Evidence map›Paper›PMID 33829685›Full record

ReviewSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2021

[AI-assisted Prediction of Lymph Node Metastasis of Breast Cancer: Current and Prospective Research].

Yan Ding, Meng-Xue Han, Yue-Ping Liu

Open access · greenAbstract readReview
In one paragraph

Review in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 96% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
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 at 1 institution in 1 country.

Yan DingDepartment of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China.
Meng-Xue HanDepartment of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China.
Yue-Ping LiuDepartment of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China.
Hebei Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the most important application of artificial intelligence (AI) in pathology is prediction, using morphological features, of patient prognosis and response to specific treatments. As one of the most common kinds of malignancies in the world and the crucial important cause of death due to malignant tumor among women, breast cancer has become the center of attention in clinical services. Axillary lymph node metastasis is an important prognostic factor in breast cancer. The accuracy of the assessment of axillary lymph node metastasis bears heavily on clinical diagnosis and treatment. At present, based on the principle of non-invasive procedures, many studies have been done to develop models that can be used to predict sentinel lymph node metastasis of breast cancer. However, different clinical and pathological parameters are used in these predictive models. How to analyze the clinical and pathological data of breast cancer patients in a more comprehensive way and how to establish a prediction model with better precision have become the future direction of development. In this paper, we describe the research progress of AI in pathology and the current status of its use in breast cancer research. We have conducted in-depth reflection and looked into the future of ways to predict effectively breast cancer lymph node metastasis and to establish more accurate and effective deep-learning algorithm based on AI assistance so as to continuously improve the diagnosis and treatment of breast cancer.

Indexed as

Breast NeoplasmsSentinel Lymph NodeArtificial IntelligenceFemaleHumansLymphatic MetastasisLymph NodesProspective StudiesSentinel Lymph Node BiopsyArtificial intelligenceBreast cancerDeep learningLymph node metastasis

Identifiers

PMID33829685
PMCPMC10408927
OpenAlexW3150099254

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.