ArticleNPJ precision oncology2025
Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- 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
- Transformer based breast cancer classification from histopathological images using sequential residual recurrent multiscale attention network.Scientific reports · 2026Article
- A distributed fusion framework for breast cancer recurrence prediction using MapReduce.Scientific reports · 2026Article
- Development and internal validation of a task-conditioned multimodal radiology-pathology model for breast cancer staging and biomarker profiling: a retrospective cohort study.Frontiers in cell and developmental biology · 2026Article
- Infrared nanoscopy for subcellular chemical imaging.QRB discovery · 2026Review
- (KAUH-BCMD) dataset: advancing mammographic breast cancer classification with multi-fusion preprocessing and residual depth-wise network.Frontiers in big data · 2025Article
- Breast lesion identification using feature fusion and multiresolution dual-tree complex wavelet transform.Digital healthArticle
- Clinical Utility and Limitations of Circulating HPV DNA in Tonsillar Squamous Cell Carcinoma: A Narrative Review.Cancer control : journal of the Moffitt Cancer CenterReview
- Immunohistochemical predictors of local recurrence in breast carcinoma: development and sensitivity validation of an IHC-based risk score.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Predicting long-term recurrence of disease in breast cancer (BC) patients remains a significant challenge for patients with early stage disease who are at low to intermediate risk of relapse as determined using current clinical tools. Prognostic assays which utilize bulk transcriptomics ignore the spatial context of the cellular material and are, therefore, of limited value in the development of mechanistic models. In this study, Fourier-transform infrared (FTIR) chemical images of BC tissue were used to train deep learning models to predict future disease recurrence. A number of deep learning models were employed, with champion models employing two-dimensional and two-dimensional-separable convolutional networks found to have predictive performance of a ROC AUC of approximately 0.64, which compares well to other clinically used prognostic assays in this space. All-digital chemical imaging may therefore provide a label-free platform for histopathological prognosis in breast cancer, opening new horizons for future deployment of these technologies.
Identifiers
What OpenQuestion 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 OpenQuestion graph.