Browsing by Author "Palta, Jatinder"

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    American Association of Physicists in Medicine Task Group 263: Standardizing Nomenclatures in Radiation Oncology.
    (International journal of radiation oncology, biology, physics, 2018-03) Mayo, Charles S; Moran, Jean M; Bosch, Walter; Xiao, Ying; McNutt, Todd; Popple, Richard; Michalski, Jeff; Feng, Mary; Marks, Lawrence B; Fuller, Clifton D; Yorke, Ellen; Palta, Jatinder; Gabriel, Peter E; Molineu, Andrea; Matuszak, Martha M; Covington, Elizabeth; Masi, Kathryn; Richardson, Susan L; Ritter, Timothy; Morgas, Tomasz; Flampouri, Stella; Santanam, Lakshmi; Moore, Joseph A; Purdie, Thomas G; Miller, Robert C; Hurkmans, Coen; Adams, Judy; Jackie Wu, Qing-Rong; Fox, Colleen J; Siochi, Ramon Alfredo; Brown, Norman L; Verbakel, Wilko; Archambault, Yves; Chmura, Steven J; Dekker, Andre L; Eagle, Don G; Fitzgerald, Thomas J; Hong, Theodore; Kapoor, Rishabh; Lansing, Beth; Jolly, Shruti; Napolitano, Mary E; Percy, James; Rose, Mark S; Siddiqui, Salim; Schadt, Christof; Simon, William E; Straube, William L; St James, Sara T; Ulin, Kenneth; Yom, Sue S; Yock, Torunn I
    A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose-volume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors. The stakeholder groups represented in the membership included the AAPM, American Society for Radiation Oncology (ASTRO), NRG Oncology, European Society for Radiation Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), Children's Oncology Group (COG), Integrating Healthcare Enterprise in Radiation Oncology (IHE-RO), and Digital Imaging and Communications in Medicine working group (DICOM WG); A nomenclature system for target and organ at risk volumes and DVH nomenclature was developed and piloted to demonstrate viability across a range of clinics and within the framework of clinical trials. The final report was approved by AAPM in October 2017. The approval process included review by 8 AAPM committees, with additional review by ASTRO, European Society for Radiation Oncology (ESTRO), and American Association of Medical Dosimetrists (AAMD). This Executive Summary of the report highlights the key recommendations for clinical practice, research, and trials.
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    Knowledge-Based Statistical Inference Method for Plan Quality Quantification.
    (Technology in cancer research & treatment, 2019-01) Zhang, Jiang; Wu, Q Jackie; Ge, Yaorong; Wang, Chunhao; Sheng, Yang; Palta, Jatinder; Salama, Joseph K; Yin, Fang-Fang; Zhang, Jiahan
    AIM:The aim of the study is to develop a geometrically adaptive and statistically robust plan quality inference method. METHODS AND MATERIALS:We propose a knowledge-based plan quality inference method that references to similar plans in the historical database for patient-specific plan quality evaluation. First, a novel plan similarity metric with high-dimension geometrical difference quantification is utilized to retrieve similar plans. Subsequently, dosimetric statistical inferences are obtained from the selected similar plans. Two plan quality metrics-dosimetric result probability and dose deviation index-are proposed to quantify plan quality among prior similar plans. To evaluate the performance of the proposed method, we exported 927 clinically approved head and neck treatment plans. Eight organs at risk, including brain stem, cord, larynx, mandible, pharynx, oral cavity, left parotid and right parotid, were analyzed. Twelve suboptimal plans identified by dosimetric result probability were replanned to validate the capability of the proposed methods in identifying inferior plans. RESULTS:After replanning, left and right parotid median doses are reduced by 31.7% and 18.2%, respectively; 83% of these cases would not be identified as suboptimal without the proposed similarity plan selection. Analysis of population plan quality reveals that average parotid sparing has been improving significantly over time (21.7% dosimetric result probability reduction from year 2006-2007 to year 2016-2017). Notably, the increasing dose sparing over time in retrospective plan quality analysis is strongly correlated with the increasing dose prescription ratios to the 2 planning targets, revealing the collective trend in planning conventions. CONCLUSIONS:The proposed similar plan retrieval and analysis methodology has been proven to be predictive of the current plan quality. Therefore, the proposed workflow can potentially be applied in the clinics as a real-time plan quality assurance tool. The proposed metrics can also serve the purpose of plan quality analytics in finding connections and historical trends in the clinical treatment planning workflow.