Trial reportEuropean journal of nuclear medicine and molecular imaging2022
Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.
Trial report in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 47 papers, 2 of them syntheses that pooled 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.
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
47 citing papers in PubMed, 2 syntheses or guidelines pooled it, 70 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
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- The Next Frontier in Quantitative Co-Clinical Imaging to Advance Functional Precision Oncology.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Response-adaptive breast cancer care in the grey zones: integrating ER and HER2 targeted PET, FDG PET/CT under immunotherapy, and ctDNA kinetics.Exploration of targeted anti-tumor therapy · 2026Review
- Review
- Review
- Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare.Scientific reports · 2025Article
- Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study.Journal of medical Internet research · 2025Article
- The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.Insights into imaging · 2024Article
- Baseline [European journal of nuclear medicine and molecular imaging · 2024Article
- Respective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer.EJNMMI research · 2024Article
- Article
- Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.Medical physics · 2024Article
- Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients.Computers in biology and medicine · 2024Article
- Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment.The oncologist · 2024Article
- Article
- Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward.BJR artificial intelligence · 2024Review
- Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer.Cancer medicine · 2023Article
- Patient-derived tumor models: a suitable tool for preclinical studies on esophageal cancer.Cancer gene therapy · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
purposeWe sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms.
methodsTNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUV
resultsSixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUV
conclusionsWe optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.
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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.