Volume 28 (2022): Issue 1 (March 2022)

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.

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.

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.

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.

Introduction: The doping of high Z nanoparticles into the tumor tissue increases the therapeutic efficiency of radiotherapy called nanoparticle enhanced radiotherapy (NERT). In the present study, we are identifying the effective types of radiation and effective doping concentration of bismuth radiosensitizer for NERT application by analyzing effective atomic number (Zeff) and photon buildup factor (PBF) of bismuth (Bi) doped soft tissue for the photon, electron, proton, alpha particle, and carbon ion interactions.

Material and methods: The direct method was used for the calculation of Zeff for photon and electron beams (10 keV-30 MeV). The phy-X/ZeXTRa software was utilized for the particle beams such as proton, alpha particle, and carbon ions (1-15 MeV). Bismuth doping concentrations of 5, 10, 15, 20, 25 and 30 mg/g were considered. The PBF was calculated over 15 keV-15 MeV energies using phy-X/PSD software.

Results: The low energy photon (<100 keV) interaction with a higher concentration of Bi dopped tissue gives the higher values of Zeff. The Zeff increased with the doping concentration of bismuth for all types of radiation. The Zeff was dependent on the type of radiation, the energy of radiation, and the concentration of Bi doping. The particle beams such as electron, proton, alpha particle, and carbon ion interaction gives the less values of Zeff has compared to photon beam interaction. On the other hand, the photon buildup factor values were decreased while increasing the Bi doping concentration.

Conclusions: According to Zeff and PBF, the low energy photon and higher concentration of radiosensitizer are the most effective for nanoparticle enhanced radiotherapy application. Based on the calculated values of Zeff, the particle beams such as electron, proton, alpha particle, and carbon ions were less effective for NERT application. The presented values of Zeff and PBF are useful for the radiation dosimetry in NERT.

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.