Trial reportInternational journal of radiation oncology, biology, physics2015
Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126.
Trial report in International journal of radiation oncology, biology, physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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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.
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Who cites it
68 citing papers in PubMed.
- Neural network dose prediction for rectal spacer stratification in dose-escalated prostate radiotherapy.Medical physics · 2022Trial
- Personalising treatment plan quality review with knowledge-based planning in the TROG 15.03 trial for stereotactic ablative body radiotherapy in primary kidney cancer.Radiation oncology (London, England) · 2021Trial
- Improving Quality and Consistency in NRG Oncology Radiation Therapy Oncology Group 0631 for Spine Radiosurgery via Knowledge-Based Planning.International journal of radiation oncology, biology, physics · 2018Trial
- The influence of dose distribution on treatment outcome in the SCOPE 1 oesophageal cancer trial.Radiation oncology (London, England) · 2016Trial
- An interactive virtual dosimetrist for adjusting radiotherapy dose quality.Physics and imaging in radiation oncology · 2026Article
- Parallel/Opposed: Automated treatment planning systems should be designed to minimize human intervention.Journal of applied clinical medical physics · 2026Article
- Towards human-centric intelligent treatment planning for radiation therapy.NPJ digital medicine · 2026Review
- Real-Time AI-Based Radiotherapy Planning for Nasopharyngeal Carcinoma: Development and Validation.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Multicenter study on the versatility and adoption of AI-driven automated radiotherapy planning across cancer types.Nature communications · 2025Article
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- Intentional creation of suboptimal, realistic dose distributions.Journal of applied clinical medical physics · 2025Article
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- Assessing ChatGPT-4 as a clinical decision support tool in neuro-oncology radiotherapy: a prospective comparative study.Journal of neuro-oncology · 2025Article
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- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- Critical assessment of knowledge-based models for craniospinal irradiation of paediatric patients.Physics and imaging in radiation oncology · 2025Article
- Automatic Treatment Planning for Radiation Therapy: A Cross-Modality and Protocol Study.Advances in radiation oncology · 2024Article
- Improving 3D dose prediction for breast radiotherapy using novel glowing masks and gradient-weighted loss functions.Medical physics · 2024Article
- Development and validation of an automated Tomotherapy planning method for cervical cancer.Radiation oncology (London, England) · 2024Article
- Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning.Journal of applied clinical medical physics · 2024Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
purposeThe purpose of this study was to quantify the frequency and clinical severity of quality deficiencies in intensity modulated radiation therapy (IMRT) planning in the Radiation Therapy Oncology Group 0126 protocol. METHODS AND MATERIALS: A total of 219 IMRT patients from the high-dose arm (79.2 Gy) of RTOG 0126 were analyzed. To quantify plan quality, we used established knowledge-based methods for patient-specific dose-volume histogram (DVH) prediction of organs at risk and a Lyman-Kutcher-Burman (LKB) model for grade ≥2 rectal complications to convert DVHs into normal tissue complication probabilities (NTCPs). The LKB model was validated by fitting dose-response parameters relative to observed toxicities. The 90th percentile (22 of 219) of plans with the lowest excess risk (difference between clinical and model-predicted NTCP) were used to create a model for the presumed best practices in the protocol (pDVH0126,top10%). Applying the resultant model to the entire sample enabled comparisons between DVHs that patients could have received to DVHs they actually received. Excess risk quantified the clinical impact of suboptimal planning. Accuracy of pDVH predictions was validated by replanning 30 of 219 patients (13.7%), including equal numbers of presumed "high-quality," "low-quality," and randomly sampled plans. NTCP-predicted toxicities were compared to adverse events on protocol.
resultsExisting models showed that bladder-sparing variations were less prevalent than rectum quality variations and that increased rectal sparing was not correlated with target metrics (dose received by 98% and 2% of the PTV, respectively). Observed toxicities were consistent with current LKB parameters. Converting DVH and pDVH0126,top10% to rectal NTCPs, we observed 94 of 219 patients (42.9%) with ≥5% excess risk, 20 of 219 patients (9.1%) with ≥10% excess risk, and 2 of 219 patients (0.9%) with ≥15% excess risk. Replanning demonstrated the predicted NTCP reductions while maintaining the volume of the PTV receiving prescription dose. An equivalent sample of high-quality plans showed fewer toxicities than low-quality plans, 6 of 73 versus 10 of 73 respectively, although these differences were not significant (P=.21) due to insufficient statistical power in this retrospective study.
conclusionsPlan quality deficiencies in RTOG 0126 exposed patients to substantial excess risk for rectal complications.
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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.