Computer-aided diagnosis: how to move from the laboratory to the clinic

Quantitative functional MRI biomarkers improved early detection of colorectal liver metastases

PURPOSE

To implement and evaluate the performance of a computerized statistical tool designed for robust and quantitative analysis of hemodynamic response imaging (HRI) -derived maps for the early identification of colorectal liver metastases (CRLM).

MATERIALS AND METHODS

CRLM-bearing mice were scanned during the early stage of tumor growth and subsequently during the advanced-stage. Three experienced radiologists marked various suspected-foci on the early stage anatomical images and classified each as either highly certain or as suspected tumors. The statistical model construction was based on HRI maps (functional-MRI combined with hypercapnia and hyperoxia) using a supervised learning paradigm which was further trained either with the advanced-stage sets (late training; LT) or with the early stage sets (early training; ET). For each group of foci, the classifier results were compared with the ground-truth.

RESULTS

The ET-based classification significantly improved the manual classification of the highly certain foci (P < 0.05) and was superior compared with the LT-based classification (P < 0.05). Additionally, the ET-based classification, offered high sensitivity (57-63%), accompanied with high positive predictive value (>94%) and high specificity (>98%) for suspected-foci.

CONCLUSION

The ET-based classifier can strengthen the radiologist's classification of highly certain foci. Additionally, it can aid in classifying suspected-foci, thus enabling earlier intervention which can often be lifesaving.

Differentiation of benign pigmented skin lesions with the aid of computer image analysis: a novel approach

BACKGROUND

The differential diagnosis of common pigmented skin lesions is important in cosmetic dermatology. The computer aided image analysis would be a potent ancillary diagnostic tool when patients are hesitant to undergo a skin biopsy.

OBJECTIVE

We investigated the numerical parameters discriminating each pigmented skin lesion from another with statistical significance.

METHODS

For each of the five magnified digital images containing clinically diagnosed nevus, lentigo and seborrheic keratosis, a total of 23 parameters describing the morphological, color, texture and topological features were calculated with the aid of a self-developed image analysis software. A novel concept of concentricity was proposed, which represents how closely the color segmentation resembles a concentric circle.

RESULTS

Morphologically, seborrheic keratosis was bigger and spikier than nevus and lentigo. The color histogram revealed that nevus was the darkest and had the widest variation in tone. In the aspect of texture, the surface of the nevus showed the highest contrast and correlation. Finally, the color segmented pattern of the nevus and lentigo was far more concentric than that of seborrheic keratosis.

CONCLUSION

We found that the subtle distinctions between nevus, lentigo and seborrheic keratosis, which are likely to be unrecognized by ocular inspection, are well emphasized and detected with the aid of software.

Three-dimensional morphological and signal intensity features for detection of intervertebral disc degeneration from magnetic resonance images

BACKGROUND AND OBJECTIVES

Advances in MRI hardware and sequences are continually increasing the amount and complexity of data such as those generated in high-resolution three-dimensional (3D) scanning of the spine. Efficient informatics tools offer considerable opportunities for research and clinically based analyses of magnetic resonance studies. In this work, we present and validate a suite of informatics tools for automated detection of degenerative changes in lumbar intervertebral discs (IVD) from both 3D isotropic and routine two-dimensional (2D) clinical T2-weighted MRI.

MATERIALS AND METHODS

An automated segmentation approach was used to extract morphological (traditional 2D radiological measures and novel 3D shape descriptors) and signal appearance (extracted from signal intensity histograms) features. The features were validated against manual reference, compared between 2D and 3D MRI scans and used for quantification and classification of IVD degeneration across magnetic resonance datasets containing IVD with early and advanced stages of degeneration.

RESULTS AND CONCLUSIONS

Combination of the novel 3D-based shape and signal intensity features on 3D (area under receiver operating curve (AUC) 0.984) and 2D (AUC 0.988) magnetic resonance data deliver a significant improvement in automated classification of IVD degeneration, compared to the combination of previously used 2D radiological measurement and signal intensity features (AUC 0.976 and 0.983, respectively). Further work is required regarding the usefulness of 2D and 3D shape data in relation to clinical scores of lower back pain. The results reveal the potential of the proposed informatics system for computer-aided IVD diagnosis from MRI in large-scale research studies and as a possible adjunct for clinical diagnosis.

Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams

The objective of this study is to assess the impact on nodule detection and efficiency using a computer-aided detection (CAD) device seamlessly integrated into a commercially available picture archiving and communication system (PACS). Forty-eight consecutive low-dose thoracic computed tomography studies were retrospectively included from an ongoing multi-institutional screening study. CAD results were sent to PACS as a separate image series for each study. Five fellowship-trained thoracic radiologists interpreted each case first on contiguous 5 mm sections, then evaluated the CAD output series (with CAD marks on corresponding axial sections). The standard of reference was based on three-reader agreement with expert adjudication. The time to interpret CAD marking was automatically recorded. A total of 134 true-positive nodules, measuring 3 mm and larger were included in our study; with 85 ≥ 4 and 50 ≥ 5 mm in size. Readers detection improved significantly in each size category when using CAD, respectively, from 44 to 57 % for ≥3 mm, 48 to 61 % for ≥4 mm, and 44 to 60 % for ≥5 mm. CAD stand-alone sensitivity was 65, 68, and 66 % for nodules ≥3, ≥4, and ≥5 mm, respectively, with CAD significantly increasing the false positives for two readers only. The average time to interpret and annotate a CAD mark was 15.1 s, after localizing it in the original image series. The integration of CAD into PACS increases reader sensitivity with minimal impact on interpretation time and supports such implementation into daily clinical practice.

Advances and perspectives in lung cancer imaging using multidetector row computed tomography

The introduction of multidetector row computed tomography (CT) into clinical practice has revolutionized many aspects of the clinical work-up. Lung cancer imaging has benefited from various breakthroughs in computing technology, with advances in the field of lung cancer detection, tissue characterization, lung cancer staging and response to therapy. Our paper discusses the problems of radiation, image visualization and CT examination comparison. It also reviews the most significant advances in lung cancer imaging and highlights the emerging clinical applications that use state of the art CT technology in the field of lung cancer diagnosis and follow-up.

Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET

Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy (TLE). When diagnosing left TLE (LTLE) or right TLE (RTLE) vs. non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% [95% confidence interval (CI) 69-90%] or 88% (95% CI 76-94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66-84%), where 89% (95% CI 77-96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement - not replace - expert analysis. In our dataset, the accuracy of manual analysis (MA) of iPET (∼80%) was similar to CAD. The square correlation between our CAD tool and MA, however, was only 30%, indicating that our CAD tool does not recreate MA. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.

Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography

Objective

To evaluate a computer-aided detection (CADe) system for lytic and blastic spinal metastases on computed tomography (CT).

Methods

We retrospectively evaluated the CADe system on 20 consecutive patients with 42 lytic and on 30 consecutive patients with 172 blastic metastases. The CADe system was trained using CT images of 114 subjects with 102 lytic and 308 blastic spinal metastases. Lesions were annotated by experienced radiologists. Detected benign lesions were considered false-positive findings. Detector sensitivity and the number of false-positive findings were calculated as the criteria for detector performance, and free-response receiver operating characteristic (FROC) analysis was conducted. Detailed analysis of false-positive and false-negative findings was performed.

Results

Algorithm runtime is 3 ± 0.5 min per patient. The system achieves a sensitivity of 83 % at 3.5 false positives per patient on average for blastic metastases and a sensitivity of 88 % at 3.7 false positives for lytic metastases. False positives appeared predominantly in the area of degenerative changes in the case of the blastic metastasis detector and in osteoporotic areas in the case of the lytic metastasis detector.

Conclusion

The CADe system reliably detects thoracolumbar spine metastases in real time. An additional study is planned to evaluate how the bone lesion CADe system improves radiologists’ accuracy and efficiency in a clinical setting.

Key Points

• Computer-aided detection (CADe) of bone metastases has been developed for spinal CT.

• The CADe system exhibits high sensitivity with a tolerable false-positive rate.

• Analysis of false-positive detection may further improve the system.

• CADe may reduce the number of missed spinal metastases at CT interpretation.