Background The number of patients diagnosed with multiple sclerosis (MS) has increased significantly over the last decade. The challenge is to identify the transition from relapsing-remitting to secondary progressive MS. Since available methods to examine patients with MS are limited, both the diagnostics and prognostication of disease progression would benefit from the multimodal approach. The latter combines the evidence obtained from disparate radiologic modalities, neurophysiological evaluation, cognitive assessment and molecular diagnostics. In this systematic review we will analyse the advantages of multimodal studies in predicting the risk of conversion to secondary progressive MS. Methods and analysis We will use peer-reviewed publications available in Web of Science, Medline/PubMed, Scopus, Embase and CINAHL databases. In vivo studies reporting the predictive value of diagnostic methods will be considered. Selected publications will be processed through Covidence software for automatic deduplication and blind screening. Two reviewers will use a predefined template to extract the data from eligible studies. We will analyse the performance metrics (1) for the classification models reflecting the risk of secondary progression: sensitivity, specificity, accuracy, area under the receiver operating characteristic curve, positive and negative predictive values; (2) for the regression models forecasting disability scores: the ratio of mean absolute error to the range of values. Then, we will create ranking charts representing performance of the algorithms for calculating disability level and MS progression. Finally, we will compare the predictive power of radiological and radiomical correlates of clinical disability and cognitive impairment in patients with MS. Ethics and dissemination The study does not require ethical approval because we will analyse publicly available literature. The project results will be published in a peer-review journal and presented at scientific conferences. PROSPERO registration number CRD42022354179.

Background: Quality of life (QoL) may change in female patients with breast cancer over time due to its dynamic construct. Recent studies have provided statistics on the diverse predictors of QoL in breast cancer patients. Still, the literature findings on the prognostication of QoL are scarce. Objectives: With this meta-analysis, we aim to describe the dynamics of overall QoL and its dimensions: mental status, physical functioning, and social interactions. Methods: To reach this aim, we will systematically evaluate the peer-reviewed literature on QoL of women who have undergone either mastectomy or breast-conserving surgery. The proposed study will focus on, but not be limited to, the analysis of BIRS, FACT-B, and EORTC QLQ-C30 questionnaires, which are most commonly used in the assessment of the quality of life of cancer patients. Then, we will extract the following outcome measures: The participants’ age, time since surgery, type of tumor removal procedure (mastectomy or breast-conserving surgery), breast reconstruction technique, mean value, and standard deviation in a QoL score. A random-effects model will then compute the pooled QoL and construct the trend lines for scores received from each diagnostic tool. The findings will be adjusted according to the reconstruction techniques and tumor removal surgery. Finally, we will model the QoL dynamics with a set of predictors identified in the extracted studies. Conclusions: The study findings may serve as a tool for stratifying female patients with breast cancer by the risk of significant reduction in QoL.