ArticleScientific reports2026
Automatic computation of breast cancer biomarkers from multiple [Formula: see text] F-FDG PET image segmentation.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Neoadjuvant chemotherapy is a standard clinical practice for tumor downsizing in breast cancer, with [Formula: see text]F-FDG Positron Emission Tomography (PET) being an essential tool for predicting complete pathological response and monitoring treatment. Our work aims to leverage PET imaging for automated segmentation of breast lesions and biomarker analysis before and after the first course of chemotherapy. We developed a system to segment primary tumor regions and extract biomarkers reflecting tumor evolution. A total of 243 baseline and 180 follow-up [Formula: see text]F-FDG PET scans were acquired. Ground truth annotations were generated semi-automatically for all baseline scans and manually for 12 test cases. A deep learning-based segmentation method was developed and evaluated using various architectures. The optimal baseline model, trained on baseline exams, was fine-tuned on 15 follow-up scans using active learning to segment tumors in follow-up exams. The pipeline extracts maximum standardized uptake value (SUV[Formula: see text]), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) to assess tumor response. Quality control procedures were used to exclude outliers. Among the tested approaches, nnUNet achieved the best tumor segmentation on PET baseline scans, with a Dice similarity coefficient of [Formula: see text] and a Hausdorff distance of [Formula: see text] mm on the test set. After fine-tuning, performance on follow-up exams reached a Dice similarity coefficient of [Formula: see text] and Hausdorff distance of [Formula: see text] mm. Biomarker analysis showed strong correlations between manual and automatic segmentations. The average [Formula: see text], [Formula: see text], and [Formula: see text] between 12 baseline and follow-up scans used for testing were [Formula: see text] (p=0.002), [Formula: see text] cm[Formula: see text] (p=0.007), and [Formula: see text] cm[Formula: see text] (p=0.010), respectively. The proposed method provides an effective automated system for breast tumor segmentation from [Formula: see text]F-FDG PET. Thanks to biomarkers extracted from the automatic segmentations on both baseline and follow-up exams, our method enables the automatic assessment of cancer progression.
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.