RESUMEN
Objectives: Recurrence risk evaluation is clinically significant for patients with locally advanced cervical cancer (LACC). We investigated the ability of transformer network in recurrence risk stratification of LACC based on computed tomography (CT) and magnetic resonance (MR) images. Methods: A total of 104 patients with pathologically diagnosed LACC between July 2017 and December 2021 were enrolled in this study. All patients underwent CT and MR scanning, and their recurrence status was identified by the biopsy. We randomly divided patients into training cohort (48 cases, non-recurrence: recurrence = 37: 11), validation cohort (21 cases, non-recurrence: recurrence = 16: 5), and testing cohort (35 cases, non-recurrence: recurrence = 27: 8), upon which we extracted 1989, 882 and 315 patches for model's development, validation and evaluation, respectively. The transformer network consisted of three modality fusion modules to extract multi-modality and multi-scale information, and a fully-connected module to perform recurrence risk prediction. The model's prediction performance was assessed by six metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, f1-score, sensitivity, specificity and precision. Univariate analysis with F-test and T-test were conducted for statistical analysis. Results: The proposed transformer network is superior to conventional radiomics methods and other deep learning networks in both training, validation and testing cohorts. Particularly, in testing cohort, the transformer network achieved the highest AUC of 0.819 ± 0.038, while four conventional radiomics methods and two deep learning networks got the AUCs of 0.680 ± 0.050, 0.720 ± 0.068, 0.777 ± 0.048, 0.691 ± 0.103, 0.743 ± 0.022 and 0.733 ± 0.027, respectively. Conclusions: The multi-modality transformer network showed promising performance in recurrence risk stratification of LACC and may be used as an effective tool to help clinicians make clinical decisions.
RESUMEN
Large areas of farmland soil in southern China are deficient in potassium (K) and are contaminated with cadmium (Cd). Previously, we suggested that the K supplementation could reduce Cd accumulation in sweet potatoes (Ipomoea batatas (L.) Lam). In the present study, we investigated the underlying physiological and molecular mechanisms. A hydroponic experiment with different K and Cd treatments was performed to compare the transcriptome profile and the cell wall structure in the roots of sweet potato using RNA sequencing, Fourier transform infrared spectroscopy (FTIR) and transmission electron microscopy (TEM). The results showed that K supply inhibits the expressions of IRT1 and YSL3, which are responsible for root Cd uptake under Cd exposure. Furthermore, the expressions of COPT5 and Nramp3 were downregulated by K, which increased Cd retention in the root vacuoles. The upregulation of POD, CAD, INT1 and SUS by K contributed to lignin and cellulose biosynthesis and thickening of root xylem cell wall, which further reduced Cd translocation to the shoot. In addition, K affected the expressions of LHT, ACS, TPS and TPP associated with the production of ethylene and trehalose, which involved in plant resistance to Cd toxicity. In general, K application could decrease the uptake and translocation of Cd in sweet potatoes by regulating the expression of genes associated with Cd transporters and root cell wall components.