Evidence map›Paper›PMID 37606817›Full record

ArticleCellular oncology (Dordrecht, Netherlands)2024

Prognostic significance of collagen signatures at breast tumor boundary obtained by combining multiphoton imaging and imaging analysis.

Xingxin Huang, Fangmeng Fu, Wenhui Guo, Deyong Kang, Xiahui Han, Liqin Zheng, Zhenlin Zhan, Chuan Wang, Qingyuan Zhang, Shu Wang and 5 more

Abstract read
In one paragraph

Article in Cellular oncology (Dordrecht, Netherlands), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

15 authors.

Xingxin HuangKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
Fangmeng FuDepartment of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
Wenhui GuoDepartment of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
Deyong KangDepartment of Pathology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
Xiahui HanKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
Liqin ZhengKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
Zhenlin ZhanKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
Chuan WangDepartment of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
Qingyuan ZhangDepartment of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, China.
Shu WangKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
Shunwu XuSchool of Electronic and Mechanical Engineering, Fujian Polytechnic Normal University, Fuqing, 350300, China.
Jianli MaDepartment of Radiation Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, China. 943328544@qq.com.
Lida QiuCollege of Physics and Electronic Information Engineering, Minjiang University, Fuzhou, 350108, China. qld.qq@163.com.
Jianxin ChenKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China. chenjianxin@fjnu.edu.cn.
Lianhuang LiKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China. lhli@fjnu.edu.cn.

Funding

National Natural Science Foundation of China 82171991Natural Science Foundation of Fujian Province 2023J01082
6 · The paper itself

Abstract

purposeCollagen features in breast tumor microenvironment is closely associated with the prognosis of patients. We aim to explore the prognostic significance of collagen features at breast tumor border by combining multiphoton imaging and imaging analysis.

methodsWe used multiphoton microscopy (MPM) to label-freely image human breast tumor samples and then constructed an automatic classification model based on deep learning to identify collagen signatures from multiphoton images. We recognized three kinds of collagen signatures at tumor boundary (CSTB I-III) in a small-scale, and furthermore obtained a CSTB score for each patient based on the combined CSTB I-III by using the ridge regression analysis. The prognostic performance of CSTB score is assessed by the area under the receiver operating characteristic curve (AUC), Cox proportional hazard regression analysis, as well as Kaplan-Meier survival analysis.

resultsAs an independent prognostic factor, statistical results reveal that the prognostic performance of CSTB score is better than that of the clinical model combining three independent prognostic indicators, molecular subtype, tumor size, and lymph nodal metastasis (AUC, Training dataset: 0.773 vs. 0.749; External validation: 0.753 vs. 0.724; HR, Training dataset: 4.18 vs. 3.92; External validation: 4.98 vs. 4.16), and as an auxiliary indicator, it can greatly improve the accuracy of prognostic prediction. And furthermore, a nomogram combining the CSTB score with the clinical model is established for prognosis prediction and clinical decision making.

conclusionThis standardized and automated imaging prognosticator may convince pathologists to adopt it as a prognostic factor, thereby customizing more effective treatment plans for patients.

Indexed as

Breast NeoplasmsMammary Neoplasms, AnimalAnimalsCollagenFemaleHumansNomogramsPrognosisTumor MicroenvironmentCollagenBreast cancerClassification modelCollagen signaturesPrognosis prediction

Identifiers

PMID37606817
PMCPMC12974034

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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.