@booklet{Gal2020,
title = {The risk of cardiovascular disease in irradiated breast cancer patients: The role of cardiac calcifications and adjuvant treatment},
author = {R. Gal, S.G. van Velzen, M.J. Emaus, D.H. van den Bongard, M.L. Gregorowitsch, E.L. Blezer, G. Sofie, N. Lessmann, M.G. Sattler, M.J. Hooning, A.J. Teske, J.J. Penninkhof, H. Meijer, J.P. Pignol, J. Verloop, I. Išgum, H.M. Verkooijen, Bragatston Study Group
},
url = {https://www.sciencedirect.com/science/article/pii/S0959804920305438?via%3Dihub},
doi = {10.1016/S0959-8049(20)30543-8},
year = {2020},
date = {2020-10-10},
journal = {European Journal of Cancer},
volume = {138},
pages = {S6},
abstract = {Background: (Neo) adjuvant treatments including anthracyclines, trastuzumab and (left-sided) radiotherapy (RT) are associated with an increased risk of cardiovascular disease (CVD). Breast cancer patients with pre-existing CVD risk factors have the highest risk of treatment induced cardiotoxicity. Coronary artery calcium (CAC) is a strong independent CVD risk factor and can be quantified on dedicated radiotherapy planning CT scans of the chest. Automated assessment of CAC scores in breast cancer patients planned for RT may be helpful in detecting patients at increased CVD risk. In the Bragatston study, we evaluate the association between automated CAC measurement on RT planning CT scans and the risk of CVD in breast cancer patients treated with RT.

Methods: In this multicenter retrospective cohort study, CAC scores of breast cancer patients receiving RT between 2005 and 2016 were automatically calculated in planning CT scans using an deep learning algorithm and classified into Agatston categories (0, 1–10, 11–100, 101–399, >400 units). Tumor and treatment characteristics were obtained from the Netherlands Cancer Registry. Data on CVD occurrence were obtained from Dutch Hospital Data and the National Cause of Death Register. Cox proportional hazard regression models were used to evaluate the association between CAC scores and CVD risk. Stratification for left- vs right-sided RT and treatment with vs without anthracyclines was performed.

Results: Data from 14,002 patients with a mean age of 58 years (SD = 11) were included. Twenty-nine percent of the patients had a CAC score of >0 (Table). At a median follow-up of 52 months (IQR: 27–82), 8% of the patients (n = 1138) were admitted to the hospital for CVD and 93 patients (1%) died from CVD. After adjustment for age and calendar year at planning CT, the risk of CVD increased with higher CAC, from 5% for patients without CAC to 28% of patients with a CAC score >400. The association between a high CAC score and CVD was strongest in patients treated with anthracyclines (HRCAC >400 = 5.4, 95%CI = 2.6–11.3).

Conclusion: CAC detected on the RT planning CT is strongly associated with CVD risk. This finding is relevant for breast cancer patients since early identification of high risk patients enables switching to less cardiotoxic breast cancer treatment (e.g. adaptation of RT target volumes or technique, chemotherapy dose reduction). Also, patients can adopt targeted cardiopreventive interventions (e.g. lifestyle changes, pharmaco-prevention, close monitoring for early detection).},
howpublished = {European Journal of Cancer, 138, pp. S6},
month = {10},
keywords = {},
pubstate = {published},
tppubtype = {booklet}
}

@booklet{vanVelzen2020c,
title = {Automatic prediction of coronary heart disease events using coronary and thoracic aorta calcium among African Americans in the Jackson Heart study},
author = {S.G.M. van Velzen, J.G. Terry, B.D. de Vos, N Lessmann, S. Nair, A. Correa, H.M. Verkooijen J.J. Carr, I. Išgum},
url = {http://archive.rsna.org/2019/19006976.html},
year = {2019},
date = {2019-12-01},
booktitle = {Radiological Society of North America, 105th Annual Meeting},
abstract = {PURPOSE
Coronary artery calcium (CAC) and thoracic aorta calcium (TAC) are predictors of CHD events. Given that CAC and TAC identification is time-consuming, methods for automatic quantification in CT have been developed. Hence, we investigate whether subjects who will experience a CHD event within 5 years from acquisition of cardiac CT can be identified using automatically extracted calcium scores.
METHOD AND MATERIALS
We included 2532 participants (age 59±11, 31% male) of the Jackson Heart Study without CHD history: 111 participants had a CHD event within 5 years from CT acquisition, defined by death certificates and medical records. For each subject a cardiac CT scan(GE Healthcare Lightspeed 16Pro, 2.5mm slice thickness, 2.5mm increment, 120kVP, 400mAs, ECG-triggered, no contrast) was available. Per-artery Agatston CAC scores (left anterior descending, left circumflex, right coronary artery) and TAC volume were automatically extracted with a previously developed AI algorithm. Scores were log transformed, combined with age and sex and all continuous variables were normalized to zero-mean and unit variance. We evaluated 3 models with 3-fold cross-validation where subjects were classified according to occurrence of CHD event using LASSO regression with 1) age, sex and CAC scores, 2) age, sex and TAC scores, and 3) all variables. Performance was evaluated with the area under the ROC curve (AUC).
RESULTS
In 1468 (58%) subjects no CAC and in 1240 (49%) no TAC was found. In remaining scans, median (range) CAC score was 78.7(1.6-5562.1): 49.5(0.0-4569.4), 0.0(0.0-2735.3), 3.9(0.0-3242.7) in the LDA, LCX and RCA, respectively. Median TAC volume was 116.8(4.7-7275.9). Prediction of CHD events using Model 1, 2 and 3 resulted in an AUC (95% CI) of 0.721(0.672-0.771), 0.735(0.686-0.785) and 0.727(0.678-0.776). Differences between the ROC curves were not significant (Model 1 and 2: p=0.80; 1 and 3: p=0.29; 2 and 3: p=0.76).
CONCLUSION
Identification of subjects at risk of a CHD event can be performed using automatically extracted CAC or TAC scores from cardiac CT.
CLINICAL RELEVANCE/APPLICATION
Prediction of CHD events from cardiac CT using TAC instead of CAC is feasible and may be advantageous in scans acquired without ECG-triggering or low image resolution.},
howpublished = {Radiological Society of North America, 105th Annual Meeting},
month = {12},
keywords = {},
pubstate = {published},
tppubtype = {booklet}
}

@booklet{vanVelzen2019,
title = {Deep learning for calcium scoring in radiotherapy treatment planning CT scans in breast cancer patients},
author = {S.G.M. van Velzen, N. Lessmann, M.J. Emaus, H. van den Bongard, H.M. Verkooijen, I. Išgum},
url = {http://archive.rsna.org/2019/19002536.html},
year = {2019},
date = {2019-12-01},
booktitle = {Radiological Society of North America, 105th Annual Meeting},
abstract = {PURPOSE
Cardiovascular disease (CVD) is an important cause of mortality in breast cancer patients. Coronary artery calcification (CAC) and thoracic aorta calcification (TAC) are strong and independent risk factors for CVD and can be detected and quantified in radiotherapy treatment planning (RTTP) CT. Manual quantification of CAC and TAC is a tedious and time-consuming task. Therefore, we evaluated the performance of an AI system, developed for automatic calcium scoring in low-dose chest CT, in RTTP CT.
METHOD AND MATERIALS
We included 1409 breast cancer patients (age 56±7 years), who participated in the UMBRELLA cohort and underwent a RTTP CT (Philips Brilliance Big Bore CT, 120kVp, no ECG-triggering, no contrast, 3.0mm slice thickness). In a first step, CAC and TAC were manually annotated in these scans. In a second step, a deep learning algorithm was applied for automated detection of CAC and TAC. A baseline system was trained with 1181 low-dose chest CTs (all major CT vendors, 120/140kVp, no ECG-triggering, no contrast, 1.0-3.0mm slice thickness) from the National Lung Screening Trail (NLST). A RTTP-specific system was trained with the NLST scans and additionally 568 RTTP scans. The remaining 841 RTTP scans were used for evaluation. CAC was quantified as Agatston and volume scores; TAC as volume scores only. Agatston score was stratified into five risk categories: 0, 1-10, 11-100, 101-400, >400. Reproducibility between manual and automatic scores was evaluated with linearly weighted κ (categories) and Intraclass Correlation Coefficient (ICC, volume scores).
RESULTS
For the baseline system, ICCs were 0.85 (95% CI 0.83-0.87) and 0.98 (0.97-0.98) for CAC and TAC volumes, respectively. ICCs for the RRTP-specific system improved to 0.92 (0.91-0.93) and 0.99 (0.98-0.99) for CAC and TAC volumes, respectively. The baseline and RTTP-specific systems achieved a κ of 0.85 (0.80-0.90) and 0.89 (0.85-0.93).
CONCLUSION
An AI system trained on low-dose chest CTs allows accurate automatic CAC and TAC scoring in RTTP CT, which improves further upon RTTP-specific training.
CLINICAL RELEVANCE/APPLICATION
Accurate, fully automatic CVD risk assessment in breast cancer patients from readily available RTTP scans allows cost-effective identification of patients who may benefit from preventive treatment.
},
howpublished = {Radiological Society of North America, 105th Annual Meeting},
month = {12},
keywords = {},
pubstate = {published},
tppubtype = {booklet}
}