AIMC Topic: Neoplasms

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Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study.

Scientific reports
Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patie...

Machine intelligence in non-invasive endocrine cancer diagnostics.

Nature reviews. Endocrinology
Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data...

The Application of Ultrasound Image in Cancer Diagnosis.

Journal of healthcare engineering
In order to solve the problem of low accuracy, high cost, and difficult detection of traditional algorithms, a new algorithm based on ultrasound imaging is proposed in this paper. The algorithm is based on fuzzy clustering to diagnose the disease and...

Machine learning random forest for predicting oncosomatic variant NGS analysis.

Scientific reports
Since 2017, we have used IonTorrent NGS platform in our hospital to diagnose and treat cancer. Analyzing variants at each run requires considerable time, and we are still struggling with some variants that appear correct on the metrics at first, but ...

The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images.

The Journal of pathology
The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digital pathology. Many color normalization algorithms have been developed in recent years in order to re...

Identification of Uncontrolled Symptoms in Cancer Patients Using Natural Language Processing.

Journal of pain and symptom management
CONTEXT: For patients with cancer, uncontrolled pain and other symptoms are the leading cause of unplanned hospitalizations. Early access to specialty palliative care (PC) is effective to reduce symptom burden, but more efficient approaches are neede...

Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers.

International journal of molecular sciences
BACKGROUND: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy.

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning.

Scientific reports
This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict local failure in patients with brain metastasis treated with hypo-fractionated stereotactic radiation ...