AIMC Topic: Neoplasms

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Artificial intelligence in healthcare applications targeting cancer diagnosis-part II: interpreting the model outputs and spotlighting the performance metrics.

Oral surgery, oral medicine, oral pathology and oral radiology
BACKGROUND: The lack of standardized performance assessment metrics and the inconsistent reporting of results can lead to the presentation of overly optimistic outcomes that fail to accurately represent key aspects of the Machine Learning framework a...

Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!

European radiology experimental
Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validati...

Artificial intelligence in healthcare applications targeting cancer diagnosis-part I: data structure, preprocessing and data organization.

Oral surgery, oral medicine, oral pathology and oral radiology
BACKGROUND: Machine learning techniques hold significant potential to support the diagnosis and prognosis of diseases. However, the success of these approaches is heavily dependent on rigorous data acquisition, preprocessing and data organization.

When multiple instance learning meets foundation models: Advancing histological whole slide image analysis.

Medical image analysis
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification. However, it remains unclear how these widely used approaches compare to each other, given the rece...

Artificial intelligence-based biomarkers for treatment decisions in oncology.

Trends in cancer
The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs) and targeted therapies has increased the complexity of the treatment landscape for solid tumors. At the current rate of annual FDA approvals, the potential trea...

Patients' attitudes toward artificial intelligence (AI) in cancer care: A scoping review protocol.

PloS one
BACKGROUND: Artificial intelligence broadly refers to computer systems that simulate intelligent behaviour with minimal human intervention. Emphasizing patient-centered care, research has explored patients' perspectives on artificial intelligence in ...

AlgaeSperm: Microalgae-Based Soft Magnetic Microrobots for Targeted Tumor Treatment.

Small (Weinheim an der Bergstrasse, Germany)
Magnetic microrobots are significant platforms for targeted drug delivery, among which sperm-inspired types have attracted much attention due to their flexible undulation. However, mass production of sperm-like soft magnetic microrobots with high-spe...

Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVES: Having a sufficient sample size is crucial when developing a clinical prediction model. We reviewed details of sample size in studies developing prediction models for binary outcomes using machine learning (ML) methods with...

Application of deep learning in automated localization and interpretation of coronary artery calcification in oncological PET/CT scans.

The international journal of cardiovascular imaging
Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gated CT or PET/CT scans. Traditional CAC assessment requires gated CT scans, leading to increased radia...

Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology.

BMC bioinformatics
BACKGROUND: Deep learning (DL) has set new standards in cancer diagnosis, significantly enhancing the accuracy of automated classification of whole slide images (WSIs) derived from biopsied tissue samples. To enable DL models to process these large i...