| 001 | 310004 | ||
| 005 | 20260223120901.0 | ||
| 024 | 7 | _ | |a 10.1016/j.ijrobp.2026.01.031 |2 doi |
| 024 | 7 | _ | |a pmid:41720170 |2 pmid |
| 024 | 7 | _ | |a 0360-3016 |2 ISSN |
| 024 | 7 | _ | |a 1879-355X |2 ISSN |
| 037 | _ | _ | |a DKFZ-2026-00417 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Reuter, Lukas M |b 0 |
| 245 | _ | _ | |a Prediction of Symptomatic Radiation Pneumonitis in Lung Cancer Patients: A Radiomics and Dosiomics Machine Learning Approach Using the Prospective Multicenter RTOG 0617 and REQUITE trials. |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2026 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1771842434_3895552 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a epub |
| 520 | _ | _ | |a Radiation-induced pneumonitis (RP) is a side effect after thoracic radiotherapy (RT). The ability to predict RP would facilitate treatment modifications. This study investigates the predictive capacity for symptomatic RP (CTCAE≥2) employing Radiomics and Dosiomics models.Computed tomography (CT) scans, along with physical and 2-Gy equivalent dose volumes (EQD2), dose-volume histograms (DVH), and clinical parameters, were evaluated for 708 multicenter lung cancer patients, among whom 89 developed RP≥2. The training cohort consisted of 441 patients from the prospective RTOG 0617 trial. External validation was carried out on 267 patients from the prospective REQUITE study. A Random Forest classifier was employed, with feature selection executed within the inner loop of a 10x5-fold nested cross-validation (nCV) utilizing the minimum-redundancy-maximum-relevance algorithm. To address class imbalances, synthetic oversampling and undersampling were implemented using SMOTE-Tomek. The QUANTEC Normal Tissue Complication Probability (NTCP) model served as a reference. Additionally, the experiments were stratified by subgroups (standard/high-dose and 3D-conformal RT (3D-CRT)/intensity-modulated RT (IMRT)).The best radiomics model identified in the nCV was trained on the standard-dose subgroup achieved a test ROC-AUC of 0.56. The baseline NTCP model showed a predictive performance with a ROC-AUC of 0.56, which was largely dependent on radiation technique (ROC-AUCS: 3D-CRT: 0.75, IMRT: 0.50). The DosiomicsEQD2 model, trained on the full training cohort, attained the second-best performance in the nCV, demonstrating the same technique-dependence (ROC-AUC of 0.75 vs. 0.39). Using a DosiomicsEQD2 ensemble model trained separately on 3D-CRT and IMRT subgroups increased overall performance to a testing ROC-AUC of 0.61, outperforming other modeling strategies for IMRT, while being outperformed by clinical models for 3D-CRT.This prospective trial-based study reveals an overall limited predictive capacity of radiomics and dosiomics models and a large influence of radiation technique. IMRT-specific models should be investigated further. |
| 536 | _ | _ | |a 899 - ohne Topic (POF4-899) |0 G:(DE-HGF)POF4-899 |c POF4-899 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de |
| 650 | _ | 7 | |a Dosiomics |2 Other |
| 650 | _ | 7 | |a Lung Cancer |2 Other |
| 650 | _ | 7 | |a NTCP |2 Other |
| 650 | _ | 7 | |a Radiation pneumonitis |2 Other |
| 650 | _ | 7 | |a Radiomics |2 Other |
| 650 | _ | 7 | |a Side Effects |2 Other |
| 700 | 1 | _ | |a Kraus, Kim |0 P:(DE-He78)6e9779a1e058ccda2d6443c44474cc3b |b 1 |
| 700 | 1 | _ | |a Fischer, Stefan M |b 2 |
| 700 | 1 | _ | |a Pletzer, Danai |b 3 |
| 700 | 1 | _ | |a Bernhardt, Denise |b 4 |
| 700 | 1 | _ | |a Combs, Stephanie E |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Schnabel, Julia A |b 6 |
| 700 | 1 | _ | |a Peeken, Jan C |0 P:(DE-HGF)0 |b 7 |
| 773 | _ | _ | |a 10.1016/j.ijrobp.2026.01.031 |g p. S0360301626003652 |0 PERI:(DE-600)1500486-7 |p nn |t International journal of radiation oncology, biology, physics |v nn |y 2026 |x 0360-3016 |
| 909 | C | O | |o oai:inrepo02.dkfz.de:310004 |p VDB |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 1 |6 P:(DE-He78)6e9779a1e058ccda2d6443c44474cc3b |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 5 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 7 |6 P:(DE-HGF)0 |
| 913 | 1 | _ | |a DE-HGF |b Programmungebundene Forschung |l ohne Programm |1 G:(DE-HGF)POF4-890 |0 G:(DE-HGF)POF4-899 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-800 |4 G:(DE-HGF)POF |v ohne Topic |x 0 |
| 914 | 1 | _ | |y 2026 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2025-11-06 |w ger |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b INT J RADIAT ONCOL : 2022 |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2025-11-06 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1110 |2 StatID |b Current Contents - Clinical Medicine |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2025-11-06 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2025-11-06 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b INT J RADIAT ONCOL : 2022 |d 2025-11-06 |
| 920 | 1 | _ | |0 I:(DE-He78)MU01-20160331 |k MU01 |l DKTK Koordinierungsstelle München |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)MU01-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|
guest ::