Deep learning for per-fraction automatic segmentation of gross tumor volume (GTV) and organs at risk (OARs) in adaptive radiotherapy of cervical cancer

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dc.contributor.authorBreto, Adrian L.
dc.contributor.authorSpieler, Benjamin
dc.contributor.authorZavala-Romero, Olmo
dc.contributor.authorAlhusseini, Mohammad
dc.contributor.authorPatel, Nirav V.
dc.contributor.authorAsher, David A.
dc.contributor.authorXu, Isaac R.
dc.contributor.authorBaikovitz, Jacqueline B.
dc.contributor.authorMellon, Eric A.
dc.contributor.authorFord, John C.
dc.contributor.authorStoyanova, Radka
dc.contributor.authorPortelance, Lorraine
dc.date.accessioned2023-03-07T16:05:03Z
dc.date.available2023-03-07T16:05:03Z
dc.date.issued2022-05-18
dc.description.abstractBackground/Hypothesis: MRI-guided online adaptive radiotherapy (MRI-g-OART) improves target coverage and organs-at-risk (OARs) sparing in radiation therapy (RT). For patients with locally advanced cervical cancer (LACC) undergoing RT, changes in bladder and rectal filling contribute to large inter-fraction target volume motion. We hypothesized that deep learning (DL) convolutional neural networks (CNN) can be trained to accurately segment gross tumor volume (GTV) and OARs both in planning and daily fractions’ MRI scans. Materials/Methods: We utilized planning and daily treatment fraction setup (RT-Fr) MRIs from LACC patients, treated with stereotactic body RT to a dose of 45-54 Gy in 25 fractions. Nine structures were manually contoured. MASK R-CNN network was trained and tested under three scenarios: (i) Leave-one-out (LOO), using the planning images of N- 1 patients for training; (ii) the same network, tested on the RT-Fr MRIs of the “left-out” patient, (iii) including the planning MRI of the “left-out” patient as an additional training sample, and tested on RT-Fr MRIs. The network performance was evaluated using the Dice Similarity Coefficient (DSC) and Hausdorff distances. The association between the structures’ volume and corresponding DSCs was investigated using Pearson’s Correlation Coefficient, r. Results: MRIs from fifteen LACC patients were analyzed. In the LOO scenario the DSC for Rectum, Femur, and Bladder was >0.8, followed by the GTV, Uterus, Mesorectum and Parametrium (0.6-0.7). The results for Vagina and Sigmoid were suboptimal. The performance of the network was similar for most organs when tested on RT-Fr MRI. Including the planning MRI in the training did not improve the segmentation of the RT-Fr MRI. There was a significant correlation between the average organ volume and the corresponding DSC (r = 0.759, p = 0.018). Conclusion: We have established a robust workflow for training MASK R-CNN to automatically segment GTV and OARs in MRI-g-OART of LACC. Albeit the small number of patients in this pilot project, the network was trained to successfully identify several structures while challenges remain, especially in relatively small organs. With the increase of the LACC cases, the performance of the network will improve. A robust auto-contouring tool would improve workflow efficiency and patient tolerance of the OART process.en_US
dc.identifier.citationBreto AL, Spieler B, Zavala-Romero O, Alhusseini M, Patel NV, Asher DA, Xu IR, Baikovitz JB, Mellon EA, Ford JC, Stoyanova R, Portelance L. Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer. Front Oncol. 2022 May 18;12:854349. doi: 10.3389/fonc.2022.854349. PMID: 35664789; PMCID: PMC9159296.en_US
dc.identifier.otherDOI: 10.3389/fonc.2022.854349
dc.identifier.urihttps://hdl.handle.net/20.500.14041/6033
dc.language.isoen_USen_US
dc.publisherFront Oncol.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectMRI-Guided Radiotherapyen_US
dc.subjectCervical Canceren_US
dc.subjectRadiotherapyen_US
dc.subjectAdaptive Radiotherapyen_US
dc.subjectDeep Learningen_US
dc.subjectConvolutional Neural Networksen_US
dc.titleDeep learning for per-fraction automatic segmentation of gross tumor volume (GTV) and organs at risk (OARs) in adaptive radiotherapy of cervical canceren_US
dc.typeArticleen_US
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