Masterclass in Imaging of Prostate Cancer

Nov 3rd, 2021
Online
https://www.icimagingsociety.org.uk/index.cfm?task=computer_workshops
Phone:07500516360
**2nd edition** This online ICIS interactive series is designed as an advanced cancer imaging course for radiologists.
Latest in Home
Early detection depends on more than technology; it depends on whether the system is built for the patient who walks through the door.
Blind spots in imaging: Dementia care in mammography and miscarriage prediction
April 29, 2026
Turning Innovation into Everyday Efficiency
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Turning Innovation into Everyday Efficiency
April 22, 2026
Uterine and placental changes during an example placental contraction. The top row shows axial MRI images at selected times points before, during and after a placental contraction (indicated by vertical lines on the graph below) with segmentations shown below. Changes in placental and non-placental volumes, wall areas and placental R2* (all measured across the whole volume of the uterus, not just the single slice shown) are plotted underneath. The legend indicates the colors used for the lines in the plots and the regions indicated in the segmentation.
MRI reveals occurrence of placental contractions
April 29, 2026
Quantification of total tumor volume (TTV) at lutetium-177 prostate-specific membrane antigen (PSMA)–617 SPECT/CT shows heterogeneous TTV responses between dose 1 (Cycle #1) and dose 2 (Cycle #2) in participants with metastatic castration-resistant prostate cancer. Posttherapy coronal attenuation-corrected noncontrast SPECT/CT maximum intensity projection images demonstrate (A) near-complete response in a 76-year-old man, (B) complete response in an 83-year-old man, (C) partial TTV reduction (not complete response) in a 69-year-old man, and (D) TTV increase (i.e. progressive disease) in a 74-year-old man. Blue-shaded regions represent semiautomatically segmented PSMA-avid tumor volume, with associated TTV values given at the bottom of each panel. The gradient bar shows standardized uptake value (SUV) from 0 to 5.
Lu-177 PSMA-SPECT/CT predicts survival in mCRPC
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More in Home
Blind spots in imaging: Dementia care in mammography and miscarriage prediction
By Claudia Tschabuschnig
Dementia in the mammography room and miscarriage in the first trimester, two blind spots that better technology alone won't fix.
April 29, 2026
Early detection depends on more than technology; it depends on whether the system is built for the patient who walks through the door.
4 CT signs help predict ischemia in small bowel obstruction
By Kate Madden Yee
The signs "could be prioritized when interpreting CT examinations in patients with SBO to help guide surgical decision-making."
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MRI reveals occurrence of placental contractions
By Kate Madden Yee
Characterizing these contractions could help clinicians better understand the placenta's function.
April 29, 2026
Uterine and placental changes during an example placental contraction. The top row shows axial MRI images at selected times points before, during and after a placental contraction (indicated by vertical lines on the graph below) with segmentations shown below. Changes in placental and non-placental volumes, wall areas and placental R2* (all measured across the whole volume of the uterus, not just the single slice shown) are plotted underneath. The legend indicates the colors used for the lines in the plots and the regions indicated in the segmentation.
Lu-177 PSMA-SPECT/CT predicts survival in mCRPC
By Will Morton
Lu-177 PSMA-617 SPECT/CT total tumor volume complete response at six weeks associated with overall survival.
April 29, 2026
Quantification of total tumor volume (TTV) at lutetium-177 prostate-specific membrane antigen (PSMA)–617 SPECT/CT shows heterogeneous TTV responses between dose 1 (Cycle #1) and dose 2 (Cycle #2) in participants with metastatic castration-resistant prostate cancer. Posttherapy coronal attenuation-corrected noncontrast SPECT/CT maximum intensity projection images demonstrate (A) near-complete response in a 76-year-old man, (B) complete response in an 83-year-old man, (C) partial TTV reduction (not complete response) in a 69-year-old man, and (D) TTV increase (i.e. progressive disease) in a 74-year-old man. Blue-shaded regions represent semiautomatically segmented PSMA-avid tumor volume, with associated TTV values given at the bottom of each panel. The gradient bar shows standardized uptake value (SUV) from 0 to 5.
AI algorithm shines in spotting early pancreatic cancer
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The findings could help shift diagnosis of PDA from late-stage terminal disease to early-stage treatable disease.
April 28, 2026
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The five-year experiment: real-world lessons from 400,000 AI-processed imaging studies
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AI models are the easy part. Governance, integration, and real-world workflows decide what survives, according to insights from five years of implementation across 20 imaging centers.
April 28, 2026
Data from the 3R Swiss Imaging Network indicates that while 91% of radiologists use AI, successful adoption depends heavily on full workflow integration.
Radiology researchers weigh in on the impact of the political climate
By Liz Carey
Group begins research into how political environments may intersect with research practice.
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AI model predicts cardiovascular risk from BACs on mammograms
By Amerigo Allegretto
An AI model showed success in using age-adjusted breast arterial calcifications from mammograms to predict cardiovascular events.
April 27, 2026
Example of Percentile Nomogram With Age-Adjusted BAC Quartiles. (A) Mammogram of a 56-year-old woman. There is evident BAC (pink arrow). This was quantified as a BAC score of 30 using the cmAngio tool. (B) Nomogram indicating that a BAC score of 30 places a 56-year-old woman within the 50th to 75th percentile for her age. Images are republished under a Creative Commons license (CC BY 4.0).
Radiography-led interventional techniques bolster patient experience
By Frances Rylands-Monk
Radiographer involvement in interventional imaging technologies and localization techniques could improve patient management, safety, and outcomes, according to experts.
April 27, 2026
Noelle Clerkin
ChatGPT matches nuclear medicine experts on FDG-PET/CT scans
By Will Morton
Models excelled on cases with well-defined metabolic patterns, such as in typical cases of Alzheimer's disease, yet were less accurate on complex cases.
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Ai Face
"You'll never walk alone": Benoît Rizk on how to make radiology AI work
By Claudia Tschabuschnig
At 3R Swiss Imaging Network, Dr. Benoît Rizk works at the center of real-world AI in radiology. In this video interview he shares why governance and integration matter more than the models themselves, and why the profession needs to have an honest conversation about the decade ahead.
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Dr. Benoît Rizk
New x-ray dataset helps clinicians develop pathology detection AI
By Kate Madden Yee
ICUs rely heavily on bedside chest x-ray to monitor patients, and the dataset could offer support for radiologic assessment.
April 23, 2026
Example radiographs with different labels for 'pulmonary congestion,' 'pleural effusion,' 'pulmonary opacities,' and 'atelectasis.'
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