Prediction model that combines with multidisciplinary analysis for clinical evaluation of malignancy risk of solid breast nodules

ObjectiveFew studies have systematically developed predictive models for clinical evaluation of the malignancy risk of solid breast nodules. We performed a retrospective review of female patients who underwent breast surgery or puncture, aiming to establish a predictive model for evaluating the clinical malignancy risk of solid breast nodules.MethodMultivariable logistic regression was used to identify independent variables and establish a predictive model based on a model group (207 nodules). The regression model was further validated using a validation group (112 nodules).ResultsWe identified six independent risk factors (X₃, boundary; X₄, margin; X₆, resistive index; X₇, S/L ratio; X₉, increase of maximum sectional area; and X₁₄, microcalcification) using multivariate analysis. The combined predictive formula for our model was: Z=−5.937 + 1.435X₃ + 1.820X₄ + 1.760X₆ + 2.312X₇ + 3.018X₉ + 2.494X₁₄. The accuracy, sensitivity, specificity, missed diagnosis rate, misdiagnosis rate, negative likelihood ratio, and positive likelihood ratio of the model were 88.39%, 90.00%, 87.80%, 10.00%, 12.20%, 7.38, and 0.11, respectively.ConclusionThis predictive model is simple, practical, and effective for evaluation of the malignancy risk of solid breast nodules in clinical settings.     

Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography

The objective of this study was to investigate the method of the combination of radiological and textural features for the differentiation of malignant from benign solitary pulmonary nodules by computed tomography. Features including 13 gray level co-occurrence matrix textural features and 12 radiological features were extracted from 2,117 CT slices, which came from 202 (116 malignant and 86 benign) patients. Lasso-type regularization to a nonlinear regression model was applied to select predictive features and a BP artificial neural network was used to build the diagnostic model. Eight radiological and two textural features were obtained after the Lasso-type regularization procedure. Twelve radiological features alone could reach an area under the ROC curve (AUC) of 0.84 in differentiating between malignant and benign lesions. The 10 selected characters improved the AUC to 0.91. The evaluation results showed that the method of selecting radiological and textural features appears to yield more effective in the distinction of malignant from benign solitary pulmonary nodules by computed tomography.     

人工智能辅助软件可提升疲劳状态下放射科规培医师对肺结节的检测效能

目的:探讨在人工智能(AI)肺结节检测软件的辅助下能否提升疲劳状态的放射科规培医师对肺结节的检测效能。方法:搜集182例患者的1 mm薄层胸部CT图像,有一位放射科规培医师分别在3种模式下进行阅片:正常状态下独立阅片(A组)、疲劳状态下(即一天日常工作满8小时以上)独立阅片(B组)、疲劳状态下使用AI软件辅助阅片(C组),三种阅片模式均间隔洗脱期(2周),分别记录每次阅片时检出结节的位置、大小和数目。将3次肺结节检出结果与金标准(由2位从事胸部影像诊断超过8年的中级医师结合AI筛查结果分别作出诊断,再由1位从事胸部影像诊断超过15年的高级医师最终审核确定)进行比较,计算敏感度和(患者)人均假阳性(误诊)结节数来评价3种模式的检测效能。结果:经金标准确认1281个肺结节,A组检出真阳性结节592个、假阳结节297个,敏感度46.21%,人均误诊结节数为1.63;B组检出真阳性结节517个、假阳结节225个,敏感度40.36%,人均误诊结节数为1.24;C组检出真阳性结节995个、假阳结节165个,敏感度77.67%,人均误诊结节数为0.91。B组的敏感度和人均误诊结节数均较A组降低,差异均有统计学意义(P<0.05);C组的敏感度较B组提高,且人均误诊结节数降低,差异均有统计学意义(P<0.05);C组的敏感度较A组提高,人均误诊结节数降低,差异均有统计学意义(P<0.05)。结论:疲劳显著降低了放射科规培医师对肺结节的检测效能,但在AI软件辅助下能明显提高疲劳状态下放射科规培医师对肺结节的检出效能,甚至超过其正常状态下的水平。     

Evaluating the validity of model for end‐stage liver disease exception points for hepatocellular carcinoma patients with multiple nodules <2 cm

Liver transplant allocation policy does not give model for end‐stage liver disease (MELD) exception points for patients with a single hepatocellular carcinoma (HCC) <2 cm in size, but does give points to patients with multiple small nodules. Because standard‐of‐care imaging for HCC struggles to differentiate HCC from other nodules, it is possible that a subset of patients receiving liver transplant for multiple nodules <2 cm in size does not have HCC. We evaluate risk of post‐transplant HCC recurrence and wait‐list dropout for patients with multiple small nodules using competing risks regression based on the Fine and Gray model. We identified 5002 adult HCC patients in the OPTN/UNOS dataset diagnosed and transplanted between January 2006 and September 2010. Compared to patients with >1 tumor <2 cm, risk of developing recurrence was significantly higher in patients with one or more tumors with only one tumor ≥2 cm (SHR 1.63, p = 0.009), as well as in patients with 2–3 tumors ≥2 cm (SHR 1.84, p = 0.02). Dropout risk was not significantly different among size categories. HCC recurrence risk was significantly lower in patients with multiple nodules <2 cm in size than in those with larger tumors, supporting the possibility that some patients received unnecessary transplants. The priority given to these patients must be re‐examined.     

A comparative phenotypical analysis of rheumatoid nodules and rheumatoid synovium with special reference to adhesion molecules and activation markers

OBJECTIVES—(1)To analyse the in situ expression of adhesion molecules in rheumatoid nodules. (2) To compare the endothelial expression of adhesion molecules in synovial tissue and subcutaneous nodules obtained from the same patients. (3) To compare the expression of adhesion molecules and activation markers on T cell lines from nodules and synovium.
METHODS—(1) Immunohistochemical analysis by APAAP technique of E selectin, CD44, ICAM-1, PECAM-1, and VCAM-1 was performed on 10 rheumatoid nodules from seven patients with rheumatoid arthritis (RA); nodules and synovium were simultaneously analysed from three patients. (2) T cell lines were generated from RA nodules (n=7) and synovium (n=7) by interleukin 2 expansion, and subsequently characterised by flow cytometry for surface expression of αEβ7, α4β7, CD44, L selectin, LFA-1a, PECAM-1, and CD30.
RESULTS—(1) In rheumatoid nodules, the palisading layer strongly stains for ICAM-1 and PECAM-1, but less pronounced for CD44. VCAM-1 staining was usually negative. ICAM-1 is upregulated in the vessels surrounding the central zone of fibrinoid necrosis. The immunohistological picture in different nodules derived from the same patient was similar. (2) The endothelial expression of adhesion molecules is comparable in RA nodules and synovium on an individual level, except for E selectin, which is overexpressed in nodule endothelium. (3) T cell lines from nodules and synovium display similar adhesion molecule profiles. However, the expression of CD30, a T cell activation marker linked with Th2 subsets, is higher in nodules compared with synovium.
CONCLUSION—These data support a recirculation hypothesis of T cells between articular and extra-articular manifestations in RA, although the activation state of the T cells in each of these localisations may differ.

Keywords: T cells; adhesion molecules; rheumatoid nodules; rheumatoid synovium     

Diagnosis of Thyroid Nodules Using Virtual Touch Tissue Quantification Value and Anteroposterior/Transverse Diameter Ratio

This paper proposes an effective approach to differential diagnosis of thyroid nodules using a hierarchical classification model based on the Virtual Touch tissue quantification (VTQ) value and anteroposterior/transverse diameter (A/T) ratio. One hundred twenty nodules (92 benign, 28 malignant) were analyzed using this approach by combining the quantitative elastic characteristic with the conventional sonographic feature. First, nodules were classified as benign (VTQ values <2.27 m/s), malignant (VTQ values >2.73 m/s) and indeterminate (2.27 m/s ≤ VTQ values ≤2.73 m/s) using two cutoff points selected on the basis of receiver operating characteristic analysis. Second, the indeterminate nodules were separated into malignant and benign nodules using an A/T ratio ≥1. The advantage of this approach was that it could alleviate the limitation of an overlap in VTQ values between benign and malignant nodules. According to the pathologic results, the accuracy of this approach was 95%. The proposed approach may potentially improve diagnostic accuracy.     

Quantitative Shear Wave Velocity Measurement on Acoustic Radiation Force Impulse Elastography for Differential Diagnosis between Benign and Malignant Thyroid Nodules: A Meta-analysis

The aim of this study was to evaluate the diagnostic performance of quantitative shear wave velocity (SWV) measurement on acoustic radiation force impulse (ARFI) elastography for differentiation between benign and malignant thyroid nodules using meta-analysis. The databases of PubMed and the Web of Science were searched. Studies published in English on assessment of the sensitivity and specificity of ARFI elastography for the differentiation of thyroid nodules were collected. The quantitative measurement of ARFI elastography was evaluated by SWV (m/s). Meta-Disc Version 1.4 software was used to describe and calculate the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and summary receiver operating characteristic curves. We analyzed a total of 13 studies, which included 1,854 thyroid nodules (including 1,339 benign nodules and 515 malignant nodules) from 1,641 patients. The summary sensitivity and specificity for differential diagnosis between benign and malignant thyroid nodules by SWV were 0.81 (95% confidence interval [CI]: 0.77–0.84) and 0.84 (95% CI: 0.81–0.86), respectively. The pooled positive and negative likelihood ratios were 5.21 (95% CI: 3.56–7.62) and 0.23 (95% CI: 0.17–0.32), respectively. The pooled diagnostic odds ratio was 27.53 (95% CI: 14.58–52.01), and the area under the summary receiver operating characteristic curve was 0.91 (Q* = 0.84). In conclusion, SWV measurement on ARFI elastography has high sensitivity and specificity for differential diagnosis between benign and malignant thyroid nodules and can be used in combination with conventional ultrasound.     

声触诊组织量化鉴别诊断甲状腺实性结节的良恶性

目的 探讨声触诊组织量化技术(VTQ)在甲状腺实性结节良恶性鉴别诊断中的应用价值。方法 采用VTQ技术测量经病理证实的101例患者共129个甲状腺实性结节及周边甲状腺组织剪切波速度(SWV),以病理诊断为金标准,绘制SWV诊断甲状腺实性恶性结节的ROC曲线,获得结节SWV诊断恶性结节的最佳截点。结果 甲状腺实性恶性结节的SWV[(3.15±1.91)m/s]大于良性结节[(2.06±0.54)m/s,P<0.01]。以SWV=2.43 m/s为截点诊断甲状腺实性恶性结节的敏感度、特异度分别为67.80%(40/59)、91.43%(64/70)。甲状腺恶性结节与周边甲状腺组织SWV比值(1.56±0.86)大于良性结节与周边甲状腺组织SWV比值(1.00±0.29,P<0.01)。结论 VTQ技术可量化甲状腺结节硬度,对鉴别诊断甲状腺实性结节具有较高的应用价值。