Background and Objectives: Preoperative prophylactic balloon-assisted occlusion (PBAO) of the internal iliac arteries minimizes blood loss and facilitates surgery performance, through reductions in the rate of uterine perfusion, which allow for better control in hysterectomy performance, with decreased rates of bleeding and surgical complications. We aimed to investigate the maternal and fetal outcomes associated with PBAO use in women with placenta increta or percreta. Material and Methods: The records of 42 consecutive patients with a diagnosis of placenta increta or percreta were retrospectively reviewed. Of 42 patients, 17 patients (40.5%) with placenta increta or percreta underwent cesarean delivery after prophylactic balloon catheter placement in the bilateral internal iliac artery (balloon group). The blood loss volume, transfusion volume, postoperative hemoglobin changes, rates of hysterectomy and hospitalization, and infant Apgar score in this group were compared to those of 25 similar women who underwent cesarean delivery without balloon placement (surgical group). Results: The mean intraoperative blood loss volume in the balloon group (2319 ± 1191 mL, range 1000–4500 mL) was significantly lower than that in the surgical group (4435 ± 1376 mL, range 1500–10,500 mL) (p = 0.037). The mean blood unit volume transfused in the balloon group (2060 ± 1154 mL, range 1200–8000 mL) was significantly lower than that in the surgical group (3840 ± 1464 mL, range 1800–15,200 mL) (p = 0.043). There was no significant difference in the postoperative hemoglobin change, hysterectomy rates, length of hospitalization, or infant Apgar score between the groups. Conclusion: PBAO of the internal iliac artery prior to cesarean delivery in patients with placenta increta or percreta is a safe and minimally invasive technique that reduces the rate of intraoperative blood loss and transfusion requirements.

Background and Objectives: The extracellular volume fraction (fECV) of the liver, as measured by contrast-enhanced computed tomography (CT), has been shown to correlate closely with the histological stages of hepatic fibrosis. This study aimed to investigate the diagnostic performance of a liver extracellular volume fraction derived from dual-energy CT (DECT) for evaluating liver functional reserve based on the Child–Pugh class in cirrhotic patients, compared with other noninvasive markers. Materials and Methods: This retrospective study included 258 patients with liver cirrhosis who underwent contrast-enhanced DECT. The fECV was measured using iodine maps derived from equilibrium phase images obtained 3 min after contrast injection at 100/140 Sn kVp. Statistical analyses included Welch’s ANOVA with post hoc tests, Spearman’s correlation, and ROC analysis. The area under the curve (AUC) was compared among fECV and other noninvasive markers (aspartate transaminase to platelet ratio index [APRI], Fibrosis-4 [FIB-4], and model for end-stage liver disease [MELD]) using DeLong’s test. Intra- and interobserver reliability of fECV was assessed with the intraclass correlation coefficient (ICC). The area under the receiver operating characteristic curve (AUC) for differentiating Child–Pugh classes was compared among the fECV and other noninvasive markers (aspartate transaminase to platelet ratio index [APRI], Fibrosis-4 [FIB-4], and model for end-stage liver disease [MELD]). Results: The fECV increased significantly with advancing Child–Pugh classes (p < 0.001), showing a moderate correlation with Child–Pugh class (r = 0.53). The mean differences in fECV among the Child–Pugh classes were 8.88 between A and B (95% confidence interval [CI], 5.85–11.92; p < 0.001) and 7.42 between B and C (95% CI, 1.92–12.91: p < 0.001). The AUC for differentiating Child–Pugh classes A and B demonstrated no significant differences among the fECV (0.84), APRI (0.83, p > 0.99) and FIB-4 (0.83, p > 0.99), except for MELD, which had a significantly higher AUC (0.94, p = 0.047). For differentiating Child-Pugh classes B and C, the fECV demonstrated a significantly higher AUC (0.78), compared with FIB-4 (0.50, p = 0.038) and APRI (0.49, p = 0.037), whereas no significant difference was observed between fECV and MELD (0.92, p = 0.12). The intra- and interobserver reliabilities of the fECV measurements were excellent (ICC, 0.93; 95% CI, 0.91–0.95 and 0.91; 95% CI, 0.88–0.92, respectively). Conclusions: DECT derived fECV is a useful noninvasive marker for assessing liver functional reserve based on the Child–Pugh classification.

Sarcopenia is a contributing factor in the development of long-COVID syndrome. We aimed to investigate how intercostal muscle mass changes over 3 months compared to other chest wall muscles following COVID-19 infection, along with identifying factors contributing to intercostal muscle loss during follow-up. We retrospectively studied 110 COVID-19 patients, analyzing muscle masses in the intercostal, pectoralis, and thoracic 12th vertebra level (T12) on initial and follow-up CT scans. Muscle mass was quantitatively assessed using density histogram analysis. We calculated the muscle difference ratio (MDR) as the following formula: (initial muscle mass - follow-up muscle mass)/initial muscle mass. Patients were categorized into 2 groups: <3 months follow-up (n = 53) and ≥ 3 months follow-up (n = 57). We employed stepwise logistic regression, using intercostal MDR ≥ 25% in follow-up as an independent variable and age < 65 years, ventilator use, steroid use, follow-up > 3 months, hospital stay > 13 days, body mass index < 18.5 kg/m², and female gender as dependent variables. The loss of intercostal muscle was the most severe among the 3 chest wall muscles in the CT follow-up. Intercostal MDR was significantly higher in the ≥ 3 months follow-up group compared to the < 3 months group (32.5 ± 23.6% vs 19.0 ± 21.1%, P = .002). There were no significant differences in pectoralis MDR or T12 MDR between the 2 groups. Stepwise logistic regression identified steroid use (3.494 (1.419–8.604), P = .007) and a follow-up period > 3 months [3.006 (1.339–6.748), P = .008] as predictors of intercostal MDR ≥ 25%. The intercostal muscle wasting was profound compared to that in the pectoralis and T12 skeletal muscles in a follow-up CT scan, and the intercostal muscle wasting was further aggravated after 3 months of COVID-19 infection. The use of steroids and a follow-up period exceeding 3 months were significant predictors for ≥ 25% of intercostal muscle wasting in follow-up.

홍석진, et al. J Korean Soc Radiol. 2020 Dec;82:e7. https://doi.org/10.3348/jksr.2020.0084

Background/Aims: To compare the prognostic value of fully automated volumetric versus single-slice computed tomography (CT)-derived body composition analysis for predicting overall survival in patients with newly diagnosed hepatocellular carcinoma (HCC).Methods: In this retrospective study of 448 patients with newly diagnosed HCC, deep-learning algorithm quantified volumetric (L1-L5) and single-slice (L3) skeletal muscle and adipose tissue metrics using CT images. Spearman correlation assessed the relationship between volumetric and single-slice body composition metrics. Cox proportional hazards models, adjusted for established clinical prognostic factors, were used to evaluate association between body composition parameters and overall survival. Harrell’s C-index was used to compare prognostic performance of Cox models.Results: Strong correlations (correlation coefficient ≥0.82) were observed between volumetric and single-slice measurements for muscle attenuation, visceral fat index, and subcutaneous fat index, while muscle index demonstrated only moderate correlation (correlation coefficient= 0.50). Single-slice low muscle index (hazard ratio [HR], 1.63; 95% confidence interval [CI], 1.15-2.31; P=0.006), single-slice low muscle attenuation (HR, 1.38; 95% CI, 1.01-1.88; P=0.045) and volumetric low muscle attenuation (HR, 1.42; 95% CI, 1.03-1.95; P=0.034) were identified as independent predictors of overall survival. Median follow-up was 6.6 years. The single-slice and volumetric models showed comparable discrimination (C-index, 0.816 vs. 0.817; P=0.11).Conclusion: Automated single-slice muscle parameters at the L3 level provide significant prognostic stratification in patients with hepatocellular carcinoma, establishing it as a viable, time-efficient tool for routine oncologic practice. In contrast, automated volumetric body composition assessment did not provide superior discrimination compared with the single-slice approach.

Purpose: To evaluate whether deep learning-based combined noise reduction and contrast enhancement reconstruction (DLR) improves image quality and resectability prediction accuracy compared to conventional iterative reconstruction (IR) in post-neoadjuvant pancreatic cancer CT assessment. Methods: This retrospective study included 114 patients with pancreatic cancer following neoadjuvant therapy. Contrast-enhanced CT images were reconstructed using conventional IR and vendor-neutral ClariACE. Three abdominal radiologists independently assessed image quality (based on 8 parameters: tumor conspicuity, tumor margin, image noise, sharpness of the main pancreatic duct, arterial depiction, venous depiction, plasticity and overall image quality) and determined tumor resectability with confidence levels. Quantitative analysis included aortic and portal venous attenuation measurements and pancreas-to-tumor contrast-to-noise ratio (CNR). Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) with DeLong’s test. Sensitivity, specificity, accuracy, and reader confidence were compared using McNemar’s test. Results: DLR demonstrated significantly superior vessel enhancement ( p < 0.001) and improved CNR ( p < 0.001) versus conventional IR. Two readers consistently rated DLR images higher across all qualitative categories ( p < 0.001) except plasticity, while the third reader favored DLR in five of eight parameters ( p < 0.001 to 0.020). However, all readers noted increased artificial appearance in DLR images ( p < 0.001). Despite image quality improvements, no significant differences were observed in resectability assessment accuracy (62.3%-65.8%), AUC values (0.485–0.520), or high-confidence diagnosis rates between reconstruction methods. Conclusion: Although deep learning-based combined noise reduction and contrast enhancement reconstruction significantly improved quantitative and subjective image quality metrics, it did not enhance diagnostic accuracy for predicting R0 resectability in post-neoadjuvant pancreatic cancer patients.