Added value of morphologic characteristics on diffusion-weighted images for characterizing lymph nodes in primary rectal cancer

Abstract Purpose To compare integrated slice-specific dynamic shimming (iShim) diffusion weighted imaging (DWI) and single-shot echo-planar imaging (SS-EPI) DWI in image quality and pathological characterization of rectal cancer. Materials and methods A total of 193 consecutive rectal tumor patients were enrolled for retrospective analysis. Among them, 101 patients underwent iShim-DWI (b = 0, 800, and 1600 s/mm2) and 92 patients underwent SS-EPI-DWI (b = 0, and 1000 s/mm2). Qualitative analyses of both DWI techniques was performed by two independent readers; including adequate fat suppression, the presence of artifacts and image quality. Quantitative analysis was performed by calculating standard deviation (SD) of the gluteus maximus, signal intensity (SI) of lesion and residual normal rectal wall, apparent diffusion coefficient (ADC) values (generated by b values of 0, 800 and 1600 s/mm2 for iShim-DWI, and by b values of 0 and 1000 s/mm2 for SS-EPI-DWI) and image quality parameters, such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of primary rectal tumor. For the primary rectal cancer, two pathological groups were divided according to pathological results: Group 1 (well-differentiated) and Group 2 (poorly differentiated). Statistical analyses were performed with p < 0.05 as significant difference. Results Compared with SS-EPI-DWI, significantly higher scores of image quality were obtained in iShim-DWI cases (P < 0.001). The SDbackground was significantly reduced on b = 1600 s/mm2 images and ADC maps of iShim-DWI. Both SNR and CNR of b = 800 s/mm2 and b = 1600 s/mm2 images in iShim-DWI were higher than those of b = 1000 s/mm2 images in SS-EPI-DWI. In primary rectal cancer of iShim-DWI cohort, SIlesion was significantly higher than SIrectum in both b = 800 and 1600 s/mm2 images. ADC values were significantly lower in Group 2 (0.732 ± 0.08) × 10− 3 mm2/s) than those in Group 1 ((0.912 ± 0.21) × 10− 3 mm2/s). ROC analyses showed significance of ADC values and SIlesion between the two groups. Conclusion iShim-DWI with b values of 0, 800 and 1600 s/mm2 is a promising technique of high image quality in rectal tumor imaging, and has potential ability to differentiate rectal cancer from normal wall and predicting pathological characterization.

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Locally Advanced Rectal Cancer: Added Value of Diffusion-weighted MR Imaging in the Evaluation of Tumor Response to Neoadjuvant Chemo- and Radiation Therapy

Radiology ◽

10.1148/radiol.2532090027 ◽

2009 ◽

Vol 253 (1) ◽

pp. 116-125 ◽

Cited By ~ 246

Author(s):

Seung Ho Kim ◽

Jeong Min Lee ◽

Sung Hyun Hong ◽

Gi Hyeon Kim ◽

Jae Young Lee ◽

...

Keyword(s):

Rectal Cancer ◽

Radiation Therapy ◽

Mr Imaging ◽

Tumor Response ◽

Locally Advanced ◽

Advanced Rectal Cancer ◽

Locally Advanced Rectal Cancer ◽

Added Value ◽

Diffusion Weighted

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Role of MRI and added value of diffusion-weighted and gadolinium-enhanced MRI for the diagnosis of local recurrence from rectal cancer

Abdominal Radiology ◽

10.1007/s00261-018-1518-z ◽

2018 ◽

Vol 43 (11) ◽

pp. 2903-2912 ◽

Cited By ~ 7

Author(s):

Valeria Molinelli ◽

Maria Gloria Angeretti ◽

Ejona Duka ◽

Nicola Tarallo ◽

Elena Bracchi ◽

...

Keyword(s):

Rectal Cancer ◽

Local Recurrence ◽

Added Value ◽

Enhanced Mri ◽

Diffusion Weighted

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Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images

BMC Medical Imaging ◽

10.1186/s12880-021-00703-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiang Liu ◽

Zhaonan Sun ◽

Chao Han ◽

Yingpu Cui ◽

Jiahao Huang ◽

...

Keyword(s):

Lymph Nodes ◽

Average Distance ◽

Model Development ◽

Area Under The Curve ◽

True Positive Rate ◽

Validation Dataset ◽

Diffusion Weighted Images ◽

Diffusion Weighted ◽

Expert Radiologist ◽

Temporal Validation

Abstract Background The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images. Methods A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for temporal validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS), Hausdorff distance (HD), the Average distance (AVD), and the Mahalanobis distance (MHD) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an hold-out test dataset between the model and radiologist was assessed using Cohen’s kappa coefficient. Results In the testing set used for model development, the Dice score, TPR, PPV, VS, HD, AVD and MHD values for the segmentation of suspicious LNs were 0.85, 0.82, 0.80, 0.86, 2.02 (mm), 2.01 (mm), and 1.54 (mm) respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the temporal validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892–0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist. Conclusion The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.

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