Prostate cancer radiomics: A study on IMRT response prediction based on MR image features and machine learning approaches

Authors

  • Abolfazl Razzaghdoust Urology and Nephrology Research Center, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  • Afshin Sadipour Department of Radiation Oncology, Shohada Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  • Bahram Mofid Department of Radiation Oncology, Shohada Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  • Hamid Abdollahi Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
  • Isaac Shiri Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
  • Mohsen Bakhshandeh Department of Radiology Technology, Allied Medicine Faculty, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  • Seied Rabi Mahdavi Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
Abstract:

Introduction: To develop different radiomic models based on radiomic features and machine learning methods to predict early intensity modulated radiation therapy (IMRT) response.   Materials and Methods: Thirty prostate patients were included. All patients underwent pre ad post-IMRT T2 weighted and apparent diffusing coefficient (ADC) magnetic resonance imaging (MRI). A wide range of radiomic features from different feature sets were extracted from all images. Delta radiomics was calculated as relative changes of pre-post-IMRT image features. Four feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 5-fold cross-validation as the criterion for feature selection and classification. For IMRT response prediction, pre, post and Delta radiomic features were analyzed. Area under the curve (AUC) was calculated as model performance value. IMRT response was obtained by changes in ADC values . Results: For IMRT response prediction, 15 models were developed. Pre-ADC model, unBalance/Select from Model/Adaptive Boosting had the highest predictive performance (AUC, 0.78). Conclusion:Radiomic models developed by MR Image features and machine learning approaches are noninvasive, easy and cost effective methods for personalized prostate cancer diagnosis and therapy.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Sports Result Prediction Based on Machine Learning and Computational Intelligence Approaches: A Survey

In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mini...

full text

Prostate cancer Radiomics using multiparametric MR imaging

full text

task-based language teaching in iran: a mixed study through constructing and validating a new questionnaire based on theoretical, sociocultural, and educational frameworks

جنبه های گوناگونی از زندگی در ایران را از جمله سبک زندگی، علم و امکانات فنی و تکنولوژیکی می توان کم یا بیش وارداتی در نظر گرفت. زبان انگلیسی و روش تدریس آن نیز از این قاعده مثتسنی نیست. با این حال گاهی سوال پیش می آید که آیا یک روش خاص با زیر ساخت های نظری، فرهنگی اجتماعی و آموزشی جامعه ایرانی سازگاری دارد یا خیر. این تحقیق بر اساس روش های ترکیبی انجام شده است.پرسش نامه ای نیز برای زبان آموزان ...

Protein Secondary Structure Prediction: a Literature Review with Focus on Machine Learning Approaches

DNA sequence, containing all genetic traits is not a functional entity. Instead, it transfers to protein sequences by transcription and translation processes. This protein sequence takes on a 3D structure later, which is a functional unit and can manage biological interactions using the information encoded in DNA. Every life process one can figure is undertaken by proteins with specific functio...

full text

Radiomics modelling of IMRT induced acute rectal toxicity using clinical and magnetic resonance imaging features

Introduction: Rectal toxicity is a dose limiting issue in prostate cancer radiotherapy. Prediction of these effects may be used to tailor the therapy. The purpose of this work was to develop predictive radiomic models based on clinical, dosimetric and radiomic features extracted from rectal wall magnetic resonance image (MRI).   Materials and Methods: This st...

full text

Time series forecasting of Bitcoin price based on ARIMA and machine learning approaches

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 15  issue Special Issue-12th. Iranian Congress of Medical Physics

pages  337- 337

publication date 2018-12-01

{@ msg @}

By following a journal you will be notified via email when a new issue of this journal is published.

Keywords

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023