Radiotherapy benefits patients with advanced esophageal squamous cell carcinoma (ESCC) on symptom relief and long-term survival. Contrarily, a substantial proportion of ESCC patients have not benefited from radiotherapy. This study aimed to establish and validate an artificial neural network-based radiomics model for the pre-treatment predicting radiotherapy response of advanced ESCC by using integrated data combined with feasible baseline characteristics of computer tomography. The 248 patients with advanced ESCC patients who underwent baseline CT and received radiotherapy were enrolled in this study and were analyzed by two types of radiomics models, including machine learning and deep learning. As a result, the Att. Resnet50 pretrained network model indicated a superior performance, with AUCs of 0.876, 0.802 and o.732 in the training, internal validation, and external validation cohort. Similarly, our Att. Resnet50 pretrained network model showed excellent calibration and significant clinical benefit according to the C index and the decision curve analysis.Herein, a novel pre-treatment radiomics model was established based on deep learning methods and could be used for radiotherapy response prediction in advanced ESCC patients, thus providing reliable evidence for therapeutic decision-making.
Recent research has shown that bilinguals outperform monolinguals on tasks
requiring non-linguistic executive control skills, thereby generating an
interest in the relationship between bilingual language processing and
non-linguistic control abilities. Based on this, the present study further
examined the bidirectional interaction between language control and
non-linguistic control in unbalanced Chinese-English bilinguals. These
bilinguals completed a Flanker task in three types of language control
contexts (i.e., L1, L2, and Mixed language contexts) in the interleaved
word-comprehension-to-Flanker sequence and performed a picture-word matching
task in three types of non-linguistic executive control contexts (i.e.,
color, shape and color-shape mixed contexts) in the interleaved
color-shape-switching-to-word-comprehension sequence. The results showed
that the Flanker effect in mixed language context was smaller than in single
(L1 and L2) context, suggesting language control leads to a better
non-linguistic control ability. Additionally, the language switching cost
was found smaller in the mixed task context (color/shape switching),
indicating that non-linguistic control can enhance the language control
ability. Therefore, we conclude that there is a bidirectional interaction
between language control and non-linguistic control even in unbalanced
bilinguals.
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