Intensity-modulated radiotherapy of nasopharyngeal carcinoma: a comparative treatment planning study of photons and protons

<p>Abstract</p> <p>Background</p> <p>The aim of this treatment planning study was to investigate the potential advantages of intensity-modulated (IM) proton therapy (IMPT) compared with IM photon therapy (IMRT) in nasopharyngeal carcinoma (NPC).</p> <p>Methods</p> <p>Eight NPC patients were chosen. The dose prescriptions in cobalt Gray equivalent (Gy_E) for gross tumor volumes of the primary tumor (GTV-T), planning target volumes of GTV-T and metastatic (PTV-TN) and elective (PTV-N) lymph node stations were 72.6 Gy_E, 66 Gy_E, and 52.8 Gy_E, respectively. For each patient, nine coplanar fields IMRT with step-and-shoot technique and 3D spot-scanned three coplanar fields IMPT plans were prepared. Both modalities were planned in 33 fractions to be delivered with a simultaneous integrated boost technique. All plans were prepared and optimized by using the research version of the inverse treatment planning system KonRad (DKFZ, Heidelberg).</p> <p>Results</p> <p>Both treatment techniques were equal in terms of averaged mean dose to target volumes. IMPT plans significantly improved the tumor coverage and conformation (<it>P </it>< 0.05) and they reduced the averaged mean dose to several organs at risk (OARs) by a factor of 2–3. The low-to-medium dose volumes (0.33–13.2 Gy_E) were more than doubled by IMRT plans.</p> <p>Conclusion</p> <p>In radiotherapy of NPC patients, three-field IMPT has greater potential than nine-field IMRT with respect to tumor coverage and reduction of the integral dose to OARs and non-specific normal tissues. The practicality of IMPT in NPC deserves further exploration when this technique becomes available on wider clinical scale.</p

Current treatment options for recurrent nasopharyngeal cancer

Loco-regional control rate of nasopharyngeal carcinoma (NPC) has improved significantly in the past decade. However, local recurrence still represents a major cause of mortality and morbidity in advanced stages, and management of local failure remains a challenging issue in NPC. The best salvage treatment for local recurrent NPC remains to be determined. The options include brachytherapy, external radiotherapy, stereotactic radiosurgery, and nasopharyngectomy, either alone or in different combinations. In this article we will discuss the different options for salvage of locally recurrent NPC. Retreatment of locally recurrent NPC using radiotherapy, alone or in combination with other treatment modalities, as well as surgery, can result in long-term local control and survival in a substantial proportion of patients. For small-volume recurrent tumors (T1–T2) treated with external radiotherapy, brachytherapy or stereotactic radiosurgery, comparable results to those obtained with surgery have been reported. In contrast, treatment results of advanced-stage locally recurrent NPC are generally more satisfactory with surgery (with or without postoperative radiotherapy) than with reirradiation

Assessment of the quality of measures of child oral health-related quality of life

Background Several measures of oral health-related quality of life have been developed for children. The most frequently used are the Child Perceptions Questionnaire (CPQ), the Child Oral Impacts on Daily Performances (C-OIDP) and the Child Oral Health Impact Profile (COHIP). The aim of this study was to assess the methodological quality of the development and testing of these three measures. Methods A systematic search strategy was used to identify eligible studies published up to December 2012, using both MEDLINE and Web of Science. Titles and abstracts were read independently by two investigators and full papers retrieved where the inclusion criteria were met. Data were extracted by two teams of two investigators using a piloted protocol. The data were used to describe the development of the measures and their use against existing criteria. The methodological quality and measurement properties of the measures were assessed using standards proposed by the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) group. Results The search strategy yielded 653 papers, of which 417 were duplicates. Following analysis of the abstracts, 119 papers met the inclusion criteria. The majority of papers reported cross-sectional studies (n = 117) with three of longitudinal design. Fifteen studies which had used the original version of the measures in their original language were included in the COSMIN analysis. The most frequently used measure was the CPQ. Reliability and construct validity appear to be adequate for all three measures. Children were not fully involved in item generation which may compromise their content validity. Internal consistency was measured using classic test theory with no evidence of modern psychometric techniques being used to test unidimensionality of the measures included in the COSMIN analysis. Conclusion The three measures evaluated appear to be able to discriminate between groups. CPQ has been most widely tested and several versions are available. COHIP employed a rigorous development strategy but has been tested in fewer populations. C-OIDP is shorter and has been used successfully in epidemiological studies. Further testing using modern psychometric techniques such as item response theory is recommended. Future developments should also focus on the development of measures which can evaluate longitudinal change

Data for: AFIF: Automatically Finding Important Features in Community Evolution Prediction for Dynamic Social Networks

Networks DBLP network is divided into eleven time windows (time span 01/01/2003 to 31/12/2013). Facebook Wall Posts network is divided into eight time windows (time span 01/01/2005 to 31/12/2008). Wiki-Talk network is segmented into six time windows (time span 24/11/2007 to 31/12/2007). Enron email network is segmented into twelve time windows (time span 01/01/2001 to 31/12/2001). Reddit-reply network is segmented into six time windows (time span 07/01/2014 to 13/01/2014). Stack Overflow network is segmented into six time windows (time span 24/01/2016 to 29/02/2016). Social group discovery Communities of each time window are discovered using Infomap, Label Propagation, and Leiden algorithms. For running the community detection algorithms, we assume that the networks are undirected and unweighted graphs. The communities whose size was smaller than two members were ignored. Community evolution tracking and chain identification In order to track community evolution, we investigate each community to find its similar community or communities from previous time windows, which is called community matching. We employed ICEM (Identification of Community Evolution by Mapping) method in order to determine the evolution events because it is a highly efficient approach to track community evolution and considers partial evolution and non-consecutive time windows (Kadkhoda Mohammadmosaferi & Naderi, 2020). ICEM has two parameters which are α and β, in this paper, the thresholds for being partially similar and very similar are set to α=10% and β=90%, respectively. Each uploaded Dataset contains chains of evolution for a network and a community detection algorithm. Reference: Kadkhoda Mohammadmosaferi, K., Naderi, H., 2020. Evolution of communities in dynamic social networks: An efficient map-based approach. Expert Syst. Appl. 147, 113221. https://doi.org/10.1016/j.eswa.2020.11322

Data for: SIFA: Selecting Important Features Automatically for predicting community evolution in dynamic social networks

Datasets DBLP dataset is divided into eleven time windows (time span 01/01/2003 to 31/12/2013). Facebook Wall Posts dataset is divided into eight time windows (time span 01/01/2005 to 31/12/2008). Wiki-Talk dataset is segmented into six time windows (time span 24/11/2007 to 31/12/2007). Enron email dataset is segmented into twelve time windows (time span 01/01/2001 to 31/12/2001). Reddit-reply dataset is segmented into six time windows (time span 07/01/2014 to 13/01/2014). Stack Overflow dataset is segmented into six time windows (time span 24/01/2016 to 29/02/2016). Social group discovery Communities of each time window are discovered using Infomap, Label Propagation, and Leiden algorithms. For running the community detection algorithms, we assume that the datasets are undirected and unweighted graphs. The communities whose size was smaller than two members were ignored. Community evolution tracking and chain identification In order to track community evolution, we investigate each community to find its similar community or communities from previous time windows, which is called community matching. We employed ICEM (Identification of Community Evolution by Mapping) (Kadkhoda Mohammadmosaferi & Naderi, 2020) method in order to determine the evolution events because it is a highly efficient approach to track community evolution and considers partial evolution and non-consecutive time windows. ICEM has two parameters which are α and β, in this paper, the thresholds for being partially similar and very similar are set to α=10% and β=90%, respectively. Each uploaded dataset contains chains of evolution for a community detection algorithm. Reference: Kadkhoda Mohammadmosaferi, K., Naderi, H., 2020. Evolution of communities in dynamic social networks: An efficient map-based approach. Expert Syst. Appl. 147, 113221

Data for: SIFA: Selecting Important Features Automatically for predicting community evolution in dynamic social networks

Datasets DBLP dataset is divided into eleven time windows (time span 01/01/2003 to 31/12/2013). Facebook Wall Posts dataset is divided into eight time windows (time span 01/01/2005 to 31/12/2008). Wiki-Talk dataset is segmented into six time windows (time span 24/11/2007 to 31/12/2007). Enron email dataset is segmented into twelve time windows (time span 01/01/2001 to 31/12/2001). Reddit-reply dataset is segmented into six time windows (time span 07/01/2014 to 13/01/2014). Stack Overflow dataset is segmented into six time windows (time span 24/01/2016 to 29/02/2016). Social group discovery Communities of each time window are discovered using Infomap, Label Propagation, and Leiden algorithms. For running the community detection algorithms, we assume that the datasets are undirected and unweighted graphs. The communities whose size was smaller than two members were ignored. Community evolution tracking and chain identification In order to track community evolution, we investigate each community to find its similar community or communities from previous time windows, which is called community matching. We employed ICEM (Identification of Community Evolution by Mapping) (Kadkhoda Mohammadmosaferi & Naderi, 2020) method in order to determine the evolution events because it is a highly efficient approach to track community evolution and considers partial evolution and non-consecutive time windows. ICEM has two parameters which are α and β, in this paper, the thresholds for being partially similar and very similar are set to α=10% and β=90%, respectively. Each uploaded dataset contains chains of evolution for a community detection algorithm. Reference: Kadkhoda Mohammadmosaferi, K., Naderi, H., 2020. Evolution of communities in dynamic social networks: An efficient map-based approach. Expert Syst. Appl. 147, 113221

Data for: AFIF: Automatically Finding Important Features in Community Evolution Prediction for Dynamic Social Networks

Networks DBLP network is divided into eleven time windows (time span 01/01/2003 to 31/12/2013). Facebook Wall Posts network is divided into eight time windows (time span 01/01/2005 to 31/12/2008). Wiki-Talk network is segmented into six time windows (time span 24/11/2007 to 31/12/2007). Enron email network is segmented into twelve time windows (time span 01/01/2001 to 31/12/2001). Reddit-reply network is segmented into six time windows (time span 07/01/2014 to 13/01/2014). Stack Overflow network is segmented into six time windows (time span 24/01/2016 to 29/02/2016). Social group discovery Communities of each time window are discovered using Infomap, Label Propagation, and Leiden algorithms. For running the community detection algorithms, we assume that the networks are undirected and unweighted graphs. The communities whose size was smaller than two members were ignored. Community evolution tracking and chain identification In order to track community evolution, we investigate each community to find its similar community or communities from previous time windows, which is called community matching. We employed ICEM (Identification of Community Evolution by Mapping) method in order to determine the evolution events because it is a highly efficient approach to track community evolution and considers partial evolution and non-consecutive time windows (Kadkhoda Mohammadmosaferi & Naderi, 2020). ICEM has two parameters which are α and β, in this paper, the thresholds for being partially similar and very similar are set to α=10% and β=90%, respectively. Each uploaded Dataset contains chains of evolution for a network and a community detection algorithm. Reference: Kadkhoda Mohammadmosaferi, K., Naderi, H., 2020. Evolution of communities in dynamic social networks: An efficient map-based approach. Expert Syst. Appl. 147, 113221. https://doi.org/10.1016/j.eswa.2020.11322

Data for: FIFA: Finding Important Features Automatically for predicting community evolution in dynamic social networks

Time segmentationDBLP dataset is divided into eleven time windows (time span 01/01/2003 to 31/12/2013). Facebook Wall Posts dataset is divided into eight time windows (time span 01/01/2005 to 31/12/2008). Wiki-Talk dataset is segmented into six time windows (time span 24/11/2007 to 31/12/2007). Enron email dataset is segmented into twelve time windows (time span 01/01/2001 to 31/12/2001). Reddit-reply dataset is segmented into six time windows (time span 07/01/2014 to 13/01/2014). Stack Overflow dataset is segmented into six time windows (time span 24/01/2016 to 29/02/2016).Social group discoveryCommunities of each time window are discovered using Infomap, Label Propagation, and Leiden algorithms. For running the community detection algorithms, we assume that the datasets are undirected and unweighted graphs. The communities whose size was smaller than two members were ignored. Community evolution tracking and chain identificationIn order to track community evolution, we investigate each community to find its similar community or communities from previous time windows, which is called community matching. We employed ICEM (Identification of Community Evolution by Mapping) (Kadkhoda Mohammadmosaferi &amp; Naderi, 2020) method in order to determine the evolution events because it is a highly efficient approach to track community evolution and considers partial evolution and non-consecutive time windows. ICEM has two parameters which are α and β, in this paper, the thresholds for being partially similar and very similar are set to α=10% and β=90%, respectively.Each uploaded dataset contains chains of evolution for a community detection algorithm.Reference:Kadkhoda Mohammadmosaferi, K., Naderi, H., 2020. Evolution of communities in dynamic social networks: An efficient map-based approach. Expert Syst. Appl. 147, 113221.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV