ArticleCancers2022
Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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.
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.
Who cites it
20 citing papers in PubMed, 32 citations in OpenAlex.
- Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkers.BMC nephrology · 2026Article
- From mechanistic models to artificial intelligence: exploring the potential of digital twins in geriatric oncology.Frontiers in artificial intelligence · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Deep learning in multi-modal breast cancer data fusion: a literature review.Quantitative imaging in medicine and surgery · 2025Review
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Role of radiomics in predicting early disease recurrence in locally advanced breast cancer patients: integration of radiomic features and RECIST criteria.La Radiologia medica · 2025Article
- Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.Discover oncology · 2025Review
- Multimodal BEHRT: transformers for multimodal electronic health records to predict breast cancer prognosis.Frontiers in oncology · 2025Article
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.PloS one · 2025Article
- Multimodal deep learning approaches for precision oncology: a comprehensive review.Briefings in bioinformatics · 2024Review
- Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.Current oncology (Toronto, Ont.) · 2024Article
- A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer.Journal of imaging informatics in medicine · 2024Article
- Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning.BMC medical informatics and decision making · 2024Article
- From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma.Cancers · 2024Article
- Diagnostics and Therapeutics in Early Stage Breast Cancer Receiving Neoadjuvant Systemic Therapy.Cancers · 2023Article
- Article
- Editorial: Explainable multimodal AI in cancer patient care: how can we reduce the gap between technology and practice?Frontiers in medicine · 2023Article
- Classification of breast cancer recurrence based on imputed data: a simulation study.BioData mining · 2022Article
- 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
Abstract
In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the cancer recur locally in the breast or in other areas of the body. We explore the use of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging (DCE, ADC, Dixon) to predict the risk of post-treatment recurrence within five years. We performed a retrospective study on a cohort of 1738 patients from Institut Curie and analyzed the data using classical machine learning, image processing, and deep learning. Our results demonstrate the ability to predict recurrence prior to NAC treatment initiation using each modality alone, and the possible improvement achieved by combining the modalities. When evaluated on holdout data, the multimodal model achieved an AUC of 0.75 (CI: 0.70, 0.80) and 0.57 specificity at 0.90 sensitivity. We then stratified the data based on known prognostic biomarkers. We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses. A version of our method won second place at the BMMR2 Challenge, with a very small margin from being first, and was a standout from the other challenge entries.
Indexed as
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.