PURPOSE: To demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiation therapy clinical trial plan quality-control system. METHODS AND MATERIALS: We analyzed 86 patients with stage IB through IVA cervical cancer treated with intensity modulated radiation therapy at 2 institutions according to the standards of the INTERTECC (International Evaluation of Radiotherapy Technology Effectiveness in Cervical Cancer, National Clinical Trials Network identifier: 01554397) protocol. The protocol used a planning target volume and 2 primary organs at risk: pelvic bone marrow (PBM) and bowel. Secondary organs at risk were rectum and bladder. Initial unfiltered dose-volume histogram (DVH) estimation models were trained using all 86 plans. Refined training sets were created by removing sub-optimal plans from the unfiltered sample, and DVH estimation models… and DVH estimation models were constructed by identifying 30 of 86 plans emphasizing PBM sparing (comparing protocol-specified dosimetric cutpoints V10 (percentage volume of PBM receiving at least 10 Gy dose) and V20 (percentage volume of PBM receiving at least 20 Gy dose) with unfiltered predictions) and another 30 of 86 plans emphasizing bowel sparing (comparing V40 (absolute volume of bowel receiving at least 40 Gy dose) and V45 (absolute volume of bowel receiving at least 45 Gy dose), 9 in common with the PBM set). To obtain deliverable KBP plans, refined models must inform patient-specific optimization objectives and/or priorities (an auto-planning "routine"). Four candidate routines emphasizing different tradeoffs were composed, and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine that best met protocol objectives in the 51-patient training sample (KBPFINAL), protocol-specific DVH metrics and normal tissue complication probability were compared for original versus KBPFINAL plans across the 35-patient validation set. Paired t tests were used to test differences between planning sets. RESULTS: KBPFINAL plans outperformed manual planning across the validation set in all protocol-specific DVH cutpoints. The mean normal tissue complication probability for gastrointestinal toxicity was lower for KBPFINAL versus validation-set plans (48.7% vs 53.8%, P<.001). Similarly, the estimated mean white blood cell count nadir was higher (2.77 vs 2.49 k/mL, P<.001) with KBPFINAL plans, indicating lowered probability of hematologic toxicity. CONCLUSIONS: This work demonstrates that a KBP system can be efficiently trained and refined for use in radiation therapy clinical trials with minimal effort. This patient-specific plan quality control resulted in improvements on protocol-specific dosimetric endpoints.

Department of Oncology and Radiotherapy University Hospital Hradec Kralove Czech Republic

Department of Radiation Medicine and Applied Sciences University of California San Diego La Jolla California

Department of Radiation Oncology Washington University in St Louis St Louis Missouri

Department of Radiation Physics University of Texas MD Anderson Cancer Center Houston Texas

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Nelms BE, Robinson G, Markham J, et al. Variation in external beam treatment plan quality: An inter-institutional study of planners and planning systems. Pract Radiat Oncol. 2012;2:296–305. PubMed

Das IJ, Cheng CW, Chopra KL, et al. Intensity-modulated radiation therapy dose prescription, recording, and delivery: Patterns of variability among institutions and treatment planning systems. J natl cancer inst United States. 2008:300–7. PubMed

Appenzoller LM, Michalski JM, Thorstad WL, et al. Predicting dose-volume histograms for organs-at-risk in imrt planning. Med Phys. 2012;39:7446–61. PubMed

Moore KL, Mutic S, Brame RS, et al. Developing predictive dose-volume relationships for a radiotherapy treatment. Book Developing predictive dose-volume relationships for a radiotherapy treatment: Google Patents. 2012 Editor, editorˆeditors.

Moore KL, Brame RS, Low DA, et al. Quantitative metrics for assessing plan quality. Seminars in radiation oncology. 2012;22:62–69. PubMed

Moore KL, Schmidt R, Moiseenko V, et al. Quantifying unnecessary normal tissue complication risks due to suboptimal planning: A secondary study of rtog 0126. Int J Radiat Oncol Biol Phys. 2015;92:228–35. PubMed PMC

Eaton BR, Pugh SL, Bradley JD, et al. Institutional enrollment and survival among nsclc patients receiving chemoradiation: Nrg oncology radiation therapy oncology group (rtog) 0617. Journal of the National Cancer Institute. 2016;108:djw034. PubMed PMC

Wu B, Ricchetti F, Sanguineti G, et al. Patient geometry-driven information retrieval for imrt treatment plan quality control. Med Phys. 2009;36:5497–505. PubMed

Moore KL, Brame RS, Low DA, et al. Int j radiat oncol biol phys. United States: Elsevier Inc; 2011. Experience-based quality control of clinical intensity-modulated radiotherapy planning; pp. 545–51. 2011. PubMed

Wu B, Ricchetti F, Sanguineti G, et al. Int j radiat oncol biol phys. United States: Elsevier Inc; 2011. Data-driven approach to generating achievable dose-volume histogram objectives in intensity-modulated radiotherapy planning; pp. 1241–7. 2011. PubMed

Wu B, McNutt T, Zahurak M, et al. Fully automated simultaneous integrated boosted-intensity modulated radiation therapy treatment planning is feasible for head-and-neck cancer: A prospective clinical study. Int J Radiat Oncol Biol Phys. 2012;84:e647–53. PubMed

Wu B, Pang D, Simari P, et al. Using overlap volume histogram and imrt plan data to guide and automate vmat planning: A head-and-neck case study. Med Phys. 2013;40:021714. PubMed

Good D, Lo J, Lee WR, et al. A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: An example application to prostate cancer planning. Int J Radiat Oncol Biol Phys. 2013;87:176–81. PubMed

Zhu X, Ge Y, Li T, et al. A planning quality evaluation tool for prostate adaptive imrt based on machine learning. Medical physics. 2011;38:719–726. PubMed

Moore K, Appenzoller L, Tan J, et al. Journal of Physics: Conference Series. IOP Publishing; 2014. Clinical implementation of dose-volume histogram predictions for organs-at-risk in imrt planning; p. 012055.

Yuan L, Wu QJ, Yin FF, et al. Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck imrt. Med Phys. 2014;41:021728. PubMed PMC

Shiraishi S, Tan J, Olsen LA, et al. Knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery. Med Phys. 2015;42:908. PubMed

Varian medical systems. Eclipse photon and electron instructions for use. Palo Alto: CA; 2014. pp. 183–199.

Varian medical systems. Eclipse photon and electron algorithms reference guide. Palo Alto: CA; 2014. pp. 220–229.

Simpson DR, Song WY, Moiseenko V, et al. Int j radiat oncol biol phys. United States: Elsevier Inc; 2012. Normal tissue complication probability analysis of acute gastrointestinal toxicity in cervical cancer patients undergoing intensity modulated radiation therapy and concurrent cisplatin; pp. e81–6. 2012. PubMed

Rose BS, Aydogan B, Liang Y, et al. Normal tissue complication probability modeling of acute hematologic toxicity in cervical cancer patients treated with chemoradiotherapy. Int J Radiat Oncol Biol Phys. 2011;79:800–7. PubMed PMC

Varian medical systems. Eclipse photon and electron reference guide. Palo Alto: CA; 2014.

Tol JP, Delaney AR, Dahele M, et al. Evaluation of a knowledge-based planning solution for head and neck cancer. International Journal of Radiation Oncology* Biology* Physics. 2015;91:612–620. PubMed

Fogliata A, Belosi F, Clivio A, et al. On the pre-clinical validation of a commercial model-based optimisation engine: Application to volumetric modulated arc therapy for patients with lung or prostate cancer. Radiotherapy and Oncology. 2014;113:385–391. PubMed

Fogliata A, Wang P-M, Belosi F, et al. Assessment of a model based optimization engine for volumetric modulated arc therapy for patients with advanced hepatocellular cancer. Radiation Oncology. 2014;9:1. PubMed PMC

Tol JP, Dahele M, Delaney AR, et al. Can knowledge-based dvh predictions be used for automated, individualized quality assurance of radiotherapy treatment plans? Radiation Oncology. 2015;10:1. PubMed PMC

Comparison of Hematologic Toxicity and Bone Marrow Compensatory Response in Head and Neck vs. Cervical Cancer Patients Undergoing Chemoradiotherapy