Well‐being trajectories in breast cancer and their predictors: A machine‐learning approach

Objective:This study aimed to described istinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months followinga breast cancer diagnosis,and identify the medical, socio-demographic,lifestyle, and psychologica lfactors that predict these trajectories.Methods:474 females (mean age=55.79 years) were enrolled in the first weeksafter surgery or biopsy. Data from seven assessmentpoints over 18 months, at 3-month intervals,were used. The two outcomeswere assessedat all points. Potential predictors were assessed at baseline and the first follow‐up. Machine‐ Learning techniques were used to detect latent patterns of change and identify the most important predictors. Results: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well‐being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune‐ related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. Conclusions: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine‐learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well‐beinginfo:eu-repo/semantics/publishedVersio

Malignant Myoepithelioma of the Breast Clinically and Histologically Masquerading as Angiosarcoma: Cytological Findings and Review of the Literature

&lt;b&gt;&lt;i&gt;Background:&lt;/i&gt;&lt;/b&gt; Malignant myoepithelioma of the breast is an exceptionally rare, aggressive tumor with a diverse morphology, the cytological features of which have only occasionally been described. &lt;b&gt;&lt;i&gt;Case Report:&lt;/i&gt;&lt;/b&gt; Our case comprises a 74-year-old woman who was admitted to our hospital with an erythematous, inflammatory-like mass of her left breast with nipple ulceration, and clinically fixed to the chest wall. The woman underwent fine-needle aspiration and biopsy. The aspirates consisted mainly of loose aggregates of large, highly pleomorphic, polygonal epithelioid cells as well as aggregates of spindle cells with prominent, easily detectable mitoses and single, multinucleated pleomorphic giant cells. The cytological diagnosis was consistent with high-grade malignancy. Histologically, the lesion consisted broadly of eosinophilic epithelioid cells with globoid cytoplasm in a reticulated, angiomatoid pattern and of spindle (sarcomatoid) cells in a storiform pattern, highly suspicious of angiosarcoma. A final diagnosis of malignant myoepithelioma was made. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; We present the cytological findings in comparison with the unusual histological features of a malignant myoepithelioma of the breast. A high degree of suspicion with a keen eye for morphological details coupled with relevant immunohistochemistry will aid in arriving at the correct diagnosis.</jats:p

Fibromatosis of the Breast: Report of a Case with Cytohistological Correlation

Background: Breast fibromatosis is a very rare, locally infiltrative lesion, without metastatic potential that arises from either stromal fibroblasts or myofibroblasts of the breast or from the pectoral fascia, extending into the breast, with its cytological and histological features only rarely being described. Case Presentation: A 58-year-old woman, with no past medical/surgical or family history, was diagnosed on regular mammographic and ultrasound examination with a nodular tumor density, in the upper inner part of her right breast. There were no calcifications or apparent lymph nodes in the right axilla. The woman underwent FNA and US-guided biopsy and final resection biopsy under hook marking. We reviewed the cytological findings of fibromatosis of the breast, as they presented in FNAC aspirates of a non-palpable mammographic finding and the histological findings in both preoperative core-needle biopsy and excision specimen. The final diagnosis was of fibromatosis of the breast. No further actions were taken. The woman is well, without recurrence, more than four years afterwards. Conclusion: Our case can make the pathologists more acquainted with the cytological and pathologic features of a rare tumor entity and the clinicians with a rare breast lesion, which can mimic malignancy both clinically and radiologically. The diagnosis of fibromatosis of the breast is more reliable in excision specimens. Nevertheless, cytology can be an invaluable adjunct to histology, pre-operatively, as it can exclude cancer and help in the preoperative planning.  </jats:p

Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.</jats:p

Digital Self-Management Intervention Paths for Early Breast Cancer Patients: Results of a Pilot Study

Background. Despite excellent prognosis of early breast cancer, the patients face problems related to decreased quality of life and mental health. There is a need for easily available interventions targeting modifiable factors related to these problems. The aim of this study was to test the use of a new digital supportive intervention platform for early breast cancer patients. Material and Methods. Ninety-seven early breast cancer patients answered questions on wellbeing, exercise, and sociodemographic factors before systemic adjuvant treatment at the Helsinki University Hospital. Based on these answers and predictive algorithms for anxiety and depression, they were guided onto one or several digital intervention paths. Patients under 56 years of age were guided onto a nutrition path, those who exercised less than the current guideline recommendations onto an exercise path, and those at risk of mental health deterioration onto an empowerment path. Information on compliance was collected at 3 months on the amount of exercise and quality of life using EORTC-C30 scale, anxiety and depression using HADS scale at baseline and 12 months, and log-in information at 3 and 12 months. Results. Thirty-two patients followed the empowerment path, 43 the nutrition path, and 75 the exercise path. On a scale of 1–5, most of the participants (mean = 3.4; SD 0.815) found the interventions helpful and would have recommended testing and supportive interventions to their peers (mean = 3.70; SD 0.961). During the 10-week intervention period, the mean number of log-ins to the empowerment path was 3.69 (SD = 4.24); the nutrition path, 4.32 (SD = 2.891); and the exercise path, 8.33 (SD = 6.293). The higher number of log-ins to the empowerment (rho = 0.531, P=0.008, and n = 24) and exercise paths (rho = 0.330, P=0.01, and n = 59) was related to better global quality of life at one year. The number of log-ins correlated to the weekly amount of exercise in the exercise path (cc 0.740, P value <0.001, and n = 20). Conclusion. Patients’ attitudes towards the interventions were positive, but they used them far less than was recommended. A randomized trial would be needed to test the effect of interventions on patients’ QoL and mental health

Quantitative identification of functional connectivity disturbances in neuropsychiatric lupus based on resting-state fMRI: a robust machine learning approach

Summarization: Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.Presented on: Brain Science