Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours

Florkow, Mateusz C.; Guerreiro, Filipa; Zijlstra, Frank; Seravalli, Enrica; Janssens, Geert O.; Maduro, John H.; Knopf, Antje; Castelein, René M.; Stralen, Marijn van; Raaymakers, B W; Seevinck, Peter R.

doi:10.1016/j.radonc.2020.09.056

Cited by 41 publications

(43 citation statements)

References 32 publications

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“…In terms of parameters such as ME, MEA, or gamma indices, our results do not seem to show improved performance when compared with the previous literature [ 15 , 18 , 19 ]. Direct comparisons among relevant studies are difficult, since sCT generation is dependent on the protocols of the dataset acquisition for training, which are extremely diverse among different studies.…”

Section: Discussioncontrasting

confidence: 87%

“…Various methodologies for sCT generation have been suggested and their feasibility has been evaluated in various studies over the years [ 10 , 12 ]. Particularly, with the recent emergence of the sCT generation technique based on the deep learning approach, MR-only simulation methods using neural networks have been developed [ 11 , 13 , 14 , 15 ]. Early investigations regarding sCT generation for RT simulation using deep learning techniques have focused on RT for cranial sites wherein inter-personal or temporal anatomical variations are limited.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, because the technique for the extraction of image features from MRI, cranial MRI has been favored due to the advances of those techniques in the brain MRI using various neural networks [ 16 , 17 ]. However, the use of sCTs for RT was expanded to other anatomic sites including the chest, abdomen, and pelvis [ 14 , 15 , 18 , 19 ]. Conventional generative adversarial networks (GANs) trained with paired image sets have been investigated most comprehensively for the generation of sCTs for RT simulations [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

Yoo

Luu

Kim

et al. 2021

Cancers

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We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (p = 0.03). The mean relative dose differences for planning target volumes or organs at risk were within 2% among the sCTs from the three deep-learning approaches. The differences in dosimetric parameters for D98% and D95% from original CT were lowest in sCT from RgGAN. In conclusion, HU conservation for soft tissue was poorest for GAN. There was the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D98% and D95% than sCTs from other methodologies.

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Section: Discussioncontrasting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

Yoo

Luu

Kim

et al. 2021

Cancers

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“…Pediatric patients represent a more heterogeneous dataset for network training, and its feasibility has been investigated first for AC in PET 70 (79 patients) and more recently for photon and proton RT. 71,72 All the models were trained to perform a regression task from the input to sCT, except for two studies where networks were trained to segment the input image into a predefined number of classes, thus performing a segmentation task. 73,74 In most of the works, training was implemented in a paired manner, with unpaired training investigated in 13 of 83 articles.…”

Section: Resultsmentioning

confidence: 99%

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

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Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy, and III) derive attenuation maps for the correction of positron emission tomography (PET).Appropriate database searching was performed on journal articles published between January 2014 and December 2020.The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DLbased sCT generation was evaluated, assessing the clinical readiness of the presented methods.

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“…In summary, AI has the potential to improve the accuracy, efficiency and quality of radiotherapy. Furthermore, MRI‐only radiotherapy [121] and real‐time adaptive radiotherapy [109] could be achieved with the implementation of effective and efficient automated segmentation, image processing, and automated treatment planning tools based on DL, which are significantly faster than standard approaches.…”

Section: Deep Leaning In Radiotherapymentioning

confidence: 99%

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Chen

Lin

et al. 2021

Cancer Communications

103

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Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti‐cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI‐powered cancer care.

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Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours

Cited by 41 publications

References 32 publications

Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

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