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INTRODUCTION: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency. METHODS: A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U-Net, ResUNet and U-Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter-observer was also evaluated. RESULTS: The AttSEResUNet with post-processing showed perfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139). CONCLUSION: Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter-observer.
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Background: The invasive pattern called spread through air spaces (STAS) is linked to an unfavorable prognosis in patients with lung adenocarcinoma (LUAD). Using computed tomography (CT) signs alone to assess STAS is subjective and lacks quantitative evaluation, whereas spectral CT can provide quantitative analysis of tumors. The aim of this study was to investigate the association between spectral CT quantitative parameters and STAS in LUAD. Methods: We retrospectively collected consecutive patients with LUAD who underwent surgical resection and preoperative spectral CT scan at our institution. The quantitative parameters included CT values at 40, 70, and 100 keV [CT40keVa/v, CT70keVa/v, and CT100keVa/v (a: arterial; v: venous)]; iodine concentration (ICa/ICv); normalized iodine concentration (NICa/NICv); and slope λHU of the spectral curve (λHUa/λHUv). Clinical and CT features of the patients were also collected. Statistical analysis was performed to identify the quantitative parameters, clinical and CT features that were significantly correlated with STAS status. We evaluated the diagnostic performance of significant factors or models which combined quantitative parameters and CT features, using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results: We enrolled a total of 47 patients, with 32 positive and 15 negative for STAS. The results revealed that CT100keVa (P=0.002), CT100keVv (P=0.007), pathologic stage (P=0.040), tumor density (P<0.001), spiculation (P=0.003), maximum solid component diameter (P=0.008), and the consolidation/tumor ratio (CTR) (P=0.001) were significantly correlated with STAS status. The tumor density demonstrated a superior diagnostic capability [AUC =0.824, 95% confidence interval (CI): 0.709-0.939, sensitivity =59.4%, specificity =100.0%] compared to other variables. CT100keVa exhibited the best diagnostic performance (AUC =0.779, 95% CI: 0.633-0.925, sensitivity =78.1%, specificity =80.0%) among the quantitative parameters. Combination models were then constructed by combining the quantitative parameters with CT features. The total combined model showed the highest diagnostic efficiency (AUC =0.952, 95% CI: 0.894-1.000, sensitivity =90.6%, specificity =86.7%). Conclusions: Spectral CT quantitative parameters CT100keVa and CT100keVv may be potentially useful parameters in distinguishing the STAS status in LUAD.
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OBJECTIVE: This study aimed to observe the value of computed tomography (CT) spectral imaging parameters in the diagnosis of solitary pulmonary nodules, during the contrast-enhanced early phase and late phase. MATERIALS AND METHODS: This study was approved by the institutional review board and written informed consent was obtained from all patients. One hundred thirty-nine patients with solitary pulmonary nodules proved by pathology underwent double-phase enhanced CT scan using gemstone spectral imaging mode on a Discovery CT750 HD, and were divided into an active inflammatory group (43 cases), a malignant group (65 cases), and a tuberculosis group (31 cases). The slope rate was calculated from the spectral curve. Iodine concentrations (ICs) were derived from iodine-based material decomposition CT images and normalized to the IC in the aorta. The Kruskal-Wallis test and Nemenyi test were performed to compare quantitative parameters among the 3 groups or between each of the 2 groups. RESULTS: There were significant differences in the slope rate, IC, and normalized IC (NIC) among the 3 groups. In the active inflammatory group, malignant group, and tuberculosis group, the mean slope rate were 3.03 ± 0.71 (SD), 1.96 ± 0.91, and 1.37 ± 0.43, respectively, during the early phase and 3.28 ± 0.67, 2.24 ± 0.82, and 1.67 ± 0.64, respectively, during the late phase. The ICs were 2.68 mg/mL ± 0.56, 1.65 mg/mL ± 0.76, and 1.10 mg/mL ± 0.34, respectively, during the early phase and 2.79 mg/mL ± 0.57, 1.90 mg/mL ± 0.71, and 1.29 mg/mL ± 0.44, respectively, during the late phase. The NIC were 0.24 ± 0.06, 0.16 ± 0.04, and 0.10 ± 0.04, respectively, during the early phase and 0.57 ± 0.10, 0.43 ± 0.11, and 0.25 ± 0.09, respectively, during the late phase. The mean slope rate, IC, and NIC for the active inflammatory group were significantly higher than these parameters for the malignant group (P < 0.05), and the parameters for malignant group were significantly higher than the tuberculosis group (P < 0.05). CONCLUSIONS: Dual-energy CT gemstone spectral imaging provides a novel method to better characterize pulmonary nodules in double-phase contrast-enhanced scanning.
