An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma.

OBJECTIVE: To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model. METHODS: This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers. We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model. RESULTS: The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P < 0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001). CONCLUSION: The RF-combined model can serve as a robust and interpretable tool to identify the appropriate crowd for cTACE sessions, sparing patients from receiving ineffective and unnecessary treatments.

Screening of osteoporosis and sarcopenia in individuals aged 50 years and older at different altitudes in Yunnan province: Protocol of a longitudinal cohort study.

Introduction: Musculoskeletal system gradually degenerates with aging, and a hypoxia environment at a high altitude may accelerate this process. However, the comprehensive effects of high-altitude environments on bones and muscles remain unclear. This study aims to compare the differences in bones and muscles at different altitudes, and to explore the mechanism and influencing factors of the high-altitude environment on the skeletal muscle system. Methods: This is a prospective, multicenter, cohort study, which will recruit a total of 4000 participants over 50 years from 12 research centers with different altitudes (50m~3500m). The study will consist of a baseline assessment and a 5-year follow-up. Participants will undergo assessments of demographic information, anthropomorphic measures, self-reported questionnaires, handgrip muscle strength assessment (HGS), short physical performance battery (SPPB), blood sample analysis, and imaging assessments (QCT and/or DXA, US) within a time frame of 3 days after inclusion. A 5-year follow-up will be conducted to evaluate the changes in muscle size, density, and fat infiltration in different muscles; the muscle function impairment; the decrease in BMD; and the osteoporotic fracture incidence. Statistical analyses will be used to compare the research results between different altitudes. Multiple linear, logistic regression and classification tree analyses will be conducted to calculate the effects of various factors (e.g., altitude, age, and physical activity) on the skeletal muscle system in a high-altitude environment. Finally, a provisional cut-off point for the diagnosis of sarcopenia in adults at different altitudes will be calculated. Ethics and dissemination: The study has been approved by the institutional research ethics committee of each study center (main center number: KHLL2021-KY056). Results will be disseminated through scientific conferences and peer-reviewed publications, as well as meetings with stakeholders. Clinical Trial registration number: http://www.chictr.org.cn/index.aspx, identifier ChiCTR2100052153.

Dynamic analysis of pulmonary computed tomography (CT) characteristics in cured coronavirus disease 2019 (COVID-19) patients.

BACKGROUND: To retrospectively analyze the pulmonary computed tomography (CT) characteristics and dynamic changes in the lungs of cured coronavirus disease 2019 (COVID-19) patients at discharge and reexamination. METHODS: A total of 155 cured COVID-19 patients admitted to designated hospitals in Yunnan Province, China, from February 1, 2020, to March 20, 2020, were included. All patients underwent pulmonary CT at discharge and at 2 weeks after discharge (during reexamination at hospital). A retrospective analysis was performed using these two pulmonary CT scans of the cured patients to observe changes in the number, distribution, morphology, and density of lesions. RESULTS: At discharge, the lung CT images of 15 cured patients showed no obvious lesions, while those of the remaining 140 patients showed different degrees of residual lesions. Patients with moderate disease mostly had multiple pulmonary lesions, mainly in the lower lobes of both lungs. At reexamination, the lung lesions in the patients with moderate disease had significantly improved (P<0.05), and the lung lesions in the patients with severe disease had partially improved, especially in patients with multi-lobe involvement (χ 2 =3.956, P<0.05). At reexamination, the lung lesions of patients with severe disease did not show significant changes (P>0.05). CONCLUSIONS: The pulmonary CT manifestations of cured COVID-19 patients had certain characteristics and variation patterns, providing a reference for the clinical evaluation of treatment efficacy and prognosis of patients.

[Comparison of head dual energy computed tomography angiography and neuro digital subtraction angiography].

OBJECTIVE: To investigate the advantages and disadvantages of dual energy with dual-source computed tomography (CT) in cerebral angiography by comparing those of neuro-digital subtraction angiography (Neuro-DSA) . METHODS: Totally 300 patients with normal CT angiography (CTA) images underwent dual energy CT angiography (DE-CTA groups, n=200) and Neuro-DSA scan (Neuro-DSA groups, n=100) using dual source CT. The comparison of two scan approaches was based on image quality, radiation does, post-processing methods, and duration of scanning and subtraction. RESULTS: The image quality of intracranial blood vessels in DE-CTA group showed no significant difference with that in Neuro-DSA group (P>0.05) , while the number of Grade I images was significantly higher than those of other three grades (P<0.01) . However, the effect of the skull base bone removal with Neuro-DSA is superior to DE-CTA (P<0.01) . The duration of scanning was significantly longer in DE-CTA group than in Neuro-DSA group (P<0.01) . However, the radiation dose, time of entire examination, and time of subtraction were significantly lower/shorter in DE-CTA group than in Neuro-DSA group (P<0.01) . The radiation dose of DE-CTA group is 26.3% lower than Neuro-DSA group. CONCLUSIONS: The image quality of intracranial blood vessels with head DE-CTA is same as Neuro-DSA, while head DE-CTA has lower radiation doses and higher inspection efficiency than Neuro-DSA. However, the effect of the skull base bone removal needs to be improved.