Jian Shu

Readout-segmented echo-planar imaging and conventional single-shot echo-planar imaging for determining cervical cancer image quality, lymphovascular space invasion, and lymph node metastasis status: a comparative study

Diffusion-weighted imaging (DWI) using single-shot echo-planar imaging (ss-EPI) is prone to artifacts, geometric distortion, and T2* blurring. Readout-segmented echo-planar imaging (rs-EPI) may improve image quality in the DWI of cervical cancer (CC). This study aimed to compare the image quality between rs-EPI and ss-EPI DWI in CC and to evaluate whether the apparent diffusion coefficient (ADC) values of ss-EPI (ssADC) and rs-EPI (rsADC) can differentiate the status of lymphovascular space invasion (LVSI) and lymph node metastasis (LNM). This prospective study included 69 patients with CC who underwent ss-EPI and rs-EPI DWI before surgery. Qualitative reader scores, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and ADC values derived from ss-EPI and rs-EPI were compared. The differences in ADC values were analyzed in patients who were (a) LNM-positive (LNM+, n = 17) and LNM-negative (LNM-, n = 52); (b) LVSI-positive (LVSI+, n = 33) and LVSI-negative (LVSI-, n = 36). The rs-EPIs of CC had higher subjective image quality scores and a lower SNR than ss-EPI (all Over a similar scan time, rs-EPI improves the qualitative image quality of DWI significantly more than ss-EPI and has good diagnostic accuracy for LNM status in CC. However, neither could predict the LVSI status. Readout-segmented EPI improves the qualitative image quality of DWI and has good diagnostic accuracy for LNM status in CC, compared with conventional ss-EPI. It is more inclined to qualitative analysis of CC foci and provides a better scheme when choosing the DWI sequence scanning strategy for CC.

DMGM: deformable-mechanism based cervical cancer staging via MRI multi-sequence *

Abstract Objective. This study aims to leverage a deep learning approach, specifically a deformable convolutional layer, for staging cervical cancer using multi-sequence MRI images. This is in response to the challenges doctors face in simultaneously identifying multiple sequences, a task that computer-aided diagnosis systems can potentially improve due to their vast information storage capabilities. Approach. To address the challenge of limited sample sizes, we introduce a sequence enhancement strategy to diversify samples and mitigate overfitting. We propose a novel deformable ConvLSTM module that integrates a deformable mechanism with ConvLSTM, enabling the model to adapt to data with varying structures. Furthermore, we introduce the deformable multi-sequence guidance model (DMGM) as an auxiliary diagnostic tool for cervical cancer staging. Main results. Through extensive testing, including comparative and ablation studies, we validate the effectiveness of the deformable ConvLSTM module and the DMGM. Our findings highlight the model’s ability to adapt to the deformation mechanism and address the challenges in cervical cancer tumor staging, thereby overcoming the overfitting issue and ensuring the synchronization of asynchronous scan sequences. The research also utilized the multi-modal data from BraTS 2019 as an external test dataset to validate the effectiveness of the proposed methodology presented in this study. Significance. The DMGM represents the first deep learning model to analyze multiple MRI sequences for cervical cancer, demonstrating strong generalization capabilities and effective staging in small dataset scenarios. This has significant implications for both deep learning applications and medical diagnostics. The source code will be made available subsequently.