BACKGROUND AND AIM:In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. METHODS:In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. RESULTS:In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. CONCLUSIONS:Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

BACKGROUND:Breast cancer is the most common malignant tumor among women, and its incidence is increasing annually. At present, the results of the study on whether optical coherence tomography (OCT) can be used as an intraoperative margin assessment method for breast-conserving surgery (BCS) are inconsistent. We herein conducted this systematic review and meta-analysis to assess the diagnostic value of OCT in BCS.METHODS:PubMed, Web of Science, Cochrane Library, and Embase were used to search relevant studies published up to September 15, 2022. We used Review Manager 5.4, Meta-Disc 1.4, and STATA 16.0 for statistical analysis.RESULTS:The results displayed 18 studies with 782 patients included according to the inclusion and exclusion criteria. Meta-analysis showed the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) of OCT in the margin assessment of BCS were 0.91 (95% CI 0.88-0.93), 0.88 (95% CI 0.83-0.92), 7.53 (95% CI 5.19-10.93), 0.11(95% CI 0.08-0.14), 70.37 (95% CI 39.78-124.47), and 0.94 (95% CI 0.92-0.96), respectively.CONCLUSIONS:OCT is a promising technique in intraoperative margin assessment of breast cancer patients.

Background Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations.Methods Our data originated from the National Health and Nutrition Examination Survey (2005-2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score.Results Deep learning had the highest AUC (0.891, 95%CI 0.869-0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904-0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors.Conclusions Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.

OBJECTIVE:The number of heart disease patients is increasing. Establishing a risk assessment model for chronic heart disease (CHD) based on risk factors is beneficial for early diagnosis and timely treatment of high-risk populations. METHODS:Four machine learning models, including logistic regression, support vector machines (SVM), random forests, and extreme gradient boosting (XGBoost), were used to evaluate the CHD among 14 971 participants in the National Health and Nutrition Examination Survey from 2011 to 2018. The area under the receiver-operator curve (AUC) is the indicator that we evaluate the model. RESULTS:In four kinds of models, SVM has the best classification performance (AUC = 0.898), and the AUC value of logistic regression and random forest were 0.895 and 0.894, respectively. Although XGBoost performed the worst with an AUC value of 0.891. There was no significant difference among the four algorithms. In the importance analysis of variables, the three most important variables were taking low-dose aspirin, chest pain or discomfort, and total amount of dietary supplements taken. CONCLUSION:All four machine learning classifiers can identify the occurrence of CHD based on population survey data. We also determined the contribution of variables in the prediction, which can further explore their effectiveness in actual clinical data.

Two types of progressive muscular dystrophy occur in Tunisian children. The first type is characterized by normal dystrophin assays and affects girls and boys in an autosomal recessive pattern of inheritance. The second type has the features of the typical Duchenne muscular dystrophy (DMD) and has abnormal dystrophin. Between 1974 and 1986, 77 patients with Duchenne muscular dystrophy were examined, 66 were biopsied. Among affected siblings and within family kindreds, we observed both clinical and histopathological variability. However, there was a close correlation between the clinical condition and the biopsy findings in each case, allowing accurate prediction of the patient's course and probable duration of the disease.

Background: Among patients with ovarian cancer (OC), the risk of contralateral OC remains controversial and few studies have focused on the occurrence of contralateral OC after conservative surgery. Methods: Basing on the Surveillance, Epidemiology, and End Results (SEER) database registered between 2000 and 2018, Logistic and Cox regressions were established to test the risk factors of contralateral OC. Kaplan-Meier mothed was used to calculate the cumulative risk curve for contralateral OC and compared using log-rank test. Furthermore, the frequency of contralateral OC and standardized incidence ratios (SIRs) were evaluated. Results: 18807 patients were included, 69 patients developed contralateral OC. Logistic and Cox regressions showed patients diagnosed >50 years had lower risk of contralateral OC (Odds ratio [OR]:0.42, 95% confidence interval [CI]: 0.24-0.73; Hazard ratios [HR]:0.44, 95%CI:0.24-0.77). Patients with radical surgery had lower contralateral OC risk (OR:0.20, 95%CI: 0.11-0.36; HR: 0.17, 95%CI: 0.09-0.30). The SIR for contralateral OC was high in all patients (SIR: 2.37, 95%CI: 1.85-3.00) and highest if patients diagnosed <50 years with conservative surgery (SIR: 27.33, 95%CI: 19.86-36.69). However, the SIR for contralateral OC was low in patients diagnosed >= 50 years with radical surgery (SIR: 0.54, 95%CI: 0.26 e1.00). No statistically significant SIRs were observed in patients diagnosed < 50 years with conservative surgery and patients diagnosed <50 years with radical surgery. Conclusions: Our study provided some information for clinicians to assess the risk of contralateral OC and suggested young patients should not undergo hysterectomy to prevent contralateral OC. Moreover, clinical surveillance cannot be relaxed. (c) 2022 Elsevier Ltd, BASO similar to The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

PURPOSE:Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI).METHODS:The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias.RESULTS:We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95).CONCLUSION:ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.

The meta-analysis was prepared to evaluate the diagnostic value of surface-enhanced Raman spectroscopy for patients with breast cancer and to provide some statistically significant reference for the detection process. PubMed, Web of Science, Cochrane library, and Embase databases were retrieved for randomized clinical trials by two independent reviewers. A comprehensive search was performed on July 15, 2022. Meta-Disc 1.4 was used to calculate Spearman correlation coefficient. STATA 14.1 software was used to evaluate the diagnostic effect and the performance of subgroup analysis, which could be conducted to explore the potential sources of heterogeneity. Review manager 5.3 software was performed for quality assessment in our meta-analysis. The results displayed 19 studies with 1299 patients were included according to the inclusion and exclusion criteria. Meta-analysis displayed the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and the area under the curve of using surface enhanced Raman spectroscopy for diagnosing the patients with breast cancer were 0.91 (95% CI 0.85-0.94), 0.94 (95% CI 0.91-0.96), 14.06 (95% CI 9.25-21.35), 0.10 (95% CI 0.06-0.17), 140.64 (95% CI 59.63-331.70), and 0.97 (95% CI 0.95-0.98), respectively. The source of specificity heterogeneity was from different sample volume mixed with nanoparticle. Surface-enhanced Raman spectroscopy had high sensitivity and specificity in the diagnosis of breast cancer. It was an accurate and effective technique for patients with breast cancer.