoma metastasis was regulated by immunosurveillance by lymphocytes or tumor cell-intrinsic mechanisms. Compared with surgery with isotype control antibody, the combination of anti-PD-1, radiotherapy, and surgery improved local recurrence-free survival (P = 0.035) and disease-free survival (P = 0.005), but not metastasis-free survival. Mice treated with radiotherapy, but not anti-PD-1, showed significantly improved local recurrence-free survival and metastasis-free survival over surgery alone (P = 0.043 and P = 0.007, respectively). The overall metastasis rate was low (∼12%) in the p53/MCA sarcoma model, which limited the power to detect further improvement in metastasis-free survival with addition of anti-PD-1 therapy. Tail vein injections of sarcoma cells into immunocompetent mice suggested that impaired metastasis was due to inability of sarcoma cells to grow in the lungs rather than a consequence of immunosurveillance. In conclusion, neoadjuvant radiotherapy improves metastasis-free survival after surgery in a primary model of STS.

Automated segmentation of lungs and lung tumors in mouse micro-CT scans

Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions.

Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip

Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems have been developed rapidly in the past decade and found important applications in drug screening and precision medicine. However, due to the complexity in the structure of both the chip-body itself and the engineered-tissue inside, the imaging and analysis of OOC have still been a big challenge for biomedical researchers. Considering that medical imaging is moving towards higher spatial and temporal resolution and has more applications in tissue engineering, this paper aims to review medical imaging methods, including CT, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role. The achievements of medical imaging assisted tissue engineering are reviewed, and the potential applications of medical imaging in organoids and OOC are discussed. Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the image analysis of 3D tissues, especially for organoids developed in novel OOC systems.