Lung nodule volumetry is used for nodule diagnosis, as well as for monitoring tumor response to therapy. Volume measurement precision and accuracy depend on a number of factors, including image-acquisition and reconstruction parameters, nodule characteristics, and the performance of algorithms for nodule segmentation and volume estimation. The purpose of this article is to provide a review of published studies relevant to the computed tomographic (CT) volumetric analysis of lung nodules. A number of underexamined areas of research regarding volumetric accuracy are identified, including the measurement of nonsolid nodules, the effects of pitch and section overlap, and the effect of respiratory motion. The need for public databases of phantom scans, as well as of clinical data, is discussed. The review points to the need for continued research to examine volumetric accuracy as a function of a multitude of interrelated variables involved in the assessment of lung nodules. Understanding and quantifying the sources of volumetric measurement error in the assessment of lung nodules with CT would be a first step toward the development of methods to minimize that error through system improvements and to correctly account for any remaining error.

The expression of the HER-2/neu (HER2) gene, a member of the epidermal growth factor receptor family, has been shown to be a valuable prognostic indicator for breast cancer. However, interobserver variability has been reported in the evaluation of HER2 with immunohistochemistry. It has been suggested that automated computer-based evaluation can provide a consistent and objective evaluation of HER2 expression. In this manuscript, we present an automated method for the quantitative assessment of HER2 using digital microscopy. The method processes microscopy images from tissue slides with a multistage algorithm, including steps of color pixel classification, nuclei segmentation, and cell membrane modeling, and extracts quantitative, continuous measures of cell membrane staining intensity and completeness. A minimum cluster distance classifier merges the features to classify the slides into HER2 categories. An evaluation based on agreement analysis with pathologist-derived HER2 scores, showed good agreement with the provided truth. Agreement varied within the different classes with highest agreement (up to 90%) for positive (3+) slides, and lowest agreement (72%-78%) for equivocal (2+) slides which contained ambiguous scoring. The developed automated method has the potential to be used as a computer aid for the immunohistochemical evaluation of HER2 expression with the objective of increasing observer reproducibility.

The evaluation of fluorescent in situ hybridization (FISH) images is one of the most widely used methods to determine Her-2/neu status of breast samples, a valuable prognostic indicator. Conventional evaluation is a difficult task since it involves manual counting of dots in multiple images. In this paper, we present a multistage algorithm for the automated classification of FISH images from breast carcinomas. The algorithm focuses not only on the detection of FISH dots per image, but also on combining results from multiple images taken from a slice for overall case classification. The algorithm includes mainly two stages for nuclei and dot detection respectively. The dot segmentation consists of a top-hat filtering stage followed by template matching to separate real signals from noise. Nuclei segmentation includes a nonlinearity correction step, global thresholding to identify candidate regions, and a geometric rule to distinguish between holes within a nucleus and holes between nuclei. Finally, the marked watershed transform is used to segment cell nuclei with markers detected as regional maxima of the distance transform. Combining the two stages allows the measurement of FISH signals ratio per cell nucleus and the collective classification of cases as positive or negative. The system was evaluated with receiver operating characteristic analysis and the results were encouraging for the further development of this method.

A number of interrelated factors can affect the precision and accuracy of lung nodule size estimation. To quantify the effect of these factors, we have been conducting phantom CT studies using an anthropomorphic thoracic phantom containing a vasculature insert to which synthetic nodules were inserted or attached. Ten repeat scans were acquired on different multi-detector scanners, using several sets of acquisition and reconstruction protocols and various nodule characteristics (size, shape, density, location). This study design enables both bias and variance analysis for the nodule size estimation task. The resulting database is in the process of becoming publicly available as a resource to facilitate the assessment of lung nodule size estimation methodologies and to enable comparisons between different methods regarding measurement error. This resource complements public databases of clinical data and will contribute towards the development of procedures that will maximize the utility of CT imaging for lung cancer screening and tumor therapy evaluation.

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This work is a part of our more general effort to probe the interrelated factors impacting the accuracy and precision of lung nodule measurement tasks. For such a task a low-bias size estimator is needed so that the true effect of factors such as acquisition and reconstruction parameters, nodule characteristics and others can be assessed. Towards this goal, we have developed a matched filter based on an adaptive model of the object acquisition and reconstruction process. Our model derives simulated reconstructed data of nodule objects (templates) which are then matched to computed tomography data produced from imaging the actual nodule in a phantom study using corresponding imaging parameters. This approach incorporates the properties of the imaging system and their effect on the discrete 3-D representation of the object of interest. Using a sum of absolute differences cost function, the derived matched filter demonstrated low bias and variance in the volume estimation of spherical synthetic nodules ranging in density from -630 to +100 HU and in size from 5 to 10 mm. This work could potentially lead to better understanding of sources of error in the task of lung nodule size measurements and may lead to new techniques to account for those errors.

For a given category of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change.

Purpose-To determine inter-related factors that contribute substantially to measurement error of pulmonary nodule measurements with CT by assessing a large-scale dataset of phantom scans and to quantitatively validate the repeatability and reproducibility of a subset containing nodules and CT acquisitions consistent with the Quantitative Imaging Biomarker Alliance (QIBA) metrology recommendations. Methods-The dataset has about 40 000 volume measurements of 48 nodules (5-20 mm, four shapes, three radiodensities) estimated by a matched-filter estimator from CT images involving 72 imaging protocols. Technical assessment was performed under a framework suggested by QIBA, which aimed to minimize the inconsistency of terminologies and techniques used in the literature. Accuracy and precision of lung nodule volume measurements were examined by analyzing the linearity, bias, variance, root mean square error (RMSE), repeatability, reproducibility, and significant and substantial factors that contribute to the measurement error. Statistical methodologies including linear regression, analysis of variance, and restricted maximum likelihood were applied to estimate the aforementioned metrics. The analysis was performed on both the whole dataset and a subset meeting the criteria proposed in the QIBA Profile document.Results-Strong linearity was observed for all data. Size, slice thickness × collimation, and randomness in attachment to vessels or chest wall were the main sources of measurement error. Grouping the data by nodule size and slice thickness × collimation, the standard deviation (3.9%-28%), and RMSE (4.4%-68%) tended to increase with smaller nodule size and larger slice thickness. For 5, 8, 10, and 20 mm nodules with reconstruction slice thickness ≤0.8, 3, 3, and 5 mm, respectively, the measurements were almost unbiased (−3.0% to 3.0%). Repeatability coefficients (RCs) were from 6.2% to 40%. Pitch of 0.9, detail kernel, and smaller slice thicknesses yielded better (smaller) RCs than those from pitch of 1.2, medium kernel, and larger slice thicknesses. Exposure showed no impact on RC. The overall reproducibility coefficient (RDC) was 45%, and reduced to about 20%-30% when the slice thickness and collimation were fixed. For nodules and CT imaging complying with the QIBA Profile (QIBA Profile subset), the measurements were highly repeatable and reproducible in spite of variations in nodule characteristics and imaging protocols. The overall measurement error was small and mostly due to a Electronic mail: qin.li1@fda.hhs.gov. HHS Public AccessAuthor manuscript Med Phys. Author manuscript; available in PMC 2017 November 01. Author Manuscript Author Manuscript Author ManuscriptAuthor Manuscript the randomness in attachment. The bias, standard deviation, and RMSE grouped by nodule size and slice thickness × collimation in the QIBA Profile subset were within ±3%, 4%, and 5%, respectively. RCs are within 11% and the overall RDC is equal to 11%. Conclusions-The authors have performed a comprehensive technical assessme...