2,554 research outputs found
Deep learning for image-based liver analysis â A comprehensive review focusing on malignant lesions
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio
Diseases of the Abdomen and Pelvis 2018-2021: Diagnostic Imaging - IDKD Book
Gastrointestinal disease; PET/CT; Radiology; X-ray; IDKD; Davo
State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma
The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor
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
Applying artificial intelligence to big data in hepatopancreatic and biliary surgery: a scoping review
Aim: Artificial Intelligence (AI) and its applications in healthcare are rapidly developing. The healthcare industry generates ever-increasing volumes of data that should be used to improve patient care. This review aims to examine the use of AI and its applications in hepatopancreatic and biliary (HPB) surgery, highlighting studies leveraging large datasets.Methods: A PRISMA-ScR compliant scoping review using Medline and Google Scholar databases was performed (5th August 2022). Studies focusing on the development and application of AI to HPB surgery were eligible for inclusion. We undertook a conceptual mapping exercise to identify key areas where AI is under active development for use in HPB surgery. We considered studies and concepts in the context of patient pathways - before surgery (including diagnostics), around the time of surgery (supporting interventions) and after surgery (including prognostication).Results: 98 studies were included. Most studies were performed in China or the USA (n = 45). Liver surgery was the most common area studied (n = 51). Research into AI in HPB surgery has increased rapidly in recent years, with almost two-thirds published since 2019 (61/98). Of these studies, 11 have focused on using âbig dataâ to develop and apply AI models. Nine of these studies came from the USA and nearly all focused on the application of Natural Language Processing. We identified several critical conceptual areas where AI is under active development, including improving preoperative optimization, image guidance and sensor fusion-assisted surgery, surgical planning and simulation, natural language processing of clinical reports for deep phenotyping and prediction, and image-based machine learning.Conclusion: Applications of AI in HPB surgery primarily focus on image analysis and computer vision to address diagnostic and prognostic uncertainties. Virtual 3D and augmented reality models to support complex HPB interventions are also under active development and likely to be used in surgical planning and education. In addition, natural language processing may be helpful in the annotation and phenotyping of disease, leading to new scientific insights
Randomized comparison of power Doppler ultrasound-directed excisional biopsy with standard excisional biopsy for the characterization of lymphadenopathies in patients with suspected lymphoma.
PURPOSE: The sensitivity of lymph node excisional biopsy requires validation. Power Doppler ultrasound (US) helps predict the malignant status of lymphadenopathies. We used power Doppler US to select for biopsy the lymph node most suspected of malignancy. PATIENTS AND METHODS: One hundred fifty-two patients having lymphadenopathies with clinical suspicion of lymphoma were divided into two well-matched groups and
randomly assigned to undergo either standard or power Doppler US-directed lymph node excisional biopsy. RESULTS: Histology showed a malignancy in 64% of patients in the standard group
(lymphoma, 49 patients; carcinoma, two patients) and in 87% of patients in the US-assisted group (lymphoma, 62 patients; carcinoma, one patient). There were significantly fewer biopsy-related complications in the assisted group than in the standard group. During the follow-up of the patients with lymph nodes
reported as being reactive, 14 of 29 patients in the standard group were rebiopsied and were found to have lymphoma (13 patients) or carcinoma at the subsequent lymph node histology, whereas none of the patients in the assisted group (nine patients) required a second biopsy. Thus, biopsy provided false-negative results for malignancy in 21% of patients affected by lymphoma in the standard group and ever in the assisted group (P <.01).
CONCLUSION: Power Doppler US is an accurate tool for screening lymphadenopathies to be removed by excisional biopsy in patients with suspected lymphoma
Liver Tumors
This book is oriented towards clinicians and scientists in the field of the management of patients with liver tumors. As many unresolved problems regarding primary and metastatic liver cancer still await investigation, I hope this book can serve as a tiny step on a long way that we need to run on the battlefield of liver tumors
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