AIMC Topic: Neoplasm Metastasis

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CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

EBioMedicine
BACKGROUND: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's pr...

Robust edge-based biomarker discovery improves prediction of breast cancer metastasis.

BMC bioinformatics
BACKGROUND: The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as p...

Serum markers improve current prediction of metastasis development in early-stage melanoma patients: a machine learning-based study.

Molecular oncology
Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. In this scenario, determination of prognostic biomarkers would be of great interest. Serum contain...

Development and validation of a deep learning system for ascites cytopathology interpretation.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Early diagnosis of Peritoneal metastasis (PM) is clinically significant regarding optimal treatment selection and avoidance of unnecessary surgical procedures. Cytopathology plays an important role in early screening of PM. We aimed to de...

Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks.

Scientific reports
Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the predi...

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.

The AAPS journal
Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to...

Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.

Scientific reports
Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical ...

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

Nature communications
In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Can...

Propofol affects the growth and metastasis of pancreatic cancer via ADAM8.

Pharmacological reports : PR
BACKGROUND: Anesthesia is a major component of surgery and recently considered an important regulator of cell phenotypes. Here we aimed to investigate propofol, an anesthesia drug, in suppressing pancreatic cancer (PDAC), focusing on A disintegrin an...