Background: Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management.Objective: This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer.Methods: We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model.Results: Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review.Conclusions: The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.

Reliable and rapid medical diagnosis is the cornerstone for improving the survival rate and quality of life of cancer patients. The problem of clinical decision-making pertaining to the management of patients with hematologic cancer is multifaceted and intricate due to the risk of therapy-induced myelosuppression, multiple infections, and febrile neutropenia (FN). Myelosuppression due to treatment increases the risk of sepsis and mortality in hematological cancer patients with febrile neutropenia. A high prevalence of multidrug-resistant organisms is also noted in such patients, which implies that these patients are left with limited or no-treatment options amidst severe health complications. Hence, early screening of patients for such organisms in their bodies is vital to enable hospital preparedness, curtail the spread to other weak patients in hospitals, and limit community outbreaks. Even though predictive models for sepsis and mortality exist, no model has been suggested for the prediction of multidrug-resistant organisms in hematological cancer patients with febrile neutropenia. Hence, for predicting three critical clinical complications, such as sepsis, the presence of multidrug-resistant organisms, and mortality, from the data available from medical records, we used 1166 febrile neutropenia episodes reported in 513 patients. The XGboost algorithm is suggested from 10-fold cross-validation on 6 candidate models. Other highlights are (1) a novel set of easily available features for the prediction of the aforementioned clinical complications and (2) the use of data augmentation methods and model-scoring-based hyperparameter tuning to address the problem of class disproportionality, a common challenge in medical datasets and often the reason behind poor event prediction rate of various predictive models reported so far. The proposed model depicts improved recall and AUC (area under the curve) for sepsis (recall = 98%, AUC = 0.85), multidrug-resistant organism (recall = 96%, AUC = 0.91), and mortality (recall = 86%, AUC = 0.88) prediction. Our results encourage the need to popularize artificial intelligence-based devices to support clinical decision-making.

Introduction: Venetoclax is a BCL-2 inhibitor with proven efficacy in patients with multiple myeloma (MM) and translocation t(11;14). However, its role in plasma cell leukemia (PCL) remains unclear. Herein, we aimed to report a case of relapsed MM with secondary PCL and t(11;14) achieving complete (CR) and durable remission with venetoclax therapy.Case presentation: A 52-year-old gentleman was diagnosed with MM-free light chain lambda (ISS III) in December 2016. He received induction therapy, followed by autologous stem cell transplant. (ASCT) in May 2017 and maintenance. A year later, the patient relapsed with secondary PCL. His cytogenetics analysis revealed t(11; 14). The patient failed salvage chemotherapy and was shifted to venetoclax with dexamethasone treatment. The patient attained complete remission (CR), which was maintained for two years and a half before he developed fatal COVID-19 pneumonia.Conclusion: In comparison with the reported literature, this case report offers the latest compilation of the available evidence on the use of venetoclax in patients with PCL. Furthermore, our patient achieved CR for the longest reported durable response in literature thus far. Prospective clinical trials are needed to elucidate the optimal dosage, combination, and duration of treatment, ensuring better representation and generalizability of the findings. Meanwhile, venetoclax may be considered as a therapeutic option in patients with PCL t(11;14).

Background: Multiple myeloma (MM) is the second-most common cancer among hematological malignancies. Patients with active disease may experience several comorbidities, including renal insufficiency and asthma, which may lead to treatment failure. The treatment of relapsed or refractory MM (RRMM) has been associated with multiple factors, causing a decline in progression-free survival as well as overall survival with subsequent lines of therapy. Data about the characteristics of this group of patients in the Greater Gulf region are lacking. Objective: The primary objective of this study is to describe the disease characteristics and various treatment approaches or regimens used in the management of patients with RRMM in the Greater Gulf region. Methods: We will conduct a regional, retrospective study collecting real-world and epidemiological data on patients with MM in countries of the Greater Gulf region. Medical records will be used to obtain the required data. Around 150 to 170 patients’ records are planned to be retrospectively reviewed over 6 months without any cross-sectional or prospective intervention. Cases will be collected from Saudi Arabia, the United Arab Emirates, Kuwait, Oman, and Qatar. Descriptive as well as analytical statistics will be performed on the extracted data. The calculated sample size will allow us to estimate the percentages of RRMM cases with acceptable precision while complying with the challenges in light of data scarcity. We will obtain a comprehensive description of the demographic profile of patients with MM; treatment outcomes; the proportion of patients with MM with renal impairment and asthma, chronic obstructive pulmonary disease, or both at the time of diagnosis and any subsequent point; and data related to treatment lines, regimens, and MM-associated morbidities. Results: Patient medical records were reviewed between June 2022 and January 2023 for eligibility and data extraction. A total of 148 patients were eligible for study inclusion, of whom 64.2% (n=95) were male and 35.8% (n=53) were female. The study is currently in its final stages of data analysis. The final manuscript is expected to be published in 2024. Conclusions: Although MM is a predominant hematological disease, data on its prevalence and patients’ characteristics in the Greater Gulf region are scarce. Therefore, this study will give us real-world insights into disease characteristics and various management approaches of patients with MM in the Greater Gulf region.

Introduction: Lambert-Eaton myasthenia syndrome (LEMS) is a rare autoimmune disorder characterized by autoantibodies targeting presynaptic neuromuscular junctions. It results in muscle weakness and autonomic dysfunction. LEMS can be idiopathic or associated with neoplastic diseases, often small-cell lung cancer. This case report describes a rare instance of paraneoplastic LEMS in a man with non-Hodgkin lymphoma. Case Presentation: A 57-year-old male with non-Hodgkin lymphoma presented with progressive muscle weakness, diminished reflexes, and autonomic symptoms. Diagnosis revealed LEMS with autoantibodies against voltage-gated calcium channels. Immunosuppressive therapy and lymphoma treatment led to significant improvement in his condition. Conclusion: This case highlights the rare occurrence of paraneoplastic LEMS in a patient with non-Hodgkin lymphoma. Recognition and timely management of LEMS alongside lymphoma treatment can lead to significant clinical improvement, emphasizing the need for increased awareness of such complex associations.

BACKGROUND Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. OBJECTIVE This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer. METHODS We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. RESULTS Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. CONCLUSIONS The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.