Evidence map›Paper›PMID 33614472›Full record

ArticleFrontiers in oncology2020

The Impact of Preoperative Radiomics Signature on the Survival of Breast Cancer Patients With Residual Tumors After NAC.

Ling Zhang, Xinhua Jiang, Xiaoming Xie, Yaopan Wu, Shaoquan Zheng, Wenwen Tian, Xinhua Xie, Li Li

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 28% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Frontiers in oncology · 2023
    Article
  5. Article
  6. 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

8 authors at 1 institution in 1 country.

Ling ZhangState Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Xinhua JiangState Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Xiaoming XieState Key Laboratory of Oncology in South China, Department of Breast Surgery, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Yaopan WuState Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Shaoquan ZhengState Key Laboratory of Oncology in South China, Department of Breast Surgery, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Wenwen TianState Key Laboratory of Oncology in South China, Department of Breast Surgery, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Xinhua XieState Key Laboratory of Oncology in South China, Department of Breast Surgery, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Li LiState Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResidual cancer cells remaining after chemotherapy may have more aggressive behavior that promotes recurrence or metastasis, and which patients would benefit from subsequent additional treatment is controversial. The purpose of our study was to evaluate the prognostic value of the preoperative radiomics features of computed tomography (CT) imaging in breast cancer (BC) patients with residual tumors after neoadjuvant chemotherapy (NAC).

methodsPost-NAC CT images were reviewed from 114 patients who had received breast surgery and had residual breast tumors. The association of the 110 radiomics features derived from CT images with 5-year disease-free survival (DFS) was assessed by log-rank test in the training cohort, resulting in 13 prognostic radiomics features.

resultsWe constructed a radiomics signature consisting of four selected features by using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, which performed well in the discrimination with an area under the curve (AUC) of 0.78 (95% CI, 0.67-0.89) and 0.73 (95% CI, 0.59-0.87) in the training and validation cohorts, respectively. Radiomics nomogram, incorporating the radiomics signature with the conventional clinical variables, also performed well in the two cohorts (training cohort: AUC, 0.84; validation cohort: AUC, 0.82). Moreover, we found that the high-risk patients determined by our radiomics nomogram could benefit from postoperative adjuvant chemotherapy, while the low-risk and total patient groups could not.

conclusionsOur novel radiomics nomogram is a promising and favorable prognostic biomarker for preoperatively predicting survival outcomes and may aid in clinical decision-making in BC patients with residual tumors after NAC.

Indexed as

breast cancerneoadjuvant chemotherapynomogramprognosisradiomics

Identifiers

PMID33614472
PMCPMC7888274
OpenAlexW3128723726

What OpenQuestion holds

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LicenceCC BY
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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.