# Quantitative and automatic plan-of-the-day assessment to facilitate adaptive radiotherapy in cervical cancer

## TL;DR

This paper introduces a new tool combining deep learning and a quantitative procedure to automatically assess daily treatment plans for cervical cancer radiotherapy, improving accuracy and efficiency.

## Contribution

A novel deep-learning segmentation model (U-Seg3) and a quantitative standard operating procedure (qSOP) for adaptive radiotherapy in cervical cancer.

## Key findings

- U-Seg3 outperformed U-Seg1 and existing models in segmenting pelvic structures with higher DSC values.
- The qSOP successfully identified optimal and acceptable treatment plans with high accuracy.
- The tool could reduce the number of trained radiographers needed for plan selection by 50%.

## Abstract

Objective. To facilitate implementation of plan-of-the-day (POTD) selection for treating locally advanced cervical cancer (LACC), we developed a POTD assessment tool for CBCT-guided radiotherapy (RT). A female pelvis segmentation model (U-Seg3) is combined with a novel quantitative standard operating procedure (qSOP) to identify optimal and acceptable plans. Approach. The planning CT[i], corresponding structure set[ii], and manually contoured CBCTs[iii] (n = 226) from 39 LACC patients treated with POTD (n = 11) or non-adaptive RT (n = 28) were used to develop U-Seg3, an algorithm incorporating deep-learning and deformable image registration techniques to segment the low-risk clinical target volume (LR-CTV), high-risk CTV (HR-CTV), bladder, rectum, and bowel bag. A single-channel input model (iii only, U-Seg1) was also developed. Contoured CBCTs from the POTD patients were (a) reserved for U-Seg3 validation/testing, (b) audited to determine optimal and acceptable plans, and (c) used to empirically derive a qSOP that maximised classification accuracy. Main results. The median (interquartile range) dice similarity coefficient (DSC) between manual and U-Seg3 contours was 0.83 [0.80], 0.78 [0.13], 0.94 [0.05], 0.86 [0.09], and 0.90 [0.05] for the LR-CTV, HR-CTV, bladder, rectum, and bowel. These were significantly higher than U-Seg1 in all structures but bladder. The qSOP classified plans as acceptable if they met target coverage thresholds (LR-CTV \documentclass[12pt]{minimal}
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$\unicode{x2A7E}$\end{document}⩾ 99%, HR-CTV \documentclass[12pt]{minimal}
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$\unicode{x2A7E}$\end{document}⩾ 99.8%), with lower LR-CTV coverage (\documentclass[12pt]{minimal}
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$\unicode{x2A7E}$\end{document}⩾95%) sometimes allowed. The acceptable plan minimizing bowel irradiation was considered optimal unless substantial bladder sparing could be achieved. With U-Seg3 embedded in the qSOP, optimal and acceptable plans were identified in 46/60 and 57/60 cases. Significance. U-Seg3 outperforms U-Seg1 and all known CBCT-based segmentation models of the female pelvis both in terms of scope and accuracy (median DSC improvement ranging from 0.03–0.06). The tool combining U-Seg3 and the qSOP identifies optimal plans with equivalent accuracy as two observers. In an implementation strategy whereby this tool serves as the second observer, plan selection confidence and decision-making time could be improved whilst simultaneously reducing the required number of POTD-trained radiographers by 50%.