Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Head and neck squamous cell carcinoma (HNSCC) presents significant challenges in clinical oncology due to its heterogeneity and high mortality rates. This study aims to leverage clinical data and machine learning (ML) principles to predict key outcomes for HNSCC patients: mortality, and relapse-free survival. Utilizing data sourced from the Cancer Imaging Archive, an extensive pipeline was imple...
The complexities inherent to leukemia, multifaceted cancer affecting white blood cells, pose considerable diagnostic and treatment challenges, primarily due to reliance on laborious morphological analyses and expert judgment that are susceptible to errors. Addressing these challenges, this study presents a refined, comprehensive strategy leveraging advanced deep-learning techniques for the class...
Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, raising mortality. This study aims to use machine lea...
Hematological malignancy is a highly heterogeneous disease with complex biological characteristics and diverse clinical manifestations. Therefore, pre...
Thousands of individuals succumb annually to leukemia alone. As artificial intelligence-driven technologies continue to evolve and advance, the ques...
Prostate cancer (Pca) continues to be a leading cause of cancer-related mortality in men, and the limitations in precision of traditional diagnostic...
Cervical cancer is a leading malignancy in female reproductive system. While AI-assisted cytology offers a cost-effective and non-invasive screening...
Motivation: An adjuvant is a chemical incorporated into vaccines that enhances their efficacy by improving the immune response. Identifying adjuvant...
Omics fusion has emerged as a crucial preprocessing approach in the field of medical image processing, providing significant assistance to several s...
Scientists often need to analyze the samples in a study that responded to treatment in order to refine their hypotheses and find potential causal dr...
Bias and inequity in palliative care disproportionately affect marginalised groups. Large language models (LLMs), such as GPT-4o, hold potential to ...
Cancers are characterized by remarkable heterogeneity and diverse prognosis. Accurate cancer classification is essential for patient stratification ...
Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic b...
Segmentation of colorectal cancer (CRC) tumors in 3D medical imaging is both complex and clinically critical, providing vital support for effective ...
Artificial intelligence-assisted imaging analysis has made substantial strides in tumor diagnosis and management. Here we present PASTA, a pan-tumor...
Neural networks have become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates t...
$\textbf{Background}$: Cancer remains one of the leading causes of morbidity and mortality worldwide. Comprehensive datasets that combine histopatho...
A key step in Adaptive Radiation Therapy (ART) workflows is the evaluation of the patient's anatomy at treatment time to ensure the accuracy of the ...
As the field of artificial intelligence evolves rapidly, these hallmarks are intended to capture fundamental, complementary concepts necessary for the...
The occurrence and progression of cancer is a significant focus of research worldwide, often accompanied by a prolonged disease course. Concurrently, ...