An Artificial Intelligence-Driven Agent for Real-Time Head-and-Neck IMRT Plan Generation using Conditional Generative Adversarial Network (cGAN).

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Abstract

To develop an Artificial-Intelligence (AI) agent for fully automated rapid head-and-neck IMRT plan generation without time-consuming dose-volume-based inverse planning.
This AI agent was trained via implementing a conditional Generative Adversarial Network (cGAN) architecture. The generator, PyraNet, is a novel Deep Learning network that implements 28 classic ResNet blocks in pyramid-like concatenations. The discriminator is a customized 4-layer DenseNet. The AI agent first generates multiple customized 2D projections at 9 template beam angles from a patient’s 3D CT volume and structures. These projections are then stacked as 4D inputs of PyraNet, from which 9 radiation fluence maps of the corresponding template beam angles are generated simultaneously. Finally, the predicted fluence maps are automatically post-processed by Gaussian deconvolution operations and imported into a commercial treatment planning system (TPS) for plan integrity check and visualization. The AI agent was built and tested upon 231 oropharyngeal IMRT plans from a TPS plan library. 200/16/15 plans were assigned for training/validation/testing, respectively. Only the primary plans in the sequential boost regime were studied. All plans were normalized to 44Gy prescription (2Gy/fx). A customized Harr wavelet loss was adopted for fluence map comparison during the training of the PyraNet. For test cases, isodose distributions in AI plans and TPS plans were qualitatively evaluated for overall dose distributions. Key dosimetric metrics were compared by Wilcoxson Signed Rank tests with a significance level of 0.05.
All 15 AI plans were successfully generated. Isodose gradients outside of PTV in AI plans were comparable to those of the TPS plans. After PTV coverage normalization, Dmean of left parotid (DAI =23.1±2.4Gy; DTPS =23.1±2.0Gy), right parotid (DAI =23.8±3.0Gy; DTPS =23.9±2.3Gy), and oral cavity (DAI =24.7±6.0Gy; DTPS =23.9±4.3Gy) in the AI plans and the TPS plans were comparable without statistical significance. AI plans achieved comparable results for maximum dose at 0.01cc of brainstem (DAI =15.0±2.1Gy; DTPS =15.5±2.7Gy) and cord+5mm (DAI =27.5±2.3Gy; DTPS =25.8±1.9Gy) without clinically-relevant differences, but body Dmax results (DAI =121.1±3.9Gy; DTPS =109.0±0.9Gy) were higher than the TPS plan results. The AI agent needed ~3s for predicting fluence maps of an IMRT plan.
With rapid and fully automated execution, the developed AI agent can generate complex head-and-neck IMRT plans with acceptable dosimetry quality. This approach holds great potential for clinical applications in pre-planning decision-making and real-time planning.
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