75th Annual Radiology, Radiation Oncology & Nuclear Medicine Meeting

Feb 3rd, 2023Feb 4th, 2023
Pasadena, CA
US
https://www.larad.org/2023-midwinter-meeting
Phone:6268365800
Topics are MSK and ABD radiology, CRS William Bradley Lecture. Radiation Oncology: H&N, lung, breast, CNS. Nuclear Medicine: Cardiology, neurology, PSMA imaging/treatment, PET, MRI, SSR.
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Overlap between manual (radiologist) and semiautomatic (algorithm) segmentations of hand joints on a standard posteroanterior radiograph. Orange overlay indicates manual radiologist segmentations, blue overlay shows algorithm segmentations, and green overlay indicates consistent regions between both methods.
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Radiomics model grades hand osteoarthritis on x-rays
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Hand osteoarthritis affects approximately 8% of men and 16% of women over the age of 50.
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Overlap between manual (radiologist) and semiautomatic (algorithm) segmentations of hand joints on a standard posteroanterior radiograph. Orange overlay indicates manual radiologist segmentations, blue overlay shows algorithm segmentations, and green overlay indicates consistent regions between both methods.
Despite pediatric ED imaging capability, race and insurance gaps remain
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TAE reduces pain in patients with chronic wrist pain
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The finding suggests that the emerging interventional radiology procedure could be a new option for patients who have exhausted conservative management.
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A 44-year-old woman with TFCC injury treated by transcatheter arterial embolization (TAE). a) Right brachial arteriography via common femoral artery access with a 5-Fr catheter demonstrates hyperstaining on the ulnar side of the wrist joint (white circle). b) An ulnar artery branch is superselected with a 1.9-Fr microcatheter, showing hypervascular staining corresponding to the pain site (white arrow). c) Delayed-phase angiography reveals early venous drainage (white arrow) adjacent to the hyperstaining, a finding often observed in TAE and considered an additional marker for embolization. d) Final angiography after injection of 0.5 mL quick-soluble gelatin sponge particles (QS-GSPs) demonstrates resolution of the hyperstaining (white circle). The VAS pain score improved from 7 at baseline to 1 at six months.
Human-AI collaboration beats AI alone for identifying PE
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Study findings highlight the continued central role of radiologists for this indication.
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Case example of a false-negative AI result. An 85-year-old female presented to the emergency department with dyspnea. Axial 0.625-mm from CT pulmonary angiography examination with contrast (CTPA) images demonstrate a thin, linear right-sided filling defect extending from the right interlobar pulmonary artery (A and B, red arrows) into the right middle lobar artery (B, yellow arrow), consistent with acute pulmonary embolism (PE). The finding was described in the radiology report; however, the AI result was negative for PE. Adjudication was consistent with acute PE.
AI model predicts 10-year breast cancer risk
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AI devices vary widely in lung cancer detection
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Three devices helped detect more cancerous tumors, whereas the other four devices helped detect fewer tumors.
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Cropped secondary capture examples. These are illustrative and not intended to imply superiority or inferiority of any device. (A) Posteroanterior radiograph in a 46-year-old female patient. The device correctly identified a right lower lobe nodule projected below the right hemidiaphragm and hilar lymphadenopathy. (B) Posteroanterior radiograph in an 86-year-old female patient with a classic Golden S sign highly suggestive of cancer. Three devices did not identify any findings. (C) The output from one device for the same radiograph as in B. The device placed a contour around the area of abnormality but mislabeled it as segmental collapse, and there are no other elements in the output to raise suspicion of cancer. (D) Posteroanterior radiograph in a 60-year-old male patient -- a case of confirmed lung cancer that was not deemed visible in retrospect. The device identified multiple false-positive abnormalities. (E) Posteroanterior radiograph in a 77-year-old female patient with two right lower lobe nodules. The device mislabeled the abnormality as infection -- a diagnostic term that could incorrectly influence clinical management. (F) Posteroanterior radiograph in a 77-year-old female patient with a right hilar tumor. Most of the lungs have been labeled by the device, with excessive overlap of the abnormality that pragmatically represents an incorrect result. All annotations shown were produced by the devices. LL = lung lesion, LO = lung opacity, PO = pleural other, TBC = tuberculosis.
MRI-based ML model shows promise in colorectal cancer subtyping
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A machine-learning model based on MRI radiomics achieved high performance in predicting colorectal cancer subtypes.
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Overview of the study design, including data collection, radiomics model construction, and biologic interpretation. (A) The primary cohort for model development and evaluation in the training and internal test sets includes 168 patients with colorectal cancer (CRC) from center 1 (Fudan University Shanghai Cancer Center), whereas external testing was performed using data from 85 patients from centers 2 (Huashan Hospital, Fudan University) and 3 (Zhongshan Hospital, Fudan University). For model interpretation, RNA sequencing (RNA-seq) data from 18 patients with CRC in center 1 and single-cell data from 35 patients from the Gene Expression Omnibus (GEO) database are incorporated. Mismatch repair (MMR) protein expression, obtained from pathologic reports in electronic medical records (EMRs), was used to identify patients with consensus molecular subtype 1 (CMS1) CRC. Tissue microarrays (TMAs) from surgically obtained CRC tissues were analyzed for immunohistochemical staining of caudal-type homeobox 2 (CDX2), 5-hydroxytryptamine receptor 2B (HTR2B), FERM domain containing 6 (FRMD6), and zinc finger E-box binding homeobox 1 (ZEB1), and the results were subsequently entered into an online classification tool that assigned patients to CMS2, 3, or 4 subtypes. (B) Radiomics workflow for CMS4 prediction. Lesions were manually delineated on T2-weighted imaging (T2WI) and contrast-enhanced (CE) T1-weighted imaging (T1WI), followed by radiomics feature extraction. Feature selection and model development were performed in the training set using machine-learning algorithms with fivefold cross-validation for model selection, and the final model was evaluated in the internal and external test sets using receiver operating characteristic (ROC) curve analysis. (C) Transcriptomic interpretation workflow. Using the radiomics-predicted CMS4 classification, bulk RNA sequencing (RNA-seq) data were used to perform differential expression analysis between predicted CMS4 and non-CMS4 groups, followed by pathway enrichment analyses to assess biologic relevance. Public single-cell RNA sequencing data from the Gene Expression Omnibus database were then used for cell-type–level interrogation to support interpretation of the associated pathways.
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