Evidence map›Paper›PMID 34629793›Full record

ReviewWorld journal of gastroenterology2021

Recent advances in blood-based and artificial intelligence-enhanced approaches for gastrointestinal cancer diagnosis.

Li-Shi Li, Xiang-Yu Guo, Kun Sun

Abstract readReview
In one paragraph

Review in World journal of gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Li-Shi LiSchool of Chemical Biology and Biotechnology, Shenzhen Graduate School, Peking University, Shenzhen 518055, Guangdong Province, China.
Xiang-Yu GuoInstitute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, Guangdong Province, China.
Kun SunInstitute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, Guangdong Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastrointestinal (GI) cancers are among the most common cancer types and leading causes of cancer-related deaths worldwide. There is a tremendous clinical need for effective early diagnosis for better healthcare of GI cancer patients. In this article, we provide a short overview of the recent advances in GI cancer diagnosis. In the first part, we discuss the applications of blood-based biomarkers, such as plasma circulating cell-free DNA, circulating tumor cells, extracellular vesicles, and circulating cell-free RNA, for cancer liquid biopsies. In the second part, we review the current trends of artificial intelligence (AI) for pathology image and tissue biopsy analysis for GI cancer, as well as deep learning-based approaches for purity assessment of tissue biopsies. We further provide our opinions on the future directions in blood-based and AI-enhanced approaches for GI cancer diagnosis, and we think that these fields will have more intensive integrations with clinical needs in the near future.

Indexed as

Cell-Free Nucleic AcidsGastrointestinal NeoplasmsArtificial IntelligenceEarly Detection of CancerHumansLiquid BiopsyCell-Free Nucleic AcidsArtificial intelligenceCirculating tumor cellExosomeExtracellular vesicleLiquid biopsyPlasma cell-free DNA

Identifiers

PMID34629793
PMCPMC8473600

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.