Emi Saegusa-Beecroft

Three-Dimensional High-Frequency Backscatter and Envelope Quantification of Cancerous Human Lymph Nodes

Quantitative imaging methods using high-frequency ultrasound (HFU) offer a means of characterizing biological tissue at the microscopic level. Previously, high-frequency, 3-D quantitative ultrasound (QUS) methods were developed to characterize 46 freshly-dissected lymph nodes of colorectal-cancer patients. 3-D ultrasound radiofrequency data were acquired using a 25.6 MHz center-frequency transducer and each node was inked before tissue fixation to recover orientation after sectioning for 3-D histological evaluation. Backscattered echo signals were processed using 3-D cylindrical regions-of-interest (ROIs) to yield four QUS estimates associated with tissue microstructure (i.e., effective scatterer size, acoustic concentration, intercept and slope). These QUS estimates, obtained by parameterizing the backscatter spectrum, showed great potential for cancer detection. In the present study, these QUS methods were applied to 112 lymph nodes from 77 colorectal and gastric cancer patients. Novel QUS methods parameterizing the envelope statistics of the ROIs using Nakagami and homodyned-K distributions were also developed; they yielded four additional QUS estimates. The ability of these eight QUS estimates to classify lymph nodes and detect cancer was evaluated using receiver operating characteristics (ROC) curves. An area under the ROC curve of 0.996 with specificity and sensitivity of 95% were obtained by combining effective scatterer size and one envelope parameter based on the homodyned-K distribution. Therefore, these advanced 3-D QUS methods potentially can be valuable for detecting small metastatic foci in dissected lymph nodes.

Three-dimensional quantitative ultrasound for detecting lymph node metastases

PURPOSE Detection of metastases in lymph nodes (LNs) is critical for cancer management. Conventional histological methods may miss metastatic foci. To date, no practical means of evaluating the entire LN volume exists. The aim of this study was to develop fast, reliable, operator-independent, high-frequency, quantitative ultrasound (QUS) methods for evaluating LNs over their entire volume to effectively detect LN metastases. METHODS We scanned freshly excised LNs at 26 MHz and digitally acquired echo-signal data over the entire three-dimensional (3D) volume. A total of 146 LNs of colorectal, 26 LNs of gastric, and 118 LNs of breast cancer patients were enrolled. We step-sectioned LNs at 50-μm intervals and later compared them with 13 QUS estimates associated with tissue microstructure. Linear-discriminant analysis classified LNs as metastatic or nonmetastatic, and we computed areas (Az) under receiver-operator characteristic curves to assess classification performance. The QUS estimates and cancer probability values derived from discriminant analysis were depicted in 3D images for comparison with 3D histology. RESULTS Of 146 LNs of colorectal cancer patients, 23 were metastatic; Az = 0.952 ± 0.021 (95% confidence interval [CI]: 0.911-0.993); sensitivity = 91.3% (specificity = 87.0%); and sensitivity = 100% (specificity = 67.5%). Of 26 LNs of gastric cancer patients, five were metastatic; Az = 0.962 ± 0.039 (95% CI: 0.807-1.000); sensitivity = 100% (specificity = 95.3%). A total of 17 of 118 LNs of breast cancer patients were metastatic; Az = 0.833 ± 0.047 (95% CI: 0.741-0.926); sensitivity = 88.2% (specificity = 62.5%); sensitivity = 100% (specificity = 50.5%). 3D cancer probability images showed good correlation with 3D histology. CONCLUSIONS These results suggest that operator- and system-independent QUS methods allow reliable entire-volume LN evaluation for detecting metastases. 3D cancer probability images can help pathologists identify metastatic foci that could be missed using conventional methods.

Entire-volume Serial Histological Examination for Detection of Micrometastases in Lymph Nodes of Colorectal Cancers

The purpose of this study was to accurately detect lymph-node micrometastases, i.e., metastatic cancer foci that have a size between 2.0 and 0.2 mm, in nodes excised from colorectal cancer (CRC) patients, and to determine how frequently micrometastases might be missed when standard histological examination procedures are used. A total of 311 lymph nodes were removed and examined from 90 patients with Stage I to IV CRC. The number of slices of histology sections ranged from 6 to 75 per node (average = 25.5; SD = 11.1), which provided a total of 7,943 slices. Lymph nodes were examined in their entire volume at every 50-μm and 100-μm intervals for nodes smaller and larger than 5 mm respectively. The total number of thin sections examined in each node and the number of thin sections where metastatic foci were present were counted. The number of thin sections with metastatic foci and the total number of slices was determined for each node. In addition, the presence or absence of metastatic foci in the “central” slice was determined. Micrometastases were found in 12/311 (3.9%) of all lymph nodes. In the 12 lymph nodes with micrometastases, the rate of metastatic slices over all slices was 39.4% (range = 6.3 to 81.3%; SD = 25.8%) In the central slice of each node, micrometastases were present only in 6 of 12 lymph nodes (50%); accordingly, they were not present in the central slice for half the micrometastatic nodes. These 6 nodes represented 1.9% of the 311 nodes and 11.1% of the 54 metastatic nodes. This study suggests that a significant fraction of micrometastases can be missed by traditional singleslice sectioning; half of the micrometastases would have been overlooked in our data set of 311 nodes.

Modeling the envelope statistics of three-dimensional high-frequency ultrasound echo signals from dissected human lymph nodes.

This work investigates the statistics of the envelope of three-dimensional (3D) high-frequency ultrasound (HFU) data acquired from dissected human lymph nodes (LNs). Nine distributions were employed, and their parameters were estimated using the method of moments. The Kolmogorov Smirnov (KS) metric was used to quantitatively compare the fit of each candidate distribution to the experimental envelope distribution. The study indicates that the generalized gamma distribution best models the statistics of the envelope data of the three media encountered: LN parenchyma, fat and phosphate-buffered saline (PBS). Furthermore, the envelope statistics of the LN parenchyma satisfy the pre-Rayleigh condition. In terms of high fitting accuracy and computationally efficient parameter estimation, the gamma distribution is the best choice to model the envelope statistics of LN parenchyma, while, the Weibull distribution is the best choice to model the envelope statistics of fat and PBS. These results will contribute to the development of more-accurate and automatic 3D segmentation of LNs for ultrasonic detection of clinically significant LN metastases.

Level-set segmentation of 2D and 3D ultrasound data using local gamma distribution fitting energy

Ultrasound (US) data suffer from speckle noise as well as intensity inhomogeneities due to underlying changes in acoustic properties of tissue structure and/or the effects of acoustic focusing and attenuation. This paper describes a 2D and 3D variational level-set method for segmenting such data. To deal with the local statistics of speckle noise, the data term of the level-set energy function is based on local gamma distributions which have shown an ability to model envelope data and gray-level pixel intensities of B-mode clinical images. Local statistics are estimated at a controllable scale using a smooth function with a compact support, a mollifyer, and the method of moments. Compared to manual segmentation, the investigated method provides a high Dice similarity coefficient (DSC) on 3D simulated data, an average DSC of 0.915 on 12 B-mode images of murine tumors acquired with a clinical US system, and average DSCs of 0.920, 0.806 and 0.975 for three media of 54 3D envelope data sets acquired with a high-frequency, focused transducer from dissected human lymph nodes. It also outperforms methods that employ local Gaussian statistics instead of local gamma statistics.

Assembling 3D histology volumes from sections of cancerous lymph nodes to match 3D high-frequency quantitative ultrasound images

High-Frequency Quantitative Ultrasound (HFQUS) imaging methods are under investigation to evaluate their ability to detect small metastases (< 2 mm) in lymph nodes freshly dissected from cancer patients. To assess the performance of these methods, 3D HFQUS must be compared to gold-standard histologic images. Histologic images have to be assembled to form volumetric histologic information. This study addresses this issue. The acquisition of high-frequency ultrasound (HFU) data with a 26-MHz center-frequency transducer and histologic preparation are described. Dissected nodes were longitudinally cut in half and pairs of histologic sections separated by 65 µm, for nodes < 5 mm, or 115 µm, for nodes > 5 mm, were photographed. Then a fully automatic method to assemble and orient a 3D histologic volume from a set of 2D images was developed and applied. Identification of the histology sections on each slide relies on a parametric shape modeling of the histologic sections with ellipses. Then a set of rigid transformations were estimated and applied to construct volumetric histologic data. The method was visually evaluated on a set of 50 lymph nodes and is valuable for comparing histologic data to HFQUS estimates in 3D.

A novel nested graph cuts method for segmenting human lymph nodes in 3D high frequency ultrasound images

Three-dimensional (3D) quantitative-ultrasound (QUS) methods were recently developed and successfully applied to detect cancerous regions in freshly-dissected lymph nodes (LNs). The 3D high frequency ultrasound (HFU) images obtained from these LNs contain three different parts: LN-parenchyma (LNP), fat, and phosphate-buffered saline (PBS). To apply QUS estimates inside the LNP region, an automatic and accurate algorithm for LNP segmentation is needed. In this paper, we describe a novel, nested-graph-cut (NGC) method that effectively exploits the nested structure of the LN images. To overcome the large variability of the intensity distribution of LNP pixels due to acoustic attenuation and focusing, we further describe an iterative self-updating framework combining NGC and spline-based robust intensity fitting. Dice similarity coefficients of 89.56±8.44% were achieved when the proposed automatic segmentation algorithm was compared to expert manual segmentation on a dataset consisting of 115 LNs.

Three-dimensional quantitative high-frequency characterization of freshly-excised human lymph nodes

High-frequency quantitative ultrasound (QUS) permits characterization of tissue microstructure using system-independent estimates. In this study, freshly-excised lymph nodes from cancer patients were evaluated using specifically designed three-dimensional (3D) QUS methods. The long-term objective was to develop 3D QUS methods for detecting metastases. Detection of metastases is critically important for cancer staging and treatment planning. A custom laboratory scanning system was used to acquire radio-frequency (RF) data in 3D from excised lymph nodes using a 26-MHz center-frequency transducer. Overlapping 1-mm cylindrical regions-of-interest (ROIs) of the RF data were processed to yield 13 QUS estimates associated with tissue microstructure. QUS estimates were obtained from more than 250 nodes from more than 150 colorectal-, gastric-, and breast-cancer patients. Cancer-detection performance was assessed for individual estimates and linear combinations of estimates. ROC results demonstrated excellent classification. For colorectal- and gastric-cancer nodes, the areas under the ROC curves (AUCs) were above 0.94. Slightly poorer results (AUC 0.87) were obtained for breast nodes. Images based on QUS parameters also permitted localization of cancer foci in some micrometastatic cases. Therefore, these advanced 3D QUS methods potentially can be valuable for detecting small metastatic foci in dissected lymph nodes.

Random forest classification and local region-based, level-set segmentation for quantitative ultrasound of human lymph nodes

To detect metastatic foci in excised human lymph nodes (LNs) using three-dimensional (3D), high-frequency quantitative ultrasound (QUS), the 3D envelope data must be accurately segmented into LN parenchyma (LNP), fat and normal saline (NS). However, automatic segmentation of the 3D data is challenging because of speckle as well as low contrast and intensity inhomogeneities caused by focusing and attenuation effects. We describe a novel method to automatically segment these media using initialization and refinement steps. In the first step, random forest classification (RFC) is employed for initial segmentation of the 3 media. To train the forest classifier and classify each voxel, features including backscattered energy, statistical parameters and contextual information are extracted from LN envelope data. In the second step, the initialization is refined by a 3-phase, local region-based, level-set segmentation method that uses the gamma probability density function as a statistical model of the envelope data. To handle depth-dependent data inhomogeneity efficiently, the gamma distribution parameters are estimated locally in transverse slices. From a database of 54 representative LNs acquired from colorectal-cancer patients, 12 LNs were used to train the random forest classifier, and the 42 remaining LNs were used for evaluation. The Dice similarity coefficient (DSC) was used to compare automatic and manual segmentation. For RFC alone, DSCs were 0.922 ± 0.022, 0.801 ± 0.075 and 0.959 ± 0.013 for LNP, fat and NS, respectively. For initialization and refinement, significantly better DSCs were obtained: 0.937 ± 0.021, 0.824 ± 0.074 and 0.961 ± 0.009 (Wilcoxon signed rank test). Results also demonstrate that accurate QUS estimates can be obtained with automatic segmentation in excised colorectal LNs, thus eliminating the need for operator-dependent, manual segmentation.

Three-dimensional quantitative ultrasound to guide pathologists towards metastatic foci in lymph nodes

The detection of metastases in freshly-excised lymph nodes from cancer patients during lymphadenectomy is critically important for cancer staging, treatment, and optimal patient management. Currently, conventional histologic methods suffer a high rate of false-negative determinations because pathologists cannot evaluate each excised lymph nodes in its entirety. Therefore, lymph nodes are undersampled and and small but clinically relevant metastatic regions can be missed. In this study, quantitative ultrasound (QUS) methods using high-frequency transducers (i.e., >; 20 MHz) were developed and evaluated for their ability to detect and guide pathologists towards suspicious regions in lymph nodes. A custom laboratory scanning system was used to acquire radio-frequency (RF) data in 3D from excised lymph nodes using a 26-MHz center-frequency transducer. Overlapping 1-mm cylindrical regions-of-interest (ROIs) of the RF data were processed to yield 13 QUS estimates quantifying tissue microstructure and organization. These QUS methods were applied to more than 260 nodes from more than 160 colorectal-, gastric-, and breast-cancer patients. Cancer-detection performance was assessed for individual estimates and linear combinations of estimates. ROC results demonstrated excellent classification. For colorectal- and gastric-cancer nodes, the areas under the ROC curves (AUCs) were greater than 0.95. Slightly poorer results (AUC=0.85) were obtained for breast-cancer nodes. Images based on QUS parameters also permitted localization of cancer foci in some micrometastatic cases.