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Text Information Retrieval Systems is a book that presents the process of information retrieval (text, abstracts, etc.) from databases by using computers. The level of presentation is basic enough that the material is accessible to all potential users.

Purpose The purposes of this work are to (a) investigate whether the use of auto‐planning and multiple iterations improves quality of head and neck (HN) radiotherapy plans; (b) determine whether delivery methods such as step‐and‐shoot (SS) and volumetric modulated arc therapy (VMAT) impact plan quality; (c) report on the observations of plan quality predictions of a commercial feasibility tool. Materials and methods Twenty HN cases were retrospectively selected from our clinical database for this study. The first ten plans were used to test setting up planning goals and other optimization parameters in the auto‐planning module. Subsequently, the other ten plans were replanned with auto‐planning using step‐and‐shoot (AP‐SS) and VMAT (AP‐VMAT) delivery methods. Dosimetric endpoints were compared between the clinical plans and the corresponding AP‐SS and AP‐VMAT plans. Finally, predicted dosimetric endpoints from a commercial program were assessed. Results All AP‐SS and AP‐VMAT plans met the clinical dose constraints. With auto‐planning, the dose coverage of the low dose planning target volume (PTV) was improved while the dose coverage of the high dose PTV was maintained. Compared to the clinical plans, the doses to critical organs, such as the brainstem, parotid, larynx, esophagus, and oral cavity were significantly reduced in the AP‐VMAT (P < 0.05); the AP‐SS plans had similar homogeneity indices (HI) and conformality indices (CI) and the AP‐VMAT plans had comparable HI and improved CI. Good agreement in dosimetric endpoints between predictions and AP‐VMAT plans were observed in five of seven critical organs. Conclusion With improved planning quality and efficiency, auto‐planning module is an effective tool to enable planners to generate HN IMRT plans that are meeting institution specific planning protocols. DVH prediction is feasible in improving workflow and plan quality.

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