Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

566
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
566
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.7K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

580
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
580

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A hybrid IGRT workflow using SGRT and CBCT for prostate SBRT: Feasibility, efficiency, and safety.

Journal of applied clinical medical physics·2025
Same author

Resilient yet productive: maize that can thrive under stress and in optimal conditions.

Frontiers in plant science·2025
Same author

Liver injury and recovery following radiation therapy for hepatocellular carcinoma: insights from functional liver imaging.

Hepatoma research·2025
Same author

Single-Pulse X-ray Acoustic Computed Tomographic Imaging for Precision Radiation Therapy.

Advances in radiation oncology·2023
Same author

Matching Protocol and Practice: The Challenge of Meeting Lung and Kidney Total Body Irradiation Constraints for Scleroderma.

Practical radiation oncology·2023
Same author

Comparison of intensity-modulated radiation therapy (IMRT), 3D conformal proton therapy and intensity-modulated proton therapy (IMPT) for the treatment of metastatic brain cancer.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists·2023

Related Experiment Video

Updated: Aug 11, 2025

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

20.4K

Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine

Mark A Newpower1, Bing-Hao Chiang1,2, Salahuddin Ahmad1

  • 1Department of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.

Journal of Applied Clinical Medical Physics
|February 7, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict proton therapy spot delivery errors, enhancing treatment plan quality checks. Even with predicted errors, intensity modulated proton therapy plans remained robust, ensuring effective dose delivery.

Keywords:
machine learningproton therapyrobustness

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.8K

Related Experiment Videos

Last Updated: Aug 11, 2025

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

20.4K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.8K

Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Machine Learning in Medicine

Background:

  • Intensity modulated proton therapy (IMPT) requires precise beam delivery.
  • Spot delivery errors can potentially impact treatment plan robustness.
  • Machine learning (ML) offers a novel approach to predict these errors.

Purpose of the Study:

  • To evaluate the robustness of IMPT treatment plans considering ML-predicted spot delivery errors.
  • To assess the impact of predicted positional and monitor unit (MU) errors on plan quality.
  • To validate a workflow for incorporating realistic spot data into IMPT plan verification.

Main Methods:

  • Trained an ML model on over 4.1 million proton spots from 6000+ IMPT machine log files.
  • Predicted spot positions and MUs using the trained ML model.
  • Recalculated two patient plans (APBI and ependymoma) with ML-predicted parameters and performed robustness analysis.

Main Results:

  • ML-predicted spot delivery plans showed reduced robustness compared to original plans.
  • Dosimetric changes in the APBI plan were not clinically significant.
  • In the ependymoma plan, a decrease in brainstem hot spot and an increase in cervical cord hot spot were observed.
  • Both recalculated plans maintained >95% CTV coverage with >95% prescription dose after robustness analysis.

Conclusions:

  • The developed ML workflow can predict IMPT spot delivery errors.
  • Despite predicted errors, the evaluated IMPT plans demonstrated overall robustness.
  • This approach can enhance quality assurance by integrating realistic spot delivery information into IMPT plan checks.