scholarly journals Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model

2021 ◽  
Vol 19 ◽  
pp. 112-119
Author(s):  
Michael Lempart ◽  
Hunor Benedek ◽  
Mikael Nilsson ◽  
Niklas Eliasson ◽  
Sven Bäck ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Treatment Plan ◽  
Volumetric Modulated Arc Therapy ◽  
Learning Model ◽  
Plan Generation ◽  
Dose Prediction ◽  
Arc Therapy ◽  
Deep Learning Model

Fully Automated Volumetric Modulated Arc Therapy Plan Generation for Prostate Cancer Patients

2014 ◽  
Vol 88 (5) ◽  
pp. 1175-1179 ◽  
Author(s):  
Peter W.J. Voet ◽  
Maarten L.P. Dirkx ◽  
Sebastiaan Breedveld ◽  
Abrahim Al-Mamgani ◽  
Luca Incrocci ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Volumetric Modulated Arc Therapy ◽  
Plan Generation ◽  
Arc Therapy

scholarly journals Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Jose M. Castillo T. ◽  
Muhammad Arif ◽  
Martijn P. A. Starmans ◽  
Wiro J. Niessen ◽  
Chris H. Bangma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Model Development ◽  
Learning Model ◽  
Multiparametric Mri ◽  
Data Sets ◽  
Data Set ◽  
Test Sets ◽  
Deep Learning Model

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

scholarly journals Validation of the relative insensitivity of volumetric-modulated arc therapy (VMAT) plan quality to gantry space resolution

2016 ◽  
Vol 58 (4) ◽  
pp. 579-590 ◽  
Author(s):  
Ghulam Murtaza ◽  
Stefania Cora ◽  
Ehsan Ullah Khan
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Treatment Plan ◽  
Volumetric Modulated Arc Therapy ◽  
Optimization Techniques ◽  
Plan Quality ◽  
Arc Therapy ◽  
Single Arc

Abstract Volumetric-modulated arc therapy (VMAT) is an efficient form of radiotherapy used to deliver intensity-modulated radiotherapy beams. The aim of this study was to investigate the relative insensitivity of VMAT plan quality to gantry angle spacing (GS). Most previous VMAT planning and dosimetric work for GS resolution has been conducted for single arc VMAT. In this work, a quantitative comparison of dose–volume indices (DIs) was made for partial-, single- and double-arc VMAT plans optimized at 2°, 3° and 4° GS, representing a large variation in deliverable multileaf collimator segments. VMAT plans of six prostate cancer and six head-and-neck cancer patients were simulated for an Elekta SynergyS® Linac (Elekta Ltd, Crawley, UK), using the SmartArc™ module of Pinnacle³ TPS, (version 9.2, Philips Healthcare). All optimization techniques generated clinically acceptable VMAT plans, except for the single-arc for the head-and-neck cancer patients. Plan quality was assessed by comparing the DIs for the planning target volume, organs at risk and normal tissue. A GS of 2°, with finest resolution and consequently highest intensity modulation, was considered to be the reference, and this was compared with GS 3° and 4°. The differences between the majority of reference DIs and compared DIs were <2%. The metrics, such as treatment plan optimization time and pretreatment (phantom) dosimetric calculation time, supported the use of a GS of 4°. The ArcCHECK™ phantom–measured dosimetric agreement verifications resulted in a >95.0% passing rate, using the criteria for γ (3%, 3 mm). In conclusion, a GS of 4° is an optimal choice for minimal usage of planning resources without compromise of plan quality.

scholarly journals Reply to Jue et al. Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on “Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335”

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1214
Author(s):  
Francesco Gentile ◽  
Matteo Ferro ◽  
Bartolomeo Della Ventura ◽  
Evelina La Civita ◽  
Antonietta Liotti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Molecular Forms ◽  
Model Diagnostics ◽  
High Grade ◽  
Psa Density ◽  
Deep Learning Model

In their comment “Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer [...]

scholarly journals Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning

Life ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1305
Author(s):  
Patiparn Kummanee ◽  
Wares Chancharoen ◽  
Kanut Tangtisanon ◽  
Todsaporn Fuangrod
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Dose Distribution ◽  
Learning Algorithm ◽  
Volumetric Modulated Arc Therapy ◽  
Dose Calculation ◽  
Generative Adversarial Network ◽  
Percentage Difference ◽  
Prediction Time ◽  
Arc Therapy

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.

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