AIMC Topic: Neoplasms

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Automated cell annotation and classification on histopathology for spatial biomarker discovery.

Nature communications
Histopathology with hematoxylin and eosin (H&E) staining is routinely employed for clinical diagnoses. Single-cell analysis of histopathology provides a powerful tool for understanding the intricate cellular interactions underlying disease progressio...

Innovations in cancer treatment: evaluating drug resistance with lab-on-a-chip technologies.

International journal of pharmaceutics
Lab-on-a-chip (LoC) technologies have emerged as transformative tools in cancer research, particularly in evaluating drug resistance, which remains a significant barrier to effective treatment. These miniaturized platforms allow for the integration o...

Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts.

BMC medical informatics and decision making
BACKGROUND: The incidence of intensive care unit (ICU) admissions and the corresponding mortality rates among cancer patients are both high. However, the existing scoring systems all lack specificity. This research seeks to establish and validate a p...

RareNet: a deep learning model for rare cancer diagnosis.

Scientific reports
Although significant advances have been made in the early detection of many cancers, challenges remain in the early diagnosis of rare cancers, including Wilms tumor, Clear Cell Sarcoma of the Kidney, Neuroblastoma, Osteosarcoma, and Acute Myeloid Leu...

Validation of an AI-enabled exome/transcriptome liquid biopsy platform for early detection, MRD, disease monitoring, and therapy selection for solid tumors.

Scientific reports
Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and w...

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

Scientific reports
In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of non-targeted drugs, as these drugs often fail to address specific genetic events. Recent advancements...

A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.

Nature communications
Despite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the limited availability of well-defined drug targets. Recent progress in generative artificial intelligen...

Simple and Rapid Tumor EV Enrichment Enabled by Long DNA Probe-Guided Entanglement.

Angewandte Chemie (International ed. in English)
Specific subpopulations of extracellular vesicles (EVs) hold significant clinical potential for biomarker discovery, disease diagnosis, and therapeutic agents. However, this field remains underutilized due to the lack of straightforward and versatile...

Machine Learning-Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones.

JCO clinical cancer informatics
PURPOSE: This study aimed to investigate whether changes in step count, measured using patients' own smartphones, could predict a clinical adverse event in the upcoming week in patients undergoing systemic anticancer treatments using machine learning...

DNA-Mediated Bioinspired MXene Gas Sensor Array with Machine Learning for Noninvasive Cancer Recognition.

ACS nano
Noninvasive odor sensing is important in environmental monitoring and medical diagnosis. The two-dimensional material MXene is widely used due to its unique sensing properties but has limitations in specifically recognizing a certain gas. This study ...