Experience-driven dose-volume histogram maps of NTCP risk as an aid for radiation treatment plan selection and optimization

2007 ◽  
Vol 35 (1) ◽  
pp. 333-343 ◽  
Author(s):  
Connor Kupchak ◽  
Jerry Battista ◽  
Jake Van Dyk
Keyword(s):  
Radiation Treatment ◽  
Treatment Plan ◽  
Dose Volume Histogram ◽  
Dose Volume ◽  
Plan Selection

Predicting dose-volume histogram of organ-at-risk using spatial geometric-encoding network for esophageal treatment planning

2021 ◽  
pp. 1-13
Author(s):  
Fudong Nian ◽  
Jie Sun ◽  
Dashan Jiang ◽  
Jingjing Zhang ◽  
Teng Li ◽  
...  
Keyword(s):  
Spatial Information ◽  
Radiation Treatment ◽  
Treatment Plan ◽  
Left Lung ◽  
Spatial Relationship ◽  
Intensity Modulated Radiation Therapy ◽  
Absolute Error ◽  
Dose Volume Histogram ◽  
Dose Volume ◽  
Proposed Model

Dose-volume histogram (DVH) is an important tool to evaluate the radiation treatment plan quality, which could be predicted based on the distance-volume spatial relationship between planning target volumes (PTV) and organs-at-risks (OARs). However, the prediction accuracy is still limited due to the complicated calculation process and the omission of detailed spatial geometric features. In this paper, we propose a spatial geometric-encoding network (SGEN) to incorporate 3D spatial information with an efficient 2D convolutional neural networks (CNN) for accurate prediction of DVH for esophageal radiation treatments. 3D computed tomography (CT) scans, 3D PTV scans and 3D distance images are used as the multi-view input of the proposed model. The dilation convolution based Multi-scale concurrent Spatial and Channel Squeeze & Excitation (msc-SE) structure in the proposed model not only can maintain comprehensive spatial information with less computation cost, but also can extract the features of organs at different scales effectively. Five-fold cross-validation on 200 intensity-modulated radiation therapy (IMRT) esophageal radiation treatment plans were used in this paper. The mean absolute error (MAE) of DVH focusing on the left lung can achieve 2.73 ± 2.36, while the MAE was 7.73 ± 3.81 using traditional machine learning prediction model. In addition, extensive ablation studies have been conducted and the quantitative results demonstrate the effectiveness of different components in the proposed method.

Practice points for radiation oncology

2016 ◽  
pp. 173-185
Author(s):  
Annekatrin Seidlitz ◽  
Stephanie E. Combs ◽  
Jürgen Debus ◽  
Michael Baumann
Keyword(s):  
Radiation Oncology ◽  
Radiation Treatment ◽  
Wide Spectrum ◽  
Treatment Plan ◽  
Radiation Oncologist ◽  
Tumour Control ◽  
Dose Volume ◽  
The Individual ◽  
Tissue Reactions ◽  
Normal Tissue Reactions

Radiotherapy is an indispensable treatment modality in modern oncology with curative potential in applying ionizing radiation in a wide spectrum of malignancies. Radiotherapy is often combined in multidisciplinary concepts with surgery or cytostatic drugs, and increasingly also with molecular-targeted therapies. The aim of radiotherapy is to achieve uncomplicated local or locoregional tumour control, that is to permanently inactivate all cancer cells in the irradiated volume without inducing severe normal tissue reactions. This aim can be reached for a substantial proportion of patients with modern high-precision radiation treatment planning and application technologies. Clinical and radiobiological principles guide the radiation oncologist in time-dose volume prescription of radiotherapy and in selection of the optimal radiation treatment plan for the individual patient. The scope of this chapter is to summarize important basic biological, physical, and clinical principles and practice points of radiotherapy of relevance for the non-radiation oncologist.

Practice points for radiation oncology

2016 ◽  
pp. 173-185
Author(s):  
Annekatrin Seidlitz ◽  
Stephanie E. Combs ◽  
Jürgen Debus ◽  
Michael Baumann
Keyword(s):  
Radiation Oncology ◽  
Radiation Treatment ◽  
Wide Spectrum ◽  
Treatment Plan ◽  
Radiation Oncologist ◽  
Tumour Control ◽  
Dose Volume ◽  
The Individual ◽  
Tissue Reactions ◽  
Normal Tissue Reactions

Radiotherapy is an indispensable treatment modality in modern oncology with curative potential in applying ionizing radiation in a wide spectrum of malignancies. Radiotherapy is often combined in multidisciplinary concepts with surgery or cytostatic drugs, and increasingly also with molecular-targeted therapies. The aim of radiotherapy is to achieve uncomplicated local or locoregional tumour control, that is to permanently inactivate all cancer cells in the irradiated volume without inducing severe normal tissue reactions. This aim can be reached for a substantial proportion of patients with modern high-precision radiation treatment planning and application technologies. Clinical and radiobiological principles guide the radiation oncologist in time-dose volume prescription of radiotherapy and in selection of the optimal radiation treatment plan for the individual patient. The scope of this chapter is to summarize important basic biological, physical, and clinical principles and practice points of radiotherapy of relevance for the non-radiation oncologist.

scholarly journals Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images

2020 ◽  
Vol 10 (1) ◽  
pp. 118
Author(s):  
Tania Pereira ◽  
Cláudia Freitas ◽  
José Luis Costa ◽  
Joana Morgado ◽  
Francisco Silva ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Targeted Therapies ◽  
Treatment Plan ◽  
Personalised Medicine ◽  
Growth Factor Receptor ◽  
Accurate Diagnosis ◽  
Cancer Development ◽  
Thoracic Computed Tomography ◽  
Plan Selection ◽  
Ct Image Analysis

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.

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