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Research on predicting the progression of multiple myeloma treated with bortezomib based on multimodal ensemble learning.

BackgroundMultiple myeloma (MM) is a malignancy characterized by abnormal plasma cell proliferation. While bortezomib has improved outcomes, significant individual variability persists. Accurate early prediction of patient progression is crucial for optimizing therapeutic intensity and improving long-term survival. Developing an automated, multimodal prediction model can provide clinicians with a robust tool for personalized prognosis, thereby reducing the burden of ineffective treatments on patients.MethodsWe enrolled 207 newly diagnosed MM (NDMM) patients treated with bortezomib. Based on 2-

Стратегии интеграции искусственного интеллекта и когнитивной оценки для прогнозирования развития инвалидности в релапсирующихся множестве склерозов: исследование по разработке моделей.

Серийная когнитивная оценка при множественном склерозе может помочь выявить пациентов, подверженных более высокому риску развития инвалидности, однако высокая масштабность данных о времени реакции затрудняет анализ. Мы разработали метод преобразования данных о времени продольной реакции в изображения и применили модели глубокого выживания для прогнозирования развития инвалидности в релапсирующих MS (RRMS). В этом когортном исследовании клинические данные были получены из регистра MSBase и когнитивных данных из компьютеризированной когнитивной батареи MSRector в период с февраля 2016 года по се

Машинное прогнозирование качества сна у пациентов с множественным склерозом.

Предыстория/задачи: нарушения сна являются распространенными, но часто недооцениваются при множественном склерозе (МС), независимо предсказывая снижение качества жизни и усиливая усталость, когнитивное расстройство и депрессию. В то время как боль, ноктура, усталость и симптомы настроения являются известными причинами, кардиометаболические и подстрекательские факторы остаются в основном неизученными, несмотря на их известные связи с плохим сном в других группах населения. В этом исследовании используется интерпретируемое машинное обучение для определения относительного вклада связанных с болез

Post-operative survival and use of systemic therapy in metastatic long-bone disease: 12 years of institutional experience.

BACKGROUND: Survival prediction in metastatic long-bone disease is essential for surgical decision-making, yet the performance of legacy prognostic models has declined in the era of immunotherapy and molecularly targeted agents. As these systemic therapies become routine, treatment patterns themselves may influence survival in ways not captured by earlier models. We, therefore, quantified preoperative systemic therapy use over the last decade and evaluated its association with postoperative overall survival in patients undergoing surgery for long-bone metastases. METHODS: We conducted a retro

Artificial intelligence powers regenerative medicine into predictive realm.

The expanding regenerative medicine toolkit is reaching a record number of lives. There is a pressing need to enhance the precision, efficiency, and effectiveness of regenerative approaches and achieve reliable outcomes. While regenerative medicine has relied on an empiric paradigm, availability of big data along with advances in informatics and artificial intelligence offer the opportunity to inform the next generation of regenerative sciences along the discovery, translation, and application pathway. Artificial intelligence can streamline discovery and development of optimized biotherapeutic

Human Versus Machine in Survival Prediction for Metastatic Spinal Cord Compression: A Retrospective Cohort Study.

BackgroundAccurate survival prediction in metastatic spinal cord compression (MSCC) is critical for guiding treatment decisions, yet remains challenging, particularly for intermediate survival durations. We compared the accuracy of oncologist judgment, surgeon-calculated Tokuhashi scores, and ChatGPT-assisted predictions in estimating survival outcomes in MSCC patients.MethodsThis retrospective study included 100 patients (n = 100) referred to the Centre for Spinal Studies and Surgery, Queen's Medical Centre, a tertiary spinal oncology center in Nottingham, United Kingdom, with radiologically

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine lear

Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment.

Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific biomarkers, and the development of personalised treatment plans. Traditional single-marker biomarkers (PD-L1, TMB, MSI) lack consistency across tumour types and cannot be used to assess tumour heterogeneity or the dynamic tumour microenvironment (TME). This review synthesises developments in multimodal AI models that combine genomics, transcriptomics, radiomics, digital pathology (pathomics), circulating biomarkers, and clinical evidence to crea

Post-marketing safety of tarlatamab in small cell lung cancer based on FAERS and WHO-VigiAccess with SHAP-based interpretable machine learning analysis of immune-related adverse events.

BACKGROUND: Tarlatamab is a DLL3-targeted bispecific T-cell engager approved for previously treated extensive-stage small cell lung cancer (ES-SCLC). However, its post-marketing safety profile in routine practice remains incompletely characterized. METHODS: We conducted a retrospective pharmacovigilance study using the FDA Adverse Event Reporting System from Q2 2024 to Q3 2025, with a descriptive cross-database comparison using WHO-VigiAccess. Disproportionality analyses were performed using reporting odds ratio, proportional reporting ratio, Bayesian confidence propagation neural network, an

Методы машинного обучения для классификации множественного склероза и болезни Альцгеймера с использованием геномных данных.

Сложные заболевания создают трудности в прогнозировании из-за их многофакторного и полигенного характера. В этом исследовании используется машинное обучение (ML) для анализа геномных данных из британского биобанка с целью прогнозирования геномной предрасположенности к таким сложным болезням, как множественный склероз (MS) и болезнь Альцгеймера (AD). Мы проверили логистическую регрессию (LR), методы ансамбля деревьев и модели глубокого обучения с этой целью. LR продемонстрировал замечательную стабильность в различных поднаборах данных, превосходя глубинные подходы к обучению, которые продемонст

Rationale and Design of the Cancer Immunotherapy Evidence Living (CIEL) Library: A Continuously Updated Clinical Trial Database of Cancer Immunotherapies.

Background and aimsImmunotherapies for cancers are tested in large numbers of clinical trials. It is difficult for clinicians and researchers to stay current with the evidence, and traditional systematic reviews and clinical guidelines are not suited to ensure a continued overview of all trials and their results. To address this problem, we designed the Cancer Immunotherapy Evidence Living (CIEL) Library.MethodsWe included planned, ongoing, and completed interventional trials of immunotherapies for cancer, regardless of trial design (e.g., randomization, blinding, and type of comparator). We s

Machine Learning-Based Model for Predicting Recurrence-Free Survival After Interventional Therapy in Malnourished Hepatocellular Carcinoma Patients.

ObjectiveThis study intends to utilize machine learning approaches to screen out the crucial factors affecting the recurrence of hepatocellular carcinoma (HCC) patients with preoperative malnutrition after interventional therapy, and based on the identified factors, develop a nomogram for predicting the patients' 1-, 3-, and 5-year recurrence-free survival (RFS).MethodsThis study encompassed the clinical data of 512 malnourished (CONUT score ≥ 2) HCC patients who received the combination treatment of transarterial chemoembolization (TACE) and radiofrequency ablation (RFA) at Beijing You'an Hos

Logic programming reveals alteration of key transcription factors in multiple myeloma.

Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected r

Artificial intelligence across the cancer care continuum.

Artificial intelligence (AI) holds significant potential to enhance various aspects of oncology, spanning the cancer care continuum. This review provides an overview of current and emerging AI applications, from risk assessment and early detection to treatment and supportive care. AI-driven tools are being developed to integrate diverse data sources, including multi-omics and electronic health records, to improve cancer risk stratification and personalize prevention strategies. In screening and diagnosis, AI algorithms show promise in augmenting the accuracy and efficiency of medical image ana

Blood cancer prediction model based on deep learning technique.

Blood cancer is among the critical health concerns among people around the world and normally emanates from genetic and environmental issues. Early detection becomes essential, as the rate of death associated with it is high, to ensure that the rate of treatment success is up, and mortality reduced. This paper focuses on improving blood cancer diagnosis using advanced deep learning techniques like ResNetRS50, RegNetX016, AlexNet, Convnext, EfficientNet, Inception_V3, Xception, and VGG19. Among the models assessed, ResNetRS50 had better accuracy and speed with minimal error rates compared with

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… A proposed approach of supervised machine learning for the prediction of blood cancer diseases, it has used a leukemia microarray gene dataset comprising 22,283 genes. …

Prospective external validation of a three-predictor frailty model for 90-day survival and complications following spinal metastasis surgery.

Background contextSurgical decision-making in patients with spinal metastases remains complex due to the need to balance potential surgical benefits with limited survival and common frailty. Predictive models can assist in this process, but their clinical utility is often limited by complexity and lack of validation.PurposeTo externally validate a simple three-predictor frailty model for 90-day survival and complications, and to compare its performance with other commonly used tools.Study design/settingProspective external validation study conducted at a single tertiary cancer center.Patient s

5-Hydroxymethylcytosine Profiles of cfDNA in Urine as Diagnostic, Differential Diagnosis and Prognostic Markers for Multiple Myeloma.

BackgroundAn effective urine-based method for the diagnosis, differential diagnosis and prognosis of multiple myeloma (MM) has not yet been developed. Urine cell-free DNA (cfDNA) carrying cancer-specific genetic and epigenetic aberrations may enable a noninvasive "liquid biopsy" for diagnosis and monitoring of cancer.MethodsWe first identified MM-specific hydroxymethylcytosine signatures by comparing 64 MM patients, 23 amyloidosis (AM) patients and 59 healthy cohort. Then, we applied a machine learning algorithm to develop diagnostic and differential diagnosis model. Finally, the prognosis of

Interpretable machine learning model to predict 90-day radiographically confirmed pneumonia after chemotherapy initiation in non-Hodgkin lymphoma: development and internal validation of a single-center cohort.

BackgroundRadiographically confirmed pneumonia within 90 days of chemotherapy initiation is a frequent and clinically important complication in patients with non-Hodgkin lymphoma, yet interpretable tools for early individualized risk estimation are limited.ObjectiveTo develop and internally validate an interpretable machine-learning model that predicts the 90-day risk of radiographically confirmed pneumonia after chemotherapy initiation in non-Hodgkin lymphoma.MethodsWe retrospectively analyzed 205 chemotherapy-treated NHL patients. A two-step feature selection (LASSO followed by random-forest

AI-Assisted Detection and Localization of Spinal Metastatic Lesions.

ObjectivesThe integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions.MethodsOur methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segme

Множественная стратификация риска склероза и прогнозирование расходов на здравоохранение с использованием машинного обучения.

СПРАВОЧНАЯ ИНФОРМАЦИЯ: Растущая доступность медицинских данных дает возможность более активно бороться с такими хроническими заболеваниями, как множественный склероз (МС). Мы поставили перед собой цель разработать подход к машинному обучению (ML) для выявления пациентов с повышенным уровнем риска и прогнозирования их расходов на медицинское обслуживание. МЕТОДЫ: Мы провели ретроспективный анализ деидентифицированных коммерческих страховых требований 267,000 (неполные лица)631 с MS), с января 2016 по июнь 2018. Ежемесячные данные о претензиях были агрегированы, и 72 ML регрессия и 63 :: были по