Currently, no standard of care or therapies have been established for patients with advanced HCC. We evaluated the efficacy and safety of conventional transarterial chemoembolization using gelatin sponges or microspheres plus lipiodol-doxorubicin (cTACE) and TACE with doxorubicin-loaded drug eluting beads (DEB-TACE). This retrospective study included 273 patients who received cTACE (n = 201) or DEB-TACE. Tumor response, survival, and adverse events were evaluated over a 5-year follow-up period. During 5-year follow-up, a greater percentage of patients treated with cTACE died than those treated with DEB-TACE (76.1% vs. 66.7%) (P = 0.045). At the last evaluation, all surviving patients had disease progression and no differences were seen between treatment groups. However, the time to disease progression differed between groups; median time to disease progression was 11.0 months for cTACE and 16.0 months for DEB-TACE (P = 0.019). The median survival time was 37 months in both treatment groups. No significant differences were observed between cTACE and DEB-TACE therapies in subgroups of patients with BCLC stage A or stage B + C either in survival time or time to disease progression (P values > 0.05). No significant differences were observed in survival status or disease progression between cTACE and DEB-TACE in patient subgroups with either tumor number > 5 or with the sum of the diameter of largest five HCC tumors being > 7 cm. DEB-TACE demonstrates greater long-term benefits than cTACE in treating treatment-naïve patients with HCC. Results of this long-term study support the use of DEB-TACE in treating HCC.

Background: Radiotherapy (RT) might lead to atherosclerotic plaque buildup and coronary artery stenosis of breast cancer (BC) survivors, and coronary artery calcium (CAC) might be a sign of preclinical atherosclerosis. This study explores possible determinants affecting the acceleration of CAC burden in BC patients after adjuvant RT. Methods: Female BC patients receiving adjuvant RT from 2002 to 2010 were included. All patients received noncontrast computed tomography (NCCT) of thorax before and after adjuvant RT. Their CAC burden was compared with healthy controls from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. The progression of the CAC burden was manifested by the increment of CAC percentiles (%CACinc). Results: Ninety-four patients, including both left- and right-side BC, were enrolled in this study. From undergoing the first to second NCCT, the %CACinc in BC patients significantly increased rather than non-BC women. In addition, the %CACinc was significantly higher in left-side than right-side BC patients (p < 0.05), and significant differences in most heart outcomes were found between the two groups. Besides, the lower the mean right coronary artery (RCA) dose, the lower the risks of CAC percentiles increase ≥ 50% after adjusting the disease's laterality. Conclusions: A significantly higher accelerated CAC burden in BC patients than non-BC women represents that BC could affect accelerated CAC. A higher risk of accelerated CAC burden was found in left-side than right-side BC patients after adjuvant RT. A decrease of the mean RCA dose could reduce more than 50% of the risk of accelerated CAC burden in BC patients.

Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.

Bronchopulmonary dysplasia (BPD) is common in preterm infants and may result in pulmonary vascular disease, compromising lung function. This study aimed to employ artificial intelligence (AI) techniques to help physicians accurately diagnose BPD in preterm infants in a timely and efficient manner. This retrospective study involves two datasets: a lung region segmentation dataset comprising 1491 chest radiographs of infants, and a BPD prediction dataset comprising 1021 chest radiographs of preterm infants. Transfer learning of a pre-trained machine learning model was employed for lung region segmentation and image fusion for BPD prediction to enhance the performance of the AI model. The lung segmentation model uses transfer learning to achieve a dice score of 0.960 for preterm infants with $$\le$$≤ 168 h postnatal age. The BPD prediction model exhibited superior diagnostic performance compared to that of experts and demonstrated consistent performance for chest radiographs obtained at $$\le$$≤ 24 h postnatal age, and those obtained at 25 to 168 h postnatal age. This study is the first to use deep learning on preterm chest radiographs for lung segmentation to develop a BPD prediction model with an early detection time of less than 24 h. Additionally, this study compared the model’s performance according to both NICHD and Jensen criteria for BPD. Results demonstrate that the AI model surpasses the diagnostic accuracy of experts in predicting lung development in preterm infants.

We aimed to set up an Automated Radiology Alert System (ARAS) for the detection of pneumothorax in chest radiographs by a deep learning model, and to compare its efficiency and diagnostic performance with the existing Manual Radiology Alert System (MRAS) at the tertiary medical center. This study retrospectively collected 1235 chest radiographs with pneumothorax labeling from 2013 to 2019, and 337 chest radiographs with negative findings in 2019 were separated into training and validation datasets for the deep learning model of ARAS. The efficiency before and after using the model was compared in terms of alert time and report time. During parallel running of the two systems from September to October 2020, chest radiographs prospectively acquired in the emergency department with age more than 6 years served as the testing dataset for comparison of diagnostic performance. The efficiency was improved after using the model, with mean alert time improving from 8.45 min to 0.69 min and the mean report time from 2.81 days to 1.59 days. The comparison of the diagnostic performance of both systems using 3739 chest radiographs acquired during parallel running showed that the ARAS was better than the MRAS as assessed in terms of sensitivity (recall), area under receiver operating characteristic curve, and F1 score (0.837 vs. 0.256, 0.914 vs. 0.628, and 0.754 vs. 0.407, respectively), but worse in terms of positive predictive value (PPV) (precision) (0.686 vs. 1.000). This study had successfully designed a deep learning model for pneumothorax detection on chest radiographs and set up an ARAS with improved efficiency and overall diagnostic performance.

Atherosclerosis and vascular inflammatory response have been considered as risk factors for non-typhoidal Salmonella (NTS) vascular infection. The study aims to assess the risk of vascular infection by measuring atherosclerosis severity, NTS vascular infection (NTSVI) score, and serum levels of inflammatory markers in people with NTS bacteremia. A prospective observational study was conducted in two medical centers and two regional hospitals. Adults aged ≥50 years with NTS bacteremia who underwent computed tomography (CT) scan for revealing vascular infections were enrolled. The degree of atherosclerosis was scaled by a calcium score determined by a CT scan. Serum concentrations of inflammatory biomarkers were determined in the patients enrolled in a medical center. Fourteen (20.3%) of 69 patients with NTS bacteremia had vascular infections. Calcium scores over the thoracic (12,540 vs. 3,261, P = 0.0005) and abdominal (9755 vs. 3,461, P = 0.0006) aorta of those with vascular infections were higher than those without vascular infection. All vascular infections were present in the high-risk group (NTSVI score ≥1), yielding a sensitivity of 100% and specificity of 30.9%. Among 17 low-risk patients (NTSVI score <1), none had vascular infections, resulting in a negative predictive value of 100%. Higher plasma concentrations of IL-1β were detected in the cases of vascular infection than those in the control group (23.6 vs. 1.06 pg/mL, P = 0.001). Atherosclerosis of the aorta which is associated with a positive NTSVI score can predict the occurrence of vascular infections and serum IL-1β could be a biomarker for vascular infection in patients with NTS bacteremia.