ArticleCancers2026
Prediction of Metastasis-Free Survival in Patients with Localized Prostate Adenocarcinoma Using Delta Radiomics from Pre-Treatment PSMA-PET/CT Scans and Dosiomics.
Apurva Singh, William Silva Mendes, Sang-Bo Oh, Ozan Cem Guler, Aysenur Elmali, Birhan Demirhan, Amit Sawant, Phuoc Tran, Cem Onal, Lei Ren
Abstract read
In one paragraphArticle in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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1 · What the graph read from itWhat it found
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4 · The recordCorrections and comments
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5 · Who and what moneyAuthors and funding
10 authors.
Apurva SinghDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.ORCID 0000-0001-9569-3513 William Silva MendesDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Sang-Bo OhDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.ORCID 0000-0001-7257-0350 Ozan Cem GulerAdana Dr Turgut Noyan Research and Treatment Center, Department of Radiation Oncology, Baskent University Faculty of Medicine, Adana 01250, Turkey.
Aysenur ElmaliDepartment of Radiation Oncology, Baskent University Faculty of Medicine, Ankara 06490, Turkey.ORCID 0000-0003-3855-3391 Birhan DemirhanAdana Dr Turgut Noyan Research and Treatment Center, Department of Radiation Oncology, Baskent University Faculty of Medicine, Adana 01250, Turkey.
Amit SawantDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Phuoc TranDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Cem OnalAdana Dr Turgut Noyan Research and Treatment Center, Department of Radiation Oncology, Baskent University Faculty of Medicine, Adana 01250, Turkey.
Lei RenDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.ORCID 0009-0005-2384-5676 Funding
Resource Sharing CoreU54CA273956 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI Luigi Marchionni · 2022 to 2026
$8.9MTumor-barcoding coupled with high-throughput sequencing of a novel chemoradiation resistant SCLC mouse modelU01CA231776 · NCI · JOHNS HOPKINS UNIVERSITY · PI HANN, CHRISTINE L., MARCHIONNI, LUIGI · 2018 to 2022
$3.9MIntegrating bioinformatics into multiscale models for hepatocellular carcinomaU01CA212007 · NCI · JOHNS HOPKINS UNIVERSITY · PI EWALD, ANDREW JOSEF, FERTIG, ELANA · 2018 to 2022
$3.2MeXtended Modular ANthropomorphic (XMAN) phantom for Imaging and Treatment Optimization in Radiotherapy.R01EB032680 · NIBIB · UNIVERSITY OF MARYLAND BALTIMORE · PI REN, LEI, YANG, XIAOFENG · 2022 to 2025
$2.4MTumor-barcoding coupled with high-throughput sequencing for quantitative radiogenomics of the abscopal response in NSCLCR01CA271540 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Phuoc T. Tran · 2023 to 2026
$2.3MHybrid virtual-MRI/CBCT: A new paradigm for image guidance in liver SBRTR01EB028324 · NIBIB · UNIVERSITY OF MARYLAND BALTIMORE · PI REN, LEI, YIN, FANG-FANG · 2019 to 2022
$2.1M3-dimensional prompt gamma imaging for online proton beam dose verificationR01CA279013 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI Jerimy C. Polf, Lei Ren · 2023 to 2026
$1.9M3D In vivo dosimetry for FLASH proton therapyU01CA288351 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Yong Chen, Lei Ren · 2024 to 2026
$1.8MDepartment of Defense W81XWH-21-1-0296NCI NIH HHS U01CA212007NCI NIH HHS U01CA231776NCI NIH HHS U01CA288351NCI NIH HHS U54 CA273956NCI NIH HHS U54CA273956NIH HHS R01CA271540NIH HHS R01CA279013NIH HHS R01EB028324NIH HHS R01EB032680
6 · The paper itselfAbstract
purposeTo develop prognostic models integrating delta radiomics from prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT) and dosiomics with clinical variables to predict metastasis-free survival (MFS) in patients with localized prostate adenocarcinoma treated with androgen deprivation therapy and external-beam radiotherapy. MATERIALS/
methodsDelta-radiomics analysis included 43 patients. Radiomics features were extracted from the primary tumor on pre- and post-treatment PSMA-PET/CT, and delta features were calculated as relative changes. Eight high-variance features were selected and combined with clinical variables (age, Gleason score, initial PSA, and a binary variable, indicating the occurrence of PSA relapse). Data was split 70:30 with training-set imbalance correction. Predictors that were significant in univariate Cox regression (
resultsFor delta radiomics, Model 1 (delta radiomics + pre-treatment radiomics + clinical) achieved the best performance (test c-score 0.58; AUC 0.70), exceeding Model 2 (pre-treatment radiomics + clinical; c-score 0.56; AUC 0.65) and Model 3 (clinical only; c-score 0.51; AUC 0.56). For dosiomics, Model 1 showed the highest performance (test c-score 0.56; AUC 0.67) compared with Model 2 (c-score 0.55; AUC 0.62) and Model 3 (c-score 0.50; AUC 0.54).
conclusionsIntegrating delta radiomics or dosiomics with pre-treatment imaging and clinical variables improves MFS prediction and supports their role as non-invasive biomarkers for individualized radiotherapy in localized prostate cancer.
Indexed as
delta radiomicsdosiomicsprognostic modelingprostate cancerPSMA-PET/CT
Identifiers
PMID41749930
PMCPMC12939975
What OpenQuestion holds
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LicenceCC BY
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