Lung nodule detection and characterization with multislice CT

Non-invasive Diagnostic Strategy in ACTH-dependent Cushing's Syndrome

CONTEXT

Inferior petrosal sinus sampling (IPSS) is used to diagnose Cushing's disease (CD) when dexamethasone-suppression and CRH tests, and pituitary magnetic resonance imaging (MRI), are negative or give discordant results. However, IPSS is an invasive procedure and its availability is limited.

OBJECTIVE

To test a noninvasive diagnostic strategy associated with 100% positive predictive value (PPV) for CD.

DESIGN

Retrospective study.

SETTING

Two university hospitals.

PATIENTS

A total of 167 patients with CD and 27 patients with ectopic ACTH-syndrome investigated between 2001 and 2016.

MAIN OUTCOME MEASURE(S)

Performance of a strategy involving the CRH and desmopressin tests with pituitary MRI followed by thin-slice whole-body computed tomography (CT) scan in patients with inconclusive results.

RESULTS

Using thresholds of a cortisol increase > 17% with an ACTH increase > 37% during the CRH test and a cortisol increase > 18% with an ACTH increase > 33% during the desmopressin test, the combination of both tests gave 73% sensitivity and 98% PPV of CD. The sensitivity and PPV for pituitary MRI were 71% and 99%, respectively. CT scan identified 67% EAS at presentation with no false-positives. The PPV for CD was 100% in patients with positive responses to both tests, with negative pituitary MRI and CT scan. The Negative Predictive Value was 100% in patients with negative responses to both tests, with negative pituitary MRI and positive CT scan. Using this strategy, IPPS could have been avoided in 47% of patients in whom it is currently recommended.

CONCLUSIONS

In conjunction with expert radiologic interpretation, the non-invasive algorithm studied significantly reduces the need for IPSS in the investigation of ACTH-dependent Cushing's syndrome.

[Growth Evaluation of Pulmonary Nodules on Chest CT]

对肺结节行计算机断层扫描（computed tomography, CT）随访并确定结节生长特性是临床针对不定性肺结节常采用的策略。依据肿瘤生长指数模型，常采用体积或质量倍增时间量化结节的生长速率。本文拟对肺癌的指数生长模型、肺结节生长量化评价的方法学、不同类型肺结节的生长特性进行综述。

[Localized pure or mixed ground-glass lung opacities]

Localized ground-glass opacities (GGOs) have been recently individualized and account for between 2.9% and 19% of all pulmonary nodules detected in high-risk patients included in CT screening series for lung cancer. These opacities, nodular, lobular or flat, correspond to benign lesions (localised infectious and inflammatory diseases, focal interstitial fibrosis, and atypical alveolar hyperplasia) or malignant lesions (bronchioloalveolar carcinoma, early-stage adenocarcinoma and sometimes metastases). Localized GGOs are more likely to be malignant than solid nodules and prognosis is related to the percentage of the ground-glass component. However, doubling time of pure localized malignant GGOs is longer than mixed localized malignant GGOs and even longer than the doubling time of solid malignant nodules. Therefore, localized GGOs warrant a dedicated diagnostic workup.

Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size

RATIONALE AND OBJECTIVES

To retrospectively investigate the effect of a computer-aided detection (CAD) system on radiologists' performance for detecting small pulmonary nodules in computed tomography (CT) examinations, with a panel of expert radiologists serving as the reference standard.

MATERIALS AND METHODS

Institutional review board approval was obtained. Our dataset contained 52 CT examinations collected by the Lung Image Database Consortium, and 33 from our institution. All CTs were read by multiple expert thoracic radiologists to identify the reference standard for detection. Six other thoracic radiologists read the CT examinations first without and then with CAD. Performance was evaluated using free-response receiver operating characteristics (FROC) and the jackknife FROC analysis methods (JAFROC) for nodules above different diameter thresholds.

RESULTS

A total of 241 nodules, ranging in size from 3.0 to 18.6 mm (mean, 5.3 mm) were identified as the reference standard. At diameter thresholds of 3, 4, 5, and 6 mm, the CAD system had a sensitivity of 54%, 64%, 68%, and 76%, respectively, with an average of 5.6 false positives (FPs) per scan. Without CAD, the average figures of merit (FOMs) for the six radiologists, obtained from JAFROC analysis, were 0.661, 0.729, 0.793, and 0.838 for the same nodule diameter thresholds, respectively. With CAD, the corresponding average FOMs improved to 0.705, 0.763, 0.810, and 0.862, respectively. The improvement achieved statistical significance for nodules at the 3 and 4 mm thresholds (P = .002 and .020, respectively), and did not achieve significance at 5 and 6 mm (P = .18 and .13, respectively). At a nodule diameter threshold of 3 mm, the radiologists' average sensitivity and FP rate were 0.56 and 0.67, respectively, without CAD, and 0.67 and 0.78 with CAD.

CONCLUSION

CAD improves thoracic radiologists' performance for detecting pulmonary nodules smaller than 5 mm on CT examinations, which are often overlooked by visual inspection alone.

An automated CT based lung nodule detection scheme using geometric analysis of signed distance field

The authors present a new computerized scheme to automatically detect lung nodules depicted on computed tomography (CT) images. The procedure is performed in the signed distance field of the CT images. To obtain an accurate signed distance field, CT images are first interpolated linearly along the axial direction to form an isotropic data set. Then a lung segmentation strategy is applied to smooth the lung border aiming to include as many juxtapleural nodules as possible while minimizing over segmentations of the lung regions. Potential nodule regions are then detected by locating local maximas of signed distances in each subvolume with values and the sizes larger than the smallest nodule of interest in the three-dimensional space. Finally, all detected candidates are scored by computing the similarity distance of their medial axis-like shapes obtained through a progressive clustering strategy combined with a marching cube algorithm from a sphere based shape. A free-response receiver operating characteristics curve is computed to assess the scheme performance. A performance test on 52 low-dose CT screening examinations that depict 184 verified lung nodules showed that during the initial stage the scheme achieved an asymptotic maximum sensitivity of 95.1% (175/184) with an average of 1200 suspicious voxels per CT examination. The nine missed nodules included two small solid nodules (with a diameter < or =3.1 mm) and seven nonsolid nodules. The final performance level after the similarity scoring stage was an absolute sensitivity level, namely, including the nine missed during the initial stage, of 81.5% (150/184) with 6.5 false-positive identifications per CT examination. This preliminary study demonstrates the feasibility of applying a simple and robust geometric model using the signed distance field to identify suspicious lung nodules. In the authors' data set the sensitivity of this scheme is not affected by nodule size. In addition to potentially being a stand alone approach, the signed distance field based method can be easily implemented as an initial filtering step in other computer-aided detection schemes.

Multi slice computed tomography in the study of pulmonary metastases

PURPOSE

This study was undertaken to assess the performance of 16-slice computed tomography (MSCT) using Multi-Planar Reformatting (MPR), Maximum Intensity Projection (MIP) and Volume Rendering (VR) reconstructions to study pulmonary metastases.

MATERIALS AND METHODS

CT studies of 32 patients with pulmonary metastases were retrospectively reviewed. Images were assessed for the following parameters: number, size, location, distribution of the nodules and the presence of the "mass-vessel sign". These parameters were evaluated by two observers on axial-source images and on MPR, MIP and VR reconstructions. Sensitivity of each reconstruction and interobserver agreement were calculated.

RESULTS

Two-dimensional (2D) axial images and MIP and VR reconstructions exhibited 100% sensitivity for lesions >10 mm. For nodules 6-10 mm, sensitivity was 49%-55% for the 2D images, 90% for MIP and 80%-85% for VR reconstructions. For metastasis <or= 5 mm, sensitivity was 22% for 2D images, 87%-89% for MIP and 55%-58% for VR reconstructions. Coronal and sagittal MPR, MIP and VR did not improve the detection rate compared with the corresponding axial images. MIP and VR provided overlapping results in detecting the "mass-vessel sign".

CONCLUSIONS

MIP are the most sensitive reconstructions for detecting small pulmonary nodules.

Adaptive border marching algorithm: automatic lung segmentation on chest CT images

Segmentation of the lungs in chest-computed tomography (CT) is often performed as a preprocessing step in lung imaging. This task is complicated especially in presence of disease. This paper presents a lung segmentation algorithm called adaptive border marching (ABM). Its novelty lies in the fact that it smoothes the lung border in a geometric way and can be used to reliably include juxtapleural nodules while minimizing oversegmentation of adjacent regions such as the abdomen and mediastinum. Our experiments using 20 datasets demonstrate that this computational geometry algorithm can re-include all juxtapleural nodules and achieve an average oversegmentation ratio of 0.43% and an average under-segmentation ratio of 1.63% relative to an expert determined reference standard. The segmentation time of a typical case is under 1min on a typical PC. As compared to other available methods, ABM is more robust, more efficient and more straightforward to implement, and once the chest CT images are input, there is no further interaction needed from users. The clinical impact of this method is in potentially avoiding false negative CAD findings due to juxtapleural nodules and improving volumetry and doubling time accuracy.

Precision of computer-aided volumetry of artificial small solid pulmonary nodules inex vivoporcine lungs

The purpose of this study was to investigate the precision of CT-based volumetric measurements of artificial small pulmonary nodules under ex vivo conditions. We implanted 322 artificial nodules in 23 inflated ex vivo porcine lungs in a dedicated chest phantom. The lungs were examined with a multislice spiral CT (20 mAs, collimation 16x0.75 mm, 1 mm slice thickness, 0.7 mm increment). A commercial volumetry software package (LungCARE VA70C-W; Siemens, Erlangen, Germany) was used for volume analysis in a semi-automatic and a manual corrected mode. After imaging, the lungs were dissected to harvest the nodules for gold standard determination. The volumes of 202 solitary, solid and well-defined lesions without contact with the pleura, greater bronchi or vessels were compared with the results of volumetry. A mean nodule diameter of 8.3 mm (+/-2.1 mm) was achieved. The mean relative deviation from the true lesion volume was -9.2% (+/-10.6%) for semi-automatic and -0.3% (+/-6.5%) for manual corrected volumetry. The subgroup of lesions from 5 mm to <10 mm in diameter showed a mean relative deviation of -8.7% (+/-10.9%) for semi-automatic volumetry and -0.3% (+/-6.9%) for manually corrected volumetry. We conclude that the presented software allowed for precise volumetry of artificial nodules in ex vivo lung tissue. This result is comparable to the findings of previous in vitro studies.

Evaluation of an automated system for temporal subtraction of thin-section thoracic CT

The purpose of this study was to objectively evaluate the registration accuracy of an automated temporal subtraction system of thin-section thoracic CT. The automated subtraction system was applied to data from 20 subjects with lung diseases. The registration accuracy was evaluated based on the concept of target registration error using 19 landmarks chosen at the bifurcations of segmental bronchi. The landmarks were identified, and the displacements of the landmarks were measured. The average displacements of the landmarks in x, y and z directions were 0.56 pixels+/-0.64 (SD), 0.56 pixels+/-0.62 (SD, standard deviation) and 0.28 sections+/-0.40 (SD), respectively. Differences in average displacements between the landmarks were not statistically significant. Our computer system demonstrated promising results. The average displacements of the landmarks were less than the dimensions of a voxel. Further study is necessary to determine whether such a computer system can improve the diagnostic performance of radiologists.

MRI of the lung: Value of different turbo spin-echo, single-shot turbo spin-echo, and 3D gradient-echo pulse sequences for the detection of pulmonary metastases

PURPOSE

To compare the value of different MRI sequences of the lung for the detection of pulmonary metastases.

MATERIALS AND METHODS

A total of 28 patients with 225 pulmonary metastases confirmed at multidetector-row computed tomography (MDCT) underwent MRI of the lung, including breathhold T2-weighted single-shot turbo spin-echo (half-Fourier single-shot turbo spin-echo [HASTE] and inversion recovery [IR]-HASTE) and conventional turbo spin-echo (TSE and short-tau inversion recovery [STIR]) sequences, a respiratory- and pulse-triggered black-blood STIR sequence (triggered STIR), and breathhold pre- and postcontrast volumetric interpolated 3D gradient-echo (VIBE) sequences. MR images were reviewed by three independent observers and results were correlated with MDCT, which served as standard of reference. Lesion-to-lung contrast-to-noise ratios (CNRs) and image artifacts were also assessed.

RESULTS

CNRs were highest on TSE images (P < 0.001). Mean sensitivities for lesion detection with triggered STIR, TSE, and STIR were 72.0%, 69.0%, and 63.4%, respectively. With HASTE, IR-HASTE, and pre- and postcontrast VIBE, significantly lower sensitivities were obtained (P < 0.05), although artifacts due to physiological motion were less distinct with these sequences compared to TSE and STIR (P < 0.05).

CONCLUSION

Conventional TSE sequences are more sensitive in depicting pulmonary metastases than single-shot TSE or 3D gradient-echo sequences. Respiratory and pulse triggering can improve lesion detection, but increases acquisition time substantially.

Computer analysis of computed tomography scans of the lung: a survey

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

Reproducibility of computer-aided volumetry of artificial small pulmonary nodules in ex vivo porcine lungs

OBJECTIVE

The main purpose of this study was to investigate the reproducibility of computed tomography (CT)-based volumetric measurements of small pulmonary nodules.

METHODS

We implanted 70 artificial pulmonary nodules in 5 ex vivo porcine lungs in a dedicated chest phantom. The lungs were scanned 5 times consecutively with multislice-CT (collimation 16 x 0.75 mm, slice thickness 1 mm, reconstruction increment 0.7 mm). A commercial software package was used for lesion volumetry. The authors differentiated between intrascan reproducibility, interscan reproducibility, and results from semiautomatic and postprocessed volumetry.

RESULTS

Analysis of intrascan reproducibility revealed a mean variation coefficient of 6.2% for semiautomatic volumetry and of 0.7% for human adapted volumetry. For interscan reproducibility a mean variation coefficient of 9.2% and for human adapted volumetry a mean of 3.7% was detected.

CONCLUSION

The presented volumetry software showed a high reproducibility that can be expected to detect nodule growth with a high degree of certainty.

Temporal Subtraction of Thin-Section Thoracic Computed Tomography Based on a 3-Dimensional Nonlinear Geometric Warping Technique

The authors developed a computer system that automatically produces temporal subtraction images on thin-section thoracic computed tomography based on a 3-dimensional nonlinear geometric warping technique. The automated subtraction system was applied to data from 20 subjects with lung diseases. Nineteen (95%) of 20 results showed good or excellent image quality. The remaining 1 (5%) showed adequate image quality. Our preliminary computer system demonstrates the potential for a clinically useful automated subtraction system.

Thin-section multiplanar reformats from multidetector-row CT data: Utility for assessment of regional tumor extent in non-small cell lung cancer

PURPOSE

To determine the clinical utility of thin-section multiplanar reformats (MPRs) from multidetector-row CT (MDCT) data sets for assessing the extent of regional tumors in non-small cell lung cancer (NSCLC) patients.

MATERIALS AND METHODS

Sixty consecutive NSCLC patients, who were considered candidates for surgical treatment, underwent contrast-enhanced MDCT examinations, surgical resection and pathological examinations. All MDCT examinations were performed with a 4-detector row computed tomography (CT). From each raw CT data set, 5mm section thickness CT images (routine CT), 1.25 mm section thickness CT images (thin-section CT) and 1.25 mm section thickness sagittal (thin-section sagittal MPR) and coronal images (thin-section coronal MPR) were reconstructed. A 4-point visual score was used to assess mediastinal, interlobar and chest wall invasions on each image set. For assessment of utility in routine clinical practice, mean reading times for each image set were compared by means of Fisher's protected least significant difference (PLSD) test. A receiver operator characteristic (ROC) analysis was performed to determine the diagnostic capability of each of the image data sets. Finally, sensitivity, specificity and accuracy of the reconstructed images were compared by McNemar test.

RESULTS

Mean reading times for thin-section sagittal and coronal MPRs were significantly shorter than those for routine CT and thin-section CT (p<0.05). Areas under the curve (Azs) showing interlobar invasion on thin-section sagittal and coronal MPRs were significantly larger than that on routine CT (p=0.03), and the Az on thin-section sagittal MPR was also significantly larger than that on routine CT (p=0.02). Accuracy of chest wall invasion by thin-section sagittal MPR was significantly higher than that by routine CT (p=0.04).

CONCLUSION

Thin-section multiplanar reformats from multidetector-row CT data sets are useful for assessing the extent of regional tumors in non-small cell lung cancer patients.

Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT

This paper proposes a robust statistical estimation and verification framework for characterizing the ellipsoidal (anisotropic) geometrical structure of pulmonary nodules in the Multislice X-ray computed tomography (CT) images. Given a marker indicating a rough location of a target, the proposed solution estimates the target's center location, ellipsoidal boundary approximation, volume, maximum/average diameters, and isotropy by robustly and efficiently fitting an anisotropic Gaussian intensity model. We propose a novel multiscale joint segmentation and model fitting solution which extends the robust mean shift-based analysis to the linear scale-space theory. The design is motivated for enhancing the robustness against margin-truncation induced by neighboring structures, data with large deviations from the chosen model, and marker location variability. A chi-square-based statistical verification and analytical volumetric measurement solutions are also proposed to complement this estimation framework. Experiments with synthetic one-dimensional and two-dimensional data clearly demonstrate the advantage of our solution in comparison with the gamma-normalized Laplacian approach (Linderberg, 1998) and the standard sample estimation approach (Matei, 2001). A quasi-real-time three-dimensional nodule characterization system is developed using this framework and validated with two clinical data sets of thin-section chest CT images. Our experiments with 1310 nodules resulted in (1) robustness against intraoperator and interoperator variability due to varying marker locations, (2) 81% correct estimation rate, (3) 3% false acceptance and 5% false rejection rates, and (4) correct characterization of clinically significant nonsolid ground-glass opacity nodules. This system processes each 33-voxel volume-of-interest by an average of 2 s with a 2.4-GHz Intel CPU. Our solution is generic and can be applied for the analysis of blob-like structures in various other applications.

Sixteen-Row multislice computed tomography: basic concepts, protocols, and enhanced clinical applications

Since its introduction, spiral computed tomography (CT) technology underwent a continuous and fast technical and clinical development. In particular, spatial and temporal resolutions were constantly increased during the last decade. The main breakthrough for clinical application was the introduction of multislice technology, first with 2-row and 4-row equipment and more recently with 16-row scanners. A high-resolution sub-millimeter CT dataset can be acquired easily, although with an increased x-ray exposure for the patient. The high speed of the scan requires up-to-date and careful protocol optimization. Scanner technology and geometry affect image formation procedure and imaging protocols should be adapted accordingly. The technical foundations of spiral CT imaging and the main scan and reconstruction parameters are described in this article. Updated protocols and clinical examples of the latest applications are also discussed.