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.

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.

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.

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.

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.

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.

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.

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.

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.

Routine individualised patient dosimetry using electronic portal imaging devices.

BACKGROUND AND PURPOSE: To analyse the results of routine EPID measurements for individualised patient dosimetry. MATERIALS AND METHODS: Calibrated camera-based EPIDs were used to measure the central field dose, which was compared with a dose prediction at the EPID level. For transit dosimetry, dose data were calculated using patient transmission and scatter, and compared with measured values. Furthermore, measured transit dose data were back-projected to an in vivo dose value at 5 cm depth in water (D(5)) and directly compared with D(5) from the treatment planning system. Dose differences per treatment session were calculated by weighting dose values with the number of monitor units per beam. Reported errors were categorised and analysed for approximately 37,500 images from 2511 patients during a period of 24 months. RESULTS: Pre-treatment measurements showed a mean dose difference per treatment session of 0.0+/-1.7% (1 SD). Transfer errors were detected and corrected prior to the first treatment session. An accelerator output variation of about 4% was found between two weekly QC measurements. Patient dosimetry showed mean transit and D(5) dose differences of -0.7+/-5.2% (1 SD) and -0.3+/-5.6% (1 SD) per treatment session, respectively. Dose differences could be related to set-up errors, organ motion, erroneous density corrections and changes in patient anatomy. CONCLUSIONS: EPIDs can be used routinely to accurately verify treatment parameter transfer and machine output. By applying transit and in vivo dosimetry, more insight can be obtained with respect to the different error sources influencing dose delivery to a patient.

Treatment verification in the presence of inhomogeneities using EPID-based three-dimensional dose reconstruction.

Treatment verification is a prerequisite for the verification of complex treatments, checking both the treatment planning process and the actual beam delivery. Pretreatment verification can detect errors introduced by the treatment planning system (TPS) or differences between planned and delivered dose distributions. In a previous paper we described the reconstruction of three-dimensional (3-D) dose distributions in homogeneous phantoms using an in-house developed model based on the beams delivered by the linear accelerator measured with an amorphous silicon electronic portal imaging device (EPID), and a dose calculation engine using the Monte Carlo code XVMC. The aim of the present study is to extend the method to situations in which tissue inhomogeneities are present and to make a comparison with the dose distributions calculated by the TPS. Dose distributions in inhomogeneous phantoms, calculated using the fast-Fourier transform convolution (FFTC) and multigrid superposition (MGS) algorithms present in the TPS, were verified using the EPID-based dose reconstruction method and compared to film and ionization chamber measurements. Differences between dose distributions were evaluated using the gamma-evaluation method (3%/3 mm) and expressed as a mean gamma and the percentage of points with gamma> 1 (P(gamma>1)). For rectangular inhomogeneous phantoms containing a low-density region, the differences between film and reconstructed dose distributions were smaller than 3%. In low-density regions there was an overestimation of the planned dose using the FFTC and MGS algorithms of the TPS up to 20% and 8%, respectively, for a 10 MV photon beam and a 3 x 3 cm2 field. For lower energies and larger fields (6 MV, 5 x 5 cm2), these differences reduced to 6% and 3%, respectively. Dose reconstruction performed in an anthropomorphic thoracic phantom for a 3-D conformal and an IMRT plan, showed good agreement between film data and reconstructed dose values (P(gamma>1) <6%). The algorithms of the TPS underestimated the dose in the low-dose regions outside the treatment field, due to an implementation error of the jaws and multileaf collimator of the linac in the TPS. The FFTC algorithm of the TPS showed differences up to 6% or 6 mm at the interface between lung and breast. Two intensity-modulated radiation therapy head and neck plans, reconstructed in a commercial phantom having a bone-equivalent insert and an air cavity, showed good agreement between film measurement, reconstructed and planned dose distributions using the FFTC and MGS algorithm, except in the bone-equivalent regions where both TPS algorithms underestimated the dose with 4%. Absolute dose verification was performed at the isocenter where both planned and reconstructed dose were within 2% of the measured dose. Reproducibility for the EPID measurements was assessed and found to be of negligible influence on the reconstructed dose distribution. Our 3-D dose verification approach is based on the actual dose measured with an EPID in combination with a Monte Carlo dose engine, and therefore independent of a TPS. Because dose values are reconstructed in 3-D, isodose surfaces and dose-volume histograms can be used to detect dose differences in target volume and normal tissues. Using our method, the combined planning and treatment delivery process is verified, offering an easy to use tool for the verification of complex treatments.

A Monte Carlo based three-dimensional dose reconstruction method derived from portal dose images.

The verification of intensity-modulated radiation therapy (IMRT) is necessary for adequate quality control of the treatment. Pretreatment verification may trace the possible differences between the planned dose and the actual dose delivered to the patient. To estimate the impact of differences between planned and delivered photon beams, a three-dimensional (3-D) dose verification method has been developed that reconstructs the dose inside a phantom. The pretreatment procedure is based on portal dose images measured with an electronic portal imaging device (EPID) of the separate beams, without the phantom in the beam and a 3-D dose calculation engine based on the Monte Carlo calculation. Measured gray scale portal images are converted into portal dose images. From these images the lateral scattered dose in the EPID is subtracted and the image is converted into energy fluence. Subsequently, a phase-space distribution is sampled from the energy fluence and a 3-D dose calculation in a phantom is started based on a Monte Carlo dose engine. The reconstruction model is compared to film and ionization chamber measurements for various field sizes. The reconstruction algorithm is also tested for an IMRT plan using 10 MV photons delivered to a phantom and measured using films at several depths in the phantom. Depth dose curves for both 6 and 10 MV photons are reconstructed with a maximum error generally smaller than 1% at depths larger than the buildup region, and smaller than 2% for the off-axis profiles, excluding the penumbra region. The absolute dose values are reconstructed to within 1.5% for square field sizes ranging from 5 to 20 cm width. For the IMRT plan, the dose was reconstructed and compared to the dose distribution with film using the gamma evaluation, with a 3% and 3 mm criterion. 99% of the pixels inside the irradiated field had a gamma value smaller than one. The absolute dose at the isocenter agreed to within 1% with the dose measured with an ionization chamber. It can be concluded that our new dose reconstruction algorithm is able to reconstruct the 3-D dose distribution in phantoms with a high accuracy. This result is obtained by combining portal dose images measured prior to treatment with an accurate dose calculation engine.

Synchronously counterpulsating extracorporeal life support enhances myocardial working conditions regardless of systemic perfusion pressure.

OBJECTIVE: A new pulsatile extracorporeal life support (pECLS) system has entered the market. We wanted to investigate what potential advantages pECLS may have over current non-pulsatile systems (NPS). Our research was focused on the pump's functional interaction with the left ventricle and the coronary circulation. METHODS: Extensive hemodynamic measurements were performed during asynchronous and synchronous pECLS in 10 calves. The two extremes regarding LV afterload, namely systolic arrival (SA) and diastolic arrival (DA) of the pump pulse were studied. RESULTS: SA was associated with increased oxygen consumption (+57%) and decreased diastolic coronary perfusion (-43%). DA increased left ventricular output (DA: 4.5+/-2.4 l/min vs SA: 3.5+/-2.2 l/min), LV ejection fraction (+10%), and ventricular efficiency (+17%). Mean aortic pressure and mean coronary flow were only marginally affected by pulse incidence. Systolic impairment was more pronounced with higher bypass flows. These results indicate that myocardial working conditions can be optimized by phasing pECLS ejection into cardiac diastole. CONCLUSION: We conclude that during pECLS, myocardial working conditions can be improved by avoidance of systolic impairment. Synchronously counterpulsating pECLS could be a more economic and versatile alternative to NPS or NPS combined with intra-aortic balloon pumping. The potential benefits of synchronously counterpulsating pECLS over the current alternatives remain to be investigated.

Intra-aortic balloon pumping in acute mitral regurgitation reduces aortic impedance and regurgitant fraction.

Acute mitral regurgitation (MR) is present in 10% of patients presenting with cardiogenic shock. To stabilize these patients, intra-aortic balloon pumping (IABP) is recommended, but the mechanism of IABP support in these patients is unknown. This animal study was designed to describe the hemodynamic effect of intra-aortic balloon pumping during cardiogenic shock induced by acute MR. In eight calves, left ventricular pressure-volume loops, aortic and left atrial pressure, and aortic, carotid artery, and coronary blood flow were recorded. Acute MR (range 36%-79%) was created by placing a metal cage in the mitral valve. Hemodynamic data was obtained at control, during acute MR, and during acute MR with 1:1 IABP support. Acute MR caused a decrease in cardiac output (-32%, P = 0.018), blood pressure, and carotid artery flow, whereas left ventricular output (+127%, P = 0.018), end-diastolic volume, and left atrial pressure all significantly increased. Stroke work, ejection fraction, and coronary blood flow were not significantly changed, and no signs of ischemia were seen on the ECG. The IABP raised average cardiac output by 31% (P = 0.012) and significantly raised blood pressure and flow to the brain while decreasing systemic vascular resistance. Left ventricular function and mean coronary blood flow did not change, but diastolic coronary flow became more important as shown by the increase in diastolic fraction from 64% to 95%. (P = 0.028). Average MR dropped by 7.5% (P = 0.025). In conclusion, application of the IABP during acute MR lowers aortic impedance, resulting in less MR and more output toward the aorta without changing left ventricular function.

Miniature intracardiac assist device provides more effective cardiac unloading and circulatory support during severe left heart failure than intraaortic balloon pumping.

BACKGROUND: Hemodynamic assistance with a miniature intracardiac pump may fill the treatment gap between use of an intraaortic balloon pump (IABP) and the current, more invasive ventricular assist devices. The objective of this study was to compare the hemodynamic efficacy of a miniature intracardiac pump device with that of IABP. METHODS AND RESULTS: Reversible acute mitral regurgitation (AMR) was induced in eight calves by stenting the mitral valve using a vena cava filter. Full and partial AMR assist were compared with maximum IABP support in each animal. In full-support mode, both assist systems increased cardiac output (miniature intracardiac pump, 13% [p < 0.05]; IABP, 3% [p < 0.05]), mean aortic pressure (miniature intracardiac pump, 13% [p < 0.05]; IABP, 8% [p < 0.05]), carotid artery flow (miniature intracardiac pump, 29% [p < 0.05]; IABP, 5% [difference not significant]), and coronary blood flow (miniature intracardiac pump, 25% [difference not significant]; IABP, 34% [p < 0.05]). Again in full-support mode, both systems reduced left atrial pressure (miniature intracardiac pump, 2.4 mm Hg [p < 0.05]; IABP, 0.7 mm Hg [p < 0.05]), peak left ventricular (LV) pressure (miniature intracardiac pump, 13% [p < 0.05]; IABP, 5% [p < 0.05]), and external LV work (miniature intracardiac pump, 29% [p < 0.05]; IABP, 3% [p < 0.05]). Only full miniature intracardiac pump support reduced both end-diastolic LV volume (7%; p < 0.05) and end-systolic LV volume (10%; p < 0.05). IABP mainly improved coronary perfusion, while the miniature intracardiac pump proved more capable of genuinely unloading the LV. CONCLUSIONS: We conclude that during severe acute LV failure, the miniature intracardiac pump is capable of more effective cardiac unloading and circulatory support than IABP.

Pressure-volume loops in patients with aortic stenosis.

BACKGROUND AND AIM OF THE STUDY: Preoperative left ventricular (LV) function is the strongest predictor of outcome after valve replacement for aortic stenosis (AS). Although pressure-volume analysis with the conductance catheter technique can provide detailed information on LV systolic and diastolic function, this technique has not yet been used in AS patients. The present study examined the potential use of LV function measurements using pressure-volume analysis with a conductance catheter during surgery for AS. METHODS: In six patients with severe symptomatic AS, a conductance catheter was placed under transesophageal echocardiographic guidance in the left ventricle via the right superior pulmonary vein. RESULTS: The procedure was successful in all patients and lengthened the duration of surgery by <30 min, but with no increase in bypass or aortic cross-clamp times. Pressure-volume analysis showed that systolic function was normal in all patients (ejection fraction 42-59%, end-systolic elastance 1.6-4.5 mmHg/ml), while diastolic dysfunction was found in all patients (Tau 32-96 ms, LV end-diastolic pressure 7-42 mmHg, atrial kick 25-60%). After valve replacement, systolic function improved, but diastolic function did not. CONCLUSION: The conductance catheter placed via the pulmonary vein can determine LV systolic and diastolic dysfunction in detail in an individual patient with AS before and after valve replacement. The technique may be used to extend diagnostic data from less-invasive modalities and to determine prognosis in the individual patient.

[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.

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.