ArticleRadiation medicine and protection2026
A comparison of dimensionality reduction approaches for pre-treatment PSMA-PET/CT radiomics in prostate adenocarcinoma outcome prediction.
Article in Radiation medicine and protection, 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
10 authors.
Funding
Abstract
Objective: To compare dimensionality-reduction methods for building prognostic models predicting metastasis-free survival (MFS) in localized prostate adenocarcinoma (PCa) patients treated with androgen-deprivation therapy and external radiotherapy using clinical factors and prostate-specific membrane antigen (PSMA)-PET/CT radiomics from primary tumor and nodal volumes. Methods: A total of 134 localized PCa patients (28 with nodal involvement) were analyzed. Gross tumor volumes for primary tumors (GTVp) and nodes (GTVn) were segmented on CT and PET scans; a 5-mm peritumoral ring was defined. Radiomic features were normalized and reduced using three techniques: principal component analysis (PCA), supervised, and unsupervised feature selection. Model 1 combined tumor and nodal radiomics via volume-weighted averaging and consisted of 12 predictors including clinical variables (age, Gleason score, initial PSA, PSA relapse) and radiomics from primary, nodal, and ring regions. Data imbalance (24 metastasis, 110 no metastasis) was addressed using a 70:30 train-test split with imbalance correction applied to train set. Univariate Cox regression ( Results: Supervised feature selection achieved highest performance. Model 1 test had c-score 0.71 (0.65-0.72). The five-year MFS test classification was sensitivity 80.1 %, specificity 85.4 %, and AUC 0.84. Unsupervised and PCA methods showed slightly lower results (test c-scores: 0.70 and 0.69, respectively). Model 1 consistently outperformed Model 2 with c-score 0.64 and AUC 0.79, as well as Model 3 with c-score 0.54 and AUC 0.68 across all dimensionality-reduction techniques. Conclusion: Supervised feature selection yielded the highest c-scores and AUCs for the models. Integrating PSMA-PET/CT radiomics from primary, nodal, and peritumoral regions with clinical factors significantly improved MFS prediction, highlighting multi-regional radiomics as a promising biomarker for personalized therapy in prostate cancer.
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.