Accurate preoperative assessment of lymph node metastasis (LNM) is crucial for treatment planning and prognostic stratification in patients with lung cancer. This study aimed to develop and validate a predictive model for LNM using radiomic features derived from non-contrast computed tomography (CT) combined with clinical characteristics. A total of 403 patients with pathologically confirmed lung cancer were retrospectively enrolled and randomly divided into a training set (n = 282) and an internal test set (n = 121). In addition,30 lung cancer patients from other hospital were collected as an external test set. Clinical variables were collected, and radiomic features were extracted from non-contrast chest CT images using the Radiomics module of 3D Slicer. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression. Multiple machine-learning models were constructed based on radiomic features and clinical features. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), and clinical utility was evaluated by decision curve analysis (DCA). Shapley additive explanations (SHAP) were applied to enhance model interpretability. Lymph node metastasis was observed in 35.5

The purpose of the study was to establish and validate a model for predicting the mutation status of epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) using magnetic resonance imaging (MRI) radiomics features combined with clinicopathological factors. Overall, 91 patients with NSCLC (72 in the training cohort and 19 in the validation cohort) were included in this study; 1708 radiomics features were extracted from the MRI (T2W and CET1w) sequences. The variance threshold method combined with the univariate selection method and the least absolute shrinkage and selection operator (LASSO) regression was used to screen important radiomics features, calculate radiomics scores, and construct a radiomics model. Multivariate logistic regression analysis was used to combine radiomics scores (Rad-scores) and independent predictive factors to construct a radiomics nomogram for predicting EGFR mutation status. The predictive performance and clinical practicality of the model were evaluated using the area under the curve (AUC), calibration curves, and clinical decision curves. EGFR mutations were identified in 30.8

To investigate the value of computed tomography imaging features combined with clinicopathological factors in predicting patients’ biliary stricture (BS) after liver transplantation and to identify patients at a high risk for BS. The imaging data and clinicopathological factors of 178 recipients who underwent liver transplantation at the First People’s Hospital of Kunming, were collected. The patients were randomly divided into training and validation set, patients were divided into BS (n = 46) and non-BS groups (n = 132). Independent risk factors to establish models were screened using logistic regression analysis. Predictive efficacy of the models was evaluated using the area under the receiver operating characteristic curve (AUC). BS occurred in 46 of 178 liver transplant recipients. Univariate analysis revealed that postoperative cholangitis, postoperative biliary calculi, and abdominal aorta and branch plaques were significant risk factors for biliary stricture after liver transplantation (p < 0.05). Further multivariate analysis showed that postoperative cholangitis (OR = 19.450, 95

Purpose:This study constructs a predictive model for hepatocellular carcinoma (HCC) transarterial chemoembolization (TACE) refractoriness using a machine learning (ML) algorithm and verifies the predictive performance of different algorithms. Patients and Methods:Clinical and magnetic resonance imaging (MRI) data of 131 patients (48 with TACE refractoriness) who underwent repeated TACE treatment for HCC were retrospectively collected. The training and validation cohorts comprised 104 and 27 cases, respectively, following an 8:2 ratio. Clinical imaging characteristics related to TACE refractoriness were identified through logistic regression analysis. HCC lesions on arterial phase, portal phase, delayed phase, and T2-weighted fat suppression MRI images before the first TACE were manually delineated as regions of interest. Dimension reduction was conducted using variance threshold, univariate selection, and least absolute shrinkage and selection operator methods. Relevant indices of TACE refractoriness were selected. ML algorithms, including a support vector machine, random forest, logistic regression and adaptive boosting, were used to construct the radiomics, clinical prediction, and combined models. The predictive performance of these models was evaluated using receiver operating characteristic curves. The optimal model was presented as a nomogram and verified through calibration and decision curve analyses. Results:In evaluating radiomics models for predicting TACE refractoriness in HCC, the LR-developed portal venous phase (VP) model achieved optimal single-sequence performance (training AUC: 0.896, 95% CI: 0.843-0.941; validation: 0.853, 0.727-0.965). Multisequence models significantly surpassed single-sequence counterparts, with the T2WI-FS+AP+VP+DP multisequence LR model demonstrating peak efficacy (training: 0.905, 0.853-0.949; validation: 0.876, 0.773-0.976). The integrated clinical-radiomics model demonstrated robust predictive performance, achieving a training cohort AUC of 0.955 (95% CI: 0.918-0.984) with 0.885 accuracy, 0.921 sensitivity, and 0.864 specificity, and maintained strong validation performance (AUC=0.941, 95% CI: 0.880-0.991). Conclusion:Multisequence clinical-radiomics model accurately predicts TACE refractoriness in hepatocellular carcinoma.

Lung cancer remains a leading cause of cancer-related mortality worldwide,with non-small cell lung cancer(NSCLC)as the most prevalent type.Accurately predicting NSCLC prognosis is crucial for optimizing patient survival.However,traditional assessment tools often lack the comprehensive and precise capability to effectively stratify patient risk.Recent research has focused on exploring the potential of imaging histology technology for NSCLC prognosis.This article delves into the core principles of imaging histology and reviews the current state of research on its application in predicting NSCLC outcomes.

Currently, standard protocols for body imaging and corresponding image processing pipelines in population-based cohort studies are unavailable, limiting the applications of body imaging. Based on the China Phenobank Project (CHPP), the present study described a body imaging protocol for multiple organs, including cardiac structures, liver, spleen, pancreas, kidneys, lung, prostate, and uterus, and the corresponding image processing pipelines promoted its development. Briefly, the body imaging protocol comprised a 40-min cardiac magnetic resonance imaging (MRI) scan, a 5-min computed tomography (CT) scan, a 20-min abdominal MRI scan, and a 10-min pelvic MRI scan. The recommended image processing pipeline utilized deep learning segmentation models to facilitate the analysis of large amount of data. This study aimed to provide a reference for planning studies based on the CHPP platform.

肝移植(liver transplantation,LT)是终末期肝病的主要治疗方式,终末期肝病主要包括肝细胞癌(hepatocellular carcinoma,HCC)及晚期肝硬化等.影像组学和深度学习(deep learning,DL)从常规使用的医学影像图像中识别出肉眼不可见的精细的影像特征,越来越多地被应用于LT后肿瘤复发的预测.以往的研究主要集中于基于影像组学及DL的各种影像图像对肿瘤复发的术前预测,希望以后能有更多的研究对LT后各种并发症进行预测.本文主要从超声、CT、MRI及正电子发射计算机断层显像(positron emission computed tomography,PET)四个方面来分析影像组学与DL在LT预后中的研究进展,主要包括以往研究的共同点与不同点、四种成像方法对LT后并发症评估的优势及不足,最后结合以往研究,总结影像组学与DL的局限性与未来的发展方向.本文旨在提高广大读者对LT的认识,增强影像科医师与临床医师对LT患者并发症的预防、早期诊断、早期治疗的意识,帮助LT患者的精准个体化治疗,提高LT患者的存活率,改善患者的预后.

肥厚型心肌病常继发心房颤动(简称房颤),导致临床结局显著变差,早期识别其房颤易感性、量化风险尤为重要.心脏磁共振成像集心脏解剖成像、功能成像于一体,可从左心房、左心室的结构、功能进行评价,可以对房颤发生风险进行早期预测、分层.本文回顾了心脏磁共振在肥厚型心肌病合并房颤时左心房、室结构、功能改变的相关进展,阐述了肥厚型心肌病发生房颤的相关机理,探讨了各种结构、功能参数在疾病发生发展过程中的应用价值及局限性,为制订更精准的诊断、治疗、管理策略提供可靠依据.

(1) Background: The microstructural alterations of the peripapillary choriocapillaris in high myopes remain elusive. Here, we used optical coherence tomography angiography (OCTA) to explore factors involved in these alterations. (2) Methods: This cross-sectional control study included 205 young adults' eyes (95 with high myopia and 110 with mild to moderate myopia). The choroidal vascular network was imaged using OCTA, and the images underwent manual adjustments to determine the peripapillary atrophy (PPA)-β zone and microvascular dropout (MvD). The area of MvD and the PPA-β zone, spherical equivalent (SE), and axial length (AL) were collected and compared across groups. (3) Results: The MvD was identified in 195 eyes (95.1%). Highly myopic eyes exhibited a significantly greater area for the PPA-β zone (1.221 ± 0.073 vs. 0.562 ± 0.383 mm2, p = 0.001) and MvD (0.248 ± 0.191 vs. 0.089 ± 0.082 mm2, p < 0.001) compared with mildly to moderately myopic eyes, and a lower average density in the choriocapillaris. Linear regression analysis showed that the MvD area correlated with age, SE, AL, and the PPA-β area (all p < 0.05). (4) Conclusions: This study found that MvDs represent choroidal microvascular alterations in young-adult high myopes, which were correlated with age, SE, AL, and the PPA-β zone. In this disorder, OCTA is important for characterizing the underlying pathophysiological adaptations.

Purpose: To evaluate longitudinal changes in choriocapillaris perfusion in patients with glaucoma with four phenotypes of optic disc damage and to explore associated factors with decreased choriocapillaris vessel density (CVD). Methods: This prospective longitudinal study included 96 eyes of 96 patients with primary open-angle glaucoma (POAG). Patients with POAG was differentiated into the optic disc phenotypes of focal ischemic type (FI), myopic type (MY), senile sclerotic type (SS), and generalized enlargement type (GE). Patients were followed up every three months. Simple linear regression was used to investigate the factors associated with a reduction in CVD. Results: The median follow-up time was 2.5 years (range, 2.0–3.0 years). Choriocapillaris perfusion tended to decrease over time, with CVD decreasing significantly faster in the FI type than in the other three types (P < 0.001). The percentage decrease in the FI type was 7.85%, 10.89%, and 8.88% faster than MY, SS and GE, respectively, after correcting for age, gender, axial length, intraocular pressure, mean deviation, retinal nerve fiber layer (RNFL), and image quality score. In multivariate regression, decreased CVD was independently associated with the rate of RNFL thinning. Conclusions: FI type had the fastest rate of CVD decline in the four phenotypes of optic disc damage, and decreased CVD was positively correlated with the rate of RNFL thinning. Translational Relevance: The role of the choriocapillaris in the pathogenesis and therapeutic potential of glaucoma require further attention to facilitate better management of glaucoma patients.

患者男,66岁,肺癌右肺上叶切除术后1日,胸部X线片提示右肺不张,予吸痰及肺泡灌洗等治疗无明显改善.查体:胸部右侧听诊呼吸音减低.复查床旁胸部X线片:右肺上野及右肺内带见条片状致密影,部分外侧缘边界清晰,右肺门角消失(图1A).胸部CT:右肺中叶大面积实变(图1B),右侧胸膜腔内见气液平面,气管三维重建示右肺中叶支气管远端截断(图1C).影像学诊断:右肺中叶肺扭转可能.开胸探查术中见右肺中叶水平裂朝向胸顶,右肺中叶局部扭转;予复位中叶、吸痰膨肺后,仍见中叶局部肺不张,予双肺通气15 min后改为单肺通气,右肺中叶仍塌陷,考虑右肺中叶顺应性差,行右肺中叶切除术.术后病理:右肺中叶部分呈暗红色,切面暗红见淤血;光镜下见肺泡细胞破坏,血管内淤血,间质细胞炎性浸润(图1D),符合肺扭转病理改变.临床诊断:右肺上叶切除术后中叶扭转.

1 病例简介 女,9岁.主诉:腹腔积液8 d,腹痛加重1 d.8 d前腹部CT示:腹腔内见大量积液.给予解痉、止痛、抽放50 ml腹水减压等对症处理,症状未明显改善.体格检查:消瘦,腹部膨隆,左下腹穿刺点见少量淡黄色腹水渗出,腹肌稍紧张,腹部压痛,移动性浊音阳性.实验室检查:血沉90.0 mm/h、血小板计数501×109/L、超敏C反应蛋白≥10 mg/L、CA125121 U/ml,腹水细胞检查可见间皮细胞及淋巴细胞.

BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts.MethodsWe established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively.ResultsThe AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively.ConclusionOur study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression.FUNDINGNational Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.

Purpose: To evaluate the frequency of and identify the factors that influence the artifacts of swept-source optical coherence tomography angiography (SS-OCTA) in glaucomatous and normal eyes. Methods: Artifacts of OCTA images of open-angle glaucoma (OAG) and normal subjects were analyzed using SS-OCTA. Univariate and multivariate logistic regression analyses were performed to evaluate the association of age, sex, best-corrected visual acuity, axial length (AL), intraocular pressure, presence and severity of OAG, and image quality score (IQS) with the presence of artifacts. Results: Images from 4426 subjects were included in the study. At least one type of artifact was present in 24.54% of the images. The most common artifacts were occurrence of motion (705 eyes, 15.93%), followed by defocus (628 eyes, 14.19%), decentration (134 eyes, 3.03%), masking (62 eyes,1.40%), and segmentation errors (23 eyes, 0.52%). Multivariate logistic analyses showed that the presence of OAG (odds ratio [OR] = 2.71; 95% confidence interval [CI], 2.09-3.51; P < 0.001), female sex (OR = 1.34; 95% CI, 1.12-1.61; P = 0.001), longer AL (OR = 1.09; 95% CI, 1.02-1.17; P = 0.017), and IQS < 40 (OR = 3.75; 95% CI, 3.15-4.48; P < 0.001) were significantly associated with higher odds for the presence of any artifact. The IQS had poor performance for detecting artifacts, with an area under the curve of 0.723, sensitivity of 73.04%, and specificity of 62.53%. Conclusions: OAG eyes had more SS-OCTA image artifacts than normal eyes. IQS is an imperfect tool for identifying artifacts. Translational Relevance: Special attention should be paid to the effect of artifacts when using SS-OCTA in the clinical setting to assess vascular parameters in patients with glaucoma.

Coronavirus disease (COVID-19) is highly infectious, has spread worldwide, and has a relatively high mortality rate. Early diagnosis and timely isolation are essential to control the spread of COVID-19. Computed tomography (CT) is considered to be an effective tool for the rapid diagnosis of COVID-19 and plays a key role in diagnosis, clinical course monitoring, and the evaluation of treatment outcomes. Artificial intelligence (AI) has emerged as a useful technology for early diagnosis, lesion quantification, and prognosis evaluation in patients with COVID-19. In this review, we discuss the role of CT in the diagnosis of COVID-19, typical CT manifestations of COVID-19 throughout the disease course, differential diagnoses, and the application of AI as a diagnostic and therapeutic tool in this patient population.

Nocardiosis, which is caused by the Nocardia bacterium, is an acute, subacute, or chronic purulent infection that mostly affects the lungs. The vast majority of these infections occur in people with impaired immune function. These infections are characterized by multiple areas of inflammation in both lungs. The initial examination of choice is chest computed tomography (CT), which can play an important role in early diagnosis and evaluation of the treatment's effectiveness. Since the disease is rare and lacks specific clinical manifestations, it is easily misdiagnosed or missed altogether, often resulting in poor patient outcomes, including death. Therefore, early diagnosis and evaluation of the effects of treatment by chest CT are essential to controlling disease progression and improving the prognosis.

Objective To develop and validate a DeepSurv nomogram based on radiomic features extracted from computed tomography images and clinicopathological factors, to predict the overall survival and guide individualized adjuvant chemotherapy in patients with non-small cell lung cancer (NSCLC). Patients and Methods This retrospective study involved 976 consecutive patients with NSCLC (training cohort, n=683; validation cohort, n=293). DeepSurv was constructed based on 1,227 radiomic features, and the risk score was calculated for each patient as the output. A clinical multivariate Cox regression model was built with clinicopathological factors to determine the independent risk factors. Finally, a DeepSurv nomogram was constructed by integrating the risk score and independent clinicopathological factors. The discrimination capability, calibration, and clinical usefulness of the nomogram performance were assessed using concordance index evaluation, the Greenwood-Nam-D’Agostino test, and decision curve analysis, respectively. The treatment strategy was analyzed using a Kaplan–Meier curve and log-rank test for the high- and low-risk groups. Results The DeepSurv nomogram yielded a significantly better concordance index (training cohort, 0.821; validation cohort 0.768) with goodness-of-fit ( P <0.05). The risk score, age, thyroid transcription factor-1, Ki-67, and disease stage were the independent risk factors for NSCLC.The Greenwood-Nam-D’Agostino test showed good calibration performance ( P =0.39). Both high- and low-risk patients did not benefit from adjuvant chemotherapy, and chemotherapy in low-risk groups may lead to a poorer prognosis. Conclusions The DeepSurv nomogram, which is based on the risk score and independent risk factors, had good predictive performance for survival outcome. Further, it could be used to guide personalized adjuvant chemotherapy in patients with NSCLC.

垂体腺瘤是颅内常见的良性肿瘤,但可表现出高侵袭性和复发率,且发病率呈逐年上升趋势.影像组学和深度学习是人工智能在医学影像领域的重要研究方向,广泛应用于肿瘤影像研究,并在垂体腺瘤的异质性诊断、疗效评估及预后预测等方面发挥着重要作用.本文就影像组学和深度学习在垂体腺瘤的应用和研究进展进行综述.

Purpose: To develop a classification system of visual field (VF) abnormalities in highly myopic eyes with and without glaucoma. Design: Secondary analysis of VF data from a longitudinal cohort study. Participants: One thousand eight hundred ninety-three VF tests from 1302 eyes (825 individuals). Methods: All participants underwent VF testing (Humphrey 24-2 Swedish interactive threshold algorithm standard program; Carl Zeiss Meditec) and detailed ophthalmic examination. A comprehensive set of VF defect patterns was defined via observation of the 1893 VF reports, literature review, and consensus meetings. The classification system comprised 4 major types of VF patterns, including normal type, glaucoma-like defects (paracentral defect, nasal step, partial arcuate defect, arcuate defect), high myopia-related defects (enlarged blind spot, vertical step, partial peripheral rim, nonspecific defect), and combined defects (nasal step with enlarged blind spot). A subset (n = 1000) of the VFs was used to evaluate the interobserver and intraobserver agreement and weighted K values of the classification system by 2 trained readers. The prevalence of various VF patterns and their associated factors were determined. Main Outcome Measures: The classification of VF in highly myopic eyes and its associated risk factors. Results: We found that normal type, glaucoma-like defects, high myopia-related defects, and combined defects accounted for 74.1%, 10.8%, 15.0%, and 0.1% of all unique VF tests, respectively. The interobserver and intraobserver agreements were > 89%, and the corresponding K values were 0.86 or more between readers. Both glaucoma-like and high myopia-related VF defects were associated with older age (odds ratios [ORs], 1.07 [95% confidence interval (CI), 1.04-1.10; P < 0.001] and 1.06 [95% CI, 1.04-1.10; P < 0.001]) and longer axial length (ORs, 1.65 [95% CI, 1.32-2.07; P < 0.001] and 1.37 [95% CI, 1.11-1.68; P = 0.003]). Longer axial length showed a stronger effect on the prevalence of glaucoma-like VF defects than on the prevalence of high myopia-related VF defects (P = 0.036). Conclusions: We propose a new and reproducible classification system of VF abnormalities for nonpathologic high myopia. Applying a comprehensive classification system will facilitate communication and comparison of findings among studies. (c) 2022 by the American Academy of Ophthalmology