Chronic lymphocytic leukemia (CLL) is a B cell neoplasm characterized by the accumulation of aberrant monoclonal B lymphocytes. CLL is the predominant type of leukemia in Western countries, accounting for 25% of cases. Although many patients remain asymptomatic, a subset may exhibit typical lymphoma symptoms, acquired immunodeficiency disorders, or autoimmune complications. Diagnosis involves blood tests showing increased lymphocytes and further examination using peripheral blood smear and flow cytometry to confirm the disease. With the significant advancements in machine learning (ML) and artificial intelligence (AI) in recent years, numerous models and algorithms have been proposed to support the diagnosis and classification of CLL. In this review, we discuss the benefits and drawbacks of recent applications of ML algorithms in the diagnosis and evaluation of patients diagnosed with CLL.

Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics.

Background: Chronic lymphocytic leukemia (CLL) is a rare hematologic malignancy to occur in pregnancy, with an estimated incidence of 1 in 75,000 pregnancies. Pregnant women with CLL face increased susceptibility to infections, due to a weakened immune system. Higher risks of fetal malformations and death are associated with CLL treatment during pregnancy, emphasizing the need for careful consideration and management in these cases. Summary: This review aimed to summarize the current evidence regarding the diagnosis, prognosis, and treatment of CLL in pregnant cases. A comprehensive search strategy was employed across multiple databases, yielding 14 case reports for inclusion. The cases were divided based on CLL diagnosis onset, either before or during pregnancy. Our results showed that patients diagnosed during pregnancy (n = 5) were mostly asymptomatic at diagnosis, with management ranging from supportive care to leukapheresis and transfusions. Postpartum treatment varied, with some patients requiring no additional therapy and others receiving chemotherapy. Pregnancy outcomes were generally favorable, with most neonates born healthy at term. However, one case of Richter transformation resulted in maternal death despite treatment. Among patients with pre-existing CLL (n = 9), the majority experienced an indolent course during pregnancy, with only supportive care required. A few cases necessitated treatment due to progressive disease or complications, including chemotherapy, leukapheresis, and splenectomy. Key Messages: This review highlights the heterogeneous nature of CLL in pregnancy and the importance of individualized management based on disease severity, gestational age, and maternal-fetal risks. Close monitoring, supportive care, and a multidisciplinary approach are essential for optimizing outcomes in this rare and complex clinical scenario.

Introduction: Stoma outlet obstruction (SOO) is a serious postoperative complication that can lead to significant morbidity, including prolonged hospitalization, increased healthcare costs, and reduced quality of life. This study, the first systematic review and meta-analysis on SOO, aims to identify and analyze key risk factors of SOO, calculate its pooled incidence, and systematically review its diagnostic features, clinical symptoms, imaging modalities, management strategies, prognosis, and associated outcomes. Methods: This systematic review and meta-analysis followed PRISMA 2020 guidelines and included 16 retrospective cohort studies, identified through a comprehensive search of multiple databases, with data on risk factors for SOO. The study analyzed four key variables reported by three or more studies, assessed study quality using the MASTER scale, and synthesized findings using the quality effects model to evaluate heterogeneity and publication bias. Results: This study included 16 retrospective cohort studies involving 2,228 patients, of whom 362 developed SOO. Increased rectus abdominis muscle thickness was found to significantly increase the risk of SOO (odds ratio [OR] 4.04, 95% confidence interval [CI] 2.36–6.93). High output stoma was another associated risk factor (OR 4.16, 95% CI 2.03–8.51). The type of ileostomy also played a critical role, with loop ileostomy showing a significantly higher risk of SOO compared to end ileostomy (OR 6.53, 95% CI 2.83–15.03). Although age was assessed as a potential risk factor, it did not show a statistically significant association with SOO (OR 1.69, 95% CI 0.44–6.54). Conclusion: This systematic review and meta-analysis identified significant risk factors for SOO, including increased rectus abdominis muscle thickness, high output stoma, loop ileostomy. We also reported other contributing factors, such as ileal pouch–anal anastomosis, shorter ileal pouch-to-ileostomy distance, oral inferior technique, smaller aperture size, higher BMI, and increased subcutaneous fat thickness. The findings emphasize the importance of tailored surgical techniques, such as stoma maturation using the oral superior technique, ensuring no twist at the mesentery, avoiding stoma limb angulation, creating the stoma slightly more proximally in cases of ileal pouch-anal anastomosis, and optimizing aperture size, along with vigilant postoperative care to reduce SOO incidence and improve patient outcomes.