Benign lesions, atypical adenomatous hyperplasia (AAH), and malignancies such as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) may feature a pure ground-glass nodule (pGGN) on a thin-slide computed tomography (CT) image. According to the World Health Organization (WHO) classification for lung cancer, the prognosis of patients with IA is worse than those with AIS and MIA. It is relatively risky to perform a core needle biopsy of a pGGN less than 2 cm to obtain a reliable pathological diagnosis. The early and adequate management of patients with IA may provide a favorable prognosis. This study aimed to disclose suggestive signs of CT to accurately predict IA among the pGGNs. A total of 181 pGGNs of less than 2 cm, in 171 patients who had preoperative CT-guided localization for surgical excision of a lung nodule between December 2013 and August 2019, were enrolled. All had CT images of 0.625 mm slice thickness during CT-guided intervention to confirm that the nodules were purely ground glass. The clinical data, CT images, and pathological reports of those 171 patients were reviewed. The CT findings of pGGNs including the location, the maximal diameter in the long axis (size-L), the maximal short axis diameter perpendicular to the size-L (size-S), and the mean value of long and short axis diameters (size-M), internal content, shape, interface, margin, lobulation, spiculation, air cavity, vessel relationship, and pleural retraction were recorded and analyzed. The final pathological diagnoses of the 181 pGGNs comprised 29 benign nodules, 14 AAHs, 25 AISs, 55 MIAs, and 58 IAs. Statistical analysis showed that there were significant differences among the aforementioned five groups with respect to size-L, size-S, and size-M (p = 0.029, 0.043, 0.025, respectively). In the univariate analysis, there were significant differences between the invasive adenocarcinomas and the non-invasive adenocarcinomas with respect to the size-L, size-S, size-M, lobulation, and air cavity (p = 0.009, 0.016, 0.008, 0.031, 0.004, respectively) between the invasive adenocarcinomas and the non-invasive adenocarcinomas. The receiver operating characteristic (ROC) curve of size for discriminating invasive adenocarcinoma also revealed similar area under curve (AUC) values among size-L (0.620), size-S (0.614), and size-M (0.623). The cut-off value of 7 mm in size-M had a sensitivity of 50.0% and a specificity of 76.4% for detecting IAs. In the multivariate analysis, the presence of air cavity was a significant predictor of IA (p = 0.042). In conclusion, the possibility of IA is higher in a pGGN when it is associated with a larger size, lobulation, and air cavity. The air cavity is the significant predictor of IA.

Combining the Segment Anything Model with a deep learning classifier produced an efficient pipeline for classifying MRI ovarian lesions, improving segmentation speed and matching radiologists’ classification performance.

(1) Background: With technological advancements, the integration of wireless sensing and artificial intelligence (AI) has significant potential for real-time monitoring and intervention. Wireless sensing devices have been applied to various medical areas for early diagnosis, monitoring, and treatment response. This review focuses on the latest advancements in wireless, AI-incorporated methods applied to clinical medicine. (2) Methods: We conducted a comprehensive search in PubMed, IEEEXplore, Embase, and Scopus for articles that describe AI-incorporated wireless sensing devices for clinical applications. We analyzed the strengths and limitations within their respective medical domains, highlighting the value of wireless sensing in precision medicine, and synthesized the literature to provide areas for future work. (3) Results: We identified 10,691 articles and selected 34 that met our inclusion criteria, focusing on real-world validation of wireless sensing. The findings indicate that these technologies demonstrate significant potential in improving diagnosis, treatment monitoring, and disease prevention. Notably, the use of acoustic signals, channel state information, and radar emerged as leading techniques, showing promising results in detecting physiological changes without invasive procedures. (4) Conclusions: This review highlights the role of wireless sensing in clinical care and suggests a growing trend towards integrating these technologies into routine healthcare, particularly patient monitoring and diagnostic support.

Background: The overlapping clinical presentations of limb-girdle muscular dystrophy (LGMD) and idiopathic inflammatory myopathy (IIM) make clinical diagnosis challenging. This study provides a comprehensive evaluation of the distributions and characteristics of muscle fat substitution and edema and aims to differentiate those two diseases.Methods: This retrospective study reviewed magnetic resonance imaging (MRI) of seventeen patients with pathologically proved diagnosis, comprising 11 with LGMD and 6 with IIM. The fat-only and water-only images from a Dixon sequence were used to evaluate muscle fat substitution and edema, respectively. The degrees of muscle fat substitution and edema were graded and compared using the appropriate statistical methods.Results: In LGMD, more than 50% of patients had high-grade fat substitution in the majority of muscle groups in the thigh and calf. However, <50% of IIM patients had high-grade fat substitution in all muscle groups. Moreover, LGMD patients had significantly higher grade fat substitution than IIM patients in all large muscle groups (p < 0.05). However, there was no significant difference in edema in the majority of muscle groups, except the adductor magnus (p = 0.012) and soleus (p = 0.009) with higher grade edema in IIM. Additionally, all the adductor magnus muscles in LGMD (100%) showed high-grade fat substitution, but none of them showed high-grade edema.Conclusions: MRI could be a valuable tool to differentiate LGMD from IIM based on the discrepancy in muscle fat substitution, and the adductor magnus muscle could provide a biosignature to categorizing LGMD.

Object. To evaluate various segment anything model (SAM) prompt strategies across four lesion datasets and to subsequently develop a reinforcement learning (RL) agent to optimize SAM prompt placement. Approach. This retrospective study included patients with four independent ovarian, lung, renal, and breast tumor datasets. Manual segmentation and SAM-assisted segmentation were performed for all lesions. An RL model was developed to predict and select SAM points to maximize segmentation performance. Statistical analysis of segmentation was conducted using pairwise t-tests. Main results. Results show that increasing the number of prompt points significantly improves segmentation accuracy, with Dice coefficients rising from 0.272 for a single point to 0.806 for five or more points in ovarian tumors. The prompt location also influenced performance, with surface and union-based prompts outperforming center-based prompts, achieving mean Dice coefficients of 0.604 and 0.724 for ovarian and breast tumors, respectively. The RL agent achieved a peak Dice coefficient of 0.595 for ovarian tumors, outperforming random and alternative RL strategies. Additionally, it significantly reduced segmentation time, achieving a nearly ten-fold improvement compared to manual methods using SAM. Significance. While increased SAM prompts and non-centered prompts generally improved segmentation accuracy, each pathology and modality has specific optimal thresholds and placement strategies. Our RL agent achieved superior performance compared to other agents while achieving a significant reduction in segmentation time.

Combined pulmonary fibrosis and emphysema (CPFE) is a well-recognized syndrome characterized by the coexistence of emphysema and fibrotic interstitial lung disease (ILD) and is associated with high mortality. However, the prognostic significance of emphysema extent relative to fibrosis pattern remains incompletely defined. We conducted an observational cohort study of participants with fibrotic ILD and radiographic emphysema. Baseline high-resolution computed tomography (HRCT) scans were independently assessed by two thoracic radiologists using a standardized semi-quantitative scoring system. Cox proportional hazards regression was used to evaluate associations between imaging features and all-cause mortality. Ninety-two participants with CPFE were followed for a median of 8 years, during which 70% died. Cumulative mortality was 4.4% at 1 year, 21.7% at 3 years, and 30.5% at 5 years. Most participants demonstrated a definite usual interstitial pneumonia pattern and centrilobular emphysema, with the majority having 5% emphysema had a significantly higher risk of death (HR 3.96, 95% CI 1.43-10.95; p = 0.008). Fibrosis pattern, emphysema subtype, and regional disease distribution were not independently associated with mortality. Semi-quantitative emphysema extent on HRCT is a strong, independent prognostic marker in CPFE. These findings support routine assessment of emphysema burden to inform risk stratification in CPFE.