PURPOSE: We aimed to evaluate the efficacy and feasibility of patient-reported outcome (PRO)-based symptom management in the early period after lung cancer surgery. METHODS: Before surgery, patients with clinically diagnosed lung cancer were randomly assigned 1:1 to receive postoperative PRO-based symptom management or usual care. All patients reported symptoms on MD Anderson Symptom Inventory-Lung Cancer presurgery, daily postsurgery, and twice a week after discharge for up to 4 weeks via an electronic PRO system. In the intervention group, treating surgeons responded to overthreshold electronic alerts driven by any of the five target symptom scores (score ≥ 4 on a 0-10 scale for pain, fatigue, disturbed sleep, shortness of breath, and coughing). The control group patients received usual care and no alerts were generated. The primary outcome was the number of symptom threshold events (any target symptom with a score of ≥ 4) at discharge. Per-protocol analyses were conducted. RESULTS: Of the 166 participants, 83 were randomly allocated to each group. At discharge, the intervention group reported fewer symptom threshold events than the control group (median [interquartile range], 0 [0-2] v 2 [0-3]; P = .007). At 4 weeks postdischarge, this difference was maintained between the intervention and control groups (median [interquartile range], 0 [0-0] v 0 [0-1]; P = .018). The intervention group had a lower complication rate than the control group (21.5% v 40.6%; P = .019). Surgeons spent a median of 3 minutes managing an alert. CONCLUSION: PRO-based symptom management after lung cancer surgery showed lower symptom burden and fewer complications than usual care for up to 4 weeks postdischarge.

Rainy days usually degrade the visual effect of images and videos. At present, most deraining models for single images adopt gradual optimization or elimination to remove rain streaks, but actually with relatively low efficiency in real tasks. An efficient one-stage deraining model, Efficient Transformer Derain Network (ETDNet), is proposed to remove rain streaks in single images efficiently. A new Transformer architecture is designed to provide rich multiple scales and context information, making the model extract features in a coarse-to-fine way. Multiple expansion filters with different expansion rates are embedded to predict the suitable kernel for each pixel of the rainy image in a multi-scale way. A multi-scale Loss Function is introduced to restore features with high-fidelity and detail textures. Experiments on Rain100L, Rain100H, and SPA datasets show that the proposed ETDNet reaches the highest PSNR and SSIM values at the fastest speed compared with other models.

To alleviate the social contradiction between limited medical resources and increasing medical needs, the medical image-assisted diagnosis based on deep learning has become the research focus in Wise Information Technology of med. Most of the existing medical segmentation models based on Convolution or Transformer have achieved relatively sound effects. However, the Convolution-based model with a limited receptive field cannot establish long-distance dependencies between features as the Network deepens. The Transformer-based model produces large computation overhead and cannot generalize the bias of local features and perceive the position feature of medical images, which are essential in medical image segmentation. To address those issues, we present Triple Gate MultiLayer Perceptron U-Net (TGMLP U-Net), a medical image segmentation model based on MLP, in which we design the Triple Gate MultiLayer Perceptron (TGMLP), composed of three parts. Firstly, considering encoding the position information of features, we propose the Triple MLP module based on MultiLayer Perceptron in this model. It uses linear projection to encode features from the high, wide, and channel dimensions, enabling the model to capture the long-distance dependence of features along the spatial dimension and the precise position information of features in three dimensions with less computational overhead. Then, we design the Local Priors and Global Perceptron module. The Global Perceptron divides the feature map into different partitions and conducts correlation modelling for each partition to establish the global dependency between partitions. The Local Priors uses multi-scale Convolution with high local feature extraction ability to explore further the relationship of context feature information within the structure. At last, we suggest a Gate-controlled Mechanism to effectively solves the problem that the dependence of position embeddings between Patches and within Patches in medical images cannot be well learned due to the relatively small number of samples in medical images segmentation data. Experimental results indicate that the proposed model outperforms other state-of-the-art models in most evaluation indicators, demonstrating its excellent performance in segmenting medical images.

Introduction. This study is conducted to investigate the correlation between perioperative fractional exhaled nitric oxide and postoperative pneumonia (POP) and the feasibility of perioperative FeNO for predicting POP in surgical lung cancer patients. Methods. Patients who were diagnosed with non-small-cell lung cancer (NSCLC) were prospectively analyzed, and the relationship between perioperative FeNO and POP was evaluated based on patients’ basic characteristics and clinical data in the hospital. Results. There were 218 patients enrolled in this study. Finally, 183 patients were involved in the study, with 19 of them in the POP group and 164 in the non-POP group. The POP group had significantly higher postoperative FeNO (median: 30.0 vs. 19.0 ppb, ) as well as change in FeNO (median: 10.0 vs. 0.0 ppb, ) before and after the surgery. For predicting POP based on the receiver operating characteristic (ROC) curve, a cutoff value of 25 ppb for postoperative FeNO (Youden’s index: 0.515, sensitivity: 78.9%, and specificity: 72.6%) and 4 ppb for change in FeNO (Youden’s index: 0.610, sensitivity: 84.2%, specificity: 76.8%) were selected. Furthermore, according to the bivariate regression analysis, FEV1/FVC (OR = 0.948, 95% CI: 0.899–0.999, ), POD1 FeNO (OR = 1.048, 95% CI: 1.019–1.077, ), and change in FeNO (OR = 1.087, 95% CI: 1.044–1.132, ) were significantly associated with occurrence of POP. Conclusions. This prospective study revealed that a high postoperative FeNO (>25 ppb), as well as an increased change in FeNO (>4 ppb), may have the potential in detecting the occurrence of POP in surgical lung cancer patients.

As for recognizing Zhuang minority pattern symbols, current recognition models often cause high computational overhead and low accuracy since Zhuang minority pattern symbols have large feature vectors and some complex features. In this paper, we present the efficient attention receptive field you only look once (Earf-YOLO), a new scheme to address those problems. Firstly, a global-local-transformer (GLocalT) structure is proposed, through which other control systems are introduced into the axial self-attention module, and global-local training strategies are also designed. The structure can use other control systems to compensate for the lost feature information along the height, width, and channel axes. The global-local training strategy can encode long-term dependencies between features and reduce local information loss, fully illustrating that the structure has high feature expression ability. Besides, strength receptive field block (SRFB) is suggested to use the dilated convolution to control the receptive field’s eccentricity and enrich the feature information of the receptive field during its training. With more branches, it can better extract multiscale features, enrich the feature space of the convolution block, and reparametrize multibranch during prediction to fuse them into the main branch, all of which contribute to the improvement of the model performance. Finally, some advanced training techniques are adopted to enhance the detection effect further. In the end, comparative experiments are conducted on the datasets of Zhuang pattern symbols and PASCAL VOC, whose results indicate that the AP and FPS of the suggested model reach their highest values, manifesting its high efficiency.