Title: Deep Learning and Radiomics-based Cardiovascular Disease Assessment on Ungated Contrast Enhanced Computed Tomography and its Integration in a Clinical Predictive Model for Patients with Non-Small Cell Lung Cancer
Funder: The Academy of Medical Sciences

Abstract: Lung cancer is the leading cause of cancer-related deaths worldwide, with most cases being non-small cell (NSCLC). Cardiovascular disease (CVD) has a well-established effect on all-cause mortality in this patient group (1), with only a proportion of at-risk patients without previously known CVD receiving treatments focusing on primary and secondary disease prevention (2). The most common imaging manifestation of CVD on computed tomography (CT) is vascular and valvular calcium deposition. In conventional clinical practice, measuring calcium deposition, or calcium scoring, is performed on gated non-contrast CT. Various studies support performing calcium scoring on ungated non-contrast chest CT scans (3–7). However, most lung cancer scans are ungated, and contrast enhanced in the portal venous phase, and there is currently no known work investigating the assessment of CVD on these scans. Radiomic features, first or higher order imaging characteristics that can be readily used for developing machine learning models, is gaining much interest recently, for their role in predicting patient outcomes in CVD on CT (8). Our study objective is to develop and validate a deep-learning model for performing automated calcium segmentation and scoring in key cardiovascular structures (coronary artery, aortic valve, and the aorta) on ungated standard lung cancer CT scans, and extracting radiomic features from these areas to facilitate an automated assessment of CVD risk in NSCLC patients, and with a scope to integrate these measurements into a cancer radiomics-based model, for improving its predictive accuracy for clinical outcomes in NSCLC, and thus optimising personalised cancer care.

Lay abstract: Lung cancer is a disease that affects many patients and the most common cause of cancer deaths worldwide. Patients would receive image scans of their lungs, known as chest CT, to detect and better understand their cancer. There is a great amount of information about their cancer contained in these scans that are not visible to naked human eye. With help of computers, scientists are now able to capture this information, and turn it into useful knowledge that could help with cancer treatment. However, none of the existing methods make use of the information about the patient’s heart, which is also imaged on these scans. Common heart diseases such as the one causing heart attack can be checked by calculating how much calcium is present in the blood vessels supplying oxygen to the heart. Similarly, we can look for calcium presence in the main heart valve, or in the vessel taking blood from the heart to the body, to check for diseases affecting those structures. Normally, patients would need to have a special scan for making these measurements. But we believe with the use of artificial intelligence tools, we could achieve this using the scan data that we already have. The benefits of our proposed work are 1) doctors can understand their cancer patients’ heart health quicker and offer treatments for heart diseases promptly, if found, and 2) more lung cancer patients could have their overall heart health considered by their cancer care team, to best plan for their treatment.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CARDIOVASCULAR;CANCER AND NEOPLASMS