Novel Pulmonary Nodule Position Detection Method Based on Multiscale Convolution / 中国医疗器械杂志

OBJECTIVE@#In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images, reduce the problem of missed detection or false detection, and effectively assist imaging doctors in the diagnosis of pulmonary nodules.@*METHODS@#Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution. First, image preprocessing methods are used to eliminate the noise and artifacts in lung CT images. Second, multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images, and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes, so as to improve the accuracy of feature extraction of pulmonary nodules. Finally, using point detection to improve the loss function of U-Net training process, the accuracy of pulmonary nodule location detection is improved.@*RESULTS@#The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively.@*CONCLUSIONS@#This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence, and can better meet the diagnostic needs of pulmonary nodules.

The performance of digital chest radiographs in the detection and diagnosis of pulmonary nodules and the consistency among readers / 中华肿瘤杂志

Objective: To investigate the detection and diagnostic efficacy of chest radiographs for ≤30 mm pulmonary nodules and the factors affecting them, and to compare the level of consistency among readers. Methods: A total of 43 patients with asymptomatic pulmonary nodules who consulted in Cancer Hospital, Chinese Academy of Medical Sciences from 2012 to 2014 and had chest CT and X-ray chest radiographs during the same period were retrospectively selected, and one nodule ≤30 mm was visible on chest CT images in the whole group (total 43 nodules in the whole group). One senior radiologist with more than 20 years of experience in imaging diagnosis reviewed CT images and recording the size, morphology, location, and density of nodules was selected retrospectively. Six radiologists with different levels of experience (2 residents, 2 attending physicians and 2 associate chief physicians independently reviewed the chest images and recorded the time of review, nodule detection, and diagnostic opinion. The CT imaging characteristics of detected and undetected nodules on X images were compared, and the factors affecting the detection of nodules on X-ray images were analyzed. Detection sensitivity and diagnosis accuracy rate of 6 radiologists were calculated, and the level of consistency among them was compared to analyze the influence of radiologists' seniority and reading time on the diagnosis results. Results: The number of nodules detected by all 6 radiologists was 17, with a sensitivity of detection of 39.5%(17/43). The number of nodules detected by ≥5, ≥4, ≥3, ≥2, and ≥1 physicians was 20, 21, 23, 25, and 28 nodules, respectively, with detection sensitivities of 46.5%, 48.8%, 53.5%, 58.1%, and 65.1%, respectively. Reasons for false-negative result of detection on X-ray images included the size, location, density, and morphology of the nodule. The sensitivity of detecting ≤30 mm, ≤20 mm, ≤15 mm, and ≤10 mm nodules was 46.5%-58.1%, 45.9%-54.1%, 36.0%-44.0%, and 36.4% for the 6 radiologists, respectively; the diagnosis accuracy rate was 19.0%-85.0%, 16.7%-6.5%, 18.2%-80.0%, and 0%-75.0%, respectively. The consistency of nodule detection among 6 doctors was good (Kappa value: 0.629-0.907) and the consistency of diagnostic results among them was moderate or poor (Kappa value: 0.350-0.653). The higher the radiologist's seniority, the shorter the time required to read the images. The reading time and the seniority of the radiologists had no significant influence on the detection and diagnosis results (P>0.05). Conclusions: The ability of radiographs to detect lung nodules ≤30 mm is limited, and the ability to determine the nature of the nodules is not sufficient, and the increase in reading time and seniority of the radiologists will not improve the diagnostic accuracy. X-ray film exam alone is not suitable for lung cancer diagnosis.

Nódulo pulmonar / Pulmonary nodule

Introducción: El nódulo pulmonar es un hallazgo generalmente incidental. Su detección ha aumentado observándose hasta en 13 por ciento de las tomografías. El diagnóstico diferencial plantea un desafío. Objetivo: Describir el caso de un paciente con nódulos pulmonares múltiples y su abordaje a partir de recomendaciones basadas en la evidencia. Caso clínico: Se presenta el caso de un paciente de 35 años con sintomatología inespecífica y nódulos pulmonares múltiples incidentales en estudios cervicotorácicos. Se analizaron etiologías posibles según datos clínicos, epidemiológicos, radiológicos y probabilidad de malignidad. Las recomendaciones basadas en la evidencia dirigieron el manejo. Dada la probabilidad intermedia de malignidad se indicó seguimiento escanográfico en 3 a 6 meses. Conclusiones: El caso presentado pretende reforzar destrezas clínicas en el manejo de esta entidad repercutiendo favorablemente en la morbimortalidad. Busca además impulsar el desarrollo de estrategias de tamizaje en poblaciones de riesgo(AU)

Introduction: The pulmonary nodule is a generally incidental finding. Its detection has increased, being observed in up to 13 percent of CT scans. Differential diagnosis poses a challenge. Objective: To describe the case of a patient with multiple pulmonary nodules and the approach to it based on evidence-based recommendations. Clinical case report: The case of a 35-year-old patient with nonspecific symptoms and incidental multiple pulmonary nodules in cervicothoracic studies is reported. Possible etiologies were analyzed according to clinical, epidemiological, radiological data and probability of malignancy. Evidence-based recommendations guided management. Given the intermediate probability of malignancy, a scan follow-up was indicated in 3 to 6 months. Conclusions: This case aims to reinforce clinical skills in the management of this entity, favourably impacting on morbidity and mortality. It also seeks to promote the development of screening strategies in populations at risk(AU)

Risk assessment of malignancy in solitary pulmonary nodules in lung computed tomography: a multivariable predictive model study / 中华医学杂志(英文版)

BACKGROUND@#Computed tomography images are easy to misjudge because of their complexity, especially images of solitary pulmonary nodules, of which diagnosis as benign or malignant is extremely important in lung cancer treatment. Therefore, there is an urgent need for a more effective strategy in lung cancer diagnosis. In our study, we aimed to externally validate and revise the Mayo model, and a new model was established.@*METHODS@#A total of 1450 patients from three centers with solitary pulmonary nodules who underwent surgery were included in the study and were divided into training, internal validation, and external validation sets (n = 849, 365, and 236, respectively). External verification and recalibration of the Mayo model and establishment of new logistic regression model were performed on the training set. Overall performance of each model was evaluated using area under receiver operating characteristic curve (AUC). Finally, the model validation was completed on the validation data set.@*RESULTS@#The AUC of the Mayo model on the training set was 0.653 (95% confidence interval [CI]: 0.613-0.694). After re-estimation of the coefficients of all covariates included in the original Mayo model, the revised Mayo model achieved an AUC of 0.671 (95% CI: 0.635-0.706). We then developed a new model that achieved a higher AUC of 0.891 (95% CI: 0.865-0.917). It had an AUC of 0.888 (95% CI: 0.842-0.934) on the internal validation set, which was significantly higher than that of the revised Mayo model (AUC: 0.577, 95% CI: 0.509-0.646) and the Mayo model (AUC: 0.609, 95% CI, 0.544-0.675) (P < 0.001). The AUC of the new model was 0.876 (95% CI: 0.831-0.920) on the external verification set, which was higher than the corresponding value of the Mayo model (AUC: 0.705, 95% CI: 0.639-0.772) and revised Mayo model (AUC: 0.706, 95% CI: 0.640-0.772) (P < 0.001). Then the prediction model was presented as a nomogram, which is easier to generalize.@*CONCLUSIONS@#After external verification and recalibration of the Mayo model, the results show that they are not suitable for the prediction of malignant pulmonary nodules in the Chinese population. Therefore, a new model was established by a backward stepwise process. The new model was constructed to rapidly discriminate benign from malignant pulmonary nodules, which could achieve accurate diagnosis of potential patients with lung cancer.

Herramienta para la detección automática de nódulos pulmonares solitarios en series de imágenes de tomografía computarizada multicorte / A tool for automated detection of solitary pulmonary nodules in series of multicut computerized tomography images

Introducción: el nódulo pulmonar solitario es uno de los problemas más frecuentes en la práctica del radiólogo, que constituye un hallazgo incidental habitual en los estudios torácicos realizados durante el ejercicio clínico diario. Objetivo: implementar un sistema de diagnóstico asistido por computadora que facilite la detección del nódulo pulmonar solitario en las series de imágenes de tomografía computarizada multicorte. Métodos: se utilizó Matlab para el desarrollo y evaluación de un conjunto de algoritmos que constituyen elementos necesarios de un sistema de diagnóstico asistido por computadora. En orden: un algoritmo para la extracción de las regiones de interés, algoritmo para la extracción de características y un algoritmo de detección de nódulo pulmonar solitario para el cual se probaron varios clasificadores. La evaluación de los algoritmos fue efectuada en base a las anotaciones realizada por especialistas a la colección de imágenes LIDC-IDRI (Lung Image Database Consortium). Resultados: el método de segmentación empleado para extracción de las regiones de interés permitió generar la adecuada división de las imágenes originales en regiones significativas. El algoritmo utilizado en la detección mostró para el conjunto de prueba además de buena exactitud (de 96,4 por ciento), un buen balance de sensibilidad (91,5 por ciento) para una tasa de 0,84 falsos positivos por imagen. Conclusiones: el trabajo de investigación y la implementación realizada se reflejan en la construcción de una interfaz gráfica en Matlab como prototipo del sistema de diagnóstico asistido por computadora, con el que se puede contribuir a detectar más fácilmente el NPS(AU)

Introduction: solitary pulmonary nodules are one of the most frequent problems in radiographic practice. They are a common incidental finding in chest studies conducted during routine clinical work. Objective: implement a computer-assisted diagnostic system facilitating detection of solitary pulmonary nodules in multicut computerized tomography image series. Methods: Matlab was used to develop and evaluate a set of algorithms constituting necessary components of a computer-assisted diagnostic system. The order was the following: an algorithm to extract regions of interest, another to extract characteristics, and another to detect solitary pulmonary nodules, for which several classifiers were tested. Evaluation of the algorithms was based on notes taken by specialists on the LIDC-IDRI (Lung Image Database Consortium) image collection. Results: the segmentation method used for extraction of regions of interest made it possible to create a suitable division of the original images into significant regions. The algorithm used for detection found that the test set exhibited good accuracy (96.4%), a good sensitivity balance (91.5%), and a 0.84 rate of false positives per image. Conclusions: the research and implementation work done is reflected in the construction of a Matlab graphic interface serving as a prototype for a computer-assisted diagnostic system which may facilitate detection of SPNs.

Valor del PET/CT semicuantitativo (SUVmax) en el estudio de nódulo pulmonar sólido / Value of Semiquantitative PET/CT (SUVmax) in study of solid lung nodule

OBJETIVO: Determinar el rendimiento diagnóstico del PET/CT en el estudio de nódulo pulmonar (NP) utilizando SUVmax. MÉTODO: Se revisó la base de datos de PET/CT, seleccionando aquellos solicitados para estudio de NP sólido. Se incluyeron sólo aquellos NP confirmados como malignos o benignos. Se excluyó NP subsólidos, masas pulmonares (> 3 cm), y pacientes con metástasis conocidas. Se midió SUVmax de las lesiones, determinando mejores valores de corte para malignidad y benignidad. RESULTADOS: De los 140 NP estudiados, el 60% (84/140) fueron confirmados como malignos y el 40% como benignos (100% y 59,6% de confirmación histológica, respectivamente). Un SUVmax ≤ 1,0 mostró sensibilidad 98,8%, valor predictivo negativo (VPN) 96,2%, y Likelihood ratio negativo (LR -) 0,027. Un SUVmax ≤ 2,5 no fue capaz de asegurar razonablemente benignidad con VPN 69,4%, y LR - 0,295. Valores de SUV > 2,5 y 5,0 se asociaron a malignidad en 83% y 93% de los casos, respectivamente (LR+ 3,333 y 8,889). CONCLUSIÓN: El PET/CT presenta alto rendimiento diagnóstico en estimar la naturaleza de un NP Un valor de SUVmax ≤ 1 es altamente predictivo de benignidad, y un valor de SUVmax ≥ 2,5 de malignidad. Valores entre 1,0 y 2,5 no permiten caracterizar eficientemente los NP.

AIM: To establish the diagnostic accuracy of PET/CT in study of solid lung nodule (LN) using SUVmax index. METHOD: We revised PET/CT data base, selecting those scans asked to evaluate a solid LN. Only confirmed malign o benign LN were included. Subsolid LN, lung masses (> 3 cm), and known or suspected lung metastases were excluded. SUVmax was measured in each LN, and best cutoff for malignant and benign lesion was calculated. RESULTS: Of the whole group of 140 LN, 60% were confirmed as malignant, and 40% as benign (100% and 59,6% of histological confirmation, respectively). SUVmax ≤ 1,0 showed sensibility of 98,8%, negative predictive value (NPV) of 96,2%, and negative likelihood ratio (LR —) of 0,027. SUVmax ≤ 2,5 was not able to guarantee reasonably benign nature of LN, showing NPV of 69,4% and LR - of 0,295. SUVmax > 2,5 and > 5,0 was associated to malign lesion in 83% and 93% of cases, respectively (LR + of 3,333 and 8,889). CONCLUSION: PET/CT shows high accuracy estimating the nature of solid LN. SUVmax ≤ 1,0 is highly predictive of benignity, and SUVmax ≥ 2,5 is highly predictive of malignancy. SUVmax values between 1,0 and 2,5 were not able to characterize efficiently LN.

Marcación de nódulos pulmonares con lipiodol bajo tomografía computada: experiencia local y correlación anatomopatológica / Marking of pulmonary nodules with lipiodol by computerized tomography: local experience and anatomopathological correlation

El objetivo de este estudio es la descripción de la novedosa y poco invasiva técnica de marcación de pequeños nódulos pulmonares con lipiodol, guiado con TAC para su posterior resección por cirugía videotoracoscópica con apoyo radioscópico. Se trata de un estudio retrospectivo que incluye 51 pacientes consecutivos en un tiempo comprendido entre junio del 2012 a septiembre del 2017, obteniendo el diagnóstico final mediante la correlación anatomopatológica, representando los procesos malignos el 81% y los benignos el 19%. Mediante esta técnica se lograron identificar y extraer con éxito el 100% de los nódulos marcados, demostrándose la efectividad y seguridad del procedimiento por las mínimas complicaciones relacionadas.

The purpose of this study is describing a novel and minimally invasive technique of CT-guided marking of small pulmonary nodules with lipiodol prior to resection by videothoracoscopic surgery with radioscopic support. This is a retrospective study that includes 51 consecutive patients between June 2012 and September 2017, with the final diagnosis confirmed by pathology. Malignant nodules represented 81% of the cases with the remaining 19% being benign nodules. Through this technique, 100% of the marked nodules were successfully identified and extracted with few procedure related complications and no adverse clinical outcome, demonstrating the effectiveness and safety of the procedure.

Differential Diagnosis of Solitary Pulmonary Inflammatory Lesions and Peripheral Lung Cancers with Contrast-enhanced Computed Tomography

OBJECTIVES: To clarify differences between solitary pulmonary inflammatory lesions and peripheral lung cancers with contrast-enhanced computed tomography. METHODS: In total, 64 and 132 patients with solitary pulmonary inflammatory masses/nodules and peripheral lung cancers, respectively, were enrolled in this study. Their computed tomographic findings were summarized and compared retrospectively. RESULTS: Compared with the peripheral lung cancers, the inflammatory lesions were located closer to the pleura (p<0.0001). The majority of the inflammatory lesions were patchy and oval-shaped (82.8%), whereas most of the tumors were lobulated (82.6%). Almost all the inflammatory cases were unclear (93.8%), whereas most of the tumors had spiculated margins (72.7%). Computed tomography values were significantly higher for the inflammatory lesions than for the cancers (p<0.0001). More than half of the inflammatory lesions had defined necrosis (59.3%). Furthermore, 49.2% of the cancers enhanced inhomogeneously, but only 24.6% had ill-defined necrosis or cavities. The peripheral zones of 98.4% of the inflammatory lesions and 72.7% of the tumors were unclear, with peripheral scattered patches (92.2%) and beam-shaped opacity (66.7%) being the most common findings, respectively. Adjacent pleural thickening was more frequent for the inflammatory lesions than the cancers (95.3% vs. 21.1%, p<0.0001), whereas pleural indentation was found in 67.4% of the subjects with cancer. In addition, hilar (p=0.034) and mediastinal (p=0.003) lymphadenopathy were more commonly detected in the cancers than in the inflammatory cases. CONCLUSIONS: Contrast-enhanced computed tomography findings for pulmonary inflammatory lesions and peripheral lung cancers were significantly different in many aspects. Developing a comprehensive understanding of these differences is helpful for directing their management.

Nódulo Pulmonar Solitário ­ manejo na prática clínica / Solitary Pulmonary Nodule

O câncer de pulmão é um permanente desafio a todos aqueles que se dedicam ao tratamento de patologias pulmonares em função de sua alta mortalidade. O diagnostico precoce é a peça fundamental para a mudança destas estatísticas. Ao longo dos últimos anos muitos avanços foram percebidos em termos de tratamento, com terapias cada vez mais personalizadas, porém pouco se modificou nos métodos diagnósticos. Os programas de rastreamento, embora já sabidamente necessários, ainda não se solidificaram em torno de um modelo ideal e a ecoendoscopia (EBUS) também, infelizmente, não é uma realidade disponível para a maioria. Entretanto os nódulos pulmonares são cada vez mais frequentes na pratica diária. O reconhecimento das características clínicas e radiológicas associadas à malignidade é fundamental para o correto manejo destas lesões com identificação precoce dos casos suspeitos e menor exposição a métodos e procedimentos desnecessários.

Lung cancer is a permanent challenge to all those dedicated to the treatment of pulmonary diseases due to the high mortality. Early diagnosis is the key to changing these statistics. Over the last few years many advances have been perceived in terms of treatment, with increasingly personalized therapies, but little has changed in diagnostic methods. The screening programs, while already known to be necessary, have not yet solidified around an ideal model, and echoendoscopy (EBUS) is also, unfortunately, not a reality available to most. However, pulmonary nodules are becoming more frequent in daily practice. The recognition of clinical and radiological features associated with malignancy is fundamental for the correct management of these lesions with early identification of suspected cases and less exposure to unnecessary methods and procedures.

Effect of the High-Pitch Mode in Dual-Source Computed Tomography on the Accuracy of Three-Dimensional Volumetry of Solid Pulmonary Nodules: A Phantom Study

OBJECTIVE: To evaluate the influence of high-pitch mode (HPM) in dual-source computed tomography (DSCT) on the accuracy of three-dimensional (3D) volumetry for solid pulmonary nodules. MATERIALS AND METHODS: A lung phantom implanted with 45 solid pulmonary nodules (n = 15 for each of 4-mm, 6-mm, and 8-mm in diameter) was scanned twice, first in conventional pitch mode (CPM) and then in HPM using DSCT. The relative percentage volume errors (RPEs) of 3D volumetry were compared between the HPM and CPM. In addition, the intermode volume variability (IVV) of 3D volumetry was calculated. RESULTS: In the measurement of the 6-mm and 8-mm nodules, there was no significant difference in RPE (p > 0.05, respectively) between the CPM and HPM (IVVs of 1.2 +/- 0.9%, and 1.7 +/- 1.5%, respectively). In the measurement of the 4-mm nodules, the mean RPE in the HPM (35.1 +/- 7.4%) was significantly greater (p < 0.01) than that in the CPM (18.4 +/- 5.3%), with an IVV of 13.1 +/- 6.6%. However, the IVVs were in an acceptable range (< 25%), regardless of nodule size. CONCLUSION: The accuracy of 3D volumetry with HPM for solid pulmonary nodule is comparable to that with CPM. However, the use of HPM may adversely affect the accuracy of 3D volumetry for smaller (< 5 mm in diameter) nodule.

Digital Tomosynthesis of the Chest: Comparison of Patient Exposure Dose and Image Quality between Standard Default Setting and Low Dose Setting

OBJECTIVE: To determine the optimum low dose (LD) digital tomosynthesis (DT) setting, and to compared the image quality of the LD DT with that of the standard default (SD) DT. MATERIALS AND METHODS: Nine DT settings, by changing tube voltage, copper filter, and dose ratio, were performed for determining the LD setting. Among combinations of DT setting, a condition providing the lowest radiation dose was determined. Eighty artificial nodules less than 1 cm in diameter (subcentimeter nodules: 40, micronodules less than 4 mm: 40) were attached to a Styrofoam and a diaphragm of the phantom. Among these, 38 nodules were located at the periphery of the lung (thin area) and 42 nodules were located at the paravertebral or sub-diaphragmatic area (thick area). Four observers counted the number of nodules detected in the thick and thin areas. The detection sensitivity in SD and LD settings were calculated separately. Data were analyzed statistically. RESULTS: The lowest LD setting was a combination of 100 kVp, 0.3 mm additional copper filter, and a 1 : 5 dose ratio. The effective dose for the LD and SD settings were 62 microSv and 140 microSv, separately. A 56.7% dose reduction was achieved in the LD setting compared with the SD setting. Detection sensitivities were not different between the SD and the LD settings except between observers 1 and 2 for the detection of micronodules in the thick area. CONCLUSION: LD DT can be effective in nodule detection bigger than 4 mm without a significant decrease in image quality compared with SD DT.

A Comparison of Two Commercial Volumetry Software Programs in the Analysis of Pulmonary Ground-Glass Nodules: Segmentation Capability and Measurement Accuracy

OBJECTIVE: To compare the segmentation capability of the 2 currently available commercial volumetry software programs with specific segmentation algorithms for pulmonary ground-glass nodules (GGNs) and to assess their measurement accuracy. MATERIALS AND METHODS: In this study, 55 patients with 66 GGNs underwent unenhanced low-dose CT. GGN segmentation was performed by using 2 volumetry software programs (LungCARE, Siemens Healthcare; LungVCAR, GE Healthcare). Successful nodule segmentation was assessed visually and morphologic features of GGNs were evaluated to determine factors affecting segmentation by both types of software. In addition, the measurement accuracy of the software programs was investigated by using an anthropomorphic chest phantom containing simulated GGNs. RESULTS: The successful nodule segmentation rate was significantly higher in LungCARE (90.9%) than in LungVCAR (72.7%) (p = 0.012). Vascular attachment was a negatively influencing morphologic feature of nodule segmentation for both software programs. As for measurement accuracy, mean relative volume measurement errors in nodules > or = 10 mm were 14.89% with LungCARE and 19.96% with LungVCAR. The mean relative attenuation measurement errors in nodules > or = 10 mm were 3.03% with LungCARE and 5.12% with LungVCAR. CONCLUSION: LungCARE shows significantly higher segmentation success rates than LungVCAR. Measurement accuracy of volume and attenuation of GGNs is acceptable in GGNs > or = 10 mm by both software programs.

CT-Guided Percutaneous Transthoracic Localization of Pulmonary Nodules Prior to Video-Assisted Thoracoscopic Surgery Using Barium Suspension

OBJECTIVE: To describe our initial experience with CT-guided percutaneous barium marking for the localization of small pulmonary nodules prior to video-assisted thoracoscopic surgery (VATS). MATERIALS AND METHODS: From October 2010 to April 2011, 10 consecutive patients (4 men and 6 women; mean age, 60 years) underwent CT-guided percutaneous barium marking for the localization of 10 small pulmonary nodules (mean size, 7.6 mm; range, 3-14 mm): 6 pure ground-glass nodules, 3 part-solid nodules, and 1 solid nodule. A 140% barium sulfate suspension (mean amount, 0.2 mL; range, 0.15-0.25 mL) was injected around the nodules with a 21-gauge needle. The technical details, surgical findings and pathologic features associated with barium localizations were evaluated. RESULTS: All nodules were marked within 3 mm (mean distance, 1.1 mm; range, 0-3 mm) from the barium ball (mean diameter, 9.6 mm; range, 8-16 mm) formed by the injected barium suspension. Pneumothorax occurred in two cases, for which one needed aspiration. However, there were no other complications. All barium balls were palpable during VATS and visible on intraoperative fluoroscopy, and were completely resected. Both the whitish barium balls and target nodules were identifiable in the frozen specimens. Pathology revealed one invasive adenocarcinoma, five adenocarcinoma-in-situ, two atypical adenomatous hyperplasias, and two benign lesions. In all cases, there were acute inflammations around the barium balls which did not hamper the histological diagnosis of the nodules. CONCLUSION: CT-guided percutaneous barium marking can be an effective, convenient and safe pre-operative localization procedure prior to VATS, enabling accurate resection and diagnosis of small or faint pulmonary nodules.

Usefulness of the CAD System for Detecting Pulmonary Nodule in Real Clinical Practice

OBJECTIVE: We wanted to evaluate the usefulness of the computer-aided detection (CAD) system for detecting pulmonary nodules in real clinical practice by using the CT images. MATERIALS AND METHODS: Our Institutional Review Board approved our retrospective study with a waiver of informed consent. This study included 166 CT examinations that were performed for the evaluation of pulmonary metastasis in 166 patients with colorectal cancer. All the CT examinations were interpreted by radiologists and they were also evaluated by the CAD system. All the nodules detected by the CAD system were evaluated with regard to whether or not they were true nodules, and they were classified into micronodules (MN, diameter < 4 mm) and significant nodules (SN, 4 < or = diameter < or = 10 mm). The radiologic reports and CAD results were compared. RESULTS: The CAD system helped detect 426 nodules; 115 (27%) of the 426 nodules were classified as true nodules and 35 (30%) of the 115 nodules were SNs, and 83 (72%) of the 115 were not mentioned in the radiologists' reports and three (4%) of the 83 nodules were non-calcified SNs. One of three non-calcified SNs was confirmed as a metastatic nodule. According to the radiologists' reports, 60 true nodules were detected, and 28 of the 60 were not detected by the CAD system. CONCLUSION: Although the CAD system missed many SNs that are detected by radiologists, it helps detect additional nodules that are missed by the radiologists in real clinical practice. Therefore, the CAD system can be useful to support a radiologist's detection performance.

Solitary Pulmonary Nodule on Helical Dynamic CT Scans: Analysis of the Enhancement Patterns Using a Computer-Aided Diagnosis (CAD) System

OBJECTIVE: We wanted to investigate the usefulness of a computer-aided diagnosis (CAD) system in assisting radiologists to diagnosis malignant solitary pulmonary nodules (SPNs), as compared with diagnosing SPNs with using direct personal drawing. MATERIALS AND METHODS: Forty patients with SPNs were analyzed. After the pre-contrast scan was performed, an additional ten series of post-contrast images were obtained at 20-second intervals. Two investigators measured the attenuation values of the SPNs: a radiologist who drew the regions of interest (ROIs), and a technician who used a CAD system. The Bland and Altman plots were used to compare the net enhancement between a CAD system and direct personal drawing. The diagnostic characteristics of the malignant SPNs were calculated by considering the CAD and direct personal drawing and with using Fisher's exact test. RESULTS: On the Bland and Altman plot, the net enhancement difference between the CAD system and direct personal drawing was not significant (within +/- 2 standard deriation). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of diagnosing malignant SPNs using CAD was 92%, 85%, 75%, 96% and 88%, respectively. The sensitivity, specificity, PPV, NPV and accuracy of diagnosing malignant SPNs using direct drawing was 92%, 89%, 79%, 92% and 88%, respectively. CONCLUSION: The CAD system was a useful tool for diagnosing malignant SPNs.

Accuracy of CT scan in detection of benign or malignant nature of solitary pulmonary nodules in comparison with pathological results-A prospective study

Solitary Pulmonary Nodule [SPN] is one of the most common diagnostic problems in chest radiology because differentiation between benign and malignant nature of the lesions based on radiologic pictures may be difficult and sometimes impossible. Nowadays, CT scan is the most accurate imaging modality for diagnosis and characterization of pulmonary nodules. In this study, 131 patients were included in whom Preliminary chest x-ray revealed SPN, chest CT scan was the next imaging investigation in these patients. Chest CT Scan confirmed SPN in 103 patients. Of these patients, Fine Needle Aspiration [FNA] and surgical resection of the nodule were taken place in 69 and 27 patients respectively. Pathological results compared with CT scan findings. Considering CT characteristics of the lesions, results were in favor of malignancy in 27 patients and in other 61 patients, CT scan findings were consistent with benign process. Mentioned data disclosed sensitivity and specificity of CT scan in prediction of malignant SPN 81/8% and 86/3% respectively. CT scan as used in this study could be useful for determining benign or malignant nature of solitary pulmonary nodules. Rate of enhancement in the lesions following IV administration of contrast agent is the most important criterion to differentiate benign and malignant lesions

value of LDH level of BAL fluid in differentiating benign from malignant solitary pulmonary nodules

Serum lactate dehydrogenase [LDH] concentration is an indicator for tissue injury. It may be secreted locally in many conditions. For the first time, this study was performed to investigate the value of LDH level in bronchoalveolar lavage fluid [BALF] in differentiation of benign from malignant single pulmonary nodules [SPNs] and to assess its relationship with serum LDH levels. This study was a prospective case-control clinical study. It included 59 patients with a SPN and 21 non-smoker healthy adult volunteers as controls. They underwent bronchoscopy with BAL, Transbronchial needle aspiration [TBNA], and transbronchial biopsy [TBB]. Both total serum and HAL LDFJ levels were measured. The range of the HAL LDH levels in the control group was 4.60 -26 mild/mI, in patients with benign nodule was 6- 83 rnlU/ml, and in those with malignant nodule was 33 -147 mIU/ml. Overall, the mean BALF LDI-I level was significantly higher in patients with a malignant pulmonary nodule [85.92 +/- 28.31] as compared with that of either patients with a benign nodule [19.08 +/- 18.35] [p<0.0001] or control group [12.16 +/- 6.18] [p<0.0001]. No significant difference between the absolute value of HAL LDH level in patients with benign pulmonary nodule and the control subjects was found [p=0.23]. There was no correlation between HALF LDH and serum LDFI level in patients with SPNs [p=0.595]. HALF LDH levels are increased in patients with malignant SPN, but had no significant rise in benign solitary pulmonary nodules. This factor is useful in differentiating benign from malignant SPNs. A low BAL fluid LDI-1 level in a patient with SPN who does not have a tissue diagnosis may be deemed acceptable for observation and follow up. This may save patients the need for operative procedures

Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists

The receiver operating characteristic (ROC) curve, which is defined as a plot of test sensitivity as the y coordinate versus its 1-specificity or false positive rate (FPR) as the x coordinate, is an effective method of evaluating the performance of diagnostic tests. The purpose of this article is to provide a nonmathematical introduction to ROC analysis. Important concepts involved in the correct use and interpretation of this analysis, such as smooth and empirical ROC curves, parametric and nonparametric methods, the area under the ROC curve and its 95% confidence interval, the sensitivity at a particular FPR, and the use of a partial area under the ROC curve are discussed. Various considerations concerning the collection of data in radiological ROC studies are briefly discussed. An introduction to the software frequently used for performing ROC analyses is also presented.