Anonymization of DICOM electronic medical records for radiation therapy

Electronic medical records (EMR) and treatment plans are used in research on patient outcomes and radiation effects. In many situations researchers must remove protected health information (PHI) from EMRs. The literature contains several studies describing the anonymization of generic Digital Imaging and Communication in Medicine (DICOM) files and DICOM image sets but no publications were found that discuss the anonymization of DICOM radiation therapy plans, a key component of an EMR in a cancer clinic. In addition to this we were unable to find a commercial software tool that met the minimum requirements for anonymization and preservation of data integrity for radiation therapy research. The purpose of this study was to develop a prototype software code to meet the requirements for the anonymization of radiation therapy treatment plans and to develop a way to validate that code and demonstrate that it properly anonymized treatment plans and preserved data integrity. We extended an open-source code to process all relevant PHI and to allow for the automatic anonymization of multiple EMRs. The prototype code successfully anonymized multiple treatment plans in less than 1. min/patient. We also tested commercial optical character recognition (OCR) algorithms for the detection of burned-in text on the images, but they were unable to reliably recognize text. In addition, we developed and tested an image filtering algorithm that allowed us to isolate and redact alpha-numeric text from a test radiograph. Validation tests verified that PHI was anonymized and data integrity, such as the relationship between DICOM unique identifiers (UID) was preserved. © 2014 Elsevier Ltd

Feasibility of generating synthetic CT from T1-weighted MRI using a linear mixed-effects regression model

Generation of synthetic computed tomography (sCT) for magnetic resonance imaging (MRI)-only radiotherapy is emerging as a promising direction because it can eliminate the registration error and simplify clinical workflow. The goal of this study was to generate accurate sCT from standard T1-weighted MRI for brain patients. CT and MRI data of twelve patients with brain tumors were retrospectively collected. Linear mixed-effects regression models were fitted between CT and T1-weighted MRI intensities for different segments in the brain. The whole brain sCTs were generated by combining predicted segments together. Mean absolute error (MAE) between real CTs and sCTs across all patients was 71.1 ±5.5 Hounsfield Unit (HU). Average differences in the HU values were 1.7 ±7.1 HU (gray matter), 0.9 ±5.1 HU (white matter), -24.7 ±8.0 HU (cerebrospinal fluid), 76.4 ±17.8 HU (bone), 20.9 ±20.4 HU (fat), -69.4 ±28.3 HU (air). A simple regression technique has been devised that is capable of producing accurate HU maps from standard T1-weighted MRI, and exceptionally low MAE values indicate accurate prediction of sCTs. Improvement is needed in segmenting MRI using a more automatic approach

An atlas-based method to predict three-dimensional dose distributions for cancer patients who receive radiotherapy

Due to the complexity of advanced radiotherapy techniques, treatment planning process is usually time consuming and plan quality can vary considerably among planners and institutions. It is also impractical to generate all possible treatment plans based on available radiotherapy techniques and select the best option for a specific patient. Automatic dose prediction will be very helpful in these situations, while there were a few studies of three-dimensional (3D) dose prediction for patients who received radiotherapy. The purpose of this work was to develop a novel atlas-based method to predict 3D dose prediction and to evaluate its performance. Previously treated nineteen left-sided post-mastectomy breast cancer patients and sixteen prostate cancer patients were included in this study. One patient was arbitrarily chosen as the reference for each type of cancer and all the remaining patients\u27 computed tomography (CT) images and contours were aligned to it using deformable image registration (DIR). Deformable vector field (DVF) for each patient i (DVFi-ref) was used to deform the original 3D dose matrix of that patient. CT scan of a test patient was also registered with the same reference patient using DIR and both direct DVF (DVFtest-ref) and inverse DVF () were derived. Similarity of atlas patients to the test patient was determined based on the similarity of DVFtest-ref to atlas DVFs (DVFi-ref) and appropriate weighting factors were calculated. Patients\u27 doses in the atlas were deformed again using to transform them from the reference patient\u27s coordinates to the test patient\u27s coordinates and the final 3D dose distribution for the test patient was predicted by summing the weighted individual 3D dose distributions. Performance of our method was evaluated and the results revealed that the proposed method was able to predict the 3D dose distributions accurately. The mean dose difference between clinical and predicted 3D dose distributions were 0.9 ± 1.1 Gy and 1.9 ± 1.2 Gy for breast and prostate plans. The proposed dose prediction method can be used to improve planning quality and facilitate plan comparisons

Postmastectomy radiotherapy for left-sided breast cancer patients: Comparison of advanced techniques

Postmastectomy radiotherapy (PMRT) has been shown to improve the overall survival for invasive breast cancer patients, and many advanced radiotherapy technologies were adopted for PMRT. The purpose of our study is to compare various advanced PMRT techniques including fixed-beam intensity-modulated radiotherapy (IMRT), non-coplanar volumetric modulated arc therapy (NC-VMAT), multiple arc VMAT (MA-VMAT), and tomotherapy (TOMO). Results of standard VMAT and mixed beam therapy that were published by our group previously were also included in the plan comparisons. Treatment plans were produced for nine PMRT patients previously treated in our clinic. The plans were evaluated based on planning target volume (PTV) coverage, dose homogeneity index (DHI), conformity index (CI), dose to organs at risk (OARs), normal tissue complication probability (NTCP) of pneumonitis, lifetime attributable risk (LAR) of second cancers, and risk of coronary events (RCE). All techniques produced clinically acceptable PMRT plans. Overall, fixed-beam IMRT delivered the lowest mean dose to contralateral breast (1.56 ± 0.4 Gy) and exhibited lowest LAR (0.6 ± 0.2%) of secondary contralateral breast cancer; NC-VMAT delivered the lowest mean dose to lungs (7.5 ± 0.8 Gy), exhibited lowest LAR (5.4 ± 2.8%) of secondary lung cancer and lowest NTCP (2.1 ± 0.4%) of pneumonitis; mixed beam therapy delivered the lowest mean dose to heart (7.1 ± 1.3 Gy) and exhibited lowest RCE (8.6 ± 7.1%); TOMO plans provided the most optimal target coverage while delivering higher dose to OARs than other techniques. Both NC-VMAT and MA-VMAT exhibited lower values of all OARs evaluation metrics compare to standard VMAT. Fixed-beam IMRT, NC-VMAT, and mixed beam therapy could be the optimal radiation technique for certain breast cancer patients after mastectomy

Comparison of conventional and advanced radiotherapy techniques for left-sided breast cancer after breast conserving surgery

Whole breast radiotherapy (WBRT) after breast conserving surgery is the standard treatment to prevent recurrence and metastasis of early stage breast cancer. This study aims to compare seven WBRT techniques including conventional tangential, field-in-field (FIF), hybrid intensity-modulated radiotherapy (IMRT), IMRT, standard volumetric modulated arc therapy (STD-VMAT), noncoplanar VMAT (NC-VMAT), and multiple arc VMAT (MA-VMAT). Fifteen patients who were previously diagnosed with left-sided early stage breast cancer and treated in our clinic were selected for this study. WBRT plans were created for these patients and were evaluated based on target coverage and normal tissue toxicities. All techniques produced clinically acceptable WBRT plans. STD-VMAT delivered the lowest mean dose (1.1 ± 0.3 Gy) and the lowest maximum dose (7.3 ± 4.9 Gy) to contralateral breast, and the second lowest lifetime attributable risk (LAR) (4.1 ± 1.4%) of secondary contralateral breast cancer. MA-VMAT delivered the lowest mean dose to lungs (4.9 ± 0.9 Gy) and heart (5.5 ± 1.2 Gy), exhibited the lowest LAR (1.7 ± 0.3%) of secondary lung cancer, normal tissue complication probability (NTCP) (1.2 ± 0.2%) of pneumonitis, risk of coronary events (RCE) (10.3 ± 2.7%), and LAR (3.9 ± 1.3%) of secondary contralateral breast cancer. NC-VMAT plans provided the most conformal target coverage, the lowest maximum lung dose (46.2 ± 4.1 Gy) and heart dose (41.1 ± 5.4 Gy), and the second lowest LAR (1.8 ± 0.4%) of secondary lung cancer and RCE (10.5 ± 2.8%). MA-VMAT and NC-VMAT could be the preferred techniques for early stage breast cancer patients after breast conserving surgery

Arquitectura innovadora de visión 3D de estructuras corporales con fines docentes

Memoria ID-114. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2019-2020.[ES]Los objetivos de este proyecto son: el desarrollo de un conjunto de imágenes anatómicas recontruidas en 3D mediante técnicas de diagnóstico por imagen y escáneres digitales, para fines docentes en las titulaciones de las ciencas de la salud; y la generación de un atlas anatómico y radiológico de utilidad para los alumno

Inter-institutional comparison of personalized risk assessments for second malignant neoplasms for a 13-year-old girl receiving proton versus photon craniospinal irradiation

Children receiving radiotherapy face the probability of a subsequent malignant neoplasm (SMN). In some cases, the predicted SMN risk can be reduced by proton therapy. The purpose of this study was to apply the most comprehensive dose assessment methods to estimate the reduction in SMN risk after proton therapy vs. photon therapy for a 13-year-old girl requiring craniospinal irradiation (CSI). We reconstructed the equivalent dose throughout the patient’s body from therapeutic and stray radiation and applied SMN incidence and mortality risk models for each modality. Excluding skin cancer, the risk of incidence after proton CSI was a third of that of photon CSI. The predicted absolute SMN risks were high. For photon CSI, the SMN incidence rates greater than 10% were for thyroid, non-melanoma skin, lung, colon, stomach, and other solid cancers, and for proton CSI they were non-melanoma skin, lung, and other solid cancers. In each setting, lung cancer accounted for half the risk of mortality. In conclusion, the predicted SMN risk for a 13-year-old girl undergoing proton CSI was reduced vs. photon CSI. This study demonstrates the feasibility of inter-institutional whole-body dose and risk assessments and also serves as a model for including risk estimation in personalized cancer care

A review of radiotherapy-induced late effects research after advanced technology treatments

The number of incident cancers and long-term cancer survivors is expected to increase substantially for at least a decade. Advanced technology radiotherapies, e.g., using beams of protons and photons, offer dosimetric advantages that theoretically yield better outcomes. In general, evidence from controlled clinical trials and epidemiology studies are lacking. To conduct these studies, new research methods and infrastructure will be needed. In the paper, we review several key research methods of relevance to late effects after advanced technology proton-beam and photon-beam radiotherapies. In particular, we focus on the determination of exposures to therapeutic and stray radiation and related uncertainties, with discussion of recent advances in exposure calculation methods, uncertainties, in silico studies, computing infrastructure, electronic medical records, and risk visualization. We identify six key areas of methodology and infrastructure that will be needed to conduct future outcome studies of radiation late effects