Cumulative Deficits Frailty Index and Relationship Status Predict Survival in Multiple Myeloma.

Several tools have been proposed for assessing frailty in multiple myeloma (MM), but most are based on clinical trial datasets. There is also limited data on the association between frailty and patient-reported outcomes, and the prognostic value of social determinants of health. This study evaluates the prognostic impact of frailty, based on the cumulative deficit frailty index (FI), and relationship and socioeconomic status (SES) in newly diagnosed MM patients.

Prognostic Impact of Patient-Reported Symptoms in Multiple Myeloma.

Patient-reported outcomes (PROs) are associated with treatment outcomes in multiple myeloma (MM) in the clinical trial setting. However, most PRO tools are time consuming, which hinders use in routine practice. Our institution incorporated a "Hematology Patient-Reported Symptom Screen" (HPRSS), a 3-item questionnaire for fatigue, pain, and quality of life (QOL).

Characteristics and survival of patients with viral versus nonviral associated hepatocellular carcinoma: a multicenter cohort study.

Background: Viral hepatitis B and C are the leading causes of hepatocellular carcinoma (HCC). With obesity, metabolic-related disorders are increasingly associated with a higher incidence of nonviral HCC. This study aimed to investigate the characteristics, tumor features, treatment outcomes, and survival of patients with viral versus nonviral HCC.

Advancing chest X-ray diagnostics: A novel CycleGAN-based preprocessing approach for enhanced lung disease classification in ChestX-Ray14.

Background And Objective: Chest radiography is a medical imaging technique widely used to diagnose thoracic diseases. However, X-ray images may contain artifacts such as irrelevant objects, medical devices, wires and electrodes that can introduce unnecessary noise, making difficult the distinction of relevant anatomical structures, and hindering accurate diagnoses. We aim in this study to address the issue of these artifacts in order to improve lung diseases classification results.