Automatic Coronary Artery Calcium Scoring on Radiotherapy Planning CT Scans of Breast Cancer Patients: Reproducibility and Association with Traditional Cardiovascular Risk Factors

Abstract

ObjectivesCoronary artery calcium (CAC) is a strong and independent predictor of cardiovascular disease (CVD) risk. This study assesses reproducibility of automatic CAC scoring on radiotherapy planning computed tomography (CT) scans of breast cancer patients, and examines its association with traditional cardiovascular risk factors.MethodsThis study included 561 breast cancer patients undergoing radiotherapy between 2013 and 2015. CAC was automatically scored with an algorithm using supervised pattern recognition, expressed as Agatston scores and categorized into five categories (0, 1–10, 11–100, 101–400, >400). Reproducibility between automatic and manual expert scoring was assessed in 79 patients with automatically determined CAC above zero and 84 randomly selected patients without automatically determined CAC. Interscan reproducibility of automatic scoring was assessed in 294 patients having received two scans (82% on the same day). Association between CAC and CVD risk factors was assessed in 36 patients with CAC scores >100, 72 randomly selected patients with scores 1–100, and 72 randomly selected patients without CAC. Reliability was assessed with linearly weighted kappa and agreement with proportional agreement.Results134 out of 561 (24%) patients had a CAC score above zero. Reliability of CVD risk categorization between automatic and manual scoring was 0.80 (95% Confidence Interval (CI): 0.74–0.87), and slightly higher for scans with breath-hold. Agreement was 0.79 (95% CI: 0.72–0.85). Interscan reliability was 0.61 (95% CI: 0.50–0.72) with an agreement of 0.84 (95% CI: 0.80–0.89). Ten out of 36 (27.8%) patients with CAC scores above 100 did not have other cardiovascular risk factors.ConclusionsAutomatic CAC scoring on radiotherapy planning CT scans is a reliable method to assess CVD risk based on Agatston scores. One in four breast cancer patients planned for radiotherapy have elevated CAC score. One in three patients with high CAC scores don't have other CVD risk factors and wouldn't have been identified as high risk.

Reporting of coronary artery calcification on chest CT studies in breast cancer patients at high risk of cancer therapy related cardiac events

“…CT scans were performed on commercially available multi-detector CTs with methods as previously described [ 10 ]. Briefly, images were non-ECG gated thoracic CT scans with or without contrast enhancement as clinically indicated [ 8 , 18 ]. Soft tissue kernel slice-thickness images ranged from 1.0–5.0 mm and were acquired using Aquillon 16-, 64-, 320-detector (Toshiba Canada Medical Systems Limited, Markham, Ontario); Lightspeed Plus 16- and Lightspeed 64-detector (General Electric Healthcare, Mississauga, Ontario,) and Definition Flash dual source 64 × 2-detector Siemens Medical Solutions Canada, Oakville, Ontario).…”

“…CAC may be detected co-incidentally on non-gated thoracic computed tomography (CT) studies [ [3] , [4] , [5] , [6] , [7] ]. Chest CT scans are performed in breast cancer patients as part of cancer staging, radiotherapy planning or to investigate clinical conditions that arise during cancer therapy [ 8 ]. Until recently there has been no consensus as to how to report arterial calcification on such studies [ 9 ].…”

Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT

“…Furthermore, previously proposed automatic calcium scoring methods are dedicated to either cardiac CT or chest CT. These methods required retraining for application in other types of CT [8], [32]. We present an automatic method that performs real-time direct calcium scoring in different types of non-contrastenhanced CT.…”

Early Detection of stenosis in Coronary Artery using Adaboost and ANN Classification

“…Two radiologists outwardly inspected the computer sectioned vessels and denoted the incorrectly half-track veins and rambling structures as false positives. In [5] Identification of pathology in computed axial tomography roentgenography (CTA) image of a heart may be a difficult task. An automatic support vector machine (SVM) based approach that detects the branches and pathology in pictures obtained from totally different rotation angles of CTA image of a heart is proposed.in this work.Coronary arteries are segmental from the projection pictures, center lines of the arteries are obtained and therefore the presence of pathology is detected by pursuit the arteries on the vessel direction.…”

“…Where, t = 1, 2… T H(x) = sign (f(x)) ht: X → {−1, 1} with least error w.r.t. dispersion H(X) ALGORITHM:-INPUTgray scale image OUTPUT-segmented image (1) Get input image from the gray scale image (2) Get the clustered value of the grouped input data C=2 (3) Apply logic for segment process (4) Plot the resultant image on bases of loop For i = 1: C i=0 -> input image i = 1 -> foreground image i=2 -> background image (5) It shows the affected region in background subtraction and that the resultant image is a segmented image from the input image. C. FEATURE EXTRACTION Feature extraction is the process that reduces the amount of resources which describes a large set of data.…”