A machine learning-based pipeline for multi-organ/tissue patient-specific radiation dosimetry in CT.

OBJECTIVES: To develop a machine learning-based pipeline for multi-organ/tissue personalized radiation dosimetry in CT. MATERIALS AND METHODS: For the study, 95 chest CT scans and 85 abdominal CT scans were collected retrospectively. For each CT scan, a personalized Monte Carlo (MC) simulation was carried out. The produced 3D dose distributions and the respective CT examinations were utilized for the development of organ/tissue-specific dose prediction deep neural networks (DNNs). A pipeline that integrates a robust open-source organ segmentation tool with the dose prediction DNNs was developed for the automatic estimation of radiation doses for 30 organs/tissues including sub-volumes of the heart and lungs. The accuracy and time efficiency of the presented methodology was assessed. Statistical analysis (t-tests) was conducted to determine if the differences between the ground truth organ/tissue radiation dose estimates and the respective dose predictions were significant. RESULTS: The lowest median percentage differences between MC-derived organ/tissue doses and DNN dose predictions were observed for the lung vessels (4.3%), small bowel (4.7%), pulmonary artery (4.7%), and colon (5.2%), while the highest differences were observed for the right lung's upper lobe (13.3%), spleen (13.1%), pancreas (12.1%), and stomach (11.6%). Statistical analysis showed that the differences were not significant (p-value > 0.18). Furthermore, the mean inference time, regarding the validation cohort, of the developed methodology was 77.0 ± 11.0 s. CONCLUSION: The proposed workflow enables fast and accurate organ/tissue radiation dose estimations. The developed algorithms and dose prediction DNNs are publicly available ( https://github.com/eltzanis/multi-structure-CT-dosimetry ). CLINICAL RELEVANCE STATEMENT: The accuracy and time efficiency of the developed pipeline compose a useful tool for personalized dosimetry in CT. By adopting the proposed workflow, institutions can utilize an automated pipeline for patient-specific dosimetry in CT. KEY POINTS: Personalized dosimetry is ideal, but is time-consuming. The proposed pipeline composes a tool for facilitating patient-specific CT dosimetry in routine clinical practice. The developed workflow integrates a robust open-source segmentation tool with organ/tissue-specific dose prediction neural networks.

The use of Dose Management Systems in Europe: Results of an ESR EuroSafe Imaging Questionnaire.

Dose management systems (DMS) are an essential tool for quality assurance and optimising patient radiation exposure. For radiologists and medical physicists, they are important for managing many radiation protection tasks. In addition, they help fulfil the requirements of Directive 2013/59/EURATOM regarding the electronic transmission of dosimetric data and the detection of unintended patient exposures. The EuroSafe Imaging Clinical Dosimetry and Dose Management Working Group launched a questionnaire on the use of DMS in European member states and analysed the results in terms of modalities, frequency of radiological procedures, involvement of medical physics experts (MPEs), legal requirements, and local issues (support by information technology (IT), modality interfaces, protocol mapping, clinical workflow, and associated costs). CRITICAL RELEVANCE STATEMENT: Despite the great advantages of dose management systems for optimising radiation protection, distribution remains insufficient. This questionnaire shows that reasons include: a lack of DICOM interfaces, insufficient harmonisation of procedure names, lack of medical physicist and IT support, and costs. KEY POINTS: Quantitative radiation dose information is essential for justification and optimisation in medical imaging. Guidelines are required to ensure radiation dose management systems quality and for acceptance testing. Verifying dose data management is crucial before dose management systems clinical implementation. Medical physics experts are professionals who have important responsibilities for the proper management of dose monitoring.

Support for the "Vancouver call for action to strengthen expertise in radiological protection worldwide": the position of organisations in formal relations with the International Commission on Radiological Protection (ICRP).

The International Commission on Radiological Protection (ICRP), recently expressed concern that "a shortage of investment in training, education, research, and infrastructure seen in many sectors and countries may compromise society's ability to properly manage radiation risks" and in 2022 announced the "Vancouver call for action to strengthen expertise in radiological protection worldwide". As representatives of organisations in formal relations with ICRP, we decided to promote this position paper to declare and emphasise that strengthening the expertise in radiological protection is a collective priority for all of us.

Is deep learning-enabled real-time personalized CT dosimetry feasible using only patient images as input?

PURPOSE: To propose a novel deep-learning based dosimetry method that allows quick and accurate estimation of organ doses for individual patients, using only their computed tomography (CT) images as input. METHODS: Despite recent advances in medical dosimetry, personalized CT dosimetry remains a labour-intensive process. Current state-of-the-art methods utilize time-consuming Monte Carlo (MC) based simulations for individual organ dose estimation in CT. The proposed method uses conditional generative adversarial networks (cGANs) to substitute MC simulations with fast dose image generation, based on image-to-image translation. The pix2pix architecture in conjunction with a regression model was utilized for the generation of the synthetic dose images. The lungs, heart, breast, bone and skin were manually segmented to estimate and compare organ doses calculated using both the original and synthetic dose images, respectively. RESULTS: The average organ dose estimation error for the proposed method was 8.3% and did not exceed 20% for any of the organs considered. The performance of the method in the clinical environment was also assessed. Using segmentation tools developed in-house, an automatic organ dose calculation pipeline was set up. Calculation of organ doses for heart and lung for each CT slice took about 2 s. CONCLUSIONS: This work shows that deep learning-enabled personalized CT dosimetry is feasible in real-time, using only patient CT images as input.

Digital phantom versus patient-specific radiation dosimetry in adult routine thorax CT examinations.

PURPOSE: The aim of this study was to compare the organ doses assessed through a digital phantom-based and a patient specific-based dosimetric tool in adult routine thorax computed tomography (CT) examinations with reference to physical dose measurements performed in anthropomorphic phantoms. METHODS: Two Monte Carlo based dose calculation tools were used to assess organ doses in routine adult thorax CT examinations. These were a digital phantom-based dosimetry tool (NCICT, National Cancer Institute, USA) and a patient-specific individualized dosimetry tool (ImpactMC, CT Imaging GmbH, Germany). Digital phantoms and patients were classified in four groups according to their water equivalent diameter (Dw). Normalized to volume computed tomography dose index (CTDIvol), organ dose was assessed for lungs, esophagus, heart, breast, active bone marrow, and skin. Organ doses were compared to measurements performed using thermoluminescent detectors (TLDs) in two physical anthropomorphic phantoms that simulate the average adult individual as a male (Alderson Research Labs, USA) and as a female (ATOM Phantoms, USA). RESULTS: The average percent difference of NCICT to TLD and ImpactMC to TLD dose measurements across all organs in both sexes was 13% and 6%, respectively. The average ± 1 standard deviation in dose values across all organs with NCICT, ImpactMC, and TLDs was ± 0.06 (mGy/mGy), ± 0.19 (mGy/mGy), and ± 0.13 (mGy/mGy), respectively. Organ doses decreased with increasing Dw in both NCICT and ImpactMC. CONCLUSION: Organ doses estimated with ImpactMC were in closer agreement to TLDs compared to NCICT. This may be attributed to the inherent property of ImpactMC methodology to generate phantoms that resemble the realistic anatomy of the examined patient as opposed to NCICT methodology that incorporates an anatomical discrepancy between phantoms and patients.

Organ dose prediction for patients undergoing radiotherapy CBCT chest examinations using artificial intelligence.

PURPOSE: To propose an artificial intelligence (AI)-based method for personalized and real-time dosimetry for chest CBCT acquisitions. METHODS: CT images from 113 patients who underwent radiotherapy treatment were collected for simulating thorax examinations using cone-beam computed tomography (CBCT) with the Monte Carlo technique. These simulations yielded organ dose data, used to train and validate specific AI algorithms. The efficacy of these AI algorithms was evaluated by comparing dose predictions with the actual doses derived from Monte Carlo simulations, which are the ground truth, utilizing Bland-Altman plots for this comparative analysis. RESULTS: The absolute mean discrepancies between the predicted doses and the ground truth are (0.9 ± 1.3)% for bones, (1.2 ± 1.2)% for the esophagus, (0.5 ± 1.3)% for the breast, (2.5 ± 1.4)% for the heart, (2.4 ± 2.1)% for lungs, (0.8 ± 0.6)% for the skin, and (1.7 ± 0.7)% for integral. Meanwhile, the maximum discrepancies between the predicted doses and the ground truth are (14.4 ± 1.3)% for bones, (12.9 ± 1.2)% for the esophagus, (9.4 ± 1.3)% for the breast, (14.6 ± 1.4)% for the heart, (21.2 ± 2.1)% for lungs, (10.0 ± 0.6)% for the skin, and (10.5 ± 0.7)% for integral. CONCLUSIONS: AI models that can make real-time predictions of the organ doses for patients undergoing CBCT thorax examinations as part of radiotherapy pre-treatment positioning were developed. The results of this study clearly show that the doses predicted by analyzed AI models are in close agreement with those calculated using Monte Carlo simulations.

A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT.

PURPOSE: To develop a machine learning-based methodology for patient-specific radiation dosimetry in thoracic and abdomen CT. METHODS: Three hundred and thirty-one thoracoabdominal radiotherapy-planning CT examinations with the respective organ/patient contours were collected retrospectively for the development and validation of segmentation 3D-UNets. Moreover, 97 diagnostic thoracic and 89 diagnostic abdomen CT examinations were collected retrospectively. For each of the diagnostic CT examinations, personalized MC dosimetry was performed. The data derived from MC simulations along with the respective CT data were used for the training and validation of a dose prediction deep neural network (DNN). An algorithm was developed to utilize the trained models and perform patient-specific organ dose estimates for thoracic and abdomen CT examinations. The doses estimated with the DNN were compared with the respective doses derived from MC simulations. A paired t-test was conducted between the DNN and MC results. Furthermore, the time efficiency of the proposed methodology was assessed. RESULTS: The mean percentage differences (range) between DNN and MC dose estimates for the lungs, liver, spleen, stomach, and kidneys were 7.2 % (0.2-24.1 %), 5.5 % (0.4-23.0 %), 7.9 % (0.6-22.3 %), 6.9 % (0.0-23.0 %) and 6.7 % (0.3-22.6 %) respectively. The differences between DNN and MC dose estimates were not significant (p-value = 0.12). Moreover, the mean processing time of the proposed workflow was 99 % lower than the respective time needed for MC-based dosimetry. CONCLUSIONS: The proposed methodology can be used for rapid and accurate patient-specific dosimetry in chest and abdomen CT.

ICRP workshop on the review and revision of the system of radiological protection: a focus on research priorities-feedback from the international community.

In September 2022, the International Commission on Radiological Protection (ICRP) organised a workshop in Estoril, Portugal, on the 'Review and Revision of the System of Radiological Protection: A Focus on Research Priorities'. The workshop, which was a side event of the European Radiation Protection Week, offered an opportunity to comment on a recent paper published by ICRP on areas of research to support the System of Radiological Protection. Altogether, about 150 individuals participated in the workshop. After the workshop, 16 of the 30 organisations in formal relations with ICRP provided written feedback. All participants and organisations followed ICRP's view that further research in various areas will offer additional support in improving the System in the short, medium, and long term. In general, it was emphasised that any research should be outcome-focused in that it should improve protection of people or the environment. Many research topics mentioned by the participants were in line with those already identified by ICRP in the paper noted above. In addition, further ideas were expressed such as, for example, that lessons learned during the COVID-19 pandemic with regards to the non-radiological social, economic and environment impacts, should be analysed for their usefulness to enhance radiological protection, and that current protection strategies and application of current radiological protection principles may need to be adapted to military scenarios like those observed recently during the military conflict in the Ukraine or the detonation of a nuclear weapon. On a broader perspective, it was discussed how radiation research and radiological protection can contribute towards the Sustainable Development Goals announced by the United Nations in 2015. This paper summarises the views expressed during the workshop and the major take home messages identified by ICRP.

Global status of medical physics human resource - The IOMP survey report.

PURPOSE: Realizing the need for periodic surveys about global medical physics human resource, the International Organization for Medical Physics (IOMP) performed a third survey following the previous two (2015 and 2018). The objective was to collect information about the current numbers of medical physicists (MPs) in individual countries, about their MP training, and accreditation pathways. METHODS: The survey was designed using Google Forms. Forms were distributed to national MP associations around the world. The data was collected during May-Nov 2022. MS Excel and SPSS software were used to perform descriptive statistics. RESULTS: 64 valid responses were received covering all continents. The largest numbers of MPs are in high income countries of Europe, Australia and North America, while the lowest numbers of MPs are seen in middle and low-income countries of Asia, Latin America and Africa. Among the respondents, 73% reported MP shortages in their countries. 69% reported the existence of an official MP training program which comprises university courses and in-service training. Furthermore, 85% of the respondents indicated the availability of MP university courses, primarily at the Master's degree level. Participation in research was between 10 and 30% of allocated work time for 42% and below 10% for 33% respondents. CONCLUSIONS: There are new findings on number of MPs per million population in different countries, with some expressing adequacy in the total number of MPs, but the data breakdown indicates a shortage in diagnostic X-ray physicists. Future surveys should also investigate in more detail data on outsourcing, and involvement in research.

A neural network-enhanced methodology for the rapid establishment of local DRLs in interventional radiology: EVAR as a case example.

PURPOSE: To develop a neural network-enhanced workflow for the automatic and rapid establishment/update of local diagnostic reference levels (DRLs) in interventional radiology (IR) using endovascular aneurysm repair (EVAR) procedures as a case example. METHODS: Radiation dose reports were collected retrospectively for 46 consecutive EVAR procedures. These reports served as demonstrative data for the development of the proposed methodology. An algorithm was developed to receive multiple dose reports, automatically extract the kerma area product (KAP), air kerma (Ka,r), number of exposure images, and fluoroscopy time (FT) from each report and calculate the first, second, third quartiles as well as the maximum and minimum values of the extracted parameters. To extract the values of interest from the dose reports, Tesseract, an open-source optical character recognition (OCR) engine was employed. Furthermore, the accuracy and time efficiency of the proposed methodology were assessed. Specifically, the values extracted from the algorithm were compared with the ground truth values and the algorithm's processing time was compared with the respective time needed to manually extract and process the values of interest. RESULTS: The OCR-based algorithm managed to correctly recognize 182 from the 184 target values, resulting in an accuracy of 99%. Moreover, the proposed pipeline reduced the processing time for the establishment of DRLs by 98%. DRL value for EVAR procedures, set as the third quartile of KAP was found to be 551 Gy*cm2. CONCLUSION: An accurate and time-efficient workflow was developed for the establishment of local DRLs in interventional radiology.

Implementation and evaluation of the AAPM TG 111 for radiation dosimetry in a state-of-the-art C-arm cone beam CT system.

The aim of this study was to assess the dosimetric characteristics of a state-of-the-art C-arm cone beam computed tomography (CBCT) system using the methodology proposed by the American Association of Physicists in Medicine (AAPM) Task Group (TG) 111. The dose measurement methodology described in AAPM TG 111 for wide cone beam acquisitions without table translation was employed to estimate equilibrium beam length (αeq‾) and equilibrium dose (feq‾) in various interventional task-specific protocols with different tube arc projection geometries. Dose profiles were derived from point dose measurements in the centre and peripheral locations of the ICRU/AAPM and standard polymethyl methacrylate (PMMA) body phantom. Dose measurements were performed in phantom and free-in-air using a solid-state point detector. Monte Carlo (MC) based simulation dosimetry was performed to quantify the inhomogeneous dose patterns imparted in the phantoms. Estimatedαeq‾andfeq‾on the ICRU/AAPM phantom was up to 49.4 cm and 6.17 mGy/100 mAs, respectively. Corresponding values determined on the PMMA phantom were 139 cm and 8.8 mGy/100 mAs, respectively. Free-in-air dose measurement ranged from 1.43 mGy/100 mAs to 5.93 mGy/100 mAs. Per cent difference inαeq‾andfeq‾between MC simulation and solid-state point detector measurement methods in the ICRU/AAPM phantom were within 16% and 18%, respectively. Manufacturers can use the presented methodology to characterize the dosimetric properties of C-arm CBCT systems. Clinical medical physicists may follow this methodology to verify corresponding data provided by the manufacturer and check for C-arm CBCT system performance dosimetric consistency.

NTCP Calculations of Five Different Irradiation Techniques for the Treatment of Thymoma.

This study provided normal tissue complication probability (NTCP) calculations from photon radiotherapy techniques in eleven patients with thymoma. Five plans were created for each participant using three-dimensional conformal radiotherapy (3D-CRT), five-field intensity modulated radiotherapy (5F-IMRT), seven-field IMRT (7F-IMRT), and volumetric modulated arc therapy with full arcs (FA-VMAT) and partial arcs (PA-VMAT). The target coverage, homogeneity index and conformation number for the planning target volume (PTV) and dosimetric parameters for the organs-at-risk (OARs) were taken from the fifty-five generated plans. The patient-specific NTCP of the lungs, heart and esophagus was calculated with an in-house software tool using differential dose-volume histograms and the equivalent uniform dose model. The PTV dose metrics from 3D-CRT were inferior to those from IMRT and VMAT plans. The dose constraints for the OARs were met in all treatment plans. The NTCP range of the lungs, heart and esophagus was 0.34-0.49%, 0.03-0.06% and 0.08-0.10%, respectively. The NTCPs of the heart for the incidence of peridarditis from IMRT and VMAT were significantly smaller than those from conformal treatment (p < 0.05). The 7F-IMRT was significantly superior to FA-VMAT in reducing the NTCP of the lungs and the risk of pneumonitis (p = 0.001). Similar superiority of 5F-IMRT over PA-VMAT for lung protection was found (p = 0.009). The presented results may be employed in the selection of the appropriate irradiation technique for restricting the complications in the adjacent OARs.

European survey on the use of patient contact shielding during radiological examinations.

OBJECTIVES: Contact shielding (CS) of patients during X-ray studies has been used for decades to protect radiosensitive organs. This practice has not changed much despite increasing evidence that CS is not useful in many cases. The Gonad And Patient Shielding (GAPS) group-founded by representatives of the main European bodies involved in radiology-promoted this survey to assess the current practice of CS among European radiology departments and the attitude towards a non-shielding policy. METHODS: Over a four-month period (15 May-15th September 2021) European Society of Radiology and European Society of Paediatric Radiology radiologist members were invited to respond to a web-based questionnaire consisting of 59 questions. RESULTS: 225 centres from 35 countries responded to this survey. CS was routinely applied in at least one radiological modality in 49.2% of centres performing studies in adults, 57.5% of centres performing studies in children, and 47.8% of centres performing studies on pregnant women. CS was most frequently used in conventional radiography, where the most frequently shielded organs were the gonads, followed by thyroid, female breasts, and eye lens. 83.6% respondents would follow European recommendations on the use of CS when provided by the main European bodies involved in radiology. CONCLUSIONS: This review shows that CS is still largely used across Europe. However, a non-shielding policy could be adopted in most departments if European professional societies provided recommendations. In this regard, a strong commitment by European and national professional societies to educate and inform practitioners, patients and carers is paramount. CLINICAL RELEVANCE STATEMENT: According to this survey expectations of patients and carers, and skepticism among professionals about the limited benefits of CS are the most important obstacles to the application of a no-shielding policy. A strong commitment from European and national professional societies to inform practitioners, patients and carers is fundamental.

The effect of added filtration on radiation dose and image quality in digital radiography of newborns.

BACKGROUND: Repeated chest and/or abdomen radiographs are needed on mobile digital radiography (DR) units to assess infants' health status. Optimization of DR tube peak kilovoltage (kVp) and tube current time product (mAs) to derive images of diagnostic quality at as low as reasonably achievable radiation dose has been a challenging task. OBJECTIVE: To investigate the effect of exposure parameters and additional filtration on entrance skin dose and image quality in newborn DR imaging. MATERIALS AND METHODS: A physical anthropomorphic phantom that simulates the average full-term neonate was used. Chest and chest/abdomen DR images were acquired at the manufacturer's recommended kVp/mAs exposure parameters followed by a series of image acquisitions at different kVp/mAs and beam filtration combinations. Entrance skin dose (ESD) and signal difference to noise ratio (SdNR) for soft tissue, bone and feeding gastric tube were estimated in raw unprocessed images. A figure of merit (FOM) analysis provided the kVp/mAs and filtration that generated images of adequate quality at the lowest ESD. RESULTS: Signal difference increased with kVp and progressively decreased with increasing filtration. Compared to the manufacturer's recommended 53 kVp/1.6 mAs exposure parameters, ESD was reduced by 76% (47.61 µGy versus 11.3 µGy) in the chest and 66% (47.61 µGy versus 16.14 µGy) in the chest/abdomen when the exposure parameters and additional beam filtration provided by the FOM analysis were used. CONCLUSION: The results derived from this phantom study suggest that additional beam filtration along with the appropriate leverage of exposure parameters can lower ESD while maintaining image quality in full-term newborns.

Education and training in radiation protection in Europe: results from the EURAMED Rocc-n-Roll project survey.

PURPOSE: To analyse the existing radiation protection (RP) education and training (E&T) capabilities in the European Union and identify associated needs, problems and challenges. METHOD: An online survey was disseminated via the EURAMED Rocc-n-Roll consortium network and prominent medical societies in the field of radiological research. The survey sections analyse the RP E&T during undergraduate, residency/internship and continuous professional development; RP E&T problems and legal implementation. Differences were analysed by European geographic regions, profession, years of professional experience and main area of practice/research. RESULTS: The majority of the 550 respondents indicated that RP topics are part of undergraduate curricula in all courses for their profession and country (55%); however, hands-on practical training is not included according to 30% of the respondents. The lack of E&T, practical aspects in current E&T, and mandatory continuing E&T were considered the major problems. The legal requirement that obtained higher implementation score was the inclusion of the practical aspects of medical radiological procedures on education (86%), and lower score was obtained for the inclusion of RP E&T on medical and dental school curriculums (61%). CONCLUSIONS: A heterogeneity in RP E&T during undergraduate, residency/internship and continuous professional development is evident across Europe. Differences were noted per area of practice/research, profession, and European geographic region. A large variation in RP E&T problem rating was also obtained.

Rapid estimation of patient-specific organ doses using a deep learning network.

BACKGROUND: Patient-specific organ-dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks, protocol optimization, and patient management. Current dose estimation techniques mainly rely on time-consuming Monte Carlo methods or/and generalized anthropomorphic phantoms. PURPOSE: We proposed a proof-of-concept rapid workflow based on deep learning networks to estimate organ doses for individuals following thorax Computed Tomography (CT) examinations. METHODS: CT scan data from 95 individuals undergoing thorax CT examinations were used. Monte Carlo simulations were performed and three-dimensional (3D) dose distributions for each patient were obtained. A fully connected sequential deep learning network model was constructed and trained for each organ considered in this study. Water-equivalent diameter (WED), scan length, and tube current were the independent variables. Organ doses for heart, lungs, esophagus, and bones were calculated from the Monte Carlo 3D distribution and used to train the deep learning networks. Organ dose predictions from each network were evaluated using an independent data set of 19 patients. RESULTS: The trained networks provided organ dose predictions within a second. There was very good agreement between the deep learning network predictions and reference organ dose values calculated from Monte Carlo simulations. The average difference was -1.5% for heart, -1.6% for esophagus, -1.0% for lungs, and -0.4% for bones in the 95 patients dataset, and -5.1%, 4.3%, 0.9%, and 1.4% respectively in the 19 patients test dataset. CONCLUSIONS: The proposed workflow demonstrated that patient-specific organ-doses can be estimated in nearly real-time using deep learning networks. The workflow can be readily implemented and requires a small set of representative data for training.

A review on fetal dose in Radiotherapy: A historical to contemporary perspective.

This paper aims to review on fetal dose in radiotherapy and extends and updates on a previous work1 to include proton therapy. Out-of-field doses, which are the doses received by regions outside of the treatment field, are unavoidable regardless of the treatment modalities used during radiotherapy. In the case of pregnant patients, fetal dose is a major concern as it has long been recognized that fetuses exposed to radiation have a higher probability of suffering from adverse effects such as anatomical malformations and even fetal death, especially when the 0.1Gy threshold is exceeded. In spite of the low occurrence of cancer during pregnancy, the radiotherapy team should be equipped with the necessary knowledge to deal with fetal dose. This is crucial so as to ensure that the fetus is adequately protected while not compromising the patient treatment outcomes. In this review paper, various aspects of fetal dose will be discussed ranging from biological, clinical to the physics aspects. Other than fetal dose resulting from conventional photon therapy, this paper will also extend the discussion to modern treatment modalities and techniques, namely proton therapy and image-guided radiotherapy, all of which have seen a significant increase in use in current radiotherapy. This review is expected to provide readers with a comprehensive understanding of fetal dose in radiotherapy, and to be fully aware of the steps to be taken in providing radiotherapy for pregnant patients.

Automatic Radiobiological Comparison of Radiation Therapy Plans: An Application to Gastric Cancer.

(1) Aim: This study was conducted to radiobiologically compare radiotherapy plans for gastric cancer with a newly developed software tool. (2) Methods: Treatment planning was performed on two computational phantoms simulating adult male and female patients. Three-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) plans for gastric cancer were generated with three-photon beam energies. The equivalent uniform dose (EUD), tumor control probability (TCP) of the target and normal tissue control probability (NTCP) of eight different critical organs were calculated. A new software was employed for these calculations using the EUD-based model and dose-volume-histogram data. (3) Results: The IMRT and VMAT plan led to TCPs of 51.3-51.5%, whereas 3D-CRT gave values up to 50.2%. The intensity-modulated techniques resulted in NTCPs of (5.3 × 10-6-3.3 × 10-1)%. The corresponding NTCPs from 3D-CRT were (3.4 × 10-7-7.4 × 10-1)%. The above biological indices were automatically calculated in less than 40 s with the software. (4) Conclusions: The direct and quick radiobiological evaluation of radiotherapy plans is feasible using the new software tool. The IMRT and VMAT reduced the probability of the appearance of late effects in most of the surrounding critical organs and slightly increased the TCP compared to 3D-CRT.