Evidence map›Paper›PMID 27829431›Full record

ArticleRadiation oncology (London, England)2016

Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.

Rakesh Shiradkar, Tarun K Podder, Ahmad Algohary, Satish Viswanath, Rodney J Ellis, Anant Madabhushi

Abstract read
In one paragraph

Article in Radiation oncology (London, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

46 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026
    Review
  5. Review
  6. Article
  7. A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025
    Review
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Article
  16. Review
  17. Review
  18. Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization.International journal of molecular sciences · 2021
    Review
  19. Review
  20. Review
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

6 authors.

Rakesh ShiradkarDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, 44106, USA. rakesh.shiradkar@case.edu.ORCID http://orcid.org/0000-0003-2852-3869
Tarun K PodderDepartment of Radiation Oncology, Case School of Medicine, Cleveland, 44106, USA.
Ahmad AlgoharyDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, 44106, USA.
Satish ViswanathDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, 44106, USA.
Rodney J EllisDepartment of Radiation Oncology, Case School of Medicine, Cleveland, 44106, USA.
Anant MadabhushiDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, 44106, USA.

Funding

Computerized histologic image predictor of cancer outcomeR01CA202752 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI FELDMAN, MICHAEL D, GANESAN, SHRIDAR · 2016 to 2020
$3.1M
Pathology Image Informatics Platform for visualization, analysis and managementU24CA199374 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI GURCAN, METIN NAFI, MADABHUSHI, ANANT · 2015 to 2019
$2.9M
Comprehensive quantitative ultrafast 3D liver MRIR01DK098503 · NIDDK · CASE WESTERN RESERVE UNIVERSITY · PI GULANI, VIKAS, SEIBERLICH, NICOLE · 2013 to 2017
$2.6M
Histologic image-based aggressiveness prediction in p16+ oropharyngeal carcinomaR21CA179327 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MADABHUSHI, ANANT · 2014 to 2015
$389k
Computerized morphologic-molecular predictor of progression in DCISR21CA195152 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI BADVE, SUNIL S., MADABHUSHI, ANANT · 2015 to 2016
$388k
NCI NIH HHS R01 CA202752NCI NIH HHS R21 CA179327NCI NIH HHS R21 CA195152NCI NIH HHS U24 CA199374NIDDK NIH HHS R01 DK098503
6 · The paper itself

Abstract

backgroundRadiomics or computer - extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans.

methodsThe Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT.

resultsRad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous ([Formula: see text]) which is the current clinical standard, radiomics based focal ([Formula: see text]), and whole gland with a radiomics based focal boost ([Formula: see text]). Comparison of [Formula: see text] against conventional [Formula: see text] revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. [Formula: see text] resulted in only a marginal increase in dosage to the OARs compared to [Formula: see text]. A similar trend was observed in case of EBRT with [Formula: see text] and [Formula: see text] compared to [Formula: see text].

conclusionsA radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.

Indexed as

HumansMachine LearningMagnetic Resonance ImagingMaleOrgans at RiskProstatic NeoplasmsRadiotherapy DosageRadiotherapy, Image-GuidedRadiotherapy Planning, Computer-AssistedRetrospective StudiesComputer aided diagnosis (CAD)prostate cancerRadiomicsTreatment planning

Identifiers

PMID27829431
PMCPMC5103611

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.