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.

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%.

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