Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology.

Background: Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whole slide images (WSIs) of thyroid fine-needle aspiration cytology (FNA), incorporating z-stacking, from institutions across the nation to develop an AI model. Methods: We conducted a multicenter retrospective diagnostic accuracy study using thyroid FNA dataset from the Open AI Dataset Project that consists of digitalized images samples collected from 3 university hospitals and 215 Korean institutions through extensive quality check during the case selection, scanning, labeling, and reviewing process. Multiple z-layer images were captured using three different scanners and image patches were extracted from WSIs and resized after focus fusion and color normalization. We pretested six AI models, determining Inception ResNet v2 as the best model using a subset of dataset, and subsequently tested the final model with total datasets. Additionally, we compared the performance of AI and cytopathologists using randomly selected 1031 image patches and reevaluated the cytopathologists' performance after reference to AI results. Results: A total of 10,332 image patches from 306 thyroid FNAs, comprising 78 malignant (papillary thyroid carcinoma) and 228 benign from 86 institutions were used for the AI training. Inception ResNet v2 achieved highest accuracy of 99.7%, 97.7%, and 94.9% for training, validation, and test dataset, respectively (sensitivity 99.9%, 99.6%, and 100% and specificity 99.6%, 96.4%, and 90.4% for training, validation, and test dataset, respectively). In the comparison between AI and human, AI model showed higher accuracy and specificity than the average expert cytopathologists beyond the two-standard deviation (accuracy 99.71% [95% confidence interval (CI), 99.38-100.00%] vs. 88.91% [95% CI, 86.99-90.83%], sensitivity 99.81% [95% CI, 99.54-100.00%] vs. 87.26% [95% CI, 85.22-89.30%], and specificity 99.61% [95% CI, 99.23-99.99%] vs. 90.58% [95% CI, 88.80-92.36%]). Moreover, after referring to the AI results, the performance of all the experts (accuracy 96%, 95%, and 96%, respectively) and the diagnostic agreement (from 0.64 to 0.84) increased. Conclusions: These results suggest that the application of AI technology to thyroid FNA cytology may improve the diagnostic accuracy as well as intra- and inter-observer variability among pathologists. Further confirmatory research is needed.

Effect of Nefopam on Dysesthesia, Postoperative Pain, and Satisfaction in Patients with Lumbar Spinal Stenosis Undergoing Spine Surgery: A Double-Blind, Randomized Study.

Postoperative residual pain and dysesthesia in patients with lumbar spinal stenosis (LSS) can reduce patient satisfaction. We investigated the effects of nefopam on dysesthesia, postoperative pain, and satisfaction in patients with LSS who underwent spine surgery. A total of 73 patients were randomly assigned to two groups: the nefopam group (n = 35), receiving a 20 mL normal saline-based solution containing nefopam 20 mg, and the control group (n = 38), which received 20 mL of normal saline 1 h before the end of the operation. Postoperative incisional pain, dysesthesia scores, and overall satisfaction with postoperative pain management were evaluated. The severity of dysesthesia within 12 and 24 h in the nefopam group was significantly lower than that in the control group (2.3 ± 1.9 and 1.7 ± 1.6 vs. 3.3 ± 2.1, and 2.6 ± 1.9, respectively; p = 0.029 and p = 0.048). Satisfaction scores for postoperative pain management were significantly higher in the nefopam group (3.7 ± 0.6 vs. 3.1 ± 1.0, respectively; p = 0.006). The administration of nefopam effectively reduced the severity of dysesthesia within 24 h of surgery in geriatric patients undergoing spine surgery and increased patient satisfaction with postoperative pain management.

Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid.

A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in cytologic diagnosis. Although artificial intelligence-based image analysis has shown promise in cytopathology research, its application in diagnosing breast cancer in pleural fluid remains unexplored. To overcome these limitations, we evaluate the diagnostic accuracy of an artificial intelligence-based model using a large collection of cytopathological slides, to detect the malignant pleural effusion cytology associated with breast cancer. This study includes a total of 569 cytological slides of malignant pleural effusion of metastatic breast cancer from various institutions. We extracted 34,221 augmented image patches from whole-slide images and trained and validated a deep convolutional neural network model (DCNN) (Inception-ResNet-V2) with the images. Using this model, we classified 845 randomly selected patches, which were reviewed by three pathologists to compare their accuracy. The DCNN model outperforms the pathologists by demonstrating higher accuracy, sensitivity, and specificity compared to the pathologists (81.1% vs. 68.7%, 95.0% vs. 72.5%, and 98.6% vs. 88.9%, respectively). The pathologists reviewed the discordant cases of DCNN. After re-examination, the average accuracy, sensitivity, and specificity of the pathologists improved to 87.9, 80.2, and 95.7%, respectively. This study shows that DCNN can accurately diagnose malignant pleural effusion cytology in breast cancer and has the potential to support pathologists.

Increase in body temperature in pediatric patients after costal cartilage harvest in microtia reconstruction: A retrospective observational study.

BACKGROUND: Previous evidence has clearly shown that maintaining normothermia in children undergoing surgery is difficult and is associated with adverse outcomes. Therefore, this study aimed to retrospectively analyze the changes in body temperature over time in 2 different types of microtia reconstruction surgeries, namely, embedding, and elevation surgeries. METHODS: We performed a retrospective chart review of patients who underwent microtia reconstruction (embedding and elevation) between July 2012 and February 2015 (n = 38). The changes in body temperature between the 2 types of surgeries were compared. RESULTS: During microtia reconstruction, the body temperature in the embedding surgery group was significantly higher than that in the elevation surgery group from 1 hour after the start of surgery to 1 day after the surgery (P < .001). Time, group, and time-group interaction were associated with an increase in body temperature (P < .001) but not the warming method. CONCLUSION: We found an increase in body temperature in patients with microtia who underwent embedding surgery (autologous costal cartilage harvest surgery), and this was related to the type of surgery and not to the warming method. Therefore, further research is warranted to determine the cause of the increase in body temperature during this surgery.

Comorbidity, Functional Impairment, and Emotional Distress: A Coping Mediation Model for Persons With Cancer.

BACKGROUND: Comorbid disease in cancer patients can substantially impact medical care, emotional distress, and mortality. However, there is a paucity of research on how coping may affect the relationship between comorbidity and emotional distress. PURPOSE: The current study investigated whether the relations between comorbidity and emotional distress and between functional impairment and emotional distress were mediated by three types of coping: action planning (AP), support/advice seeking (SAS), and disengagement (DD). METHODS: Four hundred and eighty-three persons with cancer completed a measure of functional impairment (Sickness Impact Profile), the Checklist of Comorbid Conditions, the Brief COPE, the Hospital Anxiety and Depression Scale, the Quality of Life Assessment for Cancer Survivors (Negative Feelings Scale), and the Distress Screening Schedule (Emotional Distress Scale). The latter three measures were used to form a latent construct representing the outcome, emotional distress. RESULTS: Model comparison analysis indicated that the model with DD as a mediator had a better fit than models containing AP and SAS. DD mediated the relationship between functional impairment and emotional distress, so that engaging in DD was associated with greater distress. In addition, comorbidity and functional impairment were directly and positively related to emotional distress, but the relation between comorbidity and distress was not mediated by coping type. CONCLUSIONS: Both comorbidity and functional impairment may be associated with distress, but disengagement coping only mediated the relation involving functional impairment and was positively associated with distress. Future studies can investigate whether teaching active coping or adaptive coping (e.g., through mindfulness exercises) can decrease distress in cancer patients, despite functional impairments.