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Journal articles on the topic 'Radiotherapy – Data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Zavyalov, Aleksandr A., and Dmitry A. Andreev. "Management of the radiotherapy quality control using automated Big Data processing." Health Care of the Russian Federation 64, no. 6 (2020): 368–72. http://dx.doi.org/10.46563/0044-197x-2020-64-6-368-372.

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Introduction. In Moscow, the state-of-the-art information technologies for cancer care data processing are widely used in routine practice. Data Science approaches are increasingly applied in the field of radiation oncology. Novel arrays of radiotherapy performance indices can be introduced into real-time cancer care quality and safety monitoring. The purpose of the study. The short review of the critical structural elements of automated Big Data processing and its perspectives in the light of the internal quality and safety control organization in radiation oncology departments. Material and

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Kessler, M., M. Roberson, W. Keranen, and D. McShan. "TU-FF-A3-01: Automating Data Processing for Image-Guided Adaptive Radiotherapy." Medical Physics 32, no. 6Part17 (2005): 2110. http://dx.doi.org/10.1118/1.1998454.

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Romanchikova, M., K. Harrison, S. J. Thomas, et al. "Design of electronic data processing system for radiotherapy study: lessons learned from VoxTox." Radiotherapy and Oncology 118 (February 2016): S91. http://dx.doi.org/10.1016/s0167-8140(16)30186-4.

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Philipps, L., S. Reis, S. Hindocha, M. Evison, F. McDonald, and R. Lee. "Automated data extraction and natural language processing of radiotherapy outcomes data and LNC-PATH risk score features." Lung Cancer 139 (January 2020): S1. http://dx.doi.org/10.1016/s0169-5002(20)30030-1.

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Lamamra, Kheireddine, Abdelkrim Allam, N. A. M', and hammed Afiane. "Artificial neural networks for acquisition and processing of sensors data in a radiotherapy application." International Journal of Simulation and Process Modelling 13, no. 1 (2018): 15. http://dx.doi.org/10.1504/ijspm.2018.090257.

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Afiane, M'hammed, Abdelkrim Allam, and Kheireddine Lamamra. "Artificial neural networks for acquisition and processing of sensors data in a radiotherapy application." International Journal of Simulation and Process Modelling 13, no. 1 (2018): 15. http://dx.doi.org/10.1504/ijspm.2018.10011346.

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Kahalley, Lisa S., Rachel Peterson, M. Douglas Ris, et al. "Superior Intellectual Outcomes After Proton Radiotherapy Compared With Photon Radiotherapy for Pediatric Medulloblastoma." Journal of Clinical Oncology 38, no. 5 (2020): 454–61. http://dx.doi.org/10.1200/jco.19.01706.

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PURPOSE Proton radiotherapy (PRT) may lessen the neuropsychological risk traditionally associated with cranial radiotherapy for the treatment of pediatric brain tumors by reducing the dose to normal tissue compared with that of photon radiotherapy (XRT). We examined the change in intellectual scores over time in patients with pediatric medulloblastoma treated with craniospinal PRT versus XRT. METHODS Intelligence test scores were obtained for a sample of pediatric patients treated between 2007 and 2018 on the same medulloblastoma protocols that differed only in radiotherapy modality (PRT v XRT

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Locke, C., and S. Zavgorodni. "Vega library for processing DICOM data required in Monte Carlo verification of radiotherapy treatment plans." Australasian Physics & Engineering Sciences in Medicine 31, no. 4 (2008): 290–99. http://dx.doi.org/10.1007/bf03178598.

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Syed, Khajamoinuddin, William Sleeman IV, Kevin Ivey, et al. "Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names." Healthcare 8, no. 2 (2020): 120. http://dx.doi.org/10.3390/healthcare8020120.

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The lack of standardized structure names in radiotherapy (RT) data limits interoperability, data sharing, and the ability to perform big data analysis. To standardize radiotherapy structure names, we developed an integrated natural language processing (NLP) and machine learning (ML) based system that can map the physician-given structure names to American Association of Physicists in Medicine (AAPM) Task Group 263 (TG-263) standard names. The dataset consist of 794 prostate and 754 lung cancer patients across the 40 different radiation therapy centers managed by the Veterans Health Administrat

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Pike, Lucy C., Christopher M. Thomas, Teresa Guerrero-Urbano, et al. "Guidance on the use of PET for treatment planning in radiotherapy clinical trials." British Journal of Radiology 92, no. 1103 (2019): 20190180. http://dx.doi.org/10.1259/bjr.20190180.

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The aim of this article is to propose meaningful guidance covering the practical and technical issues involved when planning or conducting clinical trials involving positron emission tomography (PET)-guided radiotherapy. The complexity of imaging requirements will depend on the study aims, design and PET methods used. Where PET is used to adapt radiotherapy, a high level of accuracy and reproducibility is required to ensure effective and safe treatment delivery. The guidance in this document is intended to assist researchers designing clinical trials involving PET-guided radiotherapy to provid

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Granja, C., J. Jakubek, P. Soukup, et al. "MiniPIX Timepix3 — a miniaturized radiation camera with onboard data processing for online characterization of wide-intensity mixed-radiation fields." Journal of Instrumentation 17, no. 03 (2022): C03019. http://dx.doi.org/10.1088/1748-0221/17/03/c03019.

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Abstract The miniaturized radiation camera MiniPIX TPX3 is designed for detailed and wide-range measurements of mixed-radiation fields present in many applications such as radiotherapy and space radiation in outer orbit. The highly integrated instrumentation utilizes a single connector for control and readout for flexible measurements and quick deployment. The device features an option to process the registered data on the same device with limited resolution and basic particle-type resolving power. A novel readout and data processing technique exploits the detector high granularity and double

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Basu, Tanmay, Simon Goldsworthy, and Georgios V. Gkoutos. "A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature." Information 12, no. 4 (2021): 139. http://dx.doi.org/10.3390/info12040139.

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The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric err

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Takahashi, Wataru, Shota Oshikawa, and Shinichiro Mori. "Real-time markerless tumour tracking with patient-specific deep learning using a personalised data generation strategy: proof of concept by phantom study." British Journal of Radiology 93, no. 1109 (2020): 20190420. http://dx.doi.org/10.1259/bjr.20190420.

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Objective: For real-time markerless tumour tracking in stereotactic lung radiotherapy, we propose a different approach which uses patient-specific deep learning (DL) using a personalised data generation strategy, avoiding the need for collection of a large patient data set. We validated our strategy with digital phantom simulation and epoxy phantom studies. Methods: We developed lung tumour tracking for radiotherapy using a convolutional neural network trained for each phantom’s lesion by using multiple digitally reconstructed radiographs (DRRs) generated from each phantom’s treatment planning

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Haidar, Ali, Farhannah Aly, and Lois Holloway. "PDCP: A Set of Tools for Extracting, Transforming, and Loading Radiotherapy Data from the Orthanc Research PACS." Software 1, no. 2 (2022): 215–22. http://dx.doi.org/10.3390/software1020009.

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The Orthanc server is a light-weight open-source picture imaging and archiving system (PACS) used to store digital imaging and communications in medicine (DICOM) data. It is widely used in research environments as it is free, open-source and scalable. To enable the use of Orthanc stored radiotherapy (RT) data in data mining and machine learning tasks, the records need to be extracted, validated, linked, and presented in a usable format. This paper reports patient data collection and processing (PDCP), a set of tools created using python for extracting, transforming, and loading RT data from Or

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Patel, I., and M. C. Kirby. "Design and implementation of an electronic data recording and processing system for physics quality control checks in external beam radiotherapy." British Journal of Radiology 80, no. 950 (2007): 126–31. http://dx.doi.org/10.1259/bjr/32367104.

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Remes, Tiina M., Emma Hovén, Niina Ritari, et al. "Neurocognitive impairment, employment, and social status in radiotherapy-treated adult survivors of childhood brain tumors." Neuro-Oncology Practice 8, no. 3 (2021): 266–77. http://dx.doi.org/10.1093/nop/npab004.

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Abstract Background Little is known of the cognitive functions, employment, and social status in adult survivors of childhood brain tumor (BT). We aimed to determine the long-term neurocognitive profile of radiotherapy-treated adult survivors of childhood BT and the relationship between cognitive functions and employment and social status. Methods Neurocognitive profiles of survivors were assessed in a Finnish national cohort of 71 radiotherapy-treated survivors of childhood BT (median follow-up time: 21 years [range: 5-33 years]) using a cross-sectional design. Neurocognitive outcomes were co

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Thakur, Varsha, Juliano Tiburcio de Freitas, Yuan Li, Keman Zhang, Alyssa Savadelis, and Barbara Bedogni. "MT1-MMP-dependent ECM processing regulates laminB1 stability and mediates replication fork restart." PLOS ONE 16, no. 7 (2021): e0253062. http://dx.doi.org/10.1371/journal.pone.0253062.

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Radiotherapy remains a mainstay of treatment for a majority of cancer patients. We have previously shown that the membrane bound matrix metalloproteinase MT1-MMP confers radio- and chemotherapy resistance to breast cancer via processing of the ECM and activation of integrinβ1/FAK signaling. Here, we further discovered that the nuclear envelope protein laminB1 is a potential target of integrinβ1/FAK. FAK interacts with laminB1 contributing to its stability. Stable laminB1 is found at replication forks (RFs) where it is likely to allow the proper positioning of RF protection factors, thus preven

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Chebrolu, Venkata V., Daniel Saenz, Dinesh Tewatia, William A. Sethares, George Cannon, and Bhudatt R. Paliwal. "Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours." Radiology Research and Practice 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/547075.

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Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT.Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (usingElastix©and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and

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Dundr, Pavel, David Cibula, Kristýna Němejcová, Ivana Tichá, Michaela Bártů, and Radek Jakša. "Pathologic Protocols for Sentinel Lymph Nodes Ultrastaging in Cervical Cancer." Archives of Pathology & Laboratory Medicine 144, no. 8 (2019): 1011–20. http://dx.doi.org/10.5858/arpa.2019-0249-ra.

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Context.— Ultrastaging of sentinel lymph nodes (SLNs) is a crucial aspect in the approach to SLN processing. No consensual protocol for pathologic ultrastaging has been approved by international societies to date. Objective.— To provide a review of the ultrastaging protocol and all its aspects related to the processing of SLNs in patients with cervical cancer. Data Sources.— In total, 127 publications reporting data from 9085 cases were identified in the literature. In 24% of studies, the information about SLN processing is entirely missing. No ultrastaging protocol was used in 7% of publicati

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Chuwdhury, Gulam Sarwar, Md Khaliluzzaman, and Md Rashed-Al Mahfuz. "Analyzing Wavelet and Bidimensional Empirical Mode Decomposition of MRI Segmentation using Fuzzy C-Means Clustering." Rajshahi University Journal of Science and Engineering 44 (November 19, 2016): 101–12. http://dx.doi.org/10.3329/rujse.v44i0.30395.

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Image segmentation is a vital step in medical image processing. Magnetic resonance imaging (MRI) is used for brain tissues extraction in white and gray matter. These tissues extraction help in image segmentation applications such as radiotherapy planning, clinical diagnosis, treatment planning. This paper presents utilization of fuzzy C-means (FCM) clustering by using wavelet and bidimensional empirical mode decomposition (BEMD) to improve the quality of noisy MR images. The signal to noise ratio (SNR) value is calculated from FCM clustering data to examine the best segmentation technique. The

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Gonella, Gloria, Elisabetta Binaghi, Paola Nocera, and Cinzia Mordacchini. "Investigating the Behaviour of Machine Learning Techniques to Segment Brain Metastases in Radiation Therapy Planning." Applied Sciences 9, no. 16 (2019): 3335. http://dx.doi.org/10.3390/app9163335.

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This work aimed to investigate whether automated classifiers belonging to feature-based and deep learning may approach brain metastases segmentation successfully. Support Vector Machine and V-Net Convolutional Neural Network are selected as representatives of the two approaches. In the experiments, we consider several configurations of the two methods to segment brain metastases on contrast-enhanced T1-weighted magnetic resonance images. Performances were evaluated and compared under critical conditions imposed by the clinical radiotherapy domain, using in-house dataset and public dataset crea

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van Iersel, Laura, Zhenghong Li, Deo Kumar Srivastava, et al. "Hypothalamic-Pituitary Disorders in Childhood Cancer Survivors: Prevalence, Risk Factors and Long-Term Health Outcomes." Journal of Clinical Endocrinology & Metabolism 104, no. 12 (2019): 6101–15. http://dx.doi.org/10.1210/jc.2019-00834.

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Abstract Context Data on hypothalamic-pituitary (HP) disorders in systematically evaluated childhood cancer survivors are limited. Objective To describe prevalence, risk factors, and associated adverse health outcomes of deficiencies in GH deficiency (GHD), TSH deficiency (TSHD), LH/FSH deficiency (LH/FSHD), and ACTH deficiency (ACTHD), and central precocious puberty (CPP). Design Retrospective with cross-sectional health outcomes analysis. Setting Established cohort; tertiary care center. Patients Participants (N = 3141; median age, 31.7 years) were followed for a median 24.1 years. Main Outc

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Santoro, Miriam, Silvia Strolin, Giulia Paolani, et al. "Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond." Applied Sciences 12, no. 7 (2022): 3223. http://dx.doi.org/10.3390/app12073223.

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In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and

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SUN, SHUH-PING, and CHING-JUNG WU. "THE APPLICATION OF FULL SCALE 3D ANTHROPOMETRIC DIGITAL MODEL SYSTEM IN RADIOTHERAPY POSITIONING AND VERIFICATION." Biomedical Engineering: Applications, Basis and Communications 16, no. 04 (2004): 173–79. http://dx.doi.org/10.4015/s1016237204000232.

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The full scale 3D Anthropometric Digital Model system is a technique combining digital imaging, three-dimensional (3D) image processing and reverse engineering to produce a full-scale solid Anthropometric Digital Model. This paper describes the Anthropometric Digital Model being made and used in radiation treatment. By using computed tomography and optical scanning, the data required for the Anthropometric Digital Model is collected. Through surface reconstruction, a model of the patient skull is made, after which rapid prototyping and rapid tooling is applied to acquire a 1:1 solid model. Thu

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Atoui, Hussein, Serge Miguet, and David Sarrut. "A FAST MORPHING-BASED INTERPOLATION FOR MEDICAL IMAGES: APPLICATION TO CONFORMAL RADIOTHERAPY." Image Analysis & Stereology 25, no. 2 (2011): 95. http://dx.doi.org/10.5566/ias.v25.p95-103.

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A method is presented for fast interpolation between medical images. The method is intended for both slice and projective interpolation. It allows offline interpolation between neighboring slices in tomographic data. Spatial correspondence between adjacent images is established using a block matching algorithm. Interpolation of image intensities is then carried out by morphing between the images. The morphing-based method is compared to standard linear interpolation, block-matching-based interpolation and registrationbased interpolation in 3D tomographic data sets. Results show that the propos

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Covele, Brent M., Kartikeya S. Puri, Karoline Kallis, James D. Murphy, and Kevin L. Moore. "ORBIT-RT: A Real-Time, Open Platform for Knowledge-Based Quality Control of Radiotherapy Treatment Planning." JCO Clinical Cancer Informatics, no. 5 (January 2021): 134–42. http://dx.doi.org/10.1200/cci.20.00093.

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PURPOSE Access to knowledge-based treatment plan quality control has been hindered by the complexity of developing models and integration with different treatment planning systems (TPS). Online Real-time Benchmarking Information Technology for RadioTherapy (ORBIT-RT) provides a free, web-based platform for knowledge-based dose estimation that can be used by clinicians worldwide to benchmark the quality of their radiotherapy plans. MATERIALS AND METHODS The ORBIT-RT platform was developed to satisfy four primary design criteria: web-based access, TPS independence, Health Insurance Portability a

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Zhang, Li. "Evaluation of Incision Margin and Radiotherapy Adjustment in Breast Cancer-Conserving Surgery Based on VR Pathology 3D Reconstruction." Journal of Healthcare Engineering 2021 (August 16, 2021): 1–11. http://dx.doi.org/10.1155/2021/2709461.

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At present, the incidence of breast cancer is high worldwide and the most effective way to treat breast cancer is surgery. This study mainly explores the application of pathological three-dimensional reconstruction based on VR technology in the evaluation of the margins of breast-conserving surgery and the adjustment of radiotherapy for breast cancer. In the process of making pathological large slices, the slice markers are counted and the upper and outer cutting edges are located and placed in a large embedding box to prevent tissue deformation. ACDSEE6.0 is used to convert the aligned image

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Hoffmann-Lamplmair, Doris, Ulrike Leiss, Andreas Peyrl, et al. "QOL-24. Evaluating the diagnostic validity & predictive value of the Cerebellar Cognitive Affective Syndrome (CCAS) in pediatric posterior fossa tumour patients." Neuro-Oncology 24, Supplement_1 (2022): i138—i139. http://dx.doi.org/10.1093/neuonc/noac079.507.

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Abstract OBJECTIVE: The present study aimed to examine the extent of deficits associated with the Cerebellar Cognitive Affective Syndrome (CCAS) in patients treated for paediatric posterior fossa tumours (PFT). Furthermore, we wanted to assess its diagnostic validity and predictive value for late sequelae. METHOD: Neuropsychological test data from 56 PFT patients treated at our pediatric neuro-onocology unit (mean age at treatment: seven years; mean age at first neuropsychological examination: nine years) were examined for severity of deficits in the CCAS core domains of executive functions, v

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Lo Faso, Enrico Antonio, Orazio Gambino, and Roberto Pirrone. "Head–Neck Cancer Delineation." Applied Sciences 11, no. 6 (2021): 2721. http://dx.doi.org/10.3390/app11062721.

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Head–Neck Cancer (HNC) has a relevant impact on the oncology patient population and for this reason, the present review is dedicated to this type of neoplastic disease. In particular, a collection of methods aimed at tumor delineation is presented, because this is a fundamental task to perform efficient radiotherapy. Such a segmentation task is often performed on uni-modal data (usually Positron Emission Tomography (PET)) even though multi-modal images are preferred (PET-Computerized Tomography (CT)/PET-Magnetic Resonance (MR)). Datasets can be private or freely provided by online repositories

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Auns Q. H. Al-Neami and Cinan Kanaan A.R. Al Khuzaay. "Medical Images Separation and Fusion Based on Artificial Neural Network." Diyala Journal of Engineering Sciences 7, no. 3 (2014): 92–105. http://dx.doi.org/10.24237/djes.2014.07306.

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During the last few decades, the field of medical image processing has been closely related to neural network methodologies and their applications. In the present investigation a 512×512 Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images for different region of the brain are registered to eliminate the dimensionality differences between the two images, then separated both of them by fast-fixed point algorithm after truncation of each image in to almost 1000 image patches of 15×15 dimension and transform them to 1-D and order them into row-wise fashion as well as reducing the

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Yılmaz, Nida Gizem, Julia C. M. Van Weert, Ellen Peters, et al. "Testing the Effects of Modality and Narration Style on Patients’ Information Use in a Lung Cancer Treatment Decision Aid." Medical Decision Making 40, no. 8 (2020): 990–1002. http://dx.doi.org/10.1177/0272989x20960436.

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Background Risk information in patient decision aids (PDAs) is often difficult for older patients to process. Providing audiovisual and narrative information may enhance the understanding and use of health-related information. We studied the effects on patients’ information processing and use of audiovisual and narrative information of an early-stage non–small-cell lung cancer treatment decision aid explaining surgery and stereotactic ablative radiotherapy. We further investigated differences between older and younger patients. Methods We conducted a 2 (modality: textual v. audiovisual) × 2 (n

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Septiani, Ike Wahyu, Abd Charis Fauzan, and Muhamat Maariful Huda. "Implementasi Algoritma K-Medoids Dengan Evaluasi Davies-Bouldin-Index Untuk Klasterisasi Harapan Hidup Pasca Operasi Pada Pasien Penderita Kanker Paru-Paru." Jurnal Sistem Komputer dan Informatika (JSON) 3, no. 4 (2022): 556. http://dx.doi.org/10.30865/json.v3i4.4055.

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Lung Cancer is a disease in which there are cells that grow in the lungs by a collection of carcionogens uncontrollably. Lung Cancer can be treated with surgery, chemotherapy and radiotherapy. Early treatment that needs to be done to reduce the mortality rate in patients with lung cancer after performing thoracic surgery, by collecting data from each patients regarding this information causes a new problem, including the data obtained including high-dimensional data and has many attributes so that it can produce less accurate information. So it is necessary to calculate data mining clustering.

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Traunwieser, Thomas, Elena Loos, Karolina Nemes, et al. "ATRT-02. Neuropsychological function in infant atypical teratoid/rhabdoid tumor versus low-grade glioma survivors reflects tumor malignancy and multimodal treatment." Neuro-Oncology 24, Supplement_1 (2022): i1—i2. http://dx.doi.org/10.1093/neuonc/noac079.001.

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Abstract BACKGROUND: Therapy of infants with brain tumors predisposes these patients to increased risks for cognitive sequelae, especially following radiotherapy. Neuropsychological outcome gains importance for those 40-60% of patients with an atypical teratoid/rhabdoid tumor (ATRT) who survive beyond 2 years. Still, reports on cognitive late-effects in children with ATRT are scarce compared to other pediatric brain tumor groups. We analyzed neuropsychological outcome for long-term ATRT-survivors registered in EU-RHAB and infant low-grade glioma (LGG) survivors from the SIOP-LGG 2004-study and

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Xin, Ying. "Clinical study of gefitinib maintenance therapy after concurrent radiotherapy and chemotherapy for locally advanced NSCLC with EGFR sensitive mutations." Journal of Global Oncology 5, suppl (2019): 87. http://dx.doi.org/10.1200/jgo.2019.5.suppl.87.

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87 Background: To investigate the value of Gefitinib, an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), in the maintenance therapy of locally advanced NSCLC after concurrent radiotherapy and chemotherapy. Methods: From Jan 2010 to Jan 2015, 166 patients with locally advanced NSCLC were treated in JCH. Total 86 patients with no progress after concurrent radiotherapy and chemotherapy were selected. Within 2 to 6 weeks after therapy, eligible patients received TKI (Gefitinib 250mg/day) or were observed for 1 year. If disease progresses or intolerable adverse reactions occu

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Nadiminti, Dr Venkata Ramana Murty, and Prof M. S. Prasad Babu. "A Novel Integrated Prognosis & Diagnosis System for Lung Cancer Disease Detection using Soft Computing Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 20 (December 2, 2020): 137–44. http://dx.doi.org/10.24297/ijct.v20i.8844.

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Nowadays, lung cancer is one of the ranking first causes of mortality worldwide among men and women. Although there are a lot of treatment options like surgery, radiotherapy, and chemotherapy, five-year survival rate for patients is quite low. However, survival rate may go up to 54% in case lung cancer is identified in an early stage. Therefore, early detection of lung cancer is vital to decrease lung cancer mortality. Medical Experts are continuously trying to find the best solution for the early prediction and diagnosis of Lung Cancer Disease; in this Research work, an attempt has been made

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Wodzinski, Marek, Izabela Ciepiela, Tomasz Kuszewski, Piotr Kedzierawski, and Andrzej Skalski. "Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization." Sensors 21, no. 12 (2021): 4085. http://dx.doi.org/10.3390/s21124085.

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Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localization and lower the radiation dose delivered to the surrounding healthy tissues. This study proposes a novel image registration method dedicated to breast tumor bed localization addressing the problem of missing data due to tumor resection that may be applied to real-time radiotherapy planning. We propose a deep learning-based nonrigid image registrati

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Heintz, A., J. Chombar, S. Boussida, et al. "P14.92 Study of Glutaminergic and Glutamatergic Metabolism in 1H-MRS Monovoxel in the Most Aggressive Part of 62 Glioblastoma Before and After 18 months Treatment." Neuro-Oncology 21, Supplement_3 (2019): iii89. http://dx.doi.org/10.1093/neuonc/noz126.327.

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Abstract BACKGROUND To study the relationships between glutaminergic metabolism (Glx/tCr), tumor proliferation (tCho/tCr) and other metabolic activities in patients with glioblastoma (GBM). MATERIAL AND METHODS Patients: 62 patients with glioblastoma, all having a STUPP Protocol (radiotherapy and concomitant chemotherapy), were selected and separated into 2 groups: Biopsies (30) and resections (32). In total, 269 NMR spectra (PRESS at GE 1.5T and 3T; multi-TEs TE=35ms and TE=144ms) were acquired. Processing: MRS data were processed with jMRUI software and quantitated using HLSVD and QUEST algo

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Syed, Khajamoinuddin, William Sleeman, Michael Hagan, Jatinder Palta, Rishabh Kapoor, and Preetam Ghosh. "Automatic Incident Triage in Radiation Oncology Incident Learning System." Healthcare 8, no. 3 (2020): 272. http://dx.doi.org/10.3390/healthcare8030272.

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The Radiotherapy Incident Reporting and Analysis System (RIRAS) receives incident reports from Radiation Oncology facilities across the US Veterans Health Affairs (VHA) enterprise and Virginia Commonwealth University (VCU). In this work, we propose a computational pipeline for analysis of radiation oncology incident reports. Our pipeline uses machine learning (ML) and natural language processing (NLP) based methods to predict the severity of the incidents reported in the RIRAS platform using the textual description of the reported incidents. These incidents in RIRAS are reviewed by a radiation

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Ott, Martina, Yuuri Hashimoto, Anantha Marisetty, et al. "MLTI-01. IMMUNOLOGICAL REPROGRAMMING IN THE CNS TUMOR MICROENVIRONMENT AND THERAPEUTIC EFFICACY OF RADIOTHERAPY WITH STAT3 BLOCKADE." Neuro-Oncology Advances 1, Supplement_1 (2019): i14. http://dx.doi.org/10.1093/noajnl/vdz014.060.

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Abstract BACKGROUND: Patients with central nervous system (CNS) tumors are typically treated with radiation therapy, but this is not curative and results in the upregulation of p-STAT3 that drives invasion, angiogenesis, and immune suppression. Therefore, we investigated the combined effect of an inhibitor of the STAT3 pathway that is currently in clinical trials (WP1066) and whole-brain radiation therapy (WBRT) in murine models of CNS malignancy. METHODS: C57BL/6 mice underwent intracerebral implantation of either B16 melanoma or GL261 glioma cells, WBRT, and treatment with WP1066 a blood-bra

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Parisi, Alessio, Chris J. Beltran, and Keith M. Furutani. "The Mayo Clinic Florida microdosimetric kinetic model of clonogenic survival: formalism and first benchmark against in vitro and in silico data." Physics in Medicine & Biology 67, no. 18 (2022): 185013. http://dx.doi.org/10.1088/1361-6560/ac7375.

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Abstract Objective. To develop a new model (Mayo Clinic Florida microdosimetric kinetic model, MCF MKM) capable of accurately describing the in vitro clonogenic survival at low and high linear energy transfer (LET) using single-event microdosimetric spectra in a single target. Methodology. The MCF MKM is based on the ‘post-processing average’ implementation of the non-Poisson microdosimetric kinetic model and includes a novel expression to compute the particle-specific quadratic-dependence of the cell survival with respect to dose (β of the linear-quadratic model). A new methodology to a prior

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Altini, Mattia, Laura Solinas, Lauro Bucchi, et al. "Assessment of Cancer Care Costs in Disease-Specific Cancer Care Pathways." International Journal of Environmental Research and Public Health 17, no. 13 (2020): 4765. http://dx.doi.org/10.3390/ijerph17134765.

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In view of an efficient use of the Italian National Health Service-funded healthcare resources, a novel data-processing strategy combining information from multiple sources was developed in a regional cancer network of northern Italy. The goal was to calculate the annual overall cost of care pathways of six disease groups in 10,486 patients. The evaluation was conceived as a population-based cost description from the perspective of the Italian National Health Service. Costs occurred during a defined time period for a cross-section of patients at varying stages of their disease were measured. T

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Kang, Kyung-Ah, Suk Jung Han, Jiyoung Chun, and Hyun-Yong Kim. "Research trends related to childhood and adolescent cancer survivors in South Korea using word co-occurrence network analysis." Child Health Nursing Research 27, no. 3 (2021): 201–10. http://dx.doi.org/10.4094/chnr.2021.27.3.201.

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Purpose: This study analyzed research trends related to childhood and adolescent cancer survivors (CACS) using word co-occurrence network analysis on studies registered in the Korean Citation Index (KCI).Methods: This word co-occurrence network analysis study explored major research trends by constructing a network based on relationships between keywords (semantic morphemes) in the abstracts of published articles. Research articles published in the KCI over the past 10 years were collected using the Biblio Data Collector tool included in the NetMiner Program (version 4), using "cancer survivor

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Mazio, Federica, Giuseppina Aloj, Grazia Maria Giovanna Pastorino, et al. "Default-Mode Network Connectivity Changes Correlate with Attention Deficits in ALL Long-Term Survivors Treated with Radio- and/or Chemotherapy." Biology 11, no. 4 (2022): 499. http://dx.doi.org/10.3390/biology11040499.

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Whether chemotherapy (ChT) and radiotherapy (RT) determine neurocognitive impairment in acute lymphoblastic leukemia long-term survivors (ALL LTSs) through similar mechanisms affecting the same brain regions is still unknown. We compared neurocognitive alterations, regional brain tissue volumes (by voxel-based morphometry), and functional connectivity of the main default-mode network hubs (by seed-based analysis of resting state functional MRI data), in 13 ALL LTSs treated with RT and ChT (Group A) and 13 treated with ChT only (Group B). Group A performed significantly worse than Group B at th

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Joly, Florence, Marie Lange, Natacha Heutte, et al. "Baseline cognitive functions among elderly patients with localized breast cancer." Journal of Clinical Oncology 31, no. 15_suppl (2013): 9510. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.9510.

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9510 Background: Cognitive deficits (CD) were reported among patients receiving chemotherapy (CT) for cancer, but could also be observed before treatment. Elderly patients were poorly studied although they are more prone to present age-related CD and CD onset or enhancement during CT. This study assessed baseline cognitive functions among elderly localized breast cancer (LBC) patients before adjuvant treatment therapy. Methods: Episodic memory, working memory, executive functions and information processing speed were assessed with neuropsychological tests. Validated questionnaires were used to

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Nestle, U., S. Kremp, D. Hellwig, et al. "Multi-centre calibration of an adaptive thresholding method for PET-based delineation of tumour volumes in radiotherapy planning of lung cancer." Nuklearmedizin 51, no. 03 (2012): 101–10. http://dx.doi.org/10.3413/nukmed-0452-11-12.

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SummaryPurpose: To evaluate the calibration of an adaptive thresholding algorithm (contrastoriented algorithm) for FDG PET-based delineation of tumour volumes in eleven centres with respect to scanner types and image data processing by phantom measurements. Methods: A cylindrical phantom with spheres of different diameters was filled with FDG realizing different signal-to-background ratios and scanned using 5 Siemens Biograph PET/CT scanners, 5 Philips Gemini PET/CT scanners, and one Siemens ECAT-ART PET scanner. All scans were analysed by the contrast-oriented algorithm implemented in two dif

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De Roeck, L., P. Spiessens, H. Vermeulen, et al. "P01.09.A Prevalence and predictors of cognitive impairment in adult glioma survivors after multimodal therapy." Neuro-Oncology 24, Supplement_2 (2022): ii25. http://dx.doi.org/10.1093/neuonc/noac174.081.

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Abstract Background Long-term survival can be achieved in an increasing number of glioma patients after treatment. Therefore, safeguarding these survivors’ quality of life (QoL) is essential. Neurocognitive decline arises in many young patients, placing a heavy burden on the social and economic aspects of the patients’ lives. A lot of debate is currently ongoing regarding the prevalence of neurocognitive impairment and individual predictors of whom is susceptible for such side effect. Material and Methods In this cross-sectional study, 37 WHO grade 2-3 adult glioma survivors, at least one year

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Zola, Paolo, Maria Elena Jacomuzzi, Simona Mazzola, et al. "Analysis of the Evolution in the Management of Endometrial Cancer in Italy: A CTF Study." Tumori Journal 88, no. 6 (2002): 481–88. http://dx.doi.org/10.1177/030089160208800610.

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Objective In 1994 we mailed questionnaires to referral centers in Italy in order to evaluate the different opinions concerning aspects of endometrial cancer treatment, which is still controversial. The data processing showed a significant nonhomogeneity in disease management and prompted the Italian Society of Gynecologic Oncology to define guidelines for endometrial cancer adjuvant treatment. In 2001, we mailed again the same questionnaire to the same referral Centers in Italy. The aim of the second enquiry was the evaluation of changes in endometrial cancer management and the effective impac

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Thenault, Ronan, Kevin Kaulanjan, Thomas Darde, et al. "The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review." Applied Sciences 10, no. 18 (2020): 6428. http://dx.doi.org/10.3390/app10186428.

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Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for origin

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Padilla, Oscar, Hanna Minns, Hong-Jian Wei, et al. "91 Impact of ultra-fast ‘FLASH’ radiotherapy on single cell immunogenomics in diffuse intrinsic pontine glioma (DIPG)." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (2021): A100. http://dx.doi.org/10.1136/jitc-2021-sitc2021.091.

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BackgroundDiffuse intrinsic pontine gliomas (DIPG’s) are immunologically inert tumors with a median survival of 9–15 months. Radiation therapy (RT) is the mainstay treatment for DIPG but is associated with immunodepletion of the tumor microenvironment (TME) at high dose ranges. FLASH, or ultra-fast dose rate RT, represents a novel ablative technique that may spare TME immune responses while decreasing tumor burden. Here, we present single-cell immune profiling of DIPG tumors treated with FLASH, conventional dose rate RT (CONV) or no RT (SHAM).MethodsMurine H3.3K27M mutant DIPG cells were stere

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Heintz, A., S. Boussida, Z. Saida, et al. "P14.91 Study glycolytic metabolism in 1H-MRS monovoxel in the most aggressive part of 62 glioblastomas before and after 18 months of treatment." Neuro-Oncology 21, Supplement_3 (2019): iii89. http://dx.doi.org/10.1093/neuonc/noz126.326.

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Abstract BACKGROUND To study the relationship between glycolytic metabolism, tumor proliferation, survival and treatment response in patients with glioblastoma (GBM). MATERIAL AND METHODS Patients: 62 patients with glioblastoma, all having a STUPP Protocol (radiotherapy and concomitant chemotherapy), were selected and separated into 2 groups: Biopsies (30) and resections (32). In total, 269 NMR spectra (PRESS at GE 1.5T and 3T; multi-TEs TE=35ms and TE=144ms) were acquired. Processing: MRS data were processed with jMRUI software and quantitated using HLSVD and QUEST algorithms. Statistical ana

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