ArticleEuropean journal of nuclear medicine and molecular imaging2023
Predictive value of
Article in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of artificial intelligence-assisted 18f-fdg pet/ct for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis.Annals of nuclear medicine · 2026Pooled it
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- PET/CT-based quantification of intratumoral heterogeneity for predicting pathological complete response in breast cancer patients treated with neoadjuvant chemotherapy: A multicentre study.European journal of nuclear medicine and molecular imaging · 2026Article
- Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer.NPJ precision oncology · 2026Article
- Development and validation of anAnnals of nuclear medicine · 2025Article
- Metabolic characterization and radiomics-based composite model for breast cancer immune microenvironment types usingEuropean journal of nuclear medicine and molecular imaging · 2025Article
- Identification of HER2-over-expression, HER2-low-expression, and HER2-zero-expression statuses in breast cancer based onCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Article
- Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based onJournal of cancer research and clinical oncology · 2024Article
- Article
- Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients.Scientific reports · 2024Article
- Radiomics based onFrontiers in oncology · 2024Article
- MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.Frontiers in oncology · 2024Article
- Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate the predictive value of an
methodsOne hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent
resultsA variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test.
conclusionIt is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.
Indexed as
Identifiers
36808002What Socratic holds
Registered trials
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 Socratic graph.