Noor Nabilah Talik Sisin, H. Akasaka, R. Sasaki, T. Tominaga, Hayato Miura, M. Nishi, M. Geso, N. Mat, K. A. Razak, W. N. Rahman
Abstract Introduction: Proton beam radiotherapy is an advanced cancer treatment technique, which would reduce the effects of radiation on the surrounding healthy cells. The usage of radiosensitizers in this technique might further elevate the radiation dose towards the cancer cells. Material and methods: The present study investigated the production of intracellular reactive oxygen species (ROS) due to the presence of individual radiosensitizers, such as bismuth oxide nanoparticles (BiONPs), cisplatin (Cis) or baicalein-rich fraction (BRF) from Oroxylum indicum plant, as well as their combinations, such as BiONPs-Cis (BC), BiONPs-BRF (BB), or BiONPs-Cis-BRF (BCB), on HCT-116 colon cancer cells under proton beam radiotherapy. Results: It was found that the ROS in the presence of Cis at 3 Gy of radiation dose was the highest, followed by BC, BiONPs, BB, BRF, and BCB treatments. The properties of bismuth as a radical scavenger, as well as the BRF as a natural compound, might contribute to the lower intracellular ROS induction. The ROS in the presence of Cis and BC combination were also time-dependent and radiation dose-dependent. Conclusions: As the prospective alternatives to the Cis, the BC combination and individual BiONPs showed the capacities to be developed as radiosensitizers for proton beam therapy.
{"title":"Effects of Bismuth Oxide Nanoparticles, Cisplatin and Baicalein-rich Fraction on ROS Generation in Proton Beam irradiated Human Colon Carcinoma Cells","authors":"Noor Nabilah Talik Sisin, H. Akasaka, R. Sasaki, T. Tominaga, Hayato Miura, M. Nishi, M. Geso, N. Mat, K. A. Razak, W. N. Rahman","doi":"10.2478/pjmpe-2022-0004","DOIUrl":"https://doi.org/10.2478/pjmpe-2022-0004","url":null,"abstract":"Abstract Introduction: Proton beam radiotherapy is an advanced cancer treatment technique, which would reduce the effects of radiation on the surrounding healthy cells. The usage of radiosensitizers in this technique might further elevate the radiation dose towards the cancer cells. Material and methods: The present study investigated the production of intracellular reactive oxygen species (ROS) due to the presence of individual radiosensitizers, such as bismuth oxide nanoparticles (BiONPs), cisplatin (Cis) or baicalein-rich fraction (BRF) from Oroxylum indicum plant, as well as their combinations, such as BiONPs-Cis (BC), BiONPs-BRF (BB), or BiONPs-Cis-BRF (BCB), on HCT-116 colon cancer cells under proton beam radiotherapy. Results: It was found that the ROS in the presence of Cis at 3 Gy of radiation dose was the highest, followed by BC, BiONPs, BB, BRF, and BCB treatments. The properties of bismuth as a radical scavenger, as well as the BRF as a natural compound, might contribute to the lower intracellular ROS induction. The ROS in the presence of Cis and BC combination were also time-dependent and radiation dose-dependent. Conclusions: As the prospective alternatives to the Cis, the BC combination and individual BiONPs showed the capacities to be developed as radiosensitizers for proton beam therapy.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"5 1","pages":"30 - 36"},"PeriodicalIF":0.4,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84351425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Shafiekhani, S. Rafiei, S. Abdollahzade, Saber Souri, Zeinab Moomeni
Abstract Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power. Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death. Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients. Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.
{"title":"Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning","authors":"S. Shafiekhani, S. Rafiei, S. Abdollahzade, Saber Souri, Zeinab Moomeni","doi":"10.2478/pjmpe-2022-0003","DOIUrl":"https://doi.org/10.2478/pjmpe-2022-0003","url":null,"abstract":"Abstract Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power. Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death. Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients. Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"68 1","pages":"19 - 29"},"PeriodicalIF":0.4,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79965814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. A. Kanti, O. Hajjaji, T. E. Bardouni, M. Mohammed
Abstract Introduction: This work aims to calculate the ambient and personal dose equivalent conversion coefficients. Material and methods: The conversion coefficients have been calculated using MC simulation. Additionally, this paper proposes a new method that depends on an analytical approach. Results: The obtained results in good agreement between MC and an analytical approach were observed. The obtained results were compared to those published in ICRU 57 report. Conclusions: We deduced that the analytical approach is as effective and suitable as the MC simulation to calculate the operational quantity conversion coefficients.
{"title":"Neutron conversion coefficients of ambient dose equivalent and personal dose equivalent","authors":"H. A. Kanti, O. Hajjaji, T. E. Bardouni, M. Mohammed","doi":"10.2478/pjmpe-2022-0006","DOIUrl":"https://doi.org/10.2478/pjmpe-2022-0006","url":null,"abstract":"Abstract Introduction: This work aims to calculate the ambient and personal dose equivalent conversion coefficients. Material and methods: The conversion coefficients have been calculated using MC simulation. Additionally, this paper proposes a new method that depends on an analytical approach. Results: The obtained results in good agreement between MC and an analytical approach were observed. The obtained results were compared to those published in ICRU 57 report. Conclusions: We deduced that the analytical approach is as effective and suitable as the MC simulation to calculate the operational quantity conversion coefficients.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"10 1","pages":"52 - 59"},"PeriodicalIF":0.4,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90982912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eyachew Misganew Tegaw, G. Tadesse, G. Geraily, S. Gholami, W. Gebreamlak
Abstract After radiotherapy (RT) of left-sided breast cancer patients, organs at risk (OARs) such as heart, left anterior descending (LAD) coronary artery, and left lung could be affected by radiation dose in the long term. The objective of this study was to perform a comprehensive meta-analysis and determine OARs dose reduction during left-sided breast cancer treatment using different RT modalities combined with deep inspiration breath-hold (DIBH) relative to free-breathing (FB). PubMed, Scopus, EMBASE, ProQuest, Google Scholar, and Cochrane Library electronic databases were used to search for studies until June 6, 2021. Nineteen eligible studies were selected and analyzed using the RevMan 5.3 statistical software package. The pooled weighted mean difference (MD) with their 95% confidence intervals for each of the three OAR mean doses were determined using a random-effects meta-analysis model to assess the dose reductions. From a total of 189 studies, 1 prospective study, 15 retrospective studies, and 3 randomized control trials (RCTs) with an overall of 634 patients were included. Reduction of doses to the heart (weighted MD = -1.79 Gy; 95% CI (-2.28, -1.30); P = 0.00001), LAD (weighted MD = -8.34 Gy; 95% CI (-11.06, -5.61); P = 0.00001), and left-lung (weighted MD = -0.90 Gy; 95% CI (-1.19, -0.61); P = 0.00001) were observed using DIBH combinations relative to FB combination. This study emphasizes that during the treatment of left-sided breast/chest wall (CW) ± supraclavicular (SCV) ± infraclavicular (ICV) ± internal mammary chain (IMC) lymph nodes (LN) ± axillary (Ax)/ cancer patients, different RT modalities combined with DIBH techniques are better options to reduce dose to OARs compared to FB, which benefits to minimize the long-term complications.
摘要左侧乳腺癌患者放射治疗后，心脏、左前降支(LAD)冠状动脉、左肺等危险器官可能长期受到辐射剂量的影响。本研究的目的是进行一项全面的荟萃分析，并确定使用不同的RT模式联合深度吸气屏气(DIBH)相对于自由呼吸(FB)治疗左侧乳腺癌期间OARs剂量的减少。使用PubMed、Scopus、EMBASE、ProQuest、Google Scholar和Cochrane Library电子数据库检索到2021年6月6日之前的研究。选取符合条件的19项研究，采用RevMan 5.3统计软件包进行分析。使用随机效应荟萃分析模型来评估剂量减少，确定了三个OAR平均剂量的合并加权平均差(MD)及其95%置信区间。共纳入189项研究，1项前瞻性研究、15项回顾性研究和3项随机对照试验(rct)，共纳入634例患者。减少心脏剂量(加权MD = -1.79 Gy;95% ci (-2.28， -1.30);P = 0.00001)， LAD(加权MD = -8.34 Gy;95% ci (-11.06， -5.61);P = 0.00001)，左肺(加权MD = -0.90 Gy;95% ci (-1.19， -0.61);P = 0.00001)。本研究强调，在左侧乳腺/胸壁(CW)±锁骨上(SCV)±锁骨下(ICV)±乳腺内链(IMC)淋巴结(LN)±腋窝(Ax)/肿瘤患者的治疗过程中，与FB相比，不同的放疗方式联合DIBH技术是减少OARs剂量的更好选择，有利于减少长期并发症。
{"title":"Comparison of organs at risk doses between deep inspiration breath-hold and free-breathing techniques during radiotherapy of left-sided breast cancer: A Meta-Analysis","authors":"Eyachew Misganew Tegaw, G. Tadesse, G. Geraily, S. Gholami, W. Gebreamlak","doi":"10.2478/pjmpe-2022-0001","DOIUrl":"https://doi.org/10.2478/pjmpe-2022-0001","url":null,"abstract":"Abstract After radiotherapy (RT) of left-sided breast cancer patients, organs at risk (OARs) such as heart, left anterior descending (LAD) coronary artery, and left lung could be affected by radiation dose in the long term. The objective of this study was to perform a comprehensive meta-analysis and determine OARs dose reduction during left-sided breast cancer treatment using different RT modalities combined with deep inspiration breath-hold (DIBH) relative to free-breathing (FB). PubMed, Scopus, EMBASE, ProQuest, Google Scholar, and Cochrane Library electronic databases were used to search for studies until June 6, 2021. Nineteen eligible studies were selected and analyzed using the RevMan 5.3 statistical software package. The pooled weighted mean difference (MD) with their 95% confidence intervals for each of the three OAR mean doses were determined using a random-effects meta-analysis model to assess the dose reductions. From a total of 189 studies, 1 prospective study, 15 retrospective studies, and 3 randomized control trials (RCTs) with an overall of 634 patients were included. Reduction of doses to the heart (weighted MD = -1.79 Gy; 95% CI (-2.28, -1.30); P = 0.00001), LAD (weighted MD = -8.34 Gy; 95% CI (-11.06, -5.61); P = 0.00001), and left-lung (weighted MD = -0.90 Gy; 95% CI (-1.19, -0.61); P = 0.00001) were observed using DIBH combinations relative to FB combination. This study emphasizes that during the treatment of left-sided breast/chest wall (CW) ± supraclavicular (SCV) ± infraclavicular (ICV) ± internal mammary chain (IMC) lymph nodes (LN) ± axillary (Ax)/ cancer patients, different RT modalities combined with DIBH techniques are better options to reduce dose to OARs compared to FB, which benefits to minimize the long-term complications.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"4 1","pages":"1 - 12"},"PeriodicalIF":0.4,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84799317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Introduction: Dosimetric verification of Intensity Modulation Radiotherapy treatment plans is usually carried out before the start of treatment. It is of special importance in the case of highly modulated plans, such as plans for patients in whom many tumors are irradiated simultaneously. In this work, we present the results of the verification of multi-target plans performed with the stereotactic HyperArc technique. Material and methods: The results of dosimetric verification of 35 patient plans in the head are presented. The results are analyzed in terms of the number of tumors, and the distance of the tumor from the isocenter. Measurements were carried out with the film method. The gamma methodology was used (3%/2mm). Results: The results showed a very good agreement between measurements and calculations. Conclusions: No dependence of the verification result on the number of targets and the distance between the center of tumor and isocenter was found.
{"title":"Dosimetric verification of multi-tumor target cases treated with SRS HyperArc technique using EBT3 radiochromic films","authors":"Aleksandra Juda, Marta Fillmann, P. Kukołowicz","doi":"10.2478/pjmpe-2022-0002","DOIUrl":"https://doi.org/10.2478/pjmpe-2022-0002","url":null,"abstract":"Abstract Introduction: Dosimetric verification of Intensity Modulation Radiotherapy treatment plans is usually carried out before the start of treatment. It is of special importance in the case of highly modulated plans, such as plans for patients in whom many tumors are irradiated simultaneously. In this work, we present the results of the verification of multi-target plans performed with the stereotactic HyperArc technique. Material and methods: The results of dosimetric verification of 35 patient plans in the head are presented. The results are analyzed in terms of the number of tumors, and the distance of the tumor from the isocenter. Measurements were carried out with the film method. The gamma methodology was used (3%/2mm). Results: The results showed a very good agreement between measurements and calculations. Conclusions: No dependence of the verification result on the number of targets and the distance between the center of tumor and isocenter was found.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"67 1","pages":"13 - 18"},"PeriodicalIF":0.4,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80877959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Polaczek-Grelik, A. Kawa-Iwanicka, Ł. Michalecki
Abstract Introduction: The accuracy of the cross-calibration procedure depends on ionization chamber type, both used as reference one and under consideration. Also, the beam energy and phantom medium could influence the precision of cross calibration coefficient, resulting in a systematic error in dose estimation and thus could influence the linac beam output checking. This will result in a systematic mismatch between dose calculated in treatment planning system and delivered to the patient. Material and methods: The usage of FC65-G, CC13 and CC01 thimble reference chambers as well as 6, 9, and 15 MeV electron beams has been analyzed. A plane-parallel PPC05 chamber was calibrated since scarce literature data are available for this dosimeter type. The influence of measurement medium and an effective point of measurement (EPOM) on obtained results are also presented. Results: Dose reconstruction precision of ~0.1% for PPC05 chamber could be obtained when cross-calibration is based on a thimble CC13 chamber. Nd,w,Qcross obtained in beam ≥ 9MeV gives 0.1 – 0.5% precision of dose reconstruction. Without beam quality correction, 15 MeV Nd,w,Qcross is 10% lower than Co-60 Nd,w,0. Various EPOM shifts resulted in up to 0.6% discrepancies in Nd,w,Qcross values. Conclusions: Ionization chamber with small active volume and tissue-equivalent materials supplies more accurate cross-calibration coefficients in the range of 6 – 15 MeV electron beams. In the case of 6 and 9 MeV beams, the exact position of an effective point of measurement is of minor importance. In-water cross-calibration coefficient can be used in a solid medium without loss of dose accuracy.
{"title":"Dosimetric accuracy of a cross-calibration coefficient for plane-parallel ionization chamber obtained in low-energy electron beams using various cylindrical dosimeters","authors":"K. Polaczek-Grelik, A. Kawa-Iwanicka, Ł. Michalecki","doi":"10.2478/pjmpe-2021-0036","DOIUrl":"https://doi.org/10.2478/pjmpe-2021-0036","url":null,"abstract":"Abstract Introduction: The accuracy of the cross-calibration procedure depends on ionization chamber type, both used as reference one and under consideration. Also, the beam energy and phantom medium could influence the precision of cross calibration coefficient, resulting in a systematic error in dose estimation and thus could influence the linac beam output checking. This will result in a systematic mismatch between dose calculated in treatment planning system and delivered to the patient. Material and methods: The usage of FC65-G, CC13 and CC01 thimble reference chambers as well as 6, 9, and 15 MeV electron beams has been analyzed. A plane-parallel PPC05 chamber was calibrated since scarce literature data are available for this dosimeter type. The influence of measurement medium and an effective point of measurement (EPOM) on obtained results are also presented. Results: Dose reconstruction precision of ~0.1% for PPC05 chamber could be obtained when cross-calibration is based on a thimble CC13 chamber. Nd,w,Qcross obtained in beam ≥ 9MeV gives 0.1 – 0.5% precision of dose reconstruction. Without beam quality correction, 15 MeV Nd,w,Qcross is 10% lower than Co-60 Nd,w,0. Various EPOM shifts resulted in up to 0.6% discrepancies in Nd,w,Qcross values. Conclusions: Ionization chamber with small active volume and tissue-equivalent materials supplies more accurate cross-calibration coefficients in the range of 6 – 15 MeV electron beams. In the case of 6 and 9 MeV beams, the exact position of an effective point of measurement is of minor importance. In-water cross-calibration coefficient can be used in a solid medium without loss of dose accuracy.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"3 2","pages":"303 - 313"},"PeriodicalIF":0.4,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72565907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elahe Sayyadi, A. Mesbahi, R. Zamiri, Farshad Seyed Nejad
Abstract Introduction: The present study aimed to investigate the radiation protection properties of silicon-based composites doped with nano-sized Bi2O3, PbO, Sm2O3, Gd2O3, WO3, and IrO2 particles. Radiation shielding properties of Sm2O3 and IrO2 nanoparticles were investigated for the first time in the current study. Material and methods: The MCNPX (2.7.0) Monte Carlo code was utilized to calculate the linear attenuation coefficients of single and multi-nano structured composites over the X-ray energy range of 10–140 keV. Homogenous distribution of spherical nanoparticles with a diameter of 100 nm in a silicon rubber matrix was simulated. The narrow beam geometry was used to calculate the photon flux after attenuation by designed nanocomposites. Results: Based on results obtained for single nanoparticle composites, three combinations of different nano-sized fillers Sm2O3+WO3+Bi2O3, Gd2O3+WO3+Bi2O3, and Sm2O3+WO3+PbO were selected, and their shielding properties were estimated. In the energy range of 20-60 keV Sm2O3 and Gd2O3 nanoparticles, in 70-100 keV energy range WO3 and for photons energy higher than 90 keV, PbO and Bi2O3 nanoparticles showed higher attenuation. Despite its higher density, IrO2 had lower attenuation compared to other nanocomposites. The results showed that the nanocomposite containing Sm2O3, WO3, and Bi2O3 nanoparticles provided better shielding among the studied samples. Conclusions: All studied multi-nanoparticle nanocomposites provided optimum shielding properties and almost 8% higher attenuation relative to single nano-based composites over a wide range of photon energy used in diagnostic radiology. Application of these new composites is recommended in radiation protection. Further experimental studies are suggested to validate our findings.
{"title":"A comprehensive Monte Carlo study to design a novel multi-nanoparticle loaded nanocomposites for augmentation of attenuation coefficient in the energy range of diagnostic X-rays","authors":"Elahe Sayyadi, A. Mesbahi, R. Zamiri, Farshad Seyed Nejad","doi":"10.2478/pjmpe-2021-0033","DOIUrl":"https://doi.org/10.2478/pjmpe-2021-0033","url":null,"abstract":"Abstract Introduction: The present study aimed to investigate the radiation protection properties of silicon-based composites doped with nano-sized Bi2O3, PbO, Sm2O3, Gd2O3, WO3, and IrO2 particles. Radiation shielding properties of Sm2O3 and IrO2 nanoparticles were investigated for the first time in the current study. Material and methods: The MCNPX (2.7.0) Monte Carlo code was utilized to calculate the linear attenuation coefficients of single and multi-nano structured composites over the X-ray energy range of 10–140 keV. Homogenous distribution of spherical nanoparticles with a diameter of 100 nm in a silicon rubber matrix was simulated. The narrow beam geometry was used to calculate the photon flux after attenuation by designed nanocomposites. Results: Based on results obtained for single nanoparticle composites, three combinations of different nano-sized fillers Sm2O3+WO3+Bi2O3, Gd2O3+WO3+Bi2O3, and Sm2O3+WO3+PbO were selected, and their shielding properties were estimated. In the energy range of 20-60 keV Sm2O3 and Gd2O3 nanoparticles, in 70-100 keV energy range WO3 and for photons energy higher than 90 keV, PbO and Bi2O3 nanoparticles showed higher attenuation. Despite its higher density, IrO2 had lower attenuation compared to other nanocomposites. The results showed that the nanocomposite containing Sm2O3, WO3, and Bi2O3 nanoparticles provided better shielding among the studied samples. Conclusions: All studied multi-nanoparticle nanocomposites provided optimum shielding properties and almost 8% higher attenuation relative to single nano-based composites over a wide range of photon energy used in diagnostic radiology. Application of these new composites is recommended in radiation protection. Further experimental studies are suggested to validate our findings.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"5 1","pages":"279 - 289"},"PeriodicalIF":0.4,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86488034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose: The aim of this study was to quantify the accuracy of 3D trajectory reconstructions performed from two planar video recordings, using three different reconstruction methods. Additionally, the recordings were carried out using easily available equipment, like built-in cellphone cameras, making the methods suitable for low-cost applications. Methods: A setup for 3D motion tracking was constructed and used to acquire 2D video recordings subsequently used to reconstruct the 3D trajectories by 1) merging appropriate coordinates, 2) merging coordinates with proportional scaling, and 3) calculating the 3D position based on markers’ projections on the viewing plane. As experimental verification, two markers moving at a fixed distance of 98.9 cm were used to assess the consistency of results. Next, gait analysis in five volunteers was carried out to quantify the differences resulting from different reconstruction methods. Results: Quantitative evaluation of the investigated 3D trajectories reconstruction methods showed significant differences between those methods, with the worst reconstruction approach resulting in a maximum error of 50% (standard deviation 13%), while the best resulting in a maximum error of 1% (standard deviation 0.44%). The gait analysis results showed differences in mean angles obtained with each reconstruction method reaching only 2°, which can be attributed to the limited measurement volume. Conclusions: Reconstructing 3D trajectory from 2D views without accounting for the “perspective error” results in significant reconstruction errors. The third method described in this study enables a significant reduction of this issue. Combined with the proposed setup, it provides a functional, low-cost gait analysis system.
{"title":"Comparison of three methods for reconstructing 3D motion from 2D video recordings for low cost gait analysis systems","authors":"S. Cygan, Adriana Specyalska","doi":"10.2478/pjmpe-2021-0032","DOIUrl":"https://doi.org/10.2478/pjmpe-2021-0032","url":null,"abstract":"Abstract Purpose: The aim of this study was to quantify the accuracy of 3D trajectory reconstructions performed from two planar video recordings, using three different reconstruction methods. Additionally, the recordings were carried out using easily available equipment, like built-in cellphone cameras, making the methods suitable for low-cost applications. Methods: A setup for 3D motion tracking was constructed and used to acquire 2D video recordings subsequently used to reconstruct the 3D trajectories by 1) merging appropriate coordinates, 2) merging coordinates with proportional scaling, and 3) calculating the 3D position based on markers’ projections on the viewing plane. As experimental verification, two markers moving at a fixed distance of 98.9 cm were used to assess the consistency of results. Next, gait analysis in five volunteers was carried out to quantify the differences resulting from different reconstruction methods. Results: Quantitative evaluation of the investigated 3D trajectories reconstruction methods showed significant differences between those methods, with the worst reconstruction approach resulting in a maximum error of 50% (standard deviation 13%), while the best resulting in a maximum error of 1% (standard deviation 0.44%). The gait analysis results showed differences in mean angles obtained with each reconstruction method reaching only 2°, which can be attributed to the limited measurement volume. Conclusions: Reconstructing 3D trajectory from 2D views without accounting for the “perspective error” results in significant reconstruction errors. The third method described in this study enables a significant reduction of this issue. Combined with the proposed setup, it provides a functional, low-cost gait analysis system.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"9 1","pages":"271 - 277"},"PeriodicalIF":0.4,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85582011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Pulmonary tuberculosis is a worldwide epidemic that can only be fought effectively with early and accurate diagnosis and proper disease management. The means of diagnosis and disease management should be easily accessible, cost effective and be readily available in the high tuberculosis burdened countries where it is most needed. Fortunately, the fast development of computer science in recent years has ensured that medical images can accurately be quantified. Radiomics is one such tool that can be used to quantify medical images. This review article focuses on the literature currently available on the application of radiomics explicitly for the purpose of diagnosis, differentiation from other pulmonary diseases and disease management of pulmonary tuberculosis. Despite using a formal search strategy, only five articles could be found on the application of radiomics to pulmonary tuberculosis. In all five articles reviewed, radiomic feature extraction was successfully used to quantify digital medical images for the purpose of comparing, or differentiating, pulmonary tuberculosis from other pulmonary diseases. This demonstrates that the use of radiomics for the purpose of tuberculosis disease management and diagnosis remains a valuable data mining opportunity not yet realised.
{"title":"Pulmonary tuberculosis diagnosis, differentiation and disease management: A review of radiomics applications","authors":"Tamarisk du Plessis, W. Rae, M. Sathekge","doi":"10.2478/pjmpe-2021-0030","DOIUrl":"https://doi.org/10.2478/pjmpe-2021-0030","url":null,"abstract":"Abstract Pulmonary tuberculosis is a worldwide epidemic that can only be fought effectively with early and accurate diagnosis and proper disease management. The means of diagnosis and disease management should be easily accessible, cost effective and be readily available in the high tuberculosis burdened countries where it is most needed. Fortunately, the fast development of computer science in recent years has ensured that medical images can accurately be quantified. Radiomics is one such tool that can be used to quantify medical images. This review article focuses on the literature currently available on the application of radiomics explicitly for the purpose of diagnosis, differentiation from other pulmonary diseases and disease management of pulmonary tuberculosis. Despite using a formal search strategy, only five articles could be found on the application of radiomics to pulmonary tuberculosis. In all five articles reviewed, radiomic feature extraction was successfully used to quantify digital medical images for the purpose of comparing, or differentiating, pulmonary tuberculosis from other pulmonary diseases. This demonstrates that the use of radiomics for the purpose of tuberculosis disease management and diagnosis remains a valuable data mining opportunity not yet realised.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"22 1","pages":"251 - 259"},"PeriodicalIF":0.4,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90407200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Introduction: This study presents an empirical method to model the electron beam percent depth dose curve (PDD) using the primary and tail functions in radiation therapy. The modeling parameters N and n can be used to derive the depth relative stopping power of the electron energy in radiation therapy. Methods and Materials: The electrons PDD curves were modeled with the primary-tail function in this study. The primary function included exponential function and main parameters of N, µ while the tail function was composed by a sigmoid function with the main parameter of n. The PDD for five electron energies were modeled by the primary and tail function by adjusting the parameters of N, µ and n. The R50 and Rp can be derived from the modeled straight line of 80% to 20% region of PDD. The same electron energy with different cone sizes was also modeled with the primary-tail function. The stopping power for different electron energies at different depths can also be derived from the parameters of N, µ and n. Percent ionization depth curve can then be derived from the percent depth dose by dividing its depth relevant stopping power for comparing with the original water phantom measurement. Results: The main parameters N, n increase, but µ decreases in primary-tail function when electron energy increased. The relationship of parameters n, N and LN(-µ) with electron energy are n = 31.667 E0 - 88, N = 0.9975 E0 - 2.8535, LN(-µ) = -0.1355 E0 - 6.0986, respectively. Stopping power of different electron energy can be derived from n and N with the equation: stopping power = (−0.042 ln NE0 + 1.072)e(−n−E0·5·10−5+0.0381·d), where d is the depth in water. Percent depth dose was derived from the percent reading curve by multiplying the stopping power relevant to the depth in water at certain electron energy. Conclusion: The PDD of electrons at different energies and field sizes can be modeled with an empirical model to deal with the stopping power calculation. The primary-tail equation provides a uncomplicated solution than a pencil beam or other numerical algorism for investigators to research the behavior of electron beam in radiation therapy.
{"title":"Empirical method for modeling the percent depth dose curves of electron beam in radiation therapy","authors":"Dongmei Chen, Yanshan Zhang, Y. Ye, Jia-Ming Wu","doi":"10.2478/pjmpe-2021-0037","DOIUrl":"https://doi.org/10.2478/pjmpe-2021-0037","url":null,"abstract":"Abstract Introduction: This study presents an empirical method to model the electron beam percent depth dose curve (PDD) using the primary and tail functions in radiation therapy. The modeling parameters N and n can be used to derive the depth relative stopping power of the electron energy in radiation therapy. Methods and Materials: The electrons PDD curves were modeled with the primary-tail function in this study. The primary function included exponential function and main parameters of N, µ while the tail function was composed by a sigmoid function with the main parameter of n. The PDD for five electron energies were modeled by the primary and tail function by adjusting the parameters of N, µ and n. The R50 and Rp can be derived from the modeled straight line of 80% to 20% region of PDD. The same electron energy with different cone sizes was also modeled with the primary-tail function. The stopping power for different electron energies at different depths can also be derived from the parameters of N, µ and n. Percent ionization depth curve can then be derived from the percent depth dose by dividing its depth relevant stopping power for comparing with the original water phantom measurement. Results: The main parameters N, n increase, but µ decreases in primary-tail function when electron energy increased. The relationship of parameters n, N and LN(-µ) with electron energy are n = 31.667 E0 - 88, N = 0.9975 E0 - 2.8535, LN(-µ) = -0.1355 E0 - 6.0986, respectively. Stopping power of different electron energy can be derived from n and N with the equation: stopping power = (−0.042 ln NE0 + 1.072)e(−n−E0·5·10−5+0.0381·d), where d is the depth in water. Percent depth dose was derived from the percent reading curve by multiplying the stopping power relevant to the depth in water at certain electron energy. Conclusion: The PDD of electrons at different energies and field sizes can be modeled with an empirical model to deal with the stopping power calculation. The primary-tail equation provides a uncomplicated solution than a pencil beam or other numerical algorism for investigators to research the behavior of electron beam in radiation therapy.","PeriodicalId":53955,"journal":{"name":"Polish Journal of Medical Physics and Engineering","volume":"43 1","pages":"315 - 321"},"PeriodicalIF":0.4,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84549262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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