Evidence map›Paper›PMID 38678522›Full record

ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2024

Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.

Enzhao Zhu, Linmei Zhang, Yixian Liu, Tianyu Ji, Jianmeng Dai, Ruichen Tang, Jiayi Wang, Chunyu Hu, Kai Chen, Qianyi Yu and 2 more

Abstract read
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In one paragraph

Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2024. 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

12 authors.

Enzhao Zhu *School of Medicine, Tongji University, Shanghai, China.
Linmei Zhang *Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology & Department of Prosthodontics, Stomatological Hospital and Dental School, Tongji University, Shanghai, 200072, China.
Yixian LiuDepartment of Gynecology and Obstetrics, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China.
Tianyu JiSchool of Medicine, Tongji University, Shanghai, China.
Jianmeng DaiSchool of Medicine, Tongji University, Shanghai, China.
Ruichen TangCollege of Electronic and Information Engineering, Tongji University, Shanghai, China.
Jiayi WangSchool of Medicine, Tongji University, Shanghai, China.
Chunyu HuTenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China.
Kai ChenCollege of Electronic and Information Engineering, Tongji University, Shanghai, China.
Qianyi YuSchool of Medicine, Tongji University, Shanghai, China.
Qiuyi LuSchool of Medicine, Tongji University, Shanghai, China.
Zisheng AiDepartment of Medical Statistics, School of Medicine, Tongji University, Shanghai, China. 2131237@tongji.edu.cn.ORCID http://orcid.org/0009-0002-2663-5621

Funding

Medical discipline Construction Health Committee of Project of Pudong Shanghai PWYgV2021-02
6 · The paper itself

Abstract

backgroundThe survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients remains controversial, especially when considering the heterogeneous characteristics of individual patients.

objectiveTo discern the variability in responses to breast cancer treatment at the individual level and propose personalized treatment recommendations utilizing deep learning (DL).

methodsSix models were developed to offer individualized treatment suggestions. Outcomes for patients whose actual treatments aligned with model recommendations were compared to those whose did not. The influence of certain baseline features of patients on NST selection was visualized and quantified by multivariate logistic regression and Poisson regression analyses.

resultsOur study included 94,487 female breast cancer patients. The Balanced Individual Treatment Effect for Survival data (BITES) model outperformed other models in performance, showing a statistically significant protective effect with inverse probability treatment weighting (IPTW)-adjusted baseline features [IPTW-adjusted hazard ratio: 0.51, 95% confidence interval (CI), 0.41-0.64; IPTW-adjusted risk difference: 21.46, 95% CI 18.90-24.01; IPTW-adjusted difference in restricted mean survival time: 21.51, 95% CI 19.37-23.80]. Adherence to BITES recommendations is associated with reduced breast cancer mortality and fewer adverse effects. BITES suggests that patients with TNM stage IIB, IIIB, triple-negative subtype, a higher number of positive axillary lymph nodes, and larger tumors are most likely to benefit from NST.

conclusionsOur results demonstrated the potential of BITES to aid in clinical treatment decisions and offer quantitative treatment insights. In our further research, these models should be validated in clinical settings and additional patient features as well as outcome measures should be studied in depth.

Indexed as

Breast NeoplasmsDeep LearningNeoadjuvant TherapyAdultAgedFemaleHumansMiddle AgedPrecision MedicineBreast cancerCausal inferenceDeep learningNeoadjuvant systemic therapy

Identifiers

PMID38678522

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.