Accuracy Of Nodule Detection Research Articles

Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier.

This study aimed to assist doctors in detecting early-stage lung cancer. To achieve this, a hierarchical system that can detect nodules in the lungs using computed tomography (CT) images was developed. In the initial phase, a preexisting model (YOLOv5s) was used to detect lung nodules. A 0.3 confidence threshold was established for identifying nodules in this phase to enhance the model's sensitivity. The primary objective of the hierarchical model was to locate and categorize all lung nodules while minimizing the false-negative rate. Following the analysis of the results from the first phase, a novel 3D convolutional neural network (CNN) classifier was developed to examine and categorize the potential nodules detected by the YOLOv5s model. The objective was to create a detection framework characterized by an extremely low false positive rate and high accuracy. The Lung Nodule Analysis 2016 (LUNA 16) dataset was used to evaluate the effectiveness of this framework. This dataset comprises 888 CT scans that include the positions of 1186 nodules and 400,000 non-nodular regions in the lungs. The YOLOv5s technique yielded numerous incorrect detections owing to its low confidence level. Nevertheless, the addition of a 3D classification system significantly enhanced the precision of nodule identification. By integrating the outcomes of the YOLOv5s approach using a 30% confidence limit and the 3D CNN classification model, the overall system achieved 98.4% nodule detection accuracy and an area under the curve of 98.9%. Despite producing some false negatives and false positives, the suggested method for identifying lung nodules from CT scans is promising as a valuable aid in decision-making for nodule detection.

Deep Convolutional Generative Adversarial Networks for Automated Segmentation and Detection of Lung Adenocarcinoma Using Red Deer Optimization Algorithm

The diagnosis of early-stage lung cancer can be challenging due to several factors. Firstly, the asymptomatic nature of the disease means that it may not present any noticeable symptoms until it has progressed to later stages. Additionally, the use of computed tomography, which can be expensive and involves repetitive radiation exposure, can further complicate the diagnostic process. Even specialists may encounter difficulties when examining lung CT imagery to identify pulmonary nodules, particularly in the case of cell lung adenocarcinoma lesions.This paper suggests a unique deep learning-based Deep Convolutional Generative Adversarial Networks (DCGAN) model for lung cancer classification. The dataset utilized for the experimental purpose is accessed from the LUNA16 challenge database. This comprises 888 CT scans of the lungs. These images are initially segmented using Quick-CapsNet (QCN) model and applied with Red Deer Optimization (RDO) algorithm to extract the optimized features. Furthermore, the categorization between benign and malignant tumors is carried out using the DC-GAN model. The pulmonary nodule detection accuracy of the proposed model is 98.65%, indicating early-stage lung cancer. It is discovered to be superior to other existing techniques, such as sophisticated deep learning, straightforward machine learning, and hybrid methods applied to lung CT scans for nodule diagnosis. According to experimental findings, the suggested way can significantly help radiologists spot early lung cancer and facilitate prompt patient management.

Detection of low‐dose computed tomography pulmonary nodules based on 3D CNN‐CapsNet

AbstractLung cancer is currently one of the diseases with the highest mortality rate. Early detection of pulmonary nodules is consistently one of the most effective ways to improve the overall survival of cancer patients. However, the existing deep learning‐based pulmonary nodule detection methods still have some problems such as low sensitivity, high false positives, and difficulty in detecting small nodules. To solve the above problems, a low‐dose computed tomography pulmonary nodule detection algorithm based on 3D convolution neural network and capsule network, namely 3D CNN‐CapsNet, is proposed in this work. Combination of full CNN and capsule network reduces the dependence of CNNs on a large amount of data. In 3D CNN‐CapsNet, the convolution kernel of different sizes is applied to the features of different channels to extract richer context information. Then, fused features of different scales are fed into the capsule network for representative feature extraction and more accurate classification. The authors evaluate their proposed method on Early Lung Cancer Program dataset. The nodule detection rate is 95.19%, the sensitivity is 92.31%, the specificity is 98.08%, and the F1‐score is 0.95 which are much better than other baseline methods. These experiments demonstrate that 3D CNN‐CapsNet can effectively improve the detection accuracy of nodules, and can better meet the diagnostic needs of pulmonary nodules.

Analysis based on machine and deep learning techniques for the accurate detection of lung nodules from CT images

The imaging modality of computed tomography (CT) is significant for the diagnosis of lung cancer. Doctors, surgeons, radiologists, and oncologists choose CT scanning to find these nodules for diagnosing lung cancer early by using computer-aided diagnosis tools. Early malignant diagnosis is necessary for providing an efficient treatment for patients who are affected by lung cancer and there are multiple methods available for the detection of these modules. Gaining knowledge about the various methods is more important for developing a novel model and hence in this research, 50 recent articles from the last five years are evaluated with a focus on lung nodule identification and a goal of highlighting the problems associated with the current approaches. Based on several variables, including performance measures, publication years and journals, achievements of the methods in numerical evaluations, and other things, the evaluation of various methods is made in this study. On the contrary, a technical analysis that considers the advantages and disadvantages of the approach is also interpreted. This study analyzed several deep learning and related techniques for locating lung nodules using CT scans that provide a deeper insight into their cons and pros. The investigation revealed that the deep learning optimization-based strategies outperformed the convolutional techniques concerning the performance measures like accuracy, sensitivity, specificity, F-score, precision, and recall for the achievements, which vary from 80 to 100%. Lastly, the report examines potential directions for future research as well as challenges to improving lung nodule detection accuracy.

Application of Artificial Intelligence in Lung Cancer.

Simple SummaryLung cancer is the leading cause of malignancy-related mortality worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and decision making to prognosis prediction. AI could reduce the labor work of LDCT, CXR, and pathology slides reading. AI as a second reader in LDCT and CXR reading reduces the effort of radiologists and increases the accuracy of nodule detection. Introducing AI to WSI in digital pathology increases the Kappa value of the pathologist and help to predict molecular phenotypes with radiomics and H&E staining. By extracting radiomics from image data and WSI from the histopathology field, clinicians could use AI to predict tumor properties such as gene mutation and PD-L1 expression. Furthermore, AI could help clinicians in decision-making by predicting treatment response, side effects, and prognosis prediction in medical treatment, surgery, and radiotherapy. Integrating AI in the future clinical workflow would be promising.Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

An Artificial Intelligence–Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study

Most early lung cancers present as pulmonary nodules on imaging, but these can be easily missed on chest radiographs. To assess if a novel artificial intelligence (AI) algorithm can help detect pulmonary nodules on radiographs at different levels of detection difficulty. This diagnostic study included 100 posteroanterior chest radiograph images taken between 2000 and 2010 of adult patients from an ambulatory health care center in Germany and a lung image database in the US. Included images were selected to represent nodules with different levels of detection difficulties (from easy to difficult), and comprised both normal and nonnormal control. All images were processed with a novel AI algorithm, the AI Rad Companion Chest X-ray. Two thoracic radiologists established the ground truth and 9 test radiologists from Germany and the US independently reviewed all images in 2 sessions (unaided and AI-aided mode) with at least a 1-month washout period. Each test radiologist recorded the presence of 5 findings (pulmonary nodules, atelectasis, consolidation, pneumothorax, and pleural effusion) and their level of confidence for detecting the individual finding on a scale of 1 to 10 (1 representing lowest confidence; 10, highest confidence). The analyzed metrics for nodules included sensitivity, specificity, accuracy, and receiver operating characteristics curve area under the curve (AUC). Images from 100 patients were included, with a mean (SD) age of 55 (20) years and including 64 men and 36 women. Mean detection accuracy across the 9 radiologists improved by 6.4% (95% CI, 2.3% to 10.6%) with AI-aided interpretation compared with unaided interpretation. Partial AUCs within the effective interval range of 0 to 0.2 false positive rate improved by 5.6% (95% CI, -1.4% to 12.0%) with AI-aided interpretation. Junior radiologists saw greater improvement in sensitivity for nodule detection with AI-aided interpretation as compared with their senior counterparts (12%; 95% CI, 4% to 19% vs 9%; 95% CI, 1% to 17%) while senior radiologists experienced similar improvement in specificity (4%; 95% CI, -2% to 9%) as compared with junior radiologists (4%; 95% CI, -3% to 5%). In this diagnostic study, an AI algorithm was associated with improved detection of pulmonary nodules on chest radiographs compared with unaided interpretation for different levels of detection difficulty and for readers with different experience.

An adaptive morphology based segmentation technique for lung nodule detection in thoracic CT image

Lung cancer is one of the most life-threatening cancers mostly indicated by the presence of nodules in the lung. Doctors and radiological experts use High-Resolution Computed Tomography (HRCT) images for nodule detection and further decision making from visual inspection. Manual detection of lung nodules is a time-consuming process. Therefore, Computer-aided detection (CADe) systems have been developed for accurate nodule detection and segmentation. CADe-based systems assist radiologists to detect lung nodules with greater confidence and a lesser amount of time and have a significant impact on the accurate, uniform, and early-stage diagnosis of lung cancer. In this research work, an adaptive morphology-based segmentation technique (AMST) has been introduced by designing an adaptive morphological filter for improved segmentation of the lung nodule region. The adaptive morphological filter detects candidate nodule regions by employing adaptive structuring element (ASE) and at the same time improves nodule detection accuracy by reducing false positives (FPs) from the Computed Tomography (CT) slices. The detected nodule candidate regions are then processed for feature extraction. In this study, morphological, texture and intensity-based features have been used with support vector machine (SVM) classifier for lung nodule detection. The performance of the proposed framework has been evaluated by incorporating a 10-fold cross-validation technique on Lung Image Database Consortium-Image Database Resource Initiative (LIDC/IDRI) dataset and on a private dataset, collected from a consultant radiologist. It has been observed that the proposed automated computer-aided detection system has achieved overall classification performance indices with 94.88% sensitivity, 93.45% specificity and 94.27% detection accuracy with 1.8 FPs/scan on LIDC/IDRI dataset and 91.43% sensitivity, 90.45% specificity, 92.83% accuracy with 3.2 FPs/scan on a private dataset. The results show that the proposed CADe system presented in this paper outperforms the other state-of-the-art methods for automatic nodule detection from the HRCT image.

Development and clinical application of deep learning model for lung nodules screening on CT images

Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland–Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists.

Ultrasound-elastic-image-assisted diagnosis of pulmonary nodules based on genetic algorithm

In the process of ultrasound elastic image detection of pulmonary nodules, due to various factors, the detection process of nodules will produce less sensitive and higher false positives, which will affect the detection accuracy of nodules. In order to improve the ultrasound-assisted diagnosis of pulmonary nodules, this study based on genetic algorithm and combined with fish-following algorithm image recognition technology to construct an improved algorithm based on genetic algorithm. In addition, this study combs and improves the algorithm through process design and sets up the simulation environment for algorithm simulation research. Finally, in order to verify the performance of the algorithm, the ultrasound image of the pulmonary nodule is analyzed by an example to obtain the processed recognition image. From the point of view of identification, the algorithm proposed in this study has certain clinical effects and can provide theoretical reference for subsequent related research.

A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans.

This research is concerned with malignant pulmonary nodule detection (PND) in low-dose CT scans. Due to its crucial role in the early diagnosis of lung cancer, PND has considerable potential in improving the survival rate of patients. We propose a two-stage framework that exploits the ever-growing advances in deep neural network models, and that is comprised of a semantic segmentation stage followed by localization and classification. We employ the recently published DeepLab model for semantic segmentation, and we show that it significantly improves the accuracy of nodule detection compared to the classical U-Net model and its most recent variants. Using the widely adopted Lung Nodule Analysis dataset (LUNA16), we evaluate the performance of the semantic segmentation stage by adopting two network backbones, namely, MobileNet-V2 and Xception. We present the impact of various model training parameters and the computational time on the detection accuracy, featuring a 79.1% mean intersection-over-union (mIoU) and an 88.34% dice coefficient. This represents a mIoU increase of 60% and a dice coefficient increase of 30% compared to U-Net. The second stage involves feeding the output of the DeepLab-based semantic segmentation to a localization-then-classification stage. The second stage is realized using Faster RCNN and SSD, with an Inception-V2 as a backbone. On LUNA16, the two-stage framework attained a sensitivity of 96.4%, outperforming other recent models in the literature, including deep models. Finally, we show that adopting a transfer learning approach, particularly, the DeepLab model weights of the first stage of the framework, to infer binary (malignant-benign) labels on the Kaggle dataset for pulmonary nodules achieves a classification accuracy of 95.66%, which represents approximately 4% improvement over the recent literature.

Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks

Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in which convolutional neural networks (CNNs) are used only as one part of the pipeline, and the whole system still needs either traditional image processing modules or human intervention to obtain final results. In this paper, we introduced a fast and fully-automated end-to-end system that can efficiently segment precise lung nodule contours from raw thoracic CT scans. Our proposed system has four major modules: candidate nodule detection with Faster regional-CNN (R-CNN), candidate merging, false positive (FP) reduction with CNN, and nodule segmentation with customized fully convolutional neural network (FCN). The entire system has no human interaction or database specific design. The average runtime is about 16 s per scan on a standard workstation. The nodule detection accuracy is 91.4% and 94.6% with an average of 1 and 4 false positives (FPs) per scan. The average dice coefficient of nodule segmentation compared to the groundtruth is 0.793.

Computer aided thyroid nodule detection system using medical ultrasound images

Abstract Thyroid nodule is one of the endocrine problem caused due to abnormal growth of cells. This survival rate can be enhanced by earlier detection of nodules. Thus, the accurate detection of nodule is of utmost importance in providing effective diagnosis to increase the survival rate. However, accuracy of nodule detection from ultrasound images is suffered due to speckle noise. It considerably deteriorates the image quality and makes the differentiation of fine details quite difficult. Most of the detection systems for the thyroid nodules are semi-automated entailing manual intervention to draw rough outline of the nodule at some level or require manual segmentation in training or testing phases that increase the inaccuracies and evaluation time. To handle this, a fully Computer-Aided Detection system is presented for speckle reduction and segmentation of nodules from thyroid ultrasound images. The proposed system has three components: speckle reduction to reduce speckle noise and preserve the diagnostic features of ultrasound image, automatic generation of Region of interest (ROI) that identifies suspicious regions and fully automatic segmentation of nodule in processed ROI image. The proposed segmentation method outperformed other methods by gaining high True Positive (TP) value (95.92 ± 3.70%), False Positive (FP) value (7.04 ± 4.21%), Dice Coefficient (DC) value (93.88 ± 2.59%), Overlap Metric (OM) (91.18 ± 7.04 pixels) and Hausdroff Distance (HD) (0.52 ± 0.20 pixels). This system can facilitate the endocrinologists by providing second opinion to improve diagnosis of nodules as benign or malignant.

Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images

Pulmonary cancer is considered as one of the major causes of death worldwide. For the detection of lung cancer, computer-assisted diagnosis (CADx) systems have been designed. Internet-of-Things (IoT) has enabled ubiquitous internet access to biomedical datasets and techniques; in result, the progress in CADx is significant. Unlike the conventional CADx, deep learning techniques have the basic advantage of an automatic exploitation feature as they have the ability to learn mid and high level image representations. We proposed a Computer-Assisted Decision Support System in Pulmonary Cancer by using the novel deep learning based model and metastasis information obtained from MBAN (Medical Body Area Network). The proposed model, DFCNet, is based on the deep fully convolutional neural network (FCNN) which is used for classification of each detected pulmonary nodule into four lung cancer stages. The performance of proposed work is evaluated on different datasets with varying scan conditions. Comparison of proposed classifier is done with the existing CNN techniques. Overall accuracy of CNN and DFCNet was 77.6% and 84.58%, respectively. Experimental results illustrate the effectiveness of proposed method for the detection and classification of lung cancer nodules. These results demonstrate the potential for the proposed technique in helping the radiologists in improving nodule detection accuracy with efficiency.

Computer aided thyroid nodule detection system using medical ultrasound images

Abstract Thyroid nodule is one of the endocrine problem caused due to abnormal growth of cells. This survival rate can be enhanced by earlier detection of nodules. Thus, the accurate detection of nodule is of utmost importance in providing effective diagnosis to increase the survival rate. However, accuracy of nodule detection from ultrasound images is suffered due to speckle noise. It considerably deteriorates the image quality and makes the differentiation of fine details quite difficult. Most of the detection systems for the thyroid nodules are semi-automated entailing manual intervention to draw rough outline of the nodule at some level or require manual segmentation in training or testing phases that increase the inaccuracies and evaluation time. To handle this, a fully Computer-Aided Detection system is presented for speckle reduction and segmentation of nodules from thyroid ultrasound images. The proposed system has three components: speckle reduction to reduce speckle noise and preserve the diagnostic features of ultrasound image, automatic generation of Region of interest (ROI) that identifies suspicious regions and fully automatic segmentation of nodule in processed ROI image. The proposed segmentation method outperformed other methods by gaining high True Positive (TP) value (95.92 ± 3.70%), False Positive (FP) value (7.04 ± 4.21%), Dice Coefficient (DC) value (93.88 ± 2.59%), Overlap Metric (OM) (91.18 ± 7.04 pixels) and Hausdroff Distance (HD) (0.52 ± 0.20 pixels). This system can facilitate the endocrinologists by providing second opinion to improve diagnosis of nodules as benign or malignant.

Size-based quality-informed framework for quantitative optimization of pediatric CT.

The purpose of this study was to formulate a systematic, evidence-based method to relate quantitative diagnostic performance to radiation dose, enabling a multidimensional system to optimize computed tomography imaging across pediatric populations. Based on two prior foundational studies, radiation dose was assessed in terms of organ doses, effective dose ([Formula: see text]), and risk index for 30 patients within nine color-coded pediatric age-size groups as a function of imaging parameters. The cases, supplemented with added noise and simulated lesions, were assessed in terms of nodule detection accuracy in an observer receiving operating characteristic study. The resulting continuous accuracy-dose relationships were used to optimize individual scan parameters. Before optimization, the nine protocols had a similar [Formula: see text] of [Formula: see text] with accuracy decreasing from 0.89 for the youngest patients to 0.67 for the oldest. After optimization, a consistent target accuracy of 0.83 was established for all patient categories with [Formula: see text] ranging from 1 to 10mSv. Alternatively, isogradient operating points targeted a consistent ratio of accuracy-per-unit-dose across the patient categories. The developed model can be used to optimize individual scan parameters and provide for consistent diagnostic performance across the broad range of body sizes in children.

An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy

Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists׳ diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules. A bidirectional chain coding method combined with a support vector machine (SVM) classifier is used to selectively smooth the lung border while minimizing the over-segmentation of adjacent regions. This automated method was tested on 233 computed tomography (CT) studies from the lung imaging database consortium (LIDC), representing 403 juxtapleural nodules. The approach obtained a 92.6% re-inclusion rate. Segmentation accuracy was further validated on 10 randomly selected CT series, finding a 0.3% average over-segmentation ratio and 2.4% under-segmentation rate when compared to manually segmented reference standards done by an expert.

Feasible Method to Assess the Performance of a Lung Cancer CT Screening CAD System in Clinical Practice: Dependence on Nodule Size and Density

Detection of small pulmonary nodules is the goal of lung cancer screening. Computer-aided detection (CAD) systems are recommended to use in lung cancer computed tomography (CT) screening to increase the accuracy of nodule detection. Size and density of lung nodules are primary factors in determining the risk of malignancy. Therefore, purpose of this study is to apply computer-simulated virtual nodules based on the point spread function (PSF) measured in same scanner (maintaining spatial resolution condition) to assess the CAD system performance dependence on nodule size and density. Virtual nodules with density differences between lung background and nodule density (ΔCT) values (200, 300 and 400 HU) and different sizes (4 to 8 mm) were generated and fused on clinical images. CAD detection was performed and free-response receiver operating characteristic (FROC) curves were obtained. Results show that both density and size of virtual nodules can affect detection efficiency. Detailed results are possible to use for quantitative analysis of a CAD system performance. This study suggests that PSF-based virtual nodules could be effectively used to assess the lung cancer CT screening CAD system performance dependence on nodule size and density.

Proposed Technique for Accurate Detection/Segmentation of Lung Nodules using Spline Wavelet Techniques

In this paper we are going to discuss and analyze the different methods which are developed to detect the Lung nodules which cause the lung cancer. At the end of analyzing different methods, the new methodology of detecting the lung nodules using Spline Wavelet technique has been proposed in this paper. Continuous modeling of data often required in medical imaging, Polynomial Splines are especially useful to consider image data as continuum rather than discrete array of pixels. The multi resolution property of Splines makes them prime candidates for constructing wavelet bases. Wavelet tool also let us to compress the original CT image to greater factor without any sacrifice in accuracy of nodule detection. Different Algorithms for segmentation/ detection of lung nodules from CT image is discussed in this paper.

Multiple diagnostic task performance in CT examination of the chest

Objectives In three experiments, we studied the detection of multiple abnormality types using the satisfaction of search (SOS) paradigm, the provision of a computer-aided detection (CAD) of pulmonary nodules and a focused nodule detection task. Methods 51 chest CT examinations (24 that demonstrated subtle pulmonary nodules and 27 that demonstrated no pulmonary nodules) were read by 15 radiology residents and fellows under two experimental conditions: (1) when there were no other abnormalities present except test abnormalities in the exams (non-SOS condition), and (2) when other abnormalities were present in the exams (SOS condition). Trials from the two conditions were intermixed. Readers were invited to return for two sessions: one in which the SOS condition was repeated with a simulated CAD; another in which only the non-SOS condition was presented. Detection accuracy was measured using receiver operating characteristic (ROC) analysis. Results An SOS effect (reduced detection accuracy for the test nodules in the presence of the diverse added abnormalities) was not found. Average accuracy was much higher when the CAD prompt was provided, without cost in the detection of the added abnormalities. Accuracy for detecting nodules appearing without intermixed SOS trials was also substantially improved. Conclusions CT interpretation was highly task dependent. Nodule detection was poor in the general search task. Therefore, CAD may offer a greater performance improvement than demonstrated in experiments assessing CAD using focused search. The absence of SOS may be due to limited nodule detection even without other abnormalities. Advances in knowledge CAD prompts of nodules increase the detection accuracy of nodules and decrease the time to detection-without impairing the detection accuracy-of non-nodule abnormalities.

Multiple diagnostic task performance in CT examination of the chest

In three experiments, we studied the detection of multiple abnormality types using the satisfaction of search (SOS) paradigm, the provision of a computer-aided detection (CAD) of pulmonary nodules and a focused nodule detection task. 51 chest CT examinations (24 that demonstrated subtle pulmonary nodules and 27 that demonstrated no pulmonary nodules) were read by 15 radiology residents and fellows under two experimental conditions: (1) when there were no other abnormalities present except test abnormalities in the exams (non-SOS condition), and (2) when other abnormalities were present in the exams (SOS condition). Trials from the two conditions were intermixed. Readers were invited to return for two sessions: one in which the SOS condition was repeated with a simulated CAD; another in which only the non-SOS condition was presented. Detection accuracy was measured using receiver operating characteristic (ROC) analysis. An SOS effect (reduced detection accuracy for the test nodules in the presence of the diverse added abnormalities) was not found. Average accuracy was much higher when the CAD prompt was provided, without cost in the detection of the added abnormalities. Accuracy for detecting nodules appearing without intermixed SOS trials was also substantially improved. CT interpretation was highly task dependent. Nodule detection was poor in the general search task. Therefore, CAD may offer a greater performance improvement than demonstrated in experiments assessing CAD using focused search. The absence of SOS may be due to limited nodule detection even without other abnormalities. Advances in knowledge CAD prompts of nodules increase the detection accuracy of nodules and decrease the time to detection-without impairing the detection accuracy-of non-nodule abnormalities.