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

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

BACKGROUND

Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases.

OBJECTIVE

This review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases.

METHODS

The well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning.

RESULTS

This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.

Assessing the impact of structured reporting on learning how to report lung cancer staging CT: A triple cohort study on inexperienced readers

PURPOSE

To assess the clinical utility of chest computed tomography (CT) reports for non-small-cell lung cancer (NSCLC) staging generated by inexperienced readers using structured reporting (SR) templates from the Royal College of Radiologists (RCR-SR) and the Italian Society of Medical and Interventional Radiology (SIRM-SR), compared to traditional non-systematic reports (NSR).

METHODS

In a cohort of 30 NSCLC patients, six third-year radiology residents reported CT examinations in two 2-month-apart separate sessions using NSR in the first and NSR, RCR-SR, or SIRM-SR in the second. Couples of expert radiologists and thoracic oncologists in consensus evaluated completeness, accuracy, and clarity. All the quality indicators were expressed on a 100-point scale. The Wilcoxon signed ranks, and Wilcoxon-Mann Whitney tests were used for statistical analyses.

RESULTS

Results showed significantly higher completeness for RCR-SR (90 %) and SIRM-SR (100 %) compared to NSR (70 %) in the second session (all p < 0.001). SIRM-SR demonstrated superior accuracy (70 % vs. 55 %, p < 0.001) over NSR, while RCR-SR and NSR accuracy did not significantly differ (60 % vs. 62.5 %, p = 0.06). In the second session, RCR-SR and SIRM-SR surpassed NSR in completeness, accuracy, and clarity (all p < 0.001, except p = 0.04 for accuracy between RCR-SR and NSR). SIRM-SR outperformed RCR-SR in completeness (100 % vs. 90 %, p < 0.001) and accuracy (70 % vs. 62.5 %, p = 0.002), with equivalent clarity (90 % for both, p = 0.27).

CONCLUSIONS

Inexperienced readers using RCR-SR and SIRM-SR demonstrated high-quality reporting, indicating their potential in radiology residency programs to enhance reporting skills for NSCLC staging and effective interaction with all the physicians involved in managing NSCLC patients.

A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images

The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular eye examinations. Age-related macular degeneration (AMD), a prevalent condition in individuals over 45, is a leading cause of vision impairment in the elderly. This paper presents a comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. This is crucial for early detection and precise diagnosis of age-related macular degeneration (AMD), enabling timely intervention and personalized treatment strategies. We have developed a novel system that extracts both local and global appearance markers from fundus images. These markers are obtained from the entire retina and iso-regions aligned with the optical disc. Applying weighted majority voting on the best classifiers improves performance, resulting in an accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 of 93.72%, ROC of 95.85%, balanced accuracy of 95.81%, and weighted sum of 95.38%. This system not only achieves high accuracy but also provides a detailed assessment of the severity of each retinal region. This approach ensures that the final diagnosis aligns with the physician's understanding of AMD, aiding them in ongoing treatment and follow-up for AMD patients.

An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis

The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing the correlation between COVID-19 lesions and the level of respiratory support provided to the patients. Computed tomography (CT) imaging will be used to analyze the three levels of respiratory support received by the patient: Level 0 (minimum support), Level 1 (non-invasive support such as soft oxygen), and Level 2 (invasive support such as mechanical ventilation). The system will begin by segmenting the COVID-19 lesions from the CT images and creating an appearance model for each lesion using a 2D, rotation-invariant, Markov-Gibbs random field (MGRF) model. Three MGRF-based models will be created, one for each level of respiratory support. This suggests that the system will be able to differentiate between different levels of severity in COVID-19 patients. The system will decide for each patient using a neural network-based fusion system, which combines the estimates of the Gibbs energy from the three MGRF-based models. The proposed system were assessed using 307 COVID-19-infected patients, achieving an accuracy of [Formula: see text], a sensitivity of [Formula: see text], and a specificity of [Formula: see text], indicating a high level of prediction accuracy.

Masquerading as Pneumonia: A Lung Neuroendocrine Tumor Case Report

The presentation of recurrent pneumonia, particularly in the same lobe, should raise suspicion for possible neuroendocrine tumors of the lung within that respective lobe. Commonly, these types of tumors will have a gastrointestinal origin with a larger incidence of carcinoid syndrome, but they may also originate in the pancreas or lungs. This case illustrates the potential for a masked lung tumor in an otherwise young and healthy 31-year-old patient, with a short history of tobacco dependence and unremarkable family history, who presents with recurrent pneumonia and dyspnea. Although rare in itself, this case was even more unique due to the partial calcification of the neuroendocrine tumor mass along with causing a collapse in the entire right middle lobe.

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study

BACKGROUND

Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy.

METHODS

This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort.

RESULTS

We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively.

CONCLUSIONS

We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.