71 research outputs found
Quantitative bone marrow lesion size in osteoarthritic knees correlates with cartilage damage and predicts longitudinal cartilage loss
<p>Abstract</p> <p>Background</p> <p>Bone marrow lesions (BMLs), common osteoarthritis-related magnetic resonance imaging findings, are associated with osteoarthritis progression and pain. However, there are no articles describing the use of 3-dimensional quantitative assessments to explore the longitudinal relationship between BMLs and hyaline cartilage loss. The purpose of this study was to assess the cross-sectional and longitudinal descriptive characteristics of BMLs with a simple measurement of approximate BML volume, and describe the cross-sectional and longitudinal relationships between BML size and the extent of hyaline cartilage damage.</p> <p>Methods</p> <p>107 participants with baseline and 24-month follow-up magnetic resonance images from a clinical trial were included with symptomatic knee osteoarthritis. An 'index' compartment was identified for each knee defined as the tibiofemoral compartment with greater disease severity. Subsequently, each knee was evaluated in four regions: index femur, index tibia, non-index femur, and non-index tibia. Approximate BML volume, the product of three linear measurements, was calculated for each BML within a region. Cartilage parameters in the index tibia and femur were measured based on manual segmentation.</p> <p>Results</p> <p>BML volume changes by region were: index femur (median [95% confidence interval of the median]) 0.1 cm<sup>3 </sup>(-0.5 to 0.9 cm<sup>3</sup>), index tibia 0.5 cm<sup>3 </sup>(-0.3 to 1.7 cm<sup>3</sup>), non-index femur 0.4 cm<sup>3 </sup>(-0.2 to 1.6 cm<sup>3</sup>), and non-index tibia 0.2 cm<sup>3 </sup>(-0.1 to 1.2 cm<sup>3</sup>). Among 44 knees with full thickness cartilage loss, baseline tibia BML volume correlated with baseline tibia full thickness cartilage lesion area (<it>r </it>= 0.63, <it>p</it>< 0.002) and baseline femur BML volume with longitudinal change in femoral full thickness cartilage lesion area (<it>r </it>= 0.48 <it>p</it>< 0.002).</p> <p>Conclusions</p> <p>Many regions had no or small longitudinal changes in approximate BML volume but some knees experienced large changes. Baseline BML size was associated to longitudinal changes in area of full thickness cartilage loss.</p
Modulation of the intramedullary pressure responses by calcium dobesilate in a rabbit knee model of osteoarthritis
Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC.
BACKGROUND: Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. METHODS: A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. RESULTS/CONCLUSIONS: Items withââ„â75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items withââ€â60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified
A multidisciplinary consensus on the morphological and functional responses to immunotherapy treatment
The implementation of immunotherapy has radically changed the treatment of oncological patients. Currently, immunotherapy is indicated in the treatment of patients with head and neck tumors, melanoma, lung cancer, bladder tumors, colon cancer, cervical cancer, breast cancer, Merkel cell carcinoma, liver cancer, leukemia and lymphomas. However, its efficacy is restricted to a limited number of cases. The challenge is, therefore, to identify which subset of patients would benefit from immunotherapy. To this end, the establishment of immunotherapy response criteria and predictive and prognostic biomarkers is of paramount interest. In this report, a group of experts of the Spanish Society of Medical Oncology (SEOM), the Spanish Society of Medical Radiology (SERAM), and Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM) provide an up-to-date review and a consensus guide on these issues
Twenty years on: RECIST as a biomarker of response in solid tumours. An EORTC Imaging Group â ESOI joint paper
Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations.
While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for reponse classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted.
There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required
Texture classification of proteins using support vector machines and bio-inspired metaheuristics
6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process
Proceedings of the International Cancer Imaging Society (ICIS) 16th Annual Teaching Course
Table of contents
O1 Tumour heterogeneity: what does it mean?
Dow-Mu Koh
O2 Skeletal sequelae in adult survivors of childhood cancer
Sue Creviston Kaste
O3 Locoregional effects of breast cancer treatment
Sarah J Vinnicombe
O4 Imaging of cancer therapy-induced CNS toxicity
Giovanni Morana, Andrea Rossi
O5 Screening for lung cancer
Christian J. Herold
O6Risk stratification of lung nodules
Theresa C. McLoud
O7 PET imaging of pulmonary nodules
Kirk A Frey
O8 Transarterial tumour therapy
Bernhard Gebauer
O9 Interventional radiology in paediatric oncology
Derek Roebuck
O10 Image guided prostate interventions
Jurgen J. FĂŒtterer
O11 Imaging cancer predisposition syndromes
Alexander J. Towbin
O12Chest and chest wall masses
Thierry AG Huisman
O13 Abdominal masses: good or bad?
Anne MJB Smets
O14 Hepatobiliary MR contrast: enhanced liver MRI for HCC diagnosis and management
Giovanni Morana
O15 Role of US elastography and multimodality fusion for managing patients with chronic liver disease and HCC
Jeong Min Lee
O16 Opportunities and challenges in imaging metastatic disease
Hersh Chandarana
O17 Diagnosis, treatment monitoring, and follow-up of lymphoma
Marius E. Mayerhoefer, Markus Raderer, Alexander Haug
O18 Managing high-risk and advanced prostate cancer
Matthias Eiber
O19 Immunotherapy: imaging challenges
Bernhard Gebauer
O20 RECIST and RECIST 1.1
Andrea Rockall
O21 Challenges of RECIST in oncology imaging basics for the trainee and novice
Aslam Sohaib
O22 Lymphoma: PET for interim and end of treatment response assessment: a usersâ guide to the Deauville Score
Victoria S Warbey
O23 Available resources
Hebert Alberto Vargas
O24 ICIS e-portal and the online learning community
Dow-Mu Koh
O25 Benign lesions that mimic pancreatic cancer
Jay P Heiken
O26 Staging and reporting pancreatic malignancies
Isaac R Francis, Mahmoud, M Al-Hawary, Ravi K Kaza
O27 Intraductal papillary mucinous neoplasm
Giovanni Morana
O28 Cystic pancreatic tumours
Mirko DâOnofrio
O29 Diffusion-weighted imaging of head and neck tumours
Harriet C. Thoeny
O30 Radiation injury in the head and neck
Ann D King
O31 PET/MR of paediatric brain tumours
Giovanni Morana, Arnoldo Piccardo, Maria Luisa GarrĂš, Andrea Rossi
O32 Structured reporting and beyond
Hebert Alberto Vargas
O33 Massachusetts General Hospital experience with structured reporting
Theresa C. McLoud
O34 The oncologistâs perspective: what the oncologist needs to know
Nick Reed
O35 Towards the cure of all children with cancer: global initiatives in pediatric oncology
Carlos Rodriguez-Galindo
O36 Multiparametric imaging of renal cancers
Hersh Chandarana
O37 Linking imaging features of renal disease and their impact on management strategies
Hebert Alberto Vargas
O38 Adrenals, retroperitoneum and peritoneum
Isaac R Francis, Ashish P Wasnik
O39 Lung and pleura
Stefan Diederich
O40 Advances in MRI
Jurgen J. FĂŒtterer
O41 Advances in molecular imaging
Wim J.G. Oyen
O42 Incorporating advanced imaging, impact on treatment selection and patient outcome
Cheng Lee Chaw, Nicholas van As
S1 Combining ADC-histogram features improves performance of MR diffusion-weighted imaging for Lymph node characterisation in cervical cancer
Igor Vieira, Frederik De Keyzer, Elleke Dresen, Sileny Han, Ignace Vergote, Philippe Moerman, Frederic Amant, Michel Koole, Vincent Vandecaveye
S2 Whole-body diffusion-weighted MRI for surgical planning in patients with colorectal cancer and peritoneal metastases
R Dresen, S De Vuysere, F De Keyzer, E Van Cutsem, A DâHoore, A Wolthuis, V Vandecaveye
S3 Role of apparent diffusion coefficient (ADC) diffusion-weighted MRI for predicting extra capsular extension of prostate cancer.
P. Pricolo ([email protected]), S. Alessi, P. Summers, E. Tagliabue, G. Petralia
S4 Generating evidence for clinical benefit of PET/CT â are management studies sufficient as surrogate for patient outcome?
C. Pfannenberg, B. GĂŒckel, SC SchĂŒle, AC MĂŒller, S. Kaufmann, N. Schwenzer, M. Reimold,C. la Fougere, K. Nikolaou, P. Martus
S5 Heterogeneity of treatment response in skeletal metastases from breast cancer with 18F-fluoride and 18F-FDG PET
GJ Cook, GK Azad, BP Taylor, M Siddique, J John, J Mansi, M Harries, V Goh
S6 Accuracy of suspicious breast imagingâcan we tell the patient?
S Seth, R Burgul, A Seth
S7 Measurement method of tumour volume changes during neoadjuvant chemotherapy affects ability to predict pathological response
S Waugh, N Muhammad Gowdh, C Purdie, A Evans, E Crowe, A Thompson, S Vinnicombe
S8 Diagnostic yield of CT IVU in haematuria screening
F. Arfeen, T. Campion, E. Goldstraw
S9 Percutaneous radiofrequency ablation of unresectable locally advanced pancreatic cancer: preliminary results
DâOnofrio M, Ciaravino V, Crosara S, De Robertis R, Pozzi Mucelli R
S10 Iodine maps from dual energy CT improve detection of metastases in staging examinations of melanoma patients
M. Uhrig, D. Simons, H. Schlemmer
S11Can contrast enhanced CT predict pelvic nodal status in malignant melanoma of the lower limb?
Kate Downey
S12 Current practice in the investigation for suspected Paraneoplastic Neurological Syndromes (PNS) and positive malignancy yield.
S Murdoch, AS Al-adhami, S Viswanathan
P1 Technical success and efficacy of Pulmonary Radiofrequency ablation: an analysis of 207 ablations
S Smith, P Jennings, D Bowers, R Soomal
P2 Lesion control and patient outcome: prospective analysis of radiofrequency abaltion in pulmonary colorectal cancer metastatic disease
S Smith, P Jennings, D Bowers, R Soomal
P3 Hepatocellular carcinoma in a post-TB patient: case of tropical infections and oncologic imaging challenges
TM Mutala, AO Odhiambo, N Harish
P4 Role of apparent diffusion coefficient (ADC) diffusion-weighted MRI for predicting extracapsular extension of prostate cancer
P. Pricolo, S. Alessi, P. Summers, E. Tagliabue, G. Petralia
P5 What a difference a decade makes; comparison of lung biopsies in Glasgow 2005 and 2015
M. Hall, M. Sproule, S. Sheridan
P6 Solid pseudopapillary tumour of pancreas: imaging features of a rare neoplasm
KY Thein, CH Tan, YL Thian, CM Ho
P7 MDCT - pathological correlation in colon adenocarcinoma staging: preliminary experience
S De Luca, C Carrera, V Blanchet, L AlarcĂłn, E Eyheremnedy
P8 Image guided biopsy of thoracic masses and reduction of pneumothorax risk: 25Â years experience
B K Choudhury, K Bujarbarua, G Barman
P9 Tumour heterogeneity analysis of 18F-FDG-PET for characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1
GJ Cook, E Lovat, M Siddique, V Goh, R Ferner, VS Warbey
P10 Impact of introduction of vacuum assisted excision (VAE) on screen detected high risk breast lesions
L Potti, B Kaye, A Beattie, K Dutton
P11 Can we reduce prevalent recall rate in breast screening?
AA Seth, F Constantinidis, H Dobson
P12 How to reduce prevalent recall rate? Identifying mammographic lesions with low Positive Predictive Value (PPV)
AA Seth ([email protected]), F Constantinidis, H Dobson
P13 Behaviour of untreated pulmonary thrombus in oncology patients diagnosed with incidental pulmonary embolism on CT
R. Bradley, G. Bozas, G. Avery, A. Stephens, A. Maraveyas
P14 A one-stop lymphoma biopsy service â is it possible?
S Bhuva, CA Johnson, M Subesinghe, N Taylor
P15 Changes in the new TNM classification for lung cancer (8th edition, effective January 2017)
LE Quint, RM Reddy, GP Kalemkerian
P16 Cancer immunotherapy: a review of adequate imaging assessment
G GonzĂĄlez Zapico, E Gainza Jauregui, R Ălvarez Francisco, S Ibåñez Alonso, I Tavera Bahillo, L MĂșgica Ălvarez
P17 Succinate dehydrogenase mutations and their associated tumours
O Francies, R Wheeler, L Childs, A Adams, A Sahdev
P18 Initial experience in the usefulness of dual energy technique in the abdomen
SE De Luca, ME Casalini Vañek, MD Pascuzzi, T Gillanders, PM Ramos, EP Eyheremendy
P19 Recognising the serious complication of Richterâs transformation in CLL patients
C Stove, M Digby
P20 Body diffusion-weighted MRI in oncologic practice: truths, tricks and tips
M. Nazar, M. Wirtz, MD. Pascuzzi, F. Troncoso, F. Saguier, EP. Eyheremendy
P21 Methotrexate-induced leukoencephalopathy in paediatric ALL Patients
D.J. Quint, L. Dang, M. Carlson, S. Leber, F. Silverstein
P22 Pitfalls in oncology CT reporting. A pictorial review
R Rueben, S Viswanathan
P23 Imaging of perineural extension in head and neck tumours
B Nazir, TH Teo, JB Khoo
P24 MRI findings of molecular subtypes of breast cancer: a pictorial primer
K Sharma, N Gupta, B Mathew, T Jeyakumar, K Harkins
P25 When cancer canât wait! A pictorial review of oncological emergencies
K Sharma, B Mathew, N Gupta, T Jeyakumar, S Joshua
P26 MRI of pancreatic neuroendocrine tumours: an approach to interpretation
D Christodoulou, S Gourtsoyianni, A Jacques, N Griffin, V Goh
P27 Gynaecological cancers in pregnancy: a review of imaging
CA Johnson, J Lee
P28 Suspected paraneoplastic neurological syndromes - review of published recommendations to date, with proposed guideline/flowchart
JA Goodfellow, AS Al-adhami, S Viswanathan
P29 Multi-parametric MRI of the pelvis for suspected local recurrence of prostate cancer after radical prostatectomy
R Bradley
P30 Utilisation of PI-RADS version 2 in multi-parametric MRI of the prostate; 12-months experience
R Bradley
P31 Radiological assessment of the post-chemotherapy liver
A Yong, S Jenkins, G Joseph
P32 Skeletal staging with MRI in breast cancer â what the radiologist needs to know
S Bhuva, K Partington
P33 Perineural spread of lympoma: an educational review of an unusual distribution of disease
CA Johnson, S Bhuva, M Subesinghe, N Taylor
P34 Visually isoattenuating pancreatic adenocarcinoma. Diagnostic imaging tools.
C Carrera, A Zanfardini, S De Luca, L AlarcĂłn, V Blanchet, EP Eyheremendy
P35 Imaging of larynx cancer: when is CT, MRI or FDG PET/CT the best test?
K Cavanagh, E Lauhttp://deepblue.lib.umich.edu/bitstream/2027.42/134651/1/40644_2016_Article_79.pd
Measurement of bone marrow lesions by MR imaging in knee osteoarthritis using quantitative segmentation methods â a reliability and sensitivity to change analysis
Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers (Jan , 10.1007/s00330-020-07598-8, 2021)
A Correction to this paper has been published:Radiolog
Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.Radiolog
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