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Breast cancer, with a high prevalence and survival rate, leads to long-term complications. A major sequel is acute or chronic postoperative pain, and we investigated the possible relationship with clinical and psychological variables. Patients undergoing breast surgery filled out the loneliness (ULS-8) and depression (HADS) questionnaires. Patients rated their pain intensity with the Numerical Rating Scale (0–10, NRS) two days, seven days, and six months after surgery. Of 124 patients, the mean age was 45.86 years old, and the pain scores on the second and seventh postoperative days were 5.33 and 3.57, respectively. Sixth-month pain was significantly correlated with the acute scores with a mean of 3.27; and in the multivariate analysis, it was significantly associated with preoperative pain (p-value = 0.007), self-reported loneliness (p-value = 0.010), and adjuvant radiotherapy (p-value = 0.004). In conclusion, loneliness may be a risk factor for postoperative pain in breast surgery.

Background Cervical cancer is a common malignant tumor of the female reproductive system and is considered a leading cause of mortality in women worldwide. The analysis of time to event, which is crucial for any clinical research, can be well done with the method of survival prediction. This study aims to systematically investigate the use of machine learning to predict survival in patients with cervical cancer. Method An electronic search of the PubMed, Scopus, and Web of Science databases was performed on October 1, 2022. All articles extracted from the databases were collected in an Excel file and duplicate articles were removed. The articles were screened twice based on the title and the abstract and checked again with the inclusion and exclusion criteria. The main inclusion criterion was machine learning algorithms for predicting cervical cancer survival. The information extracted from the articles included authors, publication year, dataset details, survival type, evaluation criteria, machine learning models, and the algorithm execution method. Results A total of 13 articles were included in this study, most of which were published from 2018 onwards. The most common machine learning models were random forest (6 articles, 46%), logistic regression (4 articles, 30%), support vector machines (3 articles, 23%), ensemble and hybrid learning (3 articles, 23%), and Deep Learning (3 articles, 23%). The number of sample datasets in the study varied between 85 and 14946 patients, and the models were internally validated except for two articles. The area under the curve (AUC) range for overall survival (0.40 to 0.99), disease-free survival (0.56 to 0.88), and progression-free survival (0.67 to 0.81), respectively from (lowest to highest) received. Finally, 15 variables with an effective role in predicting cervical cancer survival were identified. Conclusion Combining heterogeneous multidimensional data with machine learning techniques can play a very influential role in predicting cervical cancer survival. Despite the benefits of machine learning, the problem of interpretability, explainability, and imbalanced datasets is still one of the biggest challenges. Providing machine learning algorithms for survival prediction as a standard requires further studies.

Breast cancer (BC) is the most prevalent cancer in Iranian women and the fifth most common cause of cancer-related death in Iran. Among predicting factors and preventive measures for BC, the parity and breastfeeding (BF) are controversial issues. We therefore conducted this case–control study to find out the relation of parity and BF to incidence and risk of BC. A structured questionnaire that covered demographic criteria and BC risk factors was completed for case (376 cases) and control (425 subjects) groups, both matched in terms of demographic variants, reproductive issues, and socioeconomic status. Odds ratio (OR) and 95% confidence intervals (CI) were computed as measures of association from the logistic models. All p values reported are two-sided. Parity significantly reduces the risk of BC (p