Artificial intelligence-measured nodule mass for determining the invasiveness of neoplastic ground glass nodules.

Background: The nodule mass is an important indicator for evaluating the invasiveness of neoplastic ground-glass nodules (GGNs); however, the efficacy of nodule mass acquired by artificial intelligence (AI) has not been validated. This study thus aimed to determine the efficacy of nodule mass measured by AI in predicting the invasiveness of neoplastic GGNs. Methods: From May 2019 to September 2023, a retrospective study was conducted on 755 consecutive patients comprising 788 pathologically confirmed neoplastic GGNs, among which 259 were adenocarcinoma in situ (AIS), 282 minimally invasive adenocarcinoma (MIA), and 247 invasive adenocarcinoma (IAC). Nodule mass was quantified using AI software, and other computed tomography (CT) features were concurrently evaluated. Clinical data and CT features were compared using the Kruskal-Wallis test or Pearson chi-square test. The predictive efficacy of mass and CT features for evaluating invasive lesions (ILs) (MIAs and IACs) and IACs was analyzed and compared via receiver operating characteristic (ROC) analysis and the Delong test. Results: ROC curve analysis revealed that the optimal cutoff value of mass for distinguishing ILs and AISs was 225.25 mg [area under the curve (AUC) 0.821; 95% confidence interval 0.792-0.847; sensitivity 64.27%; specificity 89.19%; P<0.001], and for differentiating IACs from AISs and MIAs, it was 390.4 mg (AUC 0.883; 95% confidence interval 0.858-0.904; sensitivity 80.57%; specificity 86.32%; P<0.001). The efficacy of nodule mass in distinguishing ILs and AISs was comparable to that of size (P=0.2162) and significantly superior to other CT features (each P value <0.001). Additionally, the ability of nodule mass to differentiate IACs from AISs and MIAs was significantly better than that of CT features (each P value <0.001). Conclusions: AI-based nodule mass analysis is an effective indicator for determining the invasiveness of neoplastic GGNs.

Altitude-related features and prognosis in patients with reversible splenial lesion syndrome.

INTRODUCTION: RESLES (Reversible splenial lesion syndrome) can be observed secondary to various diseases, and intramyelinic edema may play a crucial role in the pathogenesis of SCC (Splenium of the corpus callosum). Some studies have suggested that hypoxic-ischaemic encephalopathy may constitute a risk factor for SCC lesions. However, the potential impact of high-altitude environments on SCC, especially during chronic exposure, remain obscure. METHODS: Our study included 19 patients who satisfied the diagnostic criteria of RESLES at high altitudes. Ten low-altitude patients with RESLES were included as controls. All participants received MRI (Magnetic resonance imaging) scans twice. Routine blood tests, liver, kidney and thyroid function, coagulation function, electrolytes and vitamins were detected during hospitalization and before discharge. In addition, the patients were followed up in May 2023. RESULTS: Hypoxic environments at high altitudes may increase the risk of RESLES. The two groups showed different clinical symptoms. High-altitude patients had significantly higher CRP levels than low-altitude patients. The lesion size in high-altitude patients showed a positive correlation with SaO2 levels. However, the patients at low altitudes had positive correlation trends between lesion size and several inflammatory markers (WBC, NEU and CRP). All patients had a benign prognosis that may not be affected by the use of prednisone acetate. CONCLUSIONS: Hypoxic environments at high altitudes may play a role in the aetiology of RESLES. Additionally, RESLES is a reversible disease and the administration of glucocorticoids may be dispensable for its treatment.

A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray.

Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.