Evidence map›Paper›PMID 31651890›Full record

ArticleMedicine2019

Timescale of tumor volume of a young breast cancer patient with luminal B subtype: A case report.

Xiaoyun Mao, Ming Zhou, Chuifeng Fan, Bo Chen, Feng Jin

Abstract readCase Reports
In one paragraph

Article in Medicine, 2019. 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
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. 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

5 authors.

Xiaoyun MaoDepartment of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Ming ZhouInstitute of Mathematics, University of Rostock, Ulmenstrasse 69, Haus 3, Rostock, Germany.
Chuifeng FanDepartment of Pathology, The First Affiliated Hospital and College of Basic Medical Sciences of China Medical University, Shenyang, Liaoning, People's Republic of China.
Bo ChenDepartment of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Feng JinDepartment of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

rationaleIt is largely unknown about the tumor growth of breast cancer naturally. We devised and analyzed an appropriate mathematical tool of the equations that describe how fast tumors grow without treatment on the basis of the ellipsoid shape of solid breast cancer. PATIENT CONCERNS: A 31-year-old woman presented with a painless palpable lump in her left breast for 5 months. DIAGNOSIS: Infiltrated ductal breast cancer (histologic grade II) of luminal B

interventions: The patient did not receive any therapy due to her private reasons for 2 years, the analysis of the tumor volume growth was done regarding the growth rate of the tumor in the absence of intervention. OUTCOMES: After 2 years of diagnosis of breast cancer, the tumor mass occupied the whole left breast with skin implanted and nipple abnormality. As this case indicated that the tumor's early growth rate was very slow. When the tumor volume reached 300 cm, its fast growth began without treatment. Later growth approached the maximum, when the tumor volume was more than 800 cm. LESSONS: The tumor growth is segmental without therapy. Early diagnosis and treatment is the key to good prognosis for every breast cancer patient.

Indexed as

AdultBiomarkers, TumorBreast NeoplasmsCarcinoma, Ductal, BreastErb-b2 Receptor Tyrosine KinasesFemaleHumansNeoplasm GradingTumor BurdenBiomarkers, TumorErb-b2 Receptor Tyrosine Kinases

Identifiers

PMID31651890
PMCPMC6824670

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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.