ArticleScientific reports2020
Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06370234 (The Prediction Model of Neoadjuvant Chemotherapy Response for Breast Cancer Based on The Parametric Dynamics Features of The Pretreatment and Early-Treatment MR-PET and QDS-IR Images.), which is not on this map. Cited by 50 papers, 2 of them syntheses that pooled it.
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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.
The Prediction Model of Neoadjuvant Chemotherapy Response for Breast Cancer Based on The Parametric Dynamics Features of The Pretreatment and Early-Treatment MR-PET and QDS-IR Images.
Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it, 91 citations in OpenAlex.
- Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2026Pooled it
- PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- [Breast (Edinburgh, Scotland) · 2026Trial
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Automatic computation of breast cancer biomarkers from multiple [Formula: see text] F-FDG PET image segmentation.Scientific reports · 2026Article
- Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis.Diagnostics (Basel, Switzerland) · 2026Review
- Percentage apparent diffusion coefficient change for early prediction of response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis, and a retrospective study.Quantitative imaging in medicine and surgery · 2025Article
- Immunotherapy in Triple-Negative Breast Cancer.Oncology and therapy · 2025Review
- BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images.Scientific reports · 2025Article
- Deep learning in nuclear medicine: from imaging to therapy.Annals of nuclear medicine · 2025Review
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- Advancements in real-time oncology diagnosis: harnessing AI and image fusion techniques.Frontiers in oncology · 2025Review
- Artificial intelligence integrates multi-omics data for precision stratification and drug resistance prediction in breast cancer.Frontiers in oncology · 2025Review
- Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma.Computational and structural biotechnology journal · 2024Article
- Role of Radiology in the Diagnosis and Treatment of Breast Cancer in Women: A Comprehensive Review.Cureus · 2024Review
- 18F-FDG-PET/CT in breast cancer imaging: Restaging and Implications for treatment decisions in a clinical practice setting.Acta oncologica (Stockholm, Sweden) · 2024Article
- Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.European radiology · 2024Article
- Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast [European journal of nuclear medicine and molecular imaging · 2024Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) for the pathological response of advanced breast cancer to neoadjuvant chemotherapy (NAC). The breast PET/MRI image deep learning model was introduced and compared with the conventional methods. PET/CT and MRI parameters were evaluated before and after the first NAC cycle in patients with advanced breast cancer [n = 56; all women; median age, 49 (range 26-66) years]. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained with the corresponding baseline values (SUV0, MTV0, and TLG0, respectively) and interim PET images (SUV1, MTV1, and TLG1, respectively). Mean apparent diffusion coefficients were obtained from baseline and interim diffusion MR images (ADC0 and ADC1, respectively). The differences between the baseline and interim parameters were measured (ΔSUV, ΔMTV, ΔTLG, and ΔADC). Subgroup analysis was performed for the HER2-negative and triple-negative groups. Datasets for convolutional neural network (CNN), assigned as training (80%) and test datasets (20%), were cropped from the baseline (PET0, MRI0) and interim (PET1, MRI1) images. Histopathologic responses were assessed using the Miller and Payne system, after three cycles of chemotherapy. Receiver operating characteristic curve analysis was used to assess the performance of the differentiating responders and non-responders. There were six responders (11%) and 50 non-responders (89%). The area under the curve (AUC) was the highest for ΔSUV at 0.805 (95% CI 0.677-0.899). The AUC was the highest for ΔSUV at 0.879 (95% CI 0.722-0.965) for the HER2-negative subtype. AUC improved following CNN application (SUV0:PET0 = 0.652:0.886, SUV1:PET1 = 0.687:0.980, and ADC1:MRI1 = 0.537:0.701), except for ADC0 (ADC0:MRI0 = 0.703:0.602). PET/MRI image deep learning model can predict pathological responses to NAC in patients with advanced breast cancer.
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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.