Natural Language Processing Algorithm Used for Staging Pulmonary Oncology from Free-Text Radiological Reports: "Including PET-CT and Validation Towards Clinical Use".

Natural language processing (NLP) can be used to process and structure free text, such as (free text) radiological reports. In radiology, it is important that reports are complete and accurate for clinical staging of, for instance, pulmonary oncology. A computed tomography (CT) or positron emission tomography (PET)-CT scan is of great importance in tumor staging, and NLP may be of additional value to the radiological report when used in the staging process as it may be able to extract the T and N stage of the 8th tumor-node-metastasis (TNM) classification system. The purpose of this study is to evaluate a new TN algorithm (TN-PET-CT) by adding a layer of metabolic activity to an already existing rule-based NLP algorithm (TN-CT). This new TN-PET-CT algorithm is capable of staging chest CT examinations as well as PET-CT scans. The study design made it possible to perform a subgroup analysis to test the external validation of the prior TN-CT algorithm. For information extraction and matching, pyContextNLP, SpaCy, and regular expressions were used. Overall TN accuracy score of the TN-PET-CT algorithm was 0.73 and 0.62 in the training and validation set (N = 63, N = 100). The external validation of the TN-CT classifier (N = 65) was 0.72. Overall, it is possible to adjust the TN-CT algorithm into a TN-PET-CT algorithm. However, outcomes highly depend on the accuracy of the report, the used vocabulary, and its context to express, for example, uncertainty. This is true for both the adjusted PET-CT algorithm and for the CT algorithm when applied in another hospital.

Development and validation of multicentre study on novel Artificial Intelligence-based Cardiovascular Risk Score (AICVD).

OBJECTIVE: Cardiovascular diseases (CVD) are one of the most prevalent diseases in India amounting for nearly 30% of total deaths. A dearth of research on CVD risk scores in Indian population, limited performance of conventional risk scores and inability to reproduce the initial accuracies in randomised clinical trials has led to this study on large-scale patient data. The objective is to develop an Artificial Intelligence-based Risk Score (AICVD) to predict CVD event (eg, acute myocardial infarction/acute coronary syndrome) in the next 10 years and compare the model with the Framingham Heart Risk Score (FHRS) and QRisk3. METHODS: Our study included 31 599 participants aged 18-91 years from 2009 to 2018 in six Apollo Hospitals in India. A multistep risk factors selection process using Spearman correlation coefficient and propensity score matching yielded 21 risk factors. A deep learning hazards model was built on risk factors to predict event occurrence (classification) and time to event (hazards model) using multilayered neural network. Further, the model was validated with independent retrospective cohorts of participants from India and the Netherlands and compared with FHRS and QRisk3. RESULTS: The deep learning hazards model had a good performance (area under the curve (AUC) 0.853). Validation and comparative results showed AUCs between 0.84 and 0.92 with better positive likelihood ratio (AICVD -6.16 to FHRS -2.24 and QRisk3 -1.16) and accuracy (AICVD -80.15% to FHRS 59.71% and QRisk3 51.57%). In the Netherlands cohort, AICVD also outperformed the Framingham Heart Risk Model (AUC -0.737 vs 0.707). CONCLUSIONS: This study concludes that the novel AI-based CVD Risk Score has a higher predictive performance for cardiac events than conventional risk scores in Indian population. TRIAL REGISTRATION NUMBER: CTRI/2019/07/020471.

Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer.

PURPOSE: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships. METHODS AND MATERIALS: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS: We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS: O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.

T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting.

BACKGROUND: In the era of datafication, it is important that medical data are accurate and structured for multiple applications. Especially data for oncological staging need to be accurate to stage and treat a patient, as well as population-level surveillance and outcome assessment. To support data extraction from free-text radiological reports, Dutch natural language processing (NLP) algorithm was built to quantify T-stage of pulmonary tumors according to the tumor node metastasis (TNM) classification. This structuring tool was translated and validated on English radiological free-text reports. A rule-based algorithm to classify T-stage was trained and validated on, respectively, 200 and 225 English free-text radiological reports from diagnostic computed tomography (CT) obtained for staging of patients with lung cancer. The automated T-stage extracted by the algorithm from the report was compared to manual staging. A graphical user interface was built for training purposes to visualize the results of the algorithm by highlighting the extracted concepts and its modifying context. RESULTS: Accuracy of the T-stage classifier was 0.89 in the validation set, 0.84 when considering the T-substages, and 0.76 when only considering tumor size. Results were comparable with the Dutch results (respectively, 0.88, 0.89 and 0.79). Most errors were made due to ambiguity issues that could not be solved by the rule-based nature of the algorithm. CONCLUSIONS: NLP can be successfully applied for staging lung cancer from free-text radiological reports in different languages. Focused introduction of machine learning should be introduced in a hybrid approach to improve performance.

Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network.

PURPOSE: The TNM classification system is used for prognosis, treatment, and research. Regular updates potentially break backward compatibility. Reclassification is not always possible, is labor intensive, or requires additional data. We developed a Bayesian network (BN) for reclassifying the 5th, 6th, and 7th editions of the TNM and predicting survival for non-small-cell lung cancer (NSCLC) without training data with known classifications in multiple editions. METHODS: Data were obtained from the Netherlands Cancer Registry (n = 146,084). A BN was designed with nodes for TNM edition and survival, and a group of nodes was designed for all TNM editions, with a group for edition 7 only. Before learning conditional probabilities, priors for relations between the groups were manually specified after analysis of changes between editions. For performance evaluation only, part of the 7th edition test data were manually reclassified. Performance was evaluated using sensitivity, specificity, and accuracy. Two-year survival was evaluated with the receiver operating characteristic area under the curve (AUC), and model calibration was visualized. RESULTS: Manual reclassification of 7th to 6th edition stage group as ground truth for testing was impossible in 5.6% of the patients. Predicting 6th edition stage grouping using 7th edition data and vice versa resulted in average accuracies, sensitivities, and specificities between 0.85 and 0.99. The AUC for 2-year survival was 0.81. CONCLUSION: We have successfully created a BN for reclassifying TNM stage grouping across TNM editions and predicting survival in NSCLC without knowing the true TNM classification in various editions in the training set. We suggest binary prediction of survival is less relevant than predicted probability and model calibration. For research, probabilities can be used for weighted reclassification.

Natural Language Processing in Dutch Free Text Radiology Reports: Challenges in a Small Language Area Staging Pulmonary Oncology.

Reports are the standard way of communication between the radiologist and the referring clinician. Efforts are made to improve this communication by, for instance, introducing standardization and structured reporting. Natural Language Processing (NLP) is another promising tool which can improve and enhance the radiological report by processing free text. NLP as such adds structure to the report and exposes the information, which in turn can be used for further analysis. This paper describes pre-processing and processing steps and highlights important challenges to overcome in order to successfully implement a free text mining algorithm using NLP tools and machine learning in a small language area, like Dutch. A rule-based algorithm was constructed to classify T-stage of pulmonary oncology from the original free text radiological report, based on the items tumor size, presence and involvement according to the 8th TNM classification system. PyContextNLP, spaCy and regular expressions were used as tools to extract the correct information and process the free text. Overall accuracy of the algorithm for evaluating T-stage was 0,83 in the training set and 0,87 in the validation set, which shows that the approach in this pilot study is promising. Future research with larger datasets and external validation is needed to be able to introduce more machine learning approaches and perhaps to reduce required input efforts of domain-specific knowledge. However, a hybrid NLP approach will probably achieve the best results.

American Association of Physicists in Medicine Task Group 263: Standardizing Nomenclatures in Radiation Oncology.

A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose-volume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors. The stakeholder groups represented in the membership included the AAPM, American Society for Radiation Oncology (ASTRO), NRG Oncology, European Society for Radiation Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), Children's Oncology Group (COG), Integrating Healthcare Enterprise in Radiation Oncology (IHE-RO), and Digital Imaging and Communications in Medicine working group (DICOM WG); A nomenclature system for target and organ at risk volumes and DVH nomenclature was developed and piloted to demonstrate viability across a range of clinics and within the framework of clinical trials. The final report was approved by AAPM in October 2017. The approval process included review by 8 AAPM committees, with additional review by ASTRO, European Society for Radiation Oncology (ESTRO), and American Association of Medical Dosimetrists (AAMD). This Executive Summary of the report highlights the key recommendations for clinical practice, research, and trials.

Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability.

PURPOSE: Besides basic measurements as maximum standardized uptake value (SUV)max or SUVmean derived from 18F-FDG positron emission tomography (PET) scans, more advanced quantitative imaging features (i.e. "Radiomics" features) are increasingly investigated for treatment monitoring, outcome prediction, or as potential biomarkers. With these prospected applications of Radiomics features, it is a requisite that they provide robust and reliable measurements. The aim of our study was therefore to perform an integrated stability analysis of a large number of PET-derived features in non-small cell lung carcinoma (NSCLC), based on both a test-retest and an inter-observer setup. METHODS: Eleven NSCLC patients were included in the test-retest cohort. Patients underwent repeated PET imaging within a one day interval, before any treatment was delivered. Lesions were delineated by applying a threshold of 50% of the maximum uptake value within the tumor. Twenty-three NSCLC patients were included in the inter-observer cohort. Patients underwent a diagnostic whole body PET-computed tomography (CT). Lesions were manually delineated based on fused PET-CT, using a standardized clinical delineation protocol. Delineation was performed independently by five observers, blinded to each other. Fifteen first order statistics, 39 descriptors of intensity volume histograms, eight geometric features and 44 textural features were extracted. For every feature, test-retest and inter-observer stability was assessed with the intra-class correlation coefficient (ICC) and the coefficient of variability, normalized to mean and range. Similarity between test-retest and inter-observer stability rankings of features was assessed with Spearman's rank correlation coefficient. RESULTS: Results showed that the majority of assessed features had both a high test-retest (71%) and inter-observer (91%) stability in terms of their ICC. Overall, features more stable in repeated PET imaging were also found to be more robust against inter-observer variability. CONCLUSION: Results suggest that further research of quantitative imaging features is warranted with respect to more advanced applications of PET imaging as being used for treatment monitoring, outcome prediction or imaging biomarkers.

[¹8F]fluorodeoxyglucose uptake patterns in lung before radiotherapy identify areas more susceptible to radiation-induced lung toxicity in non-small-cell lung cancer patients.

PURPOSE: Our hypothesis was that pretreatment inflammation in the lung makes pulmonary tissue more susceptible to radiation damage. The relationship between pretreatment [(18)F]fluorodeoxyglucose ([(18)F]FDG) uptake in the lungs (as a surrogate for inflammation) and the delivered radiation dose and radiation-induced lung toxicity (RILT) was investigated. METHODS AND MATERIALS: We retrospectively studied a prospectively obtained cohort of 101 non-small-cell lung cancer patients treated with (chemo)radiation therapy (RT). [(18)F]FDG-positron emission tomography-computed tomography (PET-CT) scans used for treatment planning were studied. Different parameters were used to describe [(18)F]FDG uptake patterns in the lungs, excluding clinical target volumes, and the interaction with radiation dose. An increase in the dyspnea grade of 1 (Common Terminology Criteria for Adverse Events version 3.0) or more points compared to the pre-RT score was used as an endpoint for analysis of RILT. The effect of [(18)F]FDG and CT-based variables, dose, and other patient or treatment characteristics that effected RILT was studied using logistic regression. RESULTS: Increased lung density and pretreatment [(18)F]FDG uptake were related to RILT after RT with univariable logistic regression. The 95th percentile of the [(18)F]FDG uptake in the lungs remained significant in multivariable logistic regression (p = 0.016; odds ratio [OR] = 4.3), together with age (p = 0.029; OR = 1.06), and a pre-RT dyspnea score of ≥1 (p = 0.005; OR = 0.20). Significant interaction effects were demonstrated among the 80th, 90th, and 95th percentiles and the relative lung volume receiving more than 2 and 5 Gy. CONCLUSIONS: The risk of RILT increased with the 95th percentile of the [(18)F]FDG uptake in the lungs, excluding clinical tumor volume (OR = 4.3). The effect became more pronounced as the fraction of the 5%, 10%, and 20% highest standardized uptake value voxels that received more than 2 Gy to 5 Gy increased. Therefore, the risk of RILT may be decreased by applying sophisticated radiotherapy techniques to avoid areas in the lung with high [(18)F]FDG uptake.

The ESTRO Breur Lecture 2009. From population to voxel-based radiotherapy: exploiting intra-tumour and intra-organ heterogeneity for advanced treatment of non-small cell lung cancer.

Evidence is accumulating that radiotherapy of non-small cell lung cancer patients can be optimized by escalating the tumour dose until the normal tissue tolerances are met. To further improve the therapeutic ratio between tumour control probability and the risk of normal tissue complications, we firstly need to exploit inter patient variation. This variation arises, e.g. from differences in tumour shape and size, lung function and genetic factors. Secondly improvement is achieved by taking into account intra-tumour and intra-organ heterogeneity derived from molecular and functional imaging. Additional radiation dose must be delivered to those parts of the tumour that need it the most, e.g. because of increased radio-resistance or reduced therapeutic drug uptake, and away from regions inside the lung that are most prone to complication. As the delivery of these treatments plans is very sensitive for geometrical uncertainties, probabilistic treatment planning is needed to generate robust treatment plans. The administration of these complicated dose distributions requires a quality assurance procedure that can evaluate the treatment delivery and, if necessary, adapt the treatment plan during radiotherapy.

Dose recalculation in megavoltage cone-beam CT for treatment evaluation: removal of cupping and truncation artefacts in scans of the thorax and abdomen.

PURPOSE: To correct megavoltage cone-beam CT (MVCBCT) images of the thorax and abdomen for cupping and truncation artefacts to reconstruct the 3D-delivered dose distribution for treatment evaluation. MATERIALS AND METHODS: MVCBCT scans of three phantoms, three lung and two rectal cancer patients were acquired. The cone-beam projection images were iteratively corrected for cupping and truncation artefacts and the resulting primary transmission was used for cone-beam reconstruction. The reconstructed scans were merged into the planning CT scan (MVCBCT+). Dose distributions of clinical IMRT, stereotactic and conformal treatment plans were recalculated on the uncorrected and corrected MVCBCT+ scans using the treatment planning system and compared to the planned dose distribution. RESULTS: The dose distributions on the corrected MVCBCT+ of the phantoms were accurate for 99% of the voxels within 2% or 2mm. Using this method the errors in mean GTV dose reduced from about 10% to 1% for the patients. CONCLUSIONS: The method corrects cupping and truncation artefacts in cone-beam scans of the thorax and abdomen in addition to head-and-neck (demonstrated previously). The corrected scans can be used to calculate the influence of anatomical changes on the 3D-delivered dose distribution.

In vivo dosimetry with a linear MOSFET array to evaluate the urethra dose during permanent implant brachytherapy using iodine-125.

PURPOSE: To develop a technique to monitor the dose rate in the urethra during permanent implant brachytherapy using a linear MOSFET array, with sufficient accuracy and without significantly extending the implantation time. METHODS AND MATERIALS: Phantom measurements were performed to determine the optimal conditions for clinical measurements. In vivo measurements were performed in 5 patients during the (125)I brachytherapy implant procedure. To evaluate if the urethra dose obtained in the operating room with the ultrasound transducer in the rectum and the patient in treatment position is a reference for the total accumulated dose; additional measurements were performed after the implantation procedure, in the recovery room. RESULTS: In vivo measurements during and after the implantation procedure agree very well, illustrating that the ultrasound transducer in the rectum and patient positioning do not influence the measured dose in the urethra. In vivo dose values obtained during the implantation are therefore representative for the total accumulated dose in the urethra. In 5 patients, the dose rates during and after the implantation were below the maximum dose rate of the urethra, using the planned seed distribution. CONCLUSION: In vivo dosimetry during the implantation, using a MOSFET array, is a feasible technique to evaluate the dose in the urethra during the implantation of (125)I seeds for prostate brachytherapy.

Intra-voxel heterogeneity influences the dose prescription for dose-painting with radiotherapy: a modelling study.

The purpose of this study was to increase the potential of dose redistribution by incorporating estimates of oxygen heterogeneity within imaging voxels for optimal dose determination. Cellular oxygen tension (pO(2)) distributions were estimated for imaging-size-based voxels by solving oxygen diffusion-consumption equations around capillaries placed at random locations. The linear-quadratic model was used to determine cell survival in the voxels as a function of pO(2) and dose. The dose distribution across the tumour was optimized to yield minimal survival after 30 x 2 Gy fractions by redistributing the dose based on differences in oxygen levels. Eppendorf data of a series of 69 tumours were used as a surrogate of what might be expected from oxygen imaging datasets. Dose optimizations were performed both taking into account cellular heterogeneity in oxygenation within voxels and assuming a homogeneous cellular distribution of oxygen. Our simulations show that dose redistribution based on derived cellular oxygen distributions within voxels result in dose distributions that require less total dose to obtain the same degree of cell kill as dose distributions that were optimized with a model that considered voxels as homogeneous with respect to oxygen. Moderately hypoxic tumours are expected to gain most from dose redistribution. Incorporating cellular-based distributions of radiosensitivity into dose-planning algorithms theoretically improves the potential gains from dose redistribution algorithms.

Identification of residual metabolic-active areas within individual NSCLC tumours using a pre-radiotherapy (18)Fluorodeoxyglucose-PET-CT scan.

BACKGROUND AND PURPOSE: Non-small cell lung cancer (NSCLC) tumours are mostly heterogeneous. We hypothesized that areas within the tumour with a high pre-radiation (18)F-deoxyglucose (FDG) uptake, could identify residual metabolic-active areas, ultimately enabling selective-boosting of tumour sub-volumes. MATERIAL AND METHODS: Fifty-five patients with inoperable stage I-III NSCLC treated with chemo-radiation or with radiotherapy alone were included. For each patient one pre-radiotherapy and one post-radiotherapy FDG-PET-CT scans were available. Twenty-two patients showing persistent FDG uptake in the primary tumour after radiotherapy were analyzed. Overlap fractions (OFs) were calculated between standardized uptake value (SUV) threshold-based auto-delineations on the pre- and post-radiotherapy scan. RESULTS: Patients with residual metabolic-active areas within the tumour had a significantly worse survival compared to individuals with a complete metabolic response (p=0.002). The residual metabolic-active areas within the tumour largely corresponded (OF>70%) with the 50%SUV high FDG uptake area of the pre-radiotherapy scan. The hotspot within the residual area (90%SUV) was completely within the GTV (OF=100%), and had a high overlap with the pre-radiotherapy 50%SUV threshold (OF>84%). CONCLUSIONS: The location of residual metabolic-active areas within the primary tumour after therapy corresponded with the original high FDG uptake areas pre-radiotherapy. Therefore, a single pre-treatment FDG-PET-CT scan allows for the identification of residual metabolic-active areas.

Metabolic control probability in tumour subvolumes or how to guide tumour dose redistribution in non-small cell lung cancer (NSCLC): an exploratory clinical study.

PURPOSE: To characterize the relationship between pre-radiotherapy (18)Fluorodeoxyglucose (FDG) uptake in a tumour voxel, radiation dose and the probability to achieve metabolic control in the tumour voxel after radiotherapy. MATERIALS AND METHODS: Thirty-nine patients with inoperable stage I-III non-small cell lung cancer, treated with radiotherapy (RT) alone or sequential chemo radiation were analysed retrospectively. Twenty-two showed metabolic active areas in the tumour 3 months post-radiotherapy, which is known to be a surrogate for persistent local tumour failure and worse survival. Pre- and post-RT FDG-PET-CT scans were registered and the metabolic active zones within the tumour after RT were projected on the pre-RT scan. Multi-level logistic regression was performed to determine the relation between the FDG uptake if a voxel pre-RT and its metabolic state after RT. RESULTS: The probability that a voxel is metabolically controlled (mVCP), decreased significantly with increasing FDG uptake in a voxel (SUV) (OR=0.72), increasing tumour volume (20 cm(3)) (OR=0.89) and increasing dose (Gy) (OR=0.99). Inter-patient differences in mVCP were substantial. CONCLUSION: A methodology was presented to derive relationships between FDG uptake, dose and metabolic control. Although no strong dose effect relation was demonstrated, mVCP decreased with increasing FDG uptake and tumour volume.

Tumor delineation based on time-activity curve differences assessed with dynamic fluorodeoxyglucose positron emission tomography-computed tomography in rectal cancer patients.

PURPOSE: To develop an unsupervised tumor delineation method based on time-activity curve (TAC) shape differences between tumor tissue and healthy tissue and to compare the resulting contour with the two tumor contouring methods mostly used nowadays. METHODS AND MATERIALS: Dynamic positron emission tomography-computed tomography (PET-CT) acquisition was performed for 60 min starting directly after fluorodeoxyglucose (FDG) injection. After acquisition and reconstruction, the data were filtered to attenuate noise. Correction for tissue motion during acquisition was applied. For tumor delineation, the TAC slope values were k-means clustered into two clusters. The resulting tumor contour (Contour I) was compared with a contour manually drawn by the radiation oncologist (Contour II) and a contour generated using a threshold of the maximum standardized uptake value (SUV; Contour III). RESULTS: The tumor volumes of Contours II and III were significantly larger than the tumor volumes of Contour I, with both Contours II and III containing many voxels showing flat TACs at low activities. However, in some cases, Contour II did not cover all voxels showing upward TACs. CONCLUSION: Both automated SUV contouring and manual tumor delineation possibly incorrectly assign healthy tissue, showing flat TACs, as being malignant. On the other hand, in some cases the manually drawn tumor contours do not cover all voxels showing steep upward TACs, suspected to be malignant. Further research should be conducted to validate the possible superiority of tumor delineation based on dynamic PET analysis.

In vivo dosimetry using a linear Mosfet-array dosimeter to determine the urethra dose in 125I permanent prostate implants.

PURPOSE: In vivo dosimetry during brachytherapy of the prostate with (125)I seeds is challenging because of the high dose gradients and low photon energies involved. We present the results of a study using metal-oxide-semiconductor field-effect transistor (MOSFET) dosimeters to evaluate the dose in the urethra after a permanent prostate implantation procedure. METHODS AND MATERIALS: Phantom measurements were made to validate the measurement technique, determine the measurement accuracy, and define action levels for clinical measurements. Patient measurements were performed with a MOSFET array in the urinary catheter immediately after the implantation procedure. A CT scan was performed, and dose values, calculated by the treatment planning system, were compared to in vivo dose values measured with MOSFET dosimeters. RESULTS: Corrections for temperature dependence of the MOSFET array response and photon attenuation in the catheter on the in vivo dose values are necessary. The overall uncertainty in the measurement procedure, determined in a simulation experiment, is 8.0% (1 SD). In vivo dose values were obtained for 17 patients. In the high-dose region (> 100 Gy), calculated and measured dose values agreed within 1.7% +/- 10.7% (1 SD). In the low-dose region outside the prostate (< 100 Gy), larger deviations occurred. CONCLUSIONS: MOSFET detectors are suitable for in vivo dosimetry during (125)I brachytherapy of prostate cancer. An action level of +/- 16% (2 SD) for detection of errors in the implantation procedure is achievable after validation of the detector system and measurement conditions.

Calibration of megavoltage cone-beam CT for radiotherapy dose calculations: correction of cupping artifacts and conversion of CT numbers to electron density.

Megavoltage cone-beam CT (MV CBCT) is used for three-dimensional imaging of the patient anatomy on the treatment table prior to or just after radiotherapy treatment. To use MV CBCT images for radiotherapy dose calculation purposes, reliable electron density (ED) distributions are needed. Patient scatter, beam hardening and softening effects result in cupping artifacts in MV CBCT images and distort the CT number to ED conversion. A method based on transmission images is presented to correct for these effects without using prior knowledge of the object's geometry. The scatter distribution originating from the patient is calculated with pencil beam scatter kernels that are fitted based on transmission measurements. The radiological thickness is extracted from the scatter subtracted transmission images and is then converted to the primary transmission used in the cone-beam reconstruction. These corrections are performed in an iterative manner, without using prior knowledge regarding the geometry and composition of the object. The method was tested using various homogeneous and inhomogeneous phantoms with varying shapes and compositions, including a phantom with different electron density inserts, phantoms with large density variations, and an anthropomorphic head phantom. For all phantoms, the cupping artifact was substantially removed from the images and a linear relation between the CT number and electron density was found. After correction the deviations in reconstructed ED from the true values were reduced from up to 0.30 ED units to 0.03 for the majority of the phantoms; the residual difference is equal to the amount of noise in the images. The ED distributions were evaluated in terms of absolute dose calculation accuracy for homogeneous cylinders of different size; errors decreased from 7% to below 1% in the center of the objects for the uncorrected and corrected images, respectively, and maximum differences were reduced from 17% to 2%, respectively. The presented method corrects the MV CBCT images for cupping artifacts and extracts reliable ED information of objects with varying geometries and composition, making these corrected MV CBCT images suitable for accurate dose calculation purposes.

Stability of 18F-deoxyglucose uptake locations within tumor during radiotherapy for NSCLC: a prospective study.

PURPOSE: Because individual tumors are heterogeneous, including for (18)F-deoxyglucose (FDG) uptake and, most likely, for radioresistance, selective boosting of high FDG uptake zones within the tumor has been suggested. To do this, it is critical to know whether the location of these high FDG uptake patterns within the tumor remain stable during radiotherapy (RT). METHODS AND MATERIALS: Twenty-three patients with Stage I-III non-small-cell lung cancer underwent repeated FDG positron emission tomography computed tomography scans before radical RT (Day 0) and at Days 7 and 14 of RT. On all scans, the high and low FDG uptake regions were autodelineated using several standardized uptake value thresholds, varying from 34% to 80% of the maximal standardized uptake value. The volumes and overlap fractions of these delineations were calculated to demonstrate the stability of the high FDG uptake regions during RT. RESULTS: The mean overlap fraction of the 34% uptake zones at Day 0 with Days 7 and 14 was 82.8% +/- 8.1% and 84.3% +/- 7.6%, respectively. The mean overlap fraction of the high uptake zones (60%) was 72.3% +/- 15.0% and 71.3% +/- 19.7% at Day 0 with Days 7 and 14, respectively. The volumes of the thresholds varied markedly (e.g., at Day 0, the volume of the 60% zone was 16.8 +/- 20.3 cm(3)). In contrast, although the location of the high FDG uptake patterns within the tumor during RT remained stable, the delineated volumes varied markedly. CONCLUSION: The location of the low and high FDG uptake areas within the tumor remained stable during RT. This knowledge may enable selective boosting of high FDG uptake areas within the tumor.