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Open Access 01-12-2024 | Magnetic Resonance Imaging | Research

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework

Authors: Shaojun Xia, Qingyang Li, Hai-Tao Zhu, Xiao-Yan Zhang, Yan-Jie Shi, Ding Yang, Jiaqi Wu, Zhen Guan, Qiaoyuan Lu, Xiao-Ting Li, Ying-Shi Sun

Published in: BMC Cancer | Issue 1/2024

Abstract

Purpose

Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians.

Methods

A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs.

Results

At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680–0.720), 17.73 mm (95% CI: 16.08–19.39), and 3.11 mm (95% CI: 2.67–3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm).

Conclusions

The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

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Title: Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework
Authors: Shaojun Xia
Qingyang Li
Hai-Tao Zhu
Xiao-Yan Zhang
Yan-Jie Shi
Ding Yang
Jiaqi Wu
Zhen Guan
Qiaoyuan Lu
Xiao-Ting Li
Ying-Shi Sun
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Rectal Cancer
Rectal Cancer
Published in: BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-024-11997-1

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