Organ‐at‐risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT-Reference-Cited by-同舟云学术

Organ‐at‐risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT

Published:2022-04-23 Issue:6 Volume:23 Page:
ISSN:1526-9914
Container-title:Journal of Applied Clinical Medical Physics
language:en
Short-container-title:J Applied Clin Med Phys

Author:

Brodin N. Patrik¹²,Schulte Leslie³,Velten Christian¹²,Martin William²,Shen Sydney⁴,Shen Jin²,Basavatia Amar²^ORCID,Ohri Nitin¹²,Garg Madhur K.¹²⁵⁶,Carpenter Colin³,Tomé Wolfgang A.¹²⁷^ORCID

Affiliation:

1. Institute for Onco‐Physics Albert Einstein College of Medicine Bronx NY USA

2. Department of Radiation Oncology Montefiore Medical Center Bronx NY USA

3. Siris Medical Burlingame CA USA

4. Department of Radiation Oncology Massachusetts General Hospital Boston MA USA

5. Department of Urology Montefiore Medical Center Bronx NY USA

6. Department of Otorhinolaryngology‐Head & Neck Surgery Montefiore Medical Center Bronx NY USA

7. Department of Neurology Albert Einstein College of Medicine Bronx NY USA

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging,Instrumentation,Radiation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/acm2.13609

Reference20 articles.

1. Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

2. Noninferiority Study of Automated Knowledge-Based Planning Versus Human-Driven Optimization Across Multiple Disease Sites

3. Evaluation of Automated Treatment Planning and Organ Dose Prediction for Lung Stereotactic Body Radiotherapy

4. Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution

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