Evidence map›Paper›PMID 24842891›Full record

ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2014

Tumor-Absorbed Dose Predicts Progression-Free Survival Following (131)I-Tositumomab Radioimmunotherapy.

Yuni K Dewaraja, Matthew J Schipper, Jincheng Shen, Lauren B Smith, Jure Murgic, Hatice Savas, Ehab Youssef, Denise Regan, Scott J Wilderman, Peter L Roberson and 2 more

Open access · bronzeAbstract readClinical Trial, Phase I
In one paragraph

Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 58 citations in OpenAlex.

  1. FDG PET/CT and Dosimetric Studies ofMolecular imaging and biology · 2022
    Trial
  2. Dosimetry-Driven Personalization of Radiopharmaceutical Therapies: Evidence and Opportunities.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. EANM enabling guide: how to improve the accessibility of clinical dosimetry.European journal of nuclear medicine and molecular imaging · 2023
    Article
  7. Article
  8. Radioembolization Dosimetry with Total-BodyJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022
    Article
  9. Article
  10. Review
  11. Review
  12. Review
  13. Tumor Response to Radiopharmaceutical Therapies: The Knowns and the Unknowns.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2021
    Review
  14. Dosimetry in Clinical Radiopharmaceutical Therapy of Cancer: Practicality Versus Perfection in Current Practice.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2021
    Review
  15. Review
  16. Overview of the First NRG Oncology-National Cancer Institute Workshop on Dosimetry of Systemic Radiopharmaceutical Therapy.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2021
    Review
  17. Article
  18. FDG PET/CT parameters and correlations with tumor-absorbed doses in a phase 1 trial ofEuropean journal of nuclear medicine and molecular imaging · 2021
    Article
  19. Current Status of Radiopharmaceutical Therapy.International journal of radiation oncology, biology, physics · 2021
    Review
  20. Recommendations for Multicentre Clinical Trials Involving Dosimetry for Molecular Radiotherapy.Clinical oncology (Royal College of Radiologists (Great Britain)) · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Yuni K DewarajaDepartment of Radiology, University of Michigan, Ann Arbor, Michigan yuni@umich.edu.
Matthew J SchipperDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Jincheng ShenDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan.
Lauren B SmithDepartment of Pathology, University of Michigan, Ann Arbor, Michigan.
Jure MurgicDepartment of Oncology and Nuclear Medicine, University Hospital Center Sestre Milosrdnice, Zagreb, Croatia.
Hatice SavasDepartment of Radiology, University of Michigan, Ann Arbor, Michigan.
Ehab YoussefDepartment of Radiology, University of Michigan, Ann Arbor, Michigan.
Denise ReganDepartment of Radiology, University of Michigan, Ann Arbor, Michigan.
Scott J WildermanDepartment of Nuclear Engineering, University of Michigan, Ann Arbor, Michigan; and.
Peter L RobersonDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Mark S KaminskiDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan.
Anca M AvramDepartment of Radiology, University of Michigan, Ann Arbor, Michigan.
University of Michigan–Ann Arbor · US

Funding

SPECT/CT Image-Based Dosimetry in Radionuclide TharapyR01EB001994 · NIBIB · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DEWARAJA, YUNI K · 2003 to 2014
$3.9M
NIBIB NIH HHS R01 EB001994
6 · The paper itself

Abstract

unlabelledThe study aimed at identifying patient-specific dosimetric and nondosimetric factors predicting outcome of non-Hodgkin lymphoma patients after (131)I-tositumomab radioimmunotherapy for potential use in treatment planning.

methodsTumor-absorbed dose measures were estimated for 130 tumors in 39 relapsed or refractory non-Hodgkin lymphoma patients by coupling SPECT/CT imaging with the Dose Planning Method (DPM) Monte Carlo code. Equivalent biologic effect was calculated to assess the biologic effects of nonuniform absorbed dose including the effects of the unlabeled antibody. Evaluated nondosimetric covariates included histology, presence of bulky disease, and prior treatment history. Tumor level outcome was based on volume shrinkage assessed on follow-up CT. Patient level outcome measures were overall response (OR), complete response (CR), and progression-free survival (PFS), determined from clinical assessments that included PET/CT.

resultsThe estimated mean tumor-absorbed dose had a median value of 275 cGy (range, 94-711 cGy). A high correlation was observed between tracer-predicted and therapy-delivered mean tumor-absorbed doses (P < 0.001; r = 0.85). In univariate tumor-level analysis, tumor shrinkage correlated significantly with almost all of the evaluated dosimetric factors, including equivalent biologic effect. Regression analysis showed that OR, CR, and PFS were associated with the dosimetric factors and equivalent biologic effect. Both mean tumor-absorbed dose (P = 0.025) and equivalent biologic effect (P = 0.035) were significant predictors of PFS whereas none of the nondosimetric covariates were found to be statistically significant factors affecting PFS. The most important finding of the study was that in Kaplan-Meier curves stratified by mean dose, longer PFS was observed in patients receiving mean tumor-absorbed doses greater than 200 cGy than in those receiving 200 cGy or less (median PFS, 13.6 vs. 1.9 mo for the 2 dose groups; log-rank P < 0.0001).

conclusionA higher mean tumor-absorbed dose was significantly predictive of improved PFS after (131)I-tositumomab radioimmunotherapy. Hence tumor-absorbed dose, which can be estimated before therapy, can potentially be used to design radioimmunotherapy protocols to improve efficacy.

Indexed as

Radiation DosageRadioimmunotherapyAdultAgedAged, 80 and overAntibodies, MonoclonalDisease-Free SurvivalFemaleHumansLymphoma, Non-HodgkinMaleMiddle AgedMonte Carlo MethodPrecision MedicineRadiometryRadiotherapy DosageAntibodies, Monoclonaltositumomab I-131dosimetrynon-Hodgkin lymphomaprogression free survivalradioimmunotherapySPECT/CT

Identifiers

PMID24842891
PMCPMC4237694
OpenAlexW2170554197

What OpenQuestion holds

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Registered trials

None linked

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.