Academic Radiology最新文献

Rationale and objectives: Sarcopenia, as measured at the level of the third lumbar (L3) has been shown to predict the survival of cancer patients. However, many patients with advanced non-small cell lung cancer (NSCLC) do not undergo routine abdominal imaging. The objective of this study was to investigate the association of thoracic sarcopenia with survival outcomes among patients who underwent immunotherapy for NSCLC.

Materials and methods: In this retrospective study, patients who initiated immunotherapy for advanced NSCLC from 2019 to 2022 were enrolled. and detailed patient data were collected. Cross sectional skeletal muscle area was calculated at the fifth thoracic vertebra (T5) on pretreatment chest computed tomography (CT) scan. Gender-specific lowest quartile values was used to define sarcopenia. The risk factors were analyzed using Cox analyses. The log-rank test and the random survival forest (RSF) were used to compare progression free survival (PFS). The model's performance was assessed using calibration curve and the receiver operating characteristic curve (ROC).

Results: A total of 242 patients was included (discovery cohort n = 194, validation cohort n = 48). In the discovery cohort, patients with sarcopenia exhibited significantly poorer PFS (p < 0.001) than patients without sarcopenia. Univariate cox regression revealed that sarcopenia, lung cancer stage, body mass index, smoking status, and neutrophil-to-lymphocyte ratio were predictors of poor PFS. A RSF model was constructed based on the aforementioned parameters, to evaluate the model's efficacy, the ROC curve was utilized. with an area under the curve for predicting 6-month PFS of 0.68 and for 12-month PFS of 0.69. The prediction models for survival outcomes built by the discovery cohort showed similar performance in the validation cohort.

Conclusion: Sarcopenia at T5 is independent prognostic factors in patients who received immunotherapy for advanced NSCLC.

Rationale and objectives: This study aimed to investigate the association of clinical, imaging, and pathological-molecular characteristics with the prediction of patient prognosis with stage IA invasive lung adenocarcinoma (ILADC) after sub-lobar resection.

Materials and methods: This study assessed 360 patients, including 91 and 269 with and without recurrence 3 years postoperatively, respectively, with stage IA ILADC undergoing preoperative chest computed tomography (CT) scans and subsequent sub-lobar resection at our institution. Their clinical and CT features and histological subtypes and gene mutation status were compared. Binary logistic regression analysis was conducted to identify the independent risk factors for recurrence. An external validation cohort included 113 patients, used to test the model's efficiency.

Results: For clinical features, old age, male gender, smokers, and high age-adjusted Charlson comorbidity index (ACCI) were frequently observed in patients with recurrence than those without (all p < 0.05). For CT features, large tumor size, solid-predominant density, spiculation, peripheral fibrosis, type II pleural tag, and pleural adhesion were more common in recurrent patients than non-recurrent ones (all p < 0.05). The regression model revealed old age, large tumor size, solid-predominant density, spiculation, type II pleural tag, and pleural adhesion as independent risk factors for recurrence, with an area under the curve (AUC) of 0.942. The external validation cohort obtained an AUC of 0.958. For phological-molecular features, micropapillary/solid-predominant growth pattern, KRAS, ALK, and NRAS mutation or fusion were more common in the recurrent group, whereas EGFR mutation was more frequent in the non-recurrent group (all p < 0.05).

Conclusion: Clinical and CT features help predict the prognosis of patients with stage IA ILADC after sub-lobar resection and decide for individualized treatment. Moreover, patients with different prognosis demonstrated different pathological-molecular features.

Rationale and objectives: Little is known about the long-term impact of diabetes on lung impairment in COVID-19 survivors over a three-year period. This study evaluated the long-term impact of diabetes on persistent radiological pulmonary abnormalities and lung function impairment in COVID-19 survivors over three years.

Materials and methods: In this prospective, multicenter, cohort study, pulmonary sequelae were compared between COVID-19 survivors with and without diabetes. Serial chest CT scans, symptom questionnaires and pulmonary function tests were obtained 6 months, 12 months, 2 years and 3 years post-discharge. The independent predictors for lung dysfunction at the 3-year follow-up were analyzed.

Results: A total of 278 COVID-19 survivors (63 [IQR 57-69] year-old, female: 103 [37.0%]) were included. At the 3-year follow-up, individuals in the diabetes group had higher incidences of respiratory symptoms, radiological pulmonary abnormalities and pulmonary diffusion dysfunction than those in the control group. Diabetes (OR: 2.18, 95% CI: 1.04-4.59, p = 0.034), allergy (OR: 2.26, 95% CI: 1.09-4.74, p = 0.029), female (OR: 2.70, 95% CI: 1.37-5.29, p = 0.004), severe COVID-19 (OR: 4.10, 95% CI: 1.54-10.93, p = 0.005), and fibrotic-like CT changes (OR: 5.64, 95% CI: 2.28-13.98, p < 0.001) were independent predictors of pulmonary diffusion dysfunction in COVID-19 survivors.

Conclusion: These results highlight the long-term deleterious effect of diabetes status on radiological pulmonary abnormalities and pulmonary dysfunction in COVID-19 survivors. This study provides important evidence support for long-term monitoring of lung abnormalities in COVID-19 recovery survivors with diabetes.

Rationale and objectives: This meta-analysis aimed to assess the diagnostic accuracy of multiparametric MRI (mpMRI) in detecting suspected prostate cancer (PCa) in biopsy-naive men.

Materials and methods: PubMed, Scopus, and the Cochrane Library databases were systematically searched for studies published from January 2013 to April 2024. Sixteen studies comprising 4973 patients met the inclusion criteria. Data were extracted to construct 2×2 contingency tables for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A random-effects model was used for pooled estimation, and subgroup analyses were conducted. Summary receiver operating characteristic (SROC) curves were generated to summarize overall diagnostic performance.

Results: The overall detection rate of PCa across studies was 57.3%. For detecting any PCa, mpMRI showed pooled sensitivity of 82% (95% CI, 80-83%) and specificity of 62% (95% CI, 60-64%), with positive likelihood ratio (LR) of 1.97 (95% CI, 1.71-2.26), negative LR of 0.28 (95% CI, 0.24-0.34), and diagnostic odds ratio (DOR) of 7.34 (95% CI, 5.60-9.63), and an area under the SROC curve of 0.81. For clinically significant PCa (csPCa), mpMRI had pooled sensitivity of 88% (95% CI, 87-90%) and specificity of 64% (95% CI, 63-66%), with positive LR of 2.49 (95% CI, 2.03-3.05), negative LR of 0.20 (95% CI, 0.16-0.25), DOR of 13.83 (95% CI, 9.14-20.9), and area under the curve of 0.90.

Conclusion: This meta-analysis suggests that mpMRI is effective in detecting PCa in biopsy-naive patients, particularly for csPCa. It can help reduce unnecessary biopsies and lower the risk of missing clinically significant cases, thereby guiding informed biopsy decisions.

Rationale and objectives: The preoperative diagnosis of small prevascular mediastinal nodules (SPMNs) presents a challenge, often leading to unnecessary surgical interventions. Our objective was to develop a nomogram based on preoperative CT-radiomics features, serving as a non-invasive diagnostic tool for SPMNs.

Materials and methods: Patients with surgically resected SPMNs from two medical centers between January 2018 and December 2022 were retrospectively reviewed. Radiomics features were extracted and screened from preoperative CT images. Logistic regression was employed to establish clinical, radiomics, and hybrid models for differentiating thymic epithelial tumors (TETs) from cysts. The performance of these models was validated in both internal and external test sets by area under the receiver operating characteristic curve (AUC), while also comparing their diagnostic capability with human experts.

Results: The study enrolled a total of 363 patients (median age, 53 years [IQR:45-59 years]; 175 [48.2%] males) for model development and validation, including 136 TETs and 227 cysts. Lesions' enhancement status, shape, calcification, and rad-score were identified as independent factors for distinction. The hybrid model demonstrated superior diagnostic performance compared to other models and human experts, with an AUC of 0.95 (95% CI:0.92-0.98), 0.94 (95% CI:0.89-0.99), and 0.93 (95% CI:0.83-1.00) in the training set, internal test set, and external test set respectively. The calibration curve of the model demonstrated excellent fit, while decision curve analysis underscored its clinical value.

Conclusion: The radiomics-based nomogram effectively discriminates between the most prevalent types of SPMNs, namely TETs and cysts, thus presenting a promising tool for treatment guidance.

Rationale and objectives: Proliferative hepatocellular carcinoma (HCC) is associated with high invasiveness and poor prognosis. This study aimed to investigate the preoperative risk prediction and prognostic value of different radiomics models and a nomogram for proliferative HCC.

Materials and methods: Patients were randomly divided into a training cohort (n = 156) and a validation cohort (n = 66) in a 7:3 ratio. Original and delta (the different value between imaging features extracted from two different phases) radiomics features were extracted from T1-weighted imaging (T1WI), arterial, and hepatobiliary phases to construct models using different machine learning algorithms. Logistic regression was used to select clinical independent risk factors. A nomogram was constructed by integrating the optimal radiomics model score with independent risk factors. The diagnostic efficacy and clinical utility of the models were assessed. Subsequently, patients were stratified into high-risk and low-risk subgroups based on radiomics model scores and nomogram scores, and both recurrence-free survival (RFS) and overall survival (OS) were evaluated.

Results: Multivariate logistic regression analysis showed that BCLC stage and combined radscore were independent predictors of proliferative HCC. The area under the curve (AUC) of the nomogram incorporating these factors was 0.838 and 0.801 in the training and validation cohorts, respectively, with good predictive performance. Multivariate Cox regression analysis shows that the delta radiomics model (DR)-predicted proliferative HCC can independently predict RFS and OS, with scores from the delta radiomics model performing best in prognostic risk stratification.

Conclusion: The nomogram can effectively predict proliferative HCC, while different radiomics models and the nomogram can offer varying prognostic stratification values.

Rationale and objectives: This study aims to determine the long-term prognostic value of coronary hyper-intensity plaques and left ventricular (LV) myocardial strain for major adverse cardiac events (MACEs).

Materials and methods: The study prospectively recruited 71 patients with acute coronary syndrome (ACS). All patients underwent CMR before PCI to determine the plaque-to-myocardium signal intensity ratio and LV strains. The MACEs included all-cause death, reinfarction, and new congestive heart failure. Mann-Whitney U test and chi-square test to compare patients with and without MACE, Kaplan-Meier survival analysis, Cox proportional hazards regression and C-statistics to assess prognosis, Receiver-operating characteristic (ROC) curve analysis to define the cutoff value. A P value of < 0.05 was considered statistically significant.

Results: Cox proportional hazard analysis showed that plaque-to-myocardium signal intensity ratio and global longitudinal strain (GLS) were independently associated with MACEs (plaque-to-myocardium signal intensity ratio: hazard ratio (HR) 2.80, 95% CI, 1.25-6.26, P = 0.01; GLS: HR1.21, 95% CI, 1.07-1.38, P＜0.01). ROC showed that a plaque-to-myocardium signal intensity ratio of 1.65 and a GLS of -10% were the best cutoff values for MACEs. The C-statistic values for plaque-to-myocardium signal intensity ratio, GLS, and plaque-to-myocardium signal intensity ratio+GLS for MACEs were 0.691, 0.792, and 0.825, respectively. Compared to GLS alone, the addition of plaque-to-myocardium signal intensity ratio to GLS increased the net reclassification index by 0.664 (P = 0.017).

Conclusion: Plaque-to-myocardium signal intensity ratio and GLS were significantly associated with MACEs. Adding plaque-to-myocardium signal intensity ratio to GLS substantially improved the prediction for MACEs. Our findings indicate that plaque-to-myocardium signal intensity ratio combined with GLS provides incremental prognostic value for MACEs.