Distinguishing benign from malignant pulmonary nodules with helical chest CT in children with malignant solid tumors

PURPOSE: To retrospectively assess whether computed tomographic (CT) findings can indicate the benign or malignant nature of pulmonary nodules in pediatric patients with malignant solid primary tumors. MATERIALS AND METHODS: With institutional review board approval, waived parental and patient consent, and HIPAA compliance, the authors determined the incidence of malignancy among 81 pulmonary nodules that were sampled at biopsy within 3 weeks after chest CT (January 1999 to September 2003) in 41 young patients with malignant solid tumors. Three radiologists independently and retrospectively reviewed these scans and the available previously obtained scans, classifying nodules as benign, malignant, or indeterminate on the basis of their number, unilateral versus bilateral distribution, size, margins (indistinct vs distinct), calcification, growth, and associated adenopathy. These classifications were compared with nodule histologic type, and interreviewer agreement was assessed. RESULTS: The median patient age was 14.8 years (mean, 13.7 years; range, 5-21 years). Twenty-four of the 41 patients (58%) had at least one biopsy-proved malignant nodule. Four (10%) patients had both benign and malignant nodules; 17 (42%) had only benign nodules. Reviewer 1 classified 65% (39 of 60) of nodules correctly; reviewer 2, 57% (37 of 65); and reviewer 3, 67% (43 of 64). Interreviewer agreement was slight to moderate (kappa /= .32). CONCLUSION: The frequency of benign nodules and the inconsistency of predictions based on CT features suggest the need for better predictors of pulmonary nodules being malignant or benign, so as to reduce unnecessary thoracotomy in pediatric patients with solid malignancy. .     

Differentiation of malignant and benign pulmonary nodules with first-pass dual-input perfusion CT

Objective

To assess diagnostic performance of dual-input CT perfusion for distinguishing malignant from benign solitary pulmonary nodules (SPNs).

Methods

Fifty-six consecutive subjects with SPNs underwent contrast-enhanced 320-row multidetector dynamic volume CT. The dual-input maximum slope CT perfusion analysis was employed to calculate the pulmonary flow (PF), bronchial flow (BF), and perfusion index $ \left( {\mathrm{PI},={{\mathrm{PF}} \left/ {{\left( {\mathrm{PF} + \mathrm{BF}} \right)}} \right.}} \right) $ . Differences in perfusion parameters between malignant and benign tumours were assessed with histopathological diagnosis as the gold standard. Diagnostic value of the perfusion parameters was calculated using the receiver-operating characteristic (ROC) curve analysis.

Results

Amongst 56 SPNs, statistically significant differences in all three perfusion parameters were revealed between malignant and benign tumours. The PI demonstrated the biggest difference between malignancy and benignancy: 0.30?±?0.07 vs. 0.51?±?0.13 , P?<?0.001. The area under the PI ROC curve was 0.92, the largest of the three perfusion parameters, producing a sensitivity of 0.95, specificity of 0.83, positive likelihood ratio (+LR) of 5.59, and negative likelihood ratio (?LR) of 0.06 in identifying malignancy.

Conclusions

The PI derived from the dual-input maximum slope CT perfusion analysis is a valuable biomarker for identifying malignancy in SPNs. PI may be potentially useful for lung cancer treatment planning and forecasting the therapeutic effect of radiotherapy treatment.

Key Points

? Modern CT equipment offers assessment of vascular parameters of solitary pulmonary nodules (SPNs) ? Dual vascular supply was investigated to differentiate malignant from benign SPNs. ? Different dual vascular supply patterns were found in malignant and benign SPNs. ? The perfusion index is a useful biomarker for differentiate malignancy from benignancy.     

CT图像纹理分析在鉴别良恶性肺结节中的价值

目的 评价CT纹理分析在鉴别肺良恶性结节中的价值.方法 回顾性分析110例孤立性肺结节(SPNs)(恶性55例,良性55例)的CT平扫图像,所有结节均经组织病理学或临床随访证实.提取肺结节CT平扫图像的对比度、能量、熵、相关以及一致性等5个灰度共生矩阵纹理参数进行分析.使用t检验比较良恶性结节纹理特征的差异.使用受试者工作特征曲线(ROC)分析确定各参数的最佳临界值,并评价各参数诊断肺良恶性结节的敏感性、特异性、准确性、阳性及阴性预测值.结果 5个纹理特征中,能量、一致性和熵的ROC曲线下面积(AUC)分别为0.997、0.918、0.976.恶性结节的能量、一致性小于良性结节,熵大于良性结节(P＜0.001).使用最佳临界值标准,能量、一致性和熵的敏感性分别为0.982,0.818,1.000,特异性分别为0.964,0.909,0.910,准确性为0.973,0.864,0.945,阳性预测值为0.964,0.9,0.915,阴性预测值为0.981,0.833,0.980.结论 CT纹理特征定量分析是一种有前景的能够准确鉴别良恶性肺结节的手段.     

Neural network based detection of pulmonary nodules on chest radiographs

PURPOSE: We investigated the capabilities of an artificial neural network-based Computer-Aided Diagnosis (CAD) system in improving early detection of pulmonary nodules on chest radiographs. MATERIAL AND METHODS: We used a data-set of 145 digitized chest films. Two different radiologists read the radiographs to detect the sites of possible nodules. The system uses two neural networks trained on a training-set of 100 radiographs selected from the data-set. The first network is used to focus attention on the sites of potential nodules while the second calculates the likeliness of nodule presence in ROIs. The clinical test was performed on 45 more radiographs from the training-set, but different from those in the data-set, which were positive for both benign and malignant nodules. These latter plain films showed 65 nodular lesions which differed by shape and acquisition technique. RESULTS: Sensitivity was 89% in all radiographs while specificity, evaluated by ROI, and accuracy, were 98%. CONCLUSIONS: There are potential limitations in nodule detection on plain radiographs. Some of them are operator-dependent, such as nonsystematic investigation, lesion underestimation, and poor reading, and some are technique-dependent, such as X-ray beam/tube, low voltage, patient positioning, focus-film distance and development process. CADs may contribute to improving detection of pulmonary nodules because the false-negative rate is decreased and sensitivity consequently increased. The high sensitivity and specificity rates of neural networks encourage further trials on wider data-sets to help the radiologist in the early detection of pulmonary nodules.     

Differentiation of benign from malignant thyroid nodules with acoustic radiation force impulse technique

Objective:

The aim of our study was to assess the performance of acoustic radiation force impulse (ARFI) imaging to differentiate benign from malignant thyroid nodules.

Methods:

182 patients who needed thyroid surgery were examined. All patients and 50 healthy volunteers underwent ARFI sonoelastography, which quantitatively analysed the elasticity and hardness of the nodule''s centre and periphery.

Results:

ARFI values showed a statistical significance between malignant nodules and benign nodules and common thyroid parenchyma, in both the centre and periphery of nodules (p < 0.01). There was no significant difference between benign nodules and common thyroid parenchyma in either the nodule''s centre or periphery (p > 0.05). There was no significant difference between the nodule''s centre and periphery of the elastic parameters in both the benign and malignant nodules. There was a statistically significant difference among the two areas (the central group and the peripheral group) under the receiver operating characteristic curve, and the optimal model was the peripheral group. For differentiation of malignant from benign nodules, the sensitivity and specificity were 96.3% and 96.2%, respectively, when 2.545 m s⁻¹ was chosen as a cut-off value in the peripheral group.

Conclusion:

ARFI imaging may be helpful to differentiate benign nodules from malignant thyroid nodules. The selecting measurement position is important in ARFI imaging, and it has good diagnostic value in clinical applications.

Advances in knowledge:

This study shows the diagnostic contribution of ARFI imaging in thyroid lesions.Thyroid cancer is the most common endocrine malignancy, and its incidence has increased in recent years.¹ It comprises different clinical and histological features in respect to different treatments.² The diagnostic method for thyroid cancer has very quickly progressed in recent years, but the pre-operative misdiagnosis rate is 40–70%.A newer ultrasound elastography technique called acoustic radiation force impulse (ARFI), which is performed under direct visual guidance, has recently been verified to measure the stiffness of many tissues in vivo, for example in the liver.^3,4 In our study, we investigated the mechanical properties of focal thyroid disease with ARFI. The purposes of this study were to assess the effectiveness of ARFI quantification in the diagnosis of focal thyroid nodules and differentiation of benign from malignant thyroid lesions by quantification of their stiffness.     

Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience

PURPOSE: To evaluate radiologists' performance for determining a distinction between benign and malignant pulmonary nodules on chest radiographs without and with use of a computer-aided diagnosis scheme. MATERIALS AND METHODS: Fifty-three chest radiographs that depicted 31 primary lung cancers and 22 benign nodules were used. The likelihood measure of malignancy for each nodule was determined by using an automated computerized scheme. Sixteen radiologists (nine attending radiologists and seven radiology residents) participated in an observer study in which cases were interpreted first without and then with use of the scheme. The radiologists' performance was evaluated with receiver operating characteristic analysis. RESULTS: The mean area under the best-fit binormal receiver operating characteristic curve plotted in the unit square (Az) values of radiologists who interpreted images without and with the scheme were 0.743 and 0.817, respectively. The performance of radiologists was improved significantly when the scheme was used (P =.002). However, the performance (Az = 0.889) of the computer alone exceeded these results by a substantial margin. The average change in radiologists' confidence level for interpretation without and with the scheme was highly correlated (r = 0.845) with the likelihood measure of malignancy, which was presented as computer output. CONCLUSION: This scheme for computer-aided diagnosis has the potential to improve the accuracy of radiologists' performance in the classification of benign and malignant solitary pulmonary nodules.     

Computerized search of chest radiographs for nodules

A computer program that recognizes potential pulmonary nodules in PA chest radiographs has been developed. This program produces a display of candidate nodules that require interpretation by a radiologist. Some false positives are rejected by a program, the Nodule Expert. Detection performance with and without Nodule Expert has been evaluated. Using the untrained program (no Nodule Expert), and after inspecting 45 candidate nodules, a radiologist may be confident that a nodule was inspected, if one was located by the program. When pattern recognition techniques are incorporated, the number of false positives presented for inspection is reduced. The radiologist must inspect, at most, 10 candidate nodules to be confident of having inspected a nodule, if one was located by the program. Concomitant with this decrease in the candidate nodule false-positive rate is a decrease in sensitivity (film true-positive rate) from 92 to 86%. This program was trained on candidate nodules from 37 radiographs and also tested on these 37. Some of the features used by the pattern classifier to classify candidate nodules are comparable to those used by human observers.     

Gray-scale inversion radiographic display for the detection of pulmonary nodules on chest radiographs

The purpose of this study was to investigate gray-scale inversion in nodule detection on chest radiography. Simulated nodules were superimposed randomly onto normal chest radiographs. Six radiologists interpreted 144 chest radiographs during three reading sessions: traditional presentation, inverted gray-scale, and a choice session allowing use of traditional and gray-scale inverted views. Sensitivity and specificity were used to assess accuracy based on presence or absence of a nodule. Gray-scale inversion and choice display sessions resulted in significantly higher nodule detection specificity and decreased sensitivity compared to traditional display. Gray-scale inversion may decrease false-positive nodule findings during chest X-ray interpretation.     

Subtle pulmonary nodules: detection and identification with storage phosphor radiographs and conventional chest films

To determine the value of digital storage-phosphor radiography (SR) on the detection and identification of subtle lung nodules, postero-anterior (PA) and lateral (LAT) film-screen (FR) chest radiographs were compared with isodose SR images of 45 patients with metastatic malignancies. The SR postprocessing was done with a particular mode previously optimized for routine chest radiography. Pulmonary metastases were found in 34 patients and were proved or excluded by CT (n = 28) or longterm follow-up FR (n = 17). Chest images were divided into four regions for evaluation of image quality, number of lung nodules per region and marked pulmonary structures by receiver-operating characteristics (ROC) analysis (45 patients; 125 nodules; 2810 observations; five readers). Of the nodules selected for an ROC study 82 % were 0.5–1.0 cm in diameter. Overall image quality was rated better for FR concerning lung fields (PA) and mediastinum/hilum (LAT). More lung-field nodules were detected on FR than on SR chest images (P < 0.05). Use of FR was superior to SR in the general identification of nodules (PA chest), especially concerning intermediate (P < 0.01) and subtle abnormalities (P < 0.05), whereas there was no significant difference for LAT chest images. Our results show, that currently FR still has advantages over SR in the detection and identification of subtle lung nodules in routine clinical radiography. Correspondence to: R. Scheck     

Computer-aided differentiation of malignant from benign solitary pulmonary nodules imaged by high-resolution CT

We investigated the possibility of using computer analysis of high-resolution CT images to radiologically classify the shape of pulmonary nodules. From a total of 107 HRCT images of solid, solitary pulmonary nodules with prior differentiation as benign (n=55) or malignant (n=52), we extracted the desired pulmonary nodules and calculated two quantitative parameters for characterizing nodules: circularity and second central moment. Using discriminant analysis for two thresholds in differentiating malignant from benign states resulted in a sensitivity of 76.9%, a specificity of 80%, a positive predictive value of 78.4%, and a negative predictive value of 78.6%.     

18F-FDG PET/CT结合高分辨率CT对孤立性肺结节的诊断价值

目的探讨^18F-脱氧葡萄糖（FDG）PET／CT结合高分辨率CT（HRCT）对孤立性肺结节（SPN）的鉴别诊断价值。方法25例经手术病理检查或治疗随访证实的SPN（共27个）患者，同期行^18F—FDGPET／CT显像和病灶部位HRCT检查。^18F—FDGPET／CT用目测法结合半定量法判断良恶性。HRCT则根据病灶形态学特征判断良恶性。所得^18F—FDGPET／CT结果和^18F—FDGPET／CT与HRCT相结合结果分别与病理检查结果对照比较。结果27个SPN中15个恶性，12个良性。PET／CT正确诊断14个恶性和9个良性SPN。3个良性SPNPET／CT显像为阳性，其中2个经PET／CT和HRCT联合诊断为良性。联合诊断灵敏度和单纯PET／CT相同（93．3％），但特异性、阳性预测值、阴性预测值、准确性分别高于PET／CT（91．7％、93．3％、91．7％和93．7％对75．0％、82．4％、90．0％和85．2％）。结论^18F—FDGPET／CT结合HRCT是有效的无创性鉴别SPN良恶性的方法。     

甲状腺良恶性结节的超声鉴别诊断

目的探讨甲状腺良恶性结节声像图表现及鉴别诊断。方法对经手术病理证实的60例甲状腺良性结节和21例恶性结节的超声二维及彩色多普勒血流声像图（CDFI）表现进行对比分析。结果良恶性结甲在形态,边界,内部回声,彩色血流分布,阻力指数,颈部淋巴结肿大等指标有统计学显著性差异。结论形态不规整,边界不清,内部不均匀低回声,微钙化,内部血流丰富而周边少或无血流,RI〉0．7等可以作为恶性结节的主要指标;不清晰、不完整、宽窄不一的晕环及颈部淋巴结肿大高度提示恶性;囊性结节可作为排除恶性指标。微小乳头状癌结节及良恶性并发的多源性结节常常会被误诊。