Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study

Abstract

Background Adjuvant radiation therapy improves the overall survival and loco-regional control in patients with breast cancer. However, radiation-induced heart disease, which occurs after treatment from incidental radiation exposure to the cardiac organ, is an emerging challenge. This study aimed to generate synthetic contrast-enhanced computed tomography (SCECT) from non-contrast CT (NCT) using deep learning (DL) and investigate its role in contouring cardiac substructures. We also aimed to determine its applicability for a retrospective study on the substructure volume-dose relationship for predicting radiation-induced heart disease. Methods We prepared NCT-CECT cardiac scan pairs of 59 patients. Of these, 35, 4, and 20 pairs were used for training, validation, and testing, respectively. We adopted conditional generative adversarial network as a framework to generate SCECT. SCECT was validated in the following three stages: (1) The similarity between SCECT and CECT was evaluated; (2) Manual contouring was performed on SCECT and CECT with sufficient intervals and based on this, the geometric similarity of cardiac substructures was measured between them; (3) The treatment plan was quantitatively analyzed based on the contours of SCECT and CECT. Results While the mean values (± standard deviation) of the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure between SCECT and CECT were 20.66 ± 5.29, 21.57 ± 1.85, and 0.77 ± 0.06, those were 23.95 ± 6.98, 20.67 ± 2.34, and 0.76 ± 0.07 between NCT and CECT, respectively. The Dice similarity coefficients and mean surface distance between the contours of SCECT and CECT were 0.81 ± 0.06 and 2.44 ± 0.72, respectively. The dosimetry analysis displayed error rates of 0.13 ± 0.27 Gy and 0.71 ± 1.34% for the mean heart dose and V5Gy, respectively. Conclusion Our findings displayed the feasibility of SCECT generation from NCT and its potential for cardiac substructure delineation in patients who underwent breast radiation therapy.

Advances in automatic delineation of target volume and cardiac substructure in breast cancer radiotherapy (Review)

“…The deep convolutional neural network could also generate synthetic contrast-enhanced CT images from non-enhanced contrast CT images, and then be used to contour cardiac substructures. Chun et al (77) applied this method to the automatic segmentation of cardiac substructures in patients with breast cancer undergoing radiotherapy and proved the feasibility of this method.…”

AI as a New Frontier in Contrast Media Research

“…In recent studies, AI-based approaches have demonstrated the possibility of reducing the amount of gadolinium-based contrast agents (GBCAs) in magnetic resonance imaging (MRI) [1][2][3][4][5][6][7][8] and iodine-based contrast media (ICM) in computed tomography (CT). [9][10][11][12][13][14] For both modalities, the amount of contrast agent respectively can be technically optimized in completely different ways, for example, by means of applications for the optimization of the radiation spectrum such as CareKV 15 for CT or by means of sequence, 16 molecule relaxivity, 17,18 and acquisition time optimization for MRI. 19 While at the same time, the techniques of AI-based contrast optimization and the issues regarding contrast reduction are similar.…”

“…Furthermore, AI-based methods for virtual contrast enhancement 11,[24][25][26][27][28][29][30] could raise the diagnostic quality of noncontrast data in situations where contrast agent administration is contraindicated, such as in patients with severe allergies or, in case of CT imaging, patients with severe hyperthyroidism. 31 Last but not least, new fields of image contrast modification promise to produce more than a normal contrast agent dose and may further improve the radiologist's sensitivity in detecting small lesions.…”

“…Artificial intelligence (AI) techniques, particularly generative adversarial networks (GANs), have shown significant potential in processing medical image data. In recent studies, AI-based approaches have demonstrated the possibility of reducing the amount of gadolinium-based contrast agents (GBCAs) in magnetic resonance imaging (MRI) 1–8 and iodine-based contrast media (ICM) in computed tomography (CT) 9–14 . For both modalities, the amount of contrast agent respectively can be technically optimized in completely different ways, for example, by means of applications for the optimization of the radiation spectrum such as CareKV 15 for CT or by means of sequence, 16 molecule relaxivity, 17,18 and acquisition time optimization for MRI 19 …”

“…Furthermore, AI-based methods for virtual contrast enhancement 11,24–30 could raise the diagnostic quality of noncontrast data in situations where contrast agent administration is contraindicated, such as in patients with severe allergies or, in case of CT imaging, patients with severe hyperthyroidism 31…”