Computer-aided Detection Tools Research Articles

Multi-class Breast Cancer Classification Using CNN Features Hybridization

Breast cancer has become the leading cause of cancer mortality among women worldwide. The timely diagnosis of such cancer is always in demand among researchers. This research pours light on improving the design of computer-aided detection (CAD) for earlier breast cancer classification. Meanwhile, the design of CAD tools using deep learning is becoming popular and robust in biomedical classification systems. However, deep learning gives inadequate performance when used for multilabel classification problems, especially if the dataset has an uneven distribution of output targets. And this problem is prevalent in publicly available breast cancer datasets. To overcome this, the paper integrates the learning and discrimination ability of multiple convolution neural networks such as VGG16, VGG19, ResNet50, and DenseNet121 architectures for breast cancer classification. Accordingly, the approach of fusion of hybrid deep features (FHDF) is proposed to capture more potential information and attain improved classification performance. This way, the research utilizes digital mammogram images for earlier breast tumor detection. The proposed approach is evaluated on three public breast cancer datasets: mammographic image analysis society (MIAS), curated breast imaging subset of digital database for screening mammography (CBIS-DDSM), and INbreast databases. The attained results are then compared with base convolutional neural networks (CNN) architectures and the late fusion approach. For MIAS, CBIS-DDSM, and INbreast datasets, the proposed FHDF approach provides maximum performance of 98.706%, 97.734%, and 98.834% of accuracy in classifying three classes of breast cancer severities.

Tweaking algorithms. Technopolitical issues associated with artificial intelligence based tuberculosis detection in global health.

Computer-aided detection algorithms based on artificial intelligence are increasingly being tested and used as a means for detecting tuberculosis in countries where the epidemic is still present. Computer-aided detection tools are often presented as a global solution that can be deployed in all the geographical areas concerned by tuberculosis, but at the same time, they need to be adjusted and calibrated according to local populations' characteristics. The aim of this article is to analyze the tensions between the standardization of computer-aided detection algorithms and their local adaptation and the political issues associated with these tensions. We undertook a qualitative analysis of practices associated with tuberculosis detection algorithms in different contexts, contrasting the perspectives of various stakeholders. Algorithms embed the promise of standardization through automation and the bypassing of variable human expertise such as that of radiologists, they are nonetheless objects of local practices that we have characterized as "tweaking." This work of tweaking reveals how the technology is situated but also the many concerns of the users and workers (insertion in care, control over infrastructure, and political ownership). This should be better considered to truly make computer-aided detection innovative tools for tuberculosis management in global health.

Early Detection of Pancreatic Cancer: Opportunities Provided by Cancer-induced Paraneoplastic Phenomena and Artificial Intelligence

Abstract Objective: Pancreatic ductal adenocarcinoma (PDAC) is the most lethal cancer, with a 5-year survival rate of only 11%. Surgery is the only potential cure for PDAC, but approximately 85% of patients present with unresectable tumors at diagnosis. The difficulty in early detection is attributed to the fact that early PDACs cause few or nonspecific symptoms and are frequently obscure or even invisible in imaging studies such as computed tomography (CT). This review aims to briefly summarize the status of screening/surveillance for PDAC and elaborate on the potential windows of opportunity for early detection through PDAC-induced paraneoplastic phenomena and artificial intelligence (AI)-augmented image analysis. Data Sources: Relevant studies and review articles were searched in PubMed. Study Selection: Studies and articles on human subjects were selected. Results: Surveillance for high-risk individuals with imaging-based tools (endoscopic ultrasound and magnetic resonance image) is now advocated, whereas screening for asymptomatic general populations is not warranted at present. Paraneoplastic syndromes, including pancreatic cancer-associated diabetes and cachexia, are prevalent in PDAC patients and may provide windows of opportunity for early detection. S100A9 and galectin-3 are novel PDAC-derived factors mediating pancreatic cancer-associated diabetes and have shown promise in facilitating the early detection of PDAC. Novel computer-aided detection tools based on AI technologies, including deep learning and radiomic analysis with machine learning, have achieved accurate detection and might supplement human interpretation to improve the sensitivity for early PDAC on CT images. Conclusion: Novel blood-based biomarkers and AI-augmented image analysis may be complementary and hold promise for the early detection of PDAC.

Artificial intelligence in lung cancer: current applications and perspectives.

Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.

Abstract PO-086: Detection of cancer lesions in histopathological lung images using a sparse PCA network

Abstract Purpose: Lung cancer has been the leading cause of cancer-related deaths worldwide. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMMs) have become integral in identifying and evaluating unique pathways that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer aided detection tools, for the accurate and efficient analysis of these histopathology images. Our work demonstrates a simple machine learning approach called sparse principal component analysis (PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Methods: Our method comprises four steps: 1) cascaded sparse PCA; 2) graph-based PCA hashing; 3) block-wise histograms; and 4) support vector machine (SVM) classification. In our proposed architecture, sparse PCA is employed to learn the filter banks of the multiple stages. This is followed by a graph-based PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this sparse PCA are then fed to an SVM classifier. We tested the proposed sparse PCA network on H&E slides obtained from an inducible KrasG12D lung cancer mouse model. Our dataset consists of N = 21 whole slide histopathology lung images with 9 non-tumor bearing control mice and 12 mice with visible lung tumors. Tumor lesions from 12 lung images with visible tumors were visually identified by three trained individuals, which served as ground truth. The size of each image in our dataset is 2048 × 2048 pixels. Each image was divided into non-overlapping image patches of size 20 × 20 pixels consisting of a total of 12,361 cancer lesion patches and 207,839 non-cancer patches. We used 50% of the data for training and 50% of the data for testing our proposed sparse PCA network. We evaluated our algorithm using conventional metrics that have been used for evaluation of classification algorithms, namely precision (P), recall (R), and coverage measure (F-score). Results: The automatic cancer lesion detection results were compared with manually annotated ground truth. The proposed method achieves a cancer lesion detection accuracy of 97.98% with P = 0.8624, R = 0.9062 and F-score = 0.8790. The proposed method was found to take on average 17 minutes to train and learn a good representation for accurate and efficient classification of cancerous lesions within the images. Conclusion: We demonstrated a simple machine learning methodology for detection of cancerous lesions within histopathological lung images. Experimental results show that the proposed method is able to classify the regions of interest both efficiently and accurately. Future work will focus on feature extraction of individual tumors and tumor location within lungs. Citation Format: Sundaresh Ram, Wenfei Tang, Alexander J. Bell, Cara Spencer, Alexander Buschhuas, Charles R. Hatt, Marina P. di Magliano, Stefanie Galban, Craig J. Galban. Detection of cancer lesions in histopathological lung images using a sparse PCA network [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-086.

A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers

Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions.

Prostate Cancer Detection using Deep Convolutional Neural Networks

Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNN architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools for cancer detection. In this work, we developed and implemented an automated CNN-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient. DWI images of 427 patients were used as the dataset, which contained 175 patients with PCa and 252 patients without PCa. To measure the performance of the proposed pipeline, a test set of 108 (out of 427) patients were set aside and not used in the training phase. The proposed pipeline achieved area under the receiver operating characteristic curve (AUC) of 0.87 (95{boldsymbol{ % }} Confidence Interval (CI): 0.84–0.90) and 0.84 (95{boldsymbol{ % }} CI: 0.76–0.91) at slice level and patient level, respectively.

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application—Part 2: From Clinical Implementation to Enterprise

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application—Part 1: From Methodology to Clinical Implementation

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancements in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration have ushered us into the era of radiomics, which has tremendous potential in clinical decision support as well as drug discovery. There are important issues to consider to incorporate radiomics as a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that to enterprise development (Part 2).

Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks.

Prostate cancer is a leading cause of cancer-related death among men. Multiparametric magnetic resonance imaging has become an essential part of the diagnostic evaluation of prostate cancer. The internationally accepted interpretation scheme (Pi-Rads v2) has different algorithms for scoring of the transition zone (TZ) and peripheral zone (PZ) of the prostate as tumors can appear different in these zones. Computer-aided detection tools have shown different performances in TZ and PZ and separating these zones for training and detection is essential. The TZ-PZ segmentation which requires the segmentation of prostate whole gland and TZ is typically done manually. We present a fully automatic algorithm for delineation of the prostate gland and TZ in diffusion-weighted imaging (DWI) via a stack of fully convolutional neural networks. The proposed algorithm first detects the slices that contain a portion of prostate gland within the three-dimensional DWI volume and then it segments the prostate gland and TZ automatically. The segmentation stage of the algorithm was applied to DWI images of 104 patients and median Dice similarity coefficients of 0.93 and 0.88 were achieved for the prostate gland and TZ, respectively. The detection of image slices with and without prostate gland had an average accuracy of 0.97.

Pulmonary Embolism: Computer-aided Detection at Multidetector Row Spiral Computed Tomography

We aimed to evaluate the feasibility and performance of a computer-aided detection (CAD) tool for automated detection of segmental and subsegmental pulmonary emboli. A CAD tool (ImageChecker CT, R2 Technology, Inc) for automated detection of pulmonary emboli was evaluated on multidetector-row CT studies of varying diagnostic quality in 23 patients (13 female, mean age 52 y) with pulmonary embolism (PE) and of 13 patients (all female, mean age 49 y) without PE. A collimation of 16 x 1 mm and a reconstructed section width of 1.25 mm had been used in each patient. The performance of the CAD tool for the detection of emboli in the segmental and subsegmental pulmonary arterial tree was assessed. Consensus reading of the same studies by 2 radiologists, with a third for adjudication, for the identification of segmental and subsegmental pulmonary emboli was used as the standard of reference. Consensus reading revealed 130 segmental pulmonary emboli and 107 subsegmental pulmonary emboli in the 23 patients with PE. All 23 patients with PE were correctly identified as having PE by the CAD system. In a vessel-by-vessel analysis, the sensitivity of the CAD algorithm was 92% (119/130) for the detection of segmental pulmonary emboli and 90% (92/107) for subsegmental pulmonary emboli. The overall specificity, positive predictive value (95% confidence interval) and negative predictive value (95% confidence interval) of the algorithm were 89.9%, 63.2% (57.9%-68.2%) and 97.7% (96.7%-98.4%), respectively. The average false positive rate of the CAD algorithm was 4.8 (range 1 to 9) false positive detection marks per case. CAD of segmental and subsegmental pulmonary emboli based on 1-mm multidetector-row CT studies is feasible. Application of CAD tools may improve the diagnostic accuracy and decrease the interpretation time of computed tomographic angiography for the detection of pulmonary emboli in the peripheral arterial tree and further enhance the acceptance of this test as the first line diagnostic modality for suspected PE.

An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials

Computer tomography (CT) imaging plays an important role in cancer detection and quantitative assessment in clinical trials. High-resolution imaging studies on large cohorts of patients generate vast data sets, which are infeasible to analyze through manual interpretation. In this article we describe a comprehensive architecture for computer-aided detection (CAD) and surveillance on lung nodules in CT images. Central to this architecture are the analytic components: an automated nodule detection system, nodule tracking capabilities and volume measurement, which are integrated within a data management system that includes mechanisms for receiving and archiving images, a database for storing quantitative nodule measurements and visualization, and reporting tools. We describe two studies to evaluate CAD technology within this architecture, and the potential application in large clinical trials. The first study involves performance assessment of an automated nodule detection system and its ability to increase radiologist sensitivity when used to provide a second opinion. The second study investigates nodule volume measurements on CT made using a semi-automated technique and shows that volumetric analysis yields significantly different tumor response classifications than a 2D diameter approach. These studies demonstrate the potential of automated CAD tools to assist in quantitative image analysis for clinical trials.

Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography

To determine the value of computer-aided detection (CAD) for breast cancer screening. Two sets of mammograms with known outcomes were used in two studies. Participants in both studies read the films with and without the benefit of a computer aid. In both studies, the order of reading sessions was randomised separately for each reader. The first set of 180 films, used in study 1, included 20 false-negative interval cancers and 40 screen-detected cancers. The second set of 120 films, used in study 2, was designed to be favourable to CAD: all 44 cancer cases had previously been missed by a film reader and cancers prompted by CAD were preferentially included. The studies were conducted at five UK screening centres between January 2001 and April 2003. Thirty radiologists, five breast clinicians and 15 radiographers participated. All cases in the trial were digitised and analysed using the R2 ImageChecker version 2.2. Participants all received training on the use of CAD. In the intervention condition, participants interpreted cases with a prompt sheet on which regions of potential abnormality were indicated. The sensitivity and specificity of participants were measured in both intervention and control conditions. No significant difference was found for readers' sensitivity or specificity between the prompted and unprompted conditions in study 1 [95% confidence index (CI) for sensitivity with and without CAD is 0.76 to 0.80, for specificity it is 0.81 to 0.86 without CAD and 0.81 to 0.87 with CAD]. No statistically significant difference was found between the sensitivity and specificity of different groups of film reader (95% CI for unprompted sensitivity of radiologists was 0.75 to 0.81, for radiographers it was 0.71 to 0.81, prompted sensitivity was 0.76 to 0.81 for radiologists and 0.69 to 0.79 for radiographers). Thirty-five readers participated in study 2. Sensitivity was improved in the prompted condition (0.81 from 0.78) but the difference was slightly below the threshold for statistical significance (95% CI for the difference -0.003 to 0.064). Specificity also improved (0.87 from 0.86); again, the difference was not significant at 0.05 (95% CI -0.003 to 0.034). A cost-effectiveness analysis showed that computer prompting increases cost. No significant improvement in film readers' sensitivity or specificity or gain in cost-effectiveness was established in either study. This may be due to the system's low specificity, its relatively poor sensitivity for subtle cancers or the fact the prompts cannot serve as aids to decision-making. Readers may have been better able to make use of the prompts after becoming more accustomed to working with them. Prompts may have an impact in routine use that is not detectable in an experimental setting. Although the case for CAD as an element of the NHS Breast Screening Programme is not made here, further research is required. Evaluations of new CAD tools in routine use are underway and their results should be given careful attention.

Computerassistierter Nachweis und automatisierte Volumetrie pulmonaler Rundherde in der Multislice-CT: Aktueller Stand und Perspektiven

For computer-aided detection (CAD) and volumetry of small pulmonary nodules, a number of algorithms have been developed for multislice CT data sets in recent years, with the goal of improving the diagnostic work-up and the follow-up of findings. Recent data show that the detection of small lesions may improve with CAD, suggesting that especially experienced readers may benefit from using CAD systems. This has lead to the recommendation of CAD as a replacement of the second reader in clinical practice. Furthermore, computer-aided volumetry of pulmonary nodules allows a precise determination of nodular growth rates as a prerequisite for a better classification of nodules as benign or malignant. In this article, we review recent developments of CAD and volumetry tools for pulmonary nodules, and address open questions regarding the use of these software tools in clinical routine.

Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography

To investigate the effects of incorrect computer output on the reliability of the decisions of human users. This work followed an independent UK clinical trial that evaluated the impact of computer-aided detection(CAD) in breast screening. The aim was to use data from this trial to feed into probabilistic models (similar to those used in "reliability engineering") which would detect and assess possible ways of improving the human-CAD interaction. Some analyses required extra data; therefore, two supplementary studies were conducted. Study 1 was designed to elucidate the effects of computer failure on human performance. Study 2 was conducted to clarify unexpected findings from Study 1. In Study 1, 20 film readers viewed 60 sets of mammograms (30 of which contained cancer) and provided "recall/no recall" decisions for each case. Computer output for each case was available to the participants. The test set was designed to contain an unusually large proportion (50%) of cancers for which CAD had generated incorrect output. In Study 2, 19 different readers viewed the same set of cases in similar conditions except that computer output was not available. The average sensitivity of readers in Study 1 (with CAD) was significantly lower than the average sensitivity of read-ers in Study 2 (without CAD). The difference was most marked for cancers for which CAD failed to provide correct prompting. Possible automation bias effects in CAD use deserve further study because they may degrade human decision-making for some categories of cases under certain conditions. This possibility should be taken into account in the assessment and design of CAD tools.