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\begin{document}

\title{Automating the Treatment Planning Process for 3D-Conformal Pediatric
Craniospinal Irradiation Therapy}



\author[1]{Soleil Hernandez}%
\author[2]{Callistus Nguyen}%
\author[3]{Jeannette Parkes}%
\author[4]{Hester Burger}%
\author[2]{Dong Joo Rhee}%
\author[1]{Tucker Netherton}%
\author[2]{Raymond Mumme}%
\author[2]{Jean Gumma-De La Vega}%
\author[2]{Jack Duryea}%
\author[2]{Alexandra Leone}%
\author[5]{Arnold Paulino}%
\author[6]{Carlos Cardenas}%
\author[1]{Rebecca M. Howell}%
\author[1]{David Fuentes}%
\author[1]{Julianne Pollard-Larkin}%
\author[1]{Laurence Court}%
\affil[1]{The University of Texas Graduate School of Biomedical Sciences}%
\affil[2]{The University of Texas MD Anderson Cancer Center Department of Radiation Physics}%
\affil[3]{University of Cape Town Department of Radiation Medicine}%
\affil[4]{Department Medical Physics Groote Schuur Hospital and University of Cape Town Cape Town South Africa}%
\affil[5]{The University of Texas MD Anderson Cancer Center Division of Radiation Oncology}%
\affil[6]{The University of Alabama at Birmingham Department of Radiation Oncology}%


\vspace{-1em}



  
  \date{May 23, 2024}


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\selectlanguage{english}
\begin{abstract}
\textbf{Purpose:} Pediatric patients with medulloblastoma in LMICs are
most treated with 3D conformal photon craniospinal irradiation (CSI), a
time-consuming, complex treatment to plan, especially in
resource-constrained settings. Therefore, we developed and tested a 3D
conformal CSI autoplanning tool for varying patient lengths.
\textbf{Methods and Materials}: Autocontours were generated with a deep
learning model trained:tested (80:20 ratio) on 143 pediatric
medulloblastoma CT scans (patient ages, 2-19 years, median=7 years).
Using the verified autocontours, the autoplanning tool generated 2
lateral brain fields matched to a single spine field, an extended single
spine field, or 2 matched spine fields. Additional spine sub-fields were
added to optimize the corresponding dose distribution. Feathering was
implemented (yielding 9-12 fields) to give a composite plan. Each
planning approach was tested on 6 patients (ages, 3-10 years). A
pediatric radiation oncologist assessed clinical acceptability of each
autoplan. \textbf{Results:} The autocontoured structures' average Dice
similarity coefficient ranged from 0.65-0.98. The average V95 for the
brain/spinal canal for single, extended, and multi-field spine
configurations was 99.9\selectlanguage{ngerman}±0.06\%/99.9±0.10\%, 99.9±0.07\%/99.4±0.30\%, and
99.9±0.06\%/99.4±0.40\%, respectively. The average maximum dose across
all field configurations to the brainstem, eyes (L/R), lenses (L/R) and
spinal cord were 23.7±0.08 Gy, 24.1±0.28 Gy, 13.3±5.27 Gy, 25.5±0.34 Gy,
respectively (prescription=23.4 Gy/13 fractions). Of the 18 plans
tested, all were scored as clinically acceptable as-is or clinically
acceptable with minor, time-efficient edits preferred or required. No
plans were scored as clinically unacceptable. \textbf{Conclusion:} The
autoplanning tool successfully generated pediatric CSI plans for varying
patient lengths in 3.50 ± 0.4 minutes on average, indicating potential
for an efficient planning aid in resource-constrained settings.%
\end{abstract}\selectlanguage{ngerman}%



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\includegraphics[width=0.70\columnwidth]{figures/Figure1-Workflow/Figure1-Workflow}

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\end{figure}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/Figure3-Optimization/Figure3-Optimization}

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\includegraphics[width=0.70\columnwidth]{figures/Figure4-dsc-hd/Figure4--quantitative-and-qualitative-(1)}

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\includegraphics[width=0.70\columnwidth]{figures/Figure5-DVH/Figure5-Dose-scoring-alone-+-DVH-(2)}

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\end{figure}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/Figure6-Optimize-Results/Figure6-Optimize-Results}

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\textbf{Hosted file}

\verb`CSIPaper_resubmit_revised.docx`  available at \url{https://authorea.com/users/501592/articles/581975-automating-the-treatment-planning-process-for-3d-conformal-pediatric-craniospinal-irradiation-therapy}

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