Background: We performed a dosimetric comparison of spot-scanning proton therapy (SSPT) and intensity-modulated radiation therapy (IMRT) for hepatocellular carcinoma (HCC) to investigate the impact of tumor size on the risk of radiation induced liver disease (RILD). Methods: A number of alternative plans were generated for 10 patients with HCC. The gross tumor volumes (GTV) varied from 20.1 to 2194.5 cm³. Assuming all GTVs were spherical, the nominal diameter was calculated and ranged from 3.4 to 16.1 cm. The prescription dose was 60 Gy for IMRT or 60 cobalt Gy-equivalents for SSPT with 95% planning target volume (PTV) coverage. Using IMRT and SSPT techniques, extensive comparative planning was conducted. All plans were evaluated by the risk of RILD estimated using the Lyman-normal-tissue complication probability model. Results: For IMRT the risk of RILD increased drastically between 6.3–7.8 cm nominal diameter of GTV. When the nominal diameter of GTV was more than 6.3 cm, the average risk of RILD was 94.5% for IMRT and 6.2% for SSPT. Conclusions: Regarding the risk of RILD, HCC can be more safely treated with SSPT, especially if its nominal diameter is more than 6.3 cm.

Objective: Measurement of visceral adipose tissue (VAT) needs to be accurate and sensitive to change for risk monitoring. The purpose of this study is to determine the CT slice location where VAT area can best reflect changes in VAT volume and body weight. Methods: 60 plain abdominal CT images from 30 males [mean age (range) 51 (41–68) years, mean body weight (range) 71.1 (101.9–50.9) kg] who underwent workplace screenings twice within a 1-year interval were evaluated. Automatically calculated and manually corrected areas of the VAT of various scan levels using “freeform curve” region of interest on CT were recorded and compared with body weight changes. Results: The strongest correlations of VAT area with VAT volume and body weight changes were shown in a slice 3 cm above the lower margin of L3 with r values of 0.853 and 0.902, respectively. Conclusion: VAT area measurement at a single level 3 cm above the lower margin of the L3 vertebra is feasible and can reflect changes in VAT volume and body weight. Advances in knowledge: As VAT area at a CT slice 3 cm above the lower margin of L3 can best reflect interval changes in VAT volume and body weight, VAT area measurement should be selected at this location.

To demonstrate the superiority of total psoas volume (TPV) over total psoas area (TPA) in terms of predicting post-operative complications in living-donor liver transplantation (LDLT). The TPA and TPV were assessed in 32 recipients who underwent CT before LDLT. The TPA was measured using an axial CT image at the level of the upper margin of the fourth lumbar vertebral body. The TPV was calculated using all the CT images from the muscle origin through the level of the pubic symphysis. Patients were divided into a sarcopenia group and no-sarcopenia group based on the medians of normalized TPA (nTPA) and normalized TPV (nTPV). We calculated the odds ratio (OR) of post-operative respiratory complications in relation to nTPA and nTPV, respectively. Out of 32 recipients, 17 recipients experienced at least 1 post-operative respiratory complication. The OR for males according to nTPV [OR = 15.00, 95% confidence interval (CI) = 1.03–218.31; p = 0.031] was higher than that for nTPA (OR = 3.33, 95% CI = 0.36–30.70; p = 0.280). The OR for females according to nTPV (OR = 4.00, 95% CI = 0.56–28.40; p = 0.16) was the same as that for nTPA (OR = 4.00, 95% CI = 0.56–28.40; p = 0.16). Pre-operative volume of the skeletal muscle might be a better predictor for post-operative risks in LDLT recipients than pre-operative area of the skeletal muscle. Post-operative risks for respiratory complications in LDLT recipients might be evaluated more accurately by using TPV instead of TPA.

Objective:To investigate the validity of a computer-based method using temporal subtraction in carpal joints of patients with rheumatoid arthritis (RA), which can detect the difference in joint space between two images with the joint space difference index (JSDI).Methods:The study consisted of 43 patients with RA (39 females and 4 males) who underwent radiography at baseline and at 1-year follow-up. The joint space narrowing (JSN) of carpal joints on bilateral hand radiographs was assessed by our computer-based method, using the Sharp/van der Heijde method as the standard of reference. We compared the JSDI of joints with JSN progression in the follow-up period with that of those without JSN progression. In addition, we examined whether there is a significant difference in JSDI in terms of laterality or topology of the joint.Results:The JSDI of joints with JSN progression was significantly higher than that of those without JSN progression (Mann–Whitney U test, p < 0.001). There was no statistically signifi...

Early diagnosis and initiation of treatment for fresh osteoporotic lumbar vertebral fractures (OLVF) are crucial. Magnetic resonance imaging (MRI) is generally performed to differentiate between fresh and old OLVF. However, MRIs can be intolerable for patients with severe back pain. Furthermore, it is difficult to perform in an emergency. MRI should therefore only be performed in appropriately selected patients with a high suspicion of fresh fractures. As radiography is the first-choice imaging examination for the diagnosis of OLVF, improving screening accuracy with radiographs will optimize the decision of whether an MRI is necessary. This study aimed to develop a method to automatically classify lumbar vertebrae (LV) conditions such as normal, old, or fresh OLVF using deep learning methods with radiography. A total of 3481 LV images for training, validation, and testing and 662 LV images for external validation were collected. Visual evaluation by two radiologists determined the ground truth of LV diagnoses. Three convolutional neural networks were ensembled. The accuracy, sensitivity, and specificity were 0.89, 0.83, and 0.92 in the test and 0.84, 0.76, and 0.89 in the external validation, respectively. The results suggest that the proposed method can contribute to the accurate automatic classification of LV conditions on radiography.