We evaluated the diagnostic value of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT surveillance for detecting clinically unsuspected recurrence or second primary cancer (SPC) in patients with non-small cell lung cancer (NSCLC) after curative therapy. A total of 4478 surveillance FDG PET/CT scans from 2864 NSCLC patients without suspicion of recurrence after curative therapy were reviewed retrospectively. In 274 of 2864 (9.6%) patients, recurrent NSCLC or SPC was found by surveillance PET/CT during clinical follow-up. Surveillance PET/CT scans showed sensitivity of 98.9% (274/277), specificity of 98.1% (4122/4201), accuracy of 98.2% (4396/4478), positive predictive value (PPV) of 77.6% (274/353), and negative predictive value of 99.9% (4122/4125). The specificity and accuracy in the curative surgery group were significantly higher than those in the curative radiotherapy group. PPV was significantly improved in subgroups of patients with advanced stage prior to curative therapy, PET/CT scans performed within 3 years after curative-intent therapy, and curative surgery. FDG PET/CT surveillance showed good diagnostic efficacy for detecting clinically unexpected recurrence or SPC in NSCLC patients after curative therapy. It can be more useful when performed soon after therapy in curative surgery recipients and those with an advanced disease stage considering its diagnostic efficacy and yield.

The aim of our retrospective study is to develop and externally validate an ¹⁸F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The ¹⁸F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.

We investigated the diagnostic value of the maximum standardized uptake value (SUV) at hand and wrist joints for differentiating rheumatic diseases via bone single-photon emission computed tomography (SPECT)/computed tomography (CT). A total of 84 patients manifesting hand and wrist pain (58 women; age, 49.8 ± 15.4 years) were finally diagnosed with rheumatoid arthritis (RA, n = 42), osteoarthritis (OA, n = 16), fibromyalgia (FM, n = 2), and other rheumatic diseases (n = 24). The SUV of each patient was measured in 32 joints including the distal interphalangeal (DIP), proximal interphalangeal (PIP), metacarpophalangeal (MCP), and wrist joints bilaterally. Differences in pain and SUVs between specific rheumatic diseases were assessed using the chi-squared test or one-way analysis of variance. Using the highest SUV (hSUV) in each patient, the diagnostic performance in differentiating specific diseases was evaluated by receiver operating characteristic (ROC) curve analysis. Pain symptoms were present in 886 (33.0%) sites in a total of 2688 joints. In four joint groups (DIP, PIP, MCP, and wrist), the SUVs of joints with pain were significantly higher than those of pain-free joints (all P < 0.001). Active joint sites with higher SUVs than the median value of each joint group were the most common in RA (55.1%). RA showed the greatest hSUV in the PIP (3.0 ± 2.4), MCP (3.5 ± 3.4), and wrist (3.3 ± 1.9) joint groups. FM was characterized by the lowest hSUV of all joint groups. In ROC curve analysis, the cumulative hSUV of the PIP, MCP, and wrist joint groups showed good performance for evaluating RA (area under the curve (AUC), 0.668; P = 0.005). The summation of the hSUVs at all joint groups had an excellent predictive performance for FM (AUC, 0.878; P < 0.001). Consequently, the arthritic activity of the hand and wrist joints based on SUV differed according to specific rheumatic diseases. Quantitative SPECT/CT may provide objective information related to arthritic activity for differentiating specific rheumatic diseases.

Background: The glenoid version of the shoulder joint correlates with the stability of the glenohumeral joint and the clinical results of total shoulder arthroplasty. We sought to analyze and compare the glenoid version measured by traditional axial two-dimensional (2D) computed tomography (CT) and three-dimensional (3D) reconstructed images at different levels.Methods: A total of 30 cases, including 15 male and 15 female patients, who underwent 3D shoulder CT imaging was randomly selected and matched by sex consecutively at one hospital. The angular difference between the scapular body axis and 2D CT slice axis was measured. The glenoid version was assessed at three levels (midpoint, upper one-third, and center of the lower circle of the glenoid) using Friedman’s method in the axial plane with 2D CT images and at the same level of three different transverse planes using a 3D reconstructed image. Results: The mean difference between the scapular body axis on the 3D reconstructed image and the 2D CT slice axis was 38.4°. At the level of the midpoint of the glenoid, the measurements were 1.7° ± 4.9° on the 2D CT images and −1.8° ± 4.1° in the 3D reconstructed image. At the level of the center of the lower circle, the measurements were 2.7° ± 5.2° on the 2D CT images and −0.5° ± 4.8° in the 3D reconstructed image. A statistically significant difference was found between the 2D CT and 3D reconstructed images at all three levels. Conclusions: The glenoid version is measured differently between axial 2D CT and 3D reconstructed images at three levels. Use of 3D reconstructed imaging can provide a more accurate glenoid version profile relative to 2D CT. The glenoid version is measured differently at different levels.

Spondyloarthritis (SpA) is characterized by inflammatory back pain. Magnetic resonance imaging (MRI) was the earlier gold standard technique for detecting early inflammatory change. We reassessed the diagnostic utility of sacroiliac joint/sacrum (SIS) ratios of single-photon emission computed tomography/computed tomography (SPECT/CT) for identifying sacroiliitis. We aimed to investigate of SPECT/CT in diagnosing SpA using a rheumatologist’s visual scoring of SIS ratios assessment. We conducted a single-center, medical records review study of patients with lower back pain who underwent bone SPECT/CT from August 2016 to April 2020. We employed semiquantitative visual bone scoring methods of SIS ratio. The uptake of each sacroiliac joint was compared to that of the sacrum (0–2). A score of 2 for the sacroiliac joint of either side was considered diagnostic of sacroiliitis. Of the 443 patients assessed, 40 had axial SpA (axSpA), 24 being radiographic axSpA and 16 being nonradiographic axSpA. The sensitivity, specificity, and positive and negative predictive values of SIS ratio of SPECT/CT for axSpA were 87.5%, 56.5%, 16.6%, and 97.8%, respectively. In receiver operating curve analysis, MRI better diagnosed axSpA than did SIS ratio of SPECT/CT. Although the diagnostic utility of SIS ratio of SPECT/CT was inferior to MRI, visual scoring of SPECT/CT affords high sensitivity and negative predictive value in axSpA. When MRI is inappropriate for certain patients, SIS ratio of SPECT/CT is an alternative tool for identifying axSpA in real practice.