Eugene Yanagihara

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 High-frequency Characterization of Cancerous Lymph Nodes

High-frequency ultrasound (HFU) offers a means of investigating biologic tissue at the microscopic level. High-frequency, three-dimensional (3-D) quantitative-ultrasound (QUS) methods were developed to characterize freshly-dissected lymph nodes of cancer patients. Three-dimensional ultrasound data were acquired from lymph nodes using a 25.6-MHz center-frequency transducer. Each node was inked prior to tissue fixation to recover orientation after sectioning for 3-D histologic evaluation. Backscattered echo signals were processed using 3-D cylindrical regions-of-interest to yield four QUS estimates associated with tissue microstructure (i.e., effective scatterer size, acoustic concentration, intercept and slope). QUS estimates were computed following established methods using two scattering models. In this study, 46 lymph nodes acquired from 27 patients diagnosed with colon cancer were processed. Results revealed that fully-metastatic nodes could be perfectly differentiated from cancer-free nodes using slope or scatterer-size estimates. Specifically, results indicated that metastatic nodes had an average effective scatterer size (i.e., 37.1 +/- 1.7 microm) significantly larger (p < 0.05) than that in cancer-free nodes (i.e., 26 +/- 3.3 microm). Therefore, the 3-D QUS methods could provide a useful means of identifying small metastatic foci in dissected lymph nodes that might not be detectable using current standard pathology procedures.

In vitro B-mode ultrasonographic criteria for diagnosing axillary lymph node metastasis of breast cancer.

Axillary lymph node status is an important factor for staging and treatment planning in breast cancer. Our study was performed in vitro on a node‐by‐node basis to evaluate the ability of B‐mode ultrasonographic images to distinguish metastatic from nonmetastatic nodes. Immediately prior to histologic examination, individual dissected axillary nodes were scanned in a water bath using a 10 MHz B‐mode ultrasonographic transducer. Four B‐mode features (size, circularity, border demarcation, and internal echo) were evaluated for their ability to distinguish metastatic from nonmetastatic lymph nodes. Lymph node metastasis was indicated by (1) a large size (i.e., a length of the longest axis of 10 mm or greater); (2) a circular shape (i.e., the ratio of the shortest axis to the longest axis between 0.5 and 1.0); (3) a sharply demarcated border compared with surrounding fatty tissue; and (4) a hypoechoic internal echo, with obliteration of the fatty hilum. The sensitivity and specificity were compared for all combinations of features. We examined 84 histologically characterized axillary nodes from 27 breast cancer patients, including 64 nonmetastatic and 20 metastatic nodes. Of the criteria cited, circular shape was the best single feature for distinguishing metastatic from nonmetastatic nodes (sensitivity, 65%; specificity, 73%). The best combination of sensitivity (85%) and specificity (73%) was obtained using the criterion that a lymph node contained cancer when at least three positive features were present. The present in vitro study demonstrated that the sensitivity and specificity of B‐mode ultrasonography for diagnosing lymph node metastasis were lower than 90%. Therefore, B‐mode ultrasonography may not be an optimal noninvasive screening method for diagnosing axillary lymph node metastasis in breast cancer patients, particularly under in vivo clinical conditions.

In vitro diagnosis of axillary lymph node metastases in breast cancer by spectrum analysis of radio frequency echo signals

Axillary lymph node status is of particular importance for staging and managing breast cancer. Currently, axillary lymph node dissection is performed routinely in cases of invasive breast cancer because of the lack of accurate noninvasive methods for diagnosing lymph node metastasis. We investigated the diagnostic ability of ultrasonic tissue characterization based on spectrum analysis of backscattered echo signals to detect axillary lymph node metastasis in breast cancer in vitro compared with in vitro B-mode imaging. Immediately after surgery, individual lymph nodes were isolated from axillary tissue. Each lymph node was scanned in a water bath using a 10-MHz instrument, and radio frequency data and B-mode images were acquired. Spectral parameter values were calculated, and discriminant analysis was performed to classify metastatic and nonmetastatic lymph nodes. Forty histologically characterized axillary lymph nodes were enrolled in this study, including 25 nonmetastatic and 15 metastatic lymph nodes. A significant difference existed in the spectral parameter values (slope and intercept) for metastatic and nonmetastatic lymph nodes. Spectral parameter-based discriminant function classification of metastatic vs. nonmetastatic lymph nodes provided a sensitivity of 93.3%, specificity of 92.0%, and overall accuracy of 92.5%. In comparison, B-mode ultrasound images of in vitro lymph nodes provided a sensitivity of 73.3%, specificity of 84.0%, and overall accuracy of 80.0%. Receiver operating characteristic (ROC) analysis comparing the efficacy of both methods gave an ROC curve area of 0.9888 for spectral methods, which was greater than the area of 0.8980 for B-mode ultrasound. Hence, this in vitro study suggests that the diagnostic ability of spectrum analysis may prove to be markedly superior to that of B-mode ultrasound in detecting axillary lymph node metastasis in breast cancer. Because of these encouraging results, we intend to conduct an investigation of the ability of spectral methods to classify metastatic axillary lymph nodes in vivo.

Three-dimensional segmentation of high-frequency ultrasound echo signals from dissected lymph nodes

Quantitative high-frequency ultrasound (QHFU) imaging methods are under investigation to evaluate their ability to detect small nodal metastases in lymph nodes freshly dissected from cancer patients. To apply these methods, a critical preprocessing step is 3D segmentation of the lymph-node ultrasound echo-signal dataset. Segmenting the residual fat layer and the lymph node is critical in order to avoid bias in the QHFU estimates (e.g., scatterer size and acoustic concentration) due to attenuation and to exclude estimates obtained from the fat regions. Segmentation also provides absolute measurements of lymph-node dimensions that are necessary to match 3D ultrasound with 3D histology. In this study, a 3D region-based segmentation algorithm was developed and compared quantitatively using Dices mutual-overlap criterion with 2D manual segmentation of 9 representative cross sections. The method was tested on 13 lymph nodes, and resulting Dice scores had mean values of 0.81 and 0.78 for lymph node and fat segmentation, respectively.

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.

High-Frequency Quantitative Ultrasound Imaging of Cancerous Lymph Nodes

High-frequency ultrasound (HFU) offers a means of investigating biological tissue at the microscopic level. High-frequency, quantitative-ultrasound (QUS) methods were developed to characterize freshly-dissected lymph nodes of cancer patients. Three-dimensional (3D) ultrasound data were acquired from lymph nodes using a 25.6-MHz center-frequency transducer. Each node was inked prior to 3D histological fixation to recover orientation after sectioning. Backscattered echo signals were processed to yield two QUS estimates associated with tissue microstructure: scatterer size and acoustic concentration. The QUS estimates were computed following established methods using a Gaussian scattering model. Four lymph nodes from a patient with stage-3 colon cancer were evaluated as an illustrative case. QUS images were generated for this patient by expressing QUS estimates as color-encoded pixels and overlaying them on conventional gray-scale B-mode images. The single metastatic node had an average scatterer size that was significantly larger than the average scatterer size of the other nodes, and the statistics of both QUS estimates in the metastatic node showed greater variance than the statistics of the other nodes. Results indicate that the methods may provide a useful means of identifying small metastatic foci in dissected lymph nodes that might not be detectable using current standard pathology procedures.

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.

Thrombotic Thrombocytopenic Purpura and Human T-Lymphotrophic Virus, Type 1 (HTLV-1)

Excerpt To the Editor:Several recent studies (1-4) have shown that thrombotic thrombocytopenic purpura can be a manifestation of infection with human immunodeficiency virus (HIV). Human T-lymphotro...