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Related Experiment Video

Updated: May 2, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

Statistical toolkit for analysis of radiotherapy DICOM data.

Marvin Kinz^1,2, Christina Molodowitch¹, Joseph Killoran¹

¹Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, United States of America.

Biomedical Physics & Engineering Express

|June 30, 2025

Summary

This study developed automated software tools for analyzing over 10,000 radiotherapy plans, providing statistical insights for improved treatment planning and new modalities like convergent kV RT.

Keywords:

PTV metrics convergent kV RT dose balance index python toolkit radiotherapy DICOM analysis tumor depth

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Area of Science:

Medical Physics
Radiotherapy Technology
Computational Biology

Background:

Radiotherapy (RT) planning generates extensive data requiring advanced analysis tools.
Sophisticated RT analysis can improve current treatments, Knowledge-Based Planning, and novel modalities like convergent kV RT.

Purpose of the Study:

Develop automated software for retrospective analysis of over 10,000 MeV x-ray radiotherapy plans.
Identify trends in target volumes (PTV/CTV/GTV), organs-at-risk (OAR), and plan efficiency.
Analyze topology, morphology, and dosimetry of RT plans across all treatment sites.

Main Methods:

Software tools developed in Python for large-scale RT plan analysis.
Principal component analysis for topological metrics (volume, depth, etc.).
Hounsfield Units for morphology and conformity/homogeneity indexes for dose distribution.

Main Results:

A novel toolkit for RT plan analysis and a comprehensive database analysis.
Example: Mean PTV depths of 2.5±2.3 cm (min) and 9±3 cm (max).
Quantified PTV/CTV/GTV/OAR topology, morphology, and dosimetry, alongside plan efficiency metrics.

Conclusions:

The study provides a statistical foundation and tools for radiotherapy planning.
Facilitates selection of plans for knowledge-based models and deep learning networks.
Site-specific data informs limitations and opportunities for current and future RT modalities, including convergent kV RT.