scholarly journals Impact of nominal photon energies on normal tissue sparing in knowledge-based radiotherapy treatment planning for rectal cancer patients

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0213271
Author(s):  
Yuliang Huang ◽  
Sha Li ◽  
Haizhen Yue ◽  
Meijiao Wang ◽  
Qiaoqiao Hu ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Cancer Patients ◽  
Treatment Planning ◽  
Normal Tissue ◽  
Radiotherapy Treatment Planning ◽  
Normal Tissue Sparing ◽  
Knowledge Based ◽  
Tissue Sparing ◽  
Radiotherapy Treatment

scholarly journals Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jiazhou Wang ◽  
Lijun Shen ◽  
Haoyu Zhong ◽  
Zhen Zhou ◽  
Panpan Hu ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Cancer Patients ◽  
Treatment Planning ◽  
Clinical Features ◽  
Locally Advanced ◽  
Advanced Rectal Cancer ◽  
Locally Advanced Rectal Cancer ◽  
Neoadjuvant Chemoradiation ◽  
Radiotherapy Treatment Planning ◽  
Radiotherapy Treatment

Abstract This retrospective study was to investigate whether radiomics feature come from radiotherapy treatment planning CT can predict prognosis in locally advanced rectal cancer patients treated with neoadjuvant chemoradiation followed by surgery. Four-hundred-eleven locally advanced rectal cancer patients which were treated with neoadjuvant chemoradiation enrolled in this study. All patients’ radiotherapy treatment planning CTs were collected. Tumor was delineated on these CTs by physicians. An in-house radiomics software was used to calculate 271 radiomics features. The results of test-retest and contour-recontour studies were used to filter stable radiomics (Spearman correlation coefficient > 0.7). Twenty-one radiomics features were final enrolled. The performance of prediction model with the radiomics or clinical features were calculated. The clinical outcomes include local control, distant control, disease-free survival (DFS) and overall survival (OS). Model performance C-index was evaluated by C-index. Patients are divided into two groups by cluster results. The results of chi-square test revealed that the radiomics feature cluster is independent of clinical features. Patients have significant differences in OS (p = 0.032, log rank test) for these two groups. By supervised modeling, radiomics features can improve the prediction power of OS from 0.672 [0.617 0.728] with clinical features only to 0.730 [0.658 0.801]. In conclusion, the radiomics features from radiotherapy CT can potentially predict OS for locally advanced rectal cancer patients with neoadjuvant chemoradiation treatment.

scholarly journals The benefits evaluation of abdominal deep inspiration breath hold based on knowledge-based radiotherapy treatment planning for left-sided breast cancer

2020 ◽  
Author(s):  
Jiaqi Xu ◽  
Jiazhou Wang ◽  
Feng Zhao ◽  
Weigang Hu ◽  
Luyi Bu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Treatment Planning ◽  
Breath Hold ◽  
Deep Inspiration ◽  
Breast Cancer Patients ◽  
Radiotherapy Treatment Planning ◽  
Deep Inspiration Breath Hold ◽  
Knowledge Based ◽  
Radiotherapy Treatment

Abstract Purpose Study the impact of abdominal deep inspiration breath hold (DIBH) technique on knowledge-based radiotherapy treatment planning for left-sided breast cancer to guide the application of DIBH radiotherapy technology. Methods and Materials Two kernel density estimation (KDE) models were developed based on 40 left-sided breast cancer patients with two CT acquisitions of free breathing (FB-CT) and DIBH (DIBH-CT). Each KDE model was used to predict DVHs based on DIBH-CT and FB-CT for another 10 new patients similar to our training datasets. The predicted DVHs were taken as a substitute to dose constraints and objective functions in the Eclipse treatment planning system, with the same requirements for the planning target volume (PTV). The mean doses to the heart, the left anterior descending coronary artery (LADCA) and the ipsilateral lung were evaluated and compared using the T-test among clinical plans, KDE predictions, and KDE plans.Results Our study demonstrated that the KDE model can generate deliverable simulations equivalent to clinically applicable plans. The T-test was applied to test the consistency hypothesis on another 10 left-sided breast cancer patients. In cases of the same breathing status, there was no statistically significant difference between the predicted and the clinical plans for all clinically relevant dose volume histogram (DVH) indices (p>0.05), and all predicted DVHs can be transferred into deliverable plans. For DIBH-CT images, significant differences were observed in Dmean between FB model predictions and the clinical plans (p<0.05). DIBH model prediction cannot be optimized to a deliverable plan based on FB-CT, with a counsel of perfection. Conclusion This study demonstrated that the KDE prediction results were well fitted for the same breathing condition but degrade with different breathing conditions. The benefits of DIBH can be evaluated quickly and effectively by the specific knowledge-based treatment planning for left-sided breast cancer radiotherapy. This study will help to further realize the goal of automatic treatment planning.

scholarly journals An assessment of set up position for MRI scanning for the purposes of rectal cancer radiotherapy treatment planning

2018 ◽  
Vol 65 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Ewa Juresic ◽  
Gary P. Liney ◽  
Robba Rai ◽  
Joseph Descalar ◽  
Mark Lee ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Treatment Planning ◽  
Radiotherapy Treatment Planning ◽  
Cancer Radiotherapy ◽  
Set Up ◽  
Radiotherapy Treatment

Knowledge-based radiotherapy treatment planning

2015 ◽  
Author(s):  
◽  
Lindsey Appenzoller Olsen
Keyword(s):  
Quality Control ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Radiotherapy Planning ◽  
Patient Specific ◽  
Plan Quality ◽  
Radiotherapy Treatment Planning ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Radiotherapy Treatment

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Knowledge-based planning (KBP) has become a prominent area of research in radiation oncology in the last five years. The development of KBP aims to address the lack of systematic quality control and plan quality variability in radiotherapy treatment planning by providing achievable, patient-specific optimization objectives derived from a model trained with a cohort of previously treated, site-specific plans. This dissertation intended to develop, evaluate, and implement a knowledge-based planning system to reduce variability and improve radiotherapy treatment plan quality. The project aimed to 1) develop and validate an algorithm to train mathematical models that predict dose-volume histograms for organs at risk in radiotherapy planning, 2) implement the algorithm into a software application in order to transfer the technology into clinical practice, and 3) evaluate the impact of the software system (algorithm + application) on reducing variability and improving radiotherapy treatment plan quality through knowledge transfer. The presented work demonstrates that a KBP model is beneficial to radiotherapy planning. The developed models adequately describe what is dosimetrically achievable for patient specific anatomy and have proven useful in outlier detection for quality control of radiotherapy planning. The KBP paradigm has also demonstrated ability to improve treatment plan quality through benchmarking and transfer of knowledge between institutions.

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